Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.

In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.

Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.

AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.

AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.

References

How to cite: Kellenberger, B., Tuia, D., and Morris, D.: Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12065, https://doi.org/10.5194/egusphere-egu21-12065, 2021.

Machine learning modeling for Earth events at NASA is often limited by the availability of labeled examples. For example, training classifiers for forest fires or oil spills from satellite imagery requires curating a massive and diverse dataset of example forest fires, a tedious multi-month effort requiring careful review of over 196.9 million square miles of data per day for 20 years. While such images might exist in abundance within 40 petabytes of unlabeled satellite data, finding these positive examples to include in a training dataset for a machine learning model is extremely time-consuming and requires researchers to "hunt" for positive examples, like finding a needle in a haystack. 

We present a no-code open-source tool, Curator, whose goal is to minimize the amount of human manual image labeling needed to achieve a state of the art classifier. The pipeline, purpose-built to take advantage of the massive amount of unlabeled images, consists of (1) self-supervision training to convert unlabeled images into meaningful representations, (2) search-by-example to collect a seed set of images, (3) human-in-the-loop active learning to iteratively ask for labels on uncertain examples and train on them. 

In step 1, a model capable of representing unlabeled images meaningfully is trained with a self-supervised algorithm (like SimCLR) on a random subset of the dataset (that conforms to researchers’ specified “training budget.”). Since real-world datasets are often imbalanced leading to suboptimal models, the initial model is used to generate embeddings on the entire dataset. Then, images with equidistant embeddings are sampled. This iterative training and resampling strategy improves both balanced training data and models every iteration. In step 2, researchers supply an example image of interest, and the output embeddings generated from this image are used to find other images with embeddings near the reference image’s embedding in euclidean space (hence similar looking images to the query image). These proposed candidate images contain a higher density of positive examples and are annotated manually as a seed set. In step 3, the seed labels are used to train a classifier to identify more candidate images for human inspection with active learning. Each classification training loop, candidate images for labeling are sampled from the larger unlabeled dataset based on the images that the model is most uncertain about (p ≈ 0.5).

Curator is released as an open-source package built on PyTorch-Lightning. The pipeline uses GPU-based transforms from the NVIDIA-Dali package for augmentation, leading to a 5-10x speed up in self-supervised training and is run from the command line.

By iteratively training a self-supervised model and a classifier in tandem with human manual annotation, this pipeline is able to unearth more positive examples from severely imbalanced datasets which were previously untrainable with self-supervision algorithms. In applications such as detecting wildfires, atmospheric dust, or turning outward with telescopic surveys, increasing the number of positive candidates presented to humans for manual inspection increases the efficacy of classifiers and multiplies the efficiency of researchers’ data curation efforts.

How to cite: Venguswamy, R., Levy, M., Koul, A., Praveen, S., Narayanan, T., Krishnan, A., Peterson, J., Ganju, S., and Kasam, M.: Curator: A No-Code Self-Supervised Learning and Active Labeling Tool to Create Labeled Image Datasets from Petabyte-Scale Imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6853, https://doi.org/10.5194/egusphere-egu21-6853, 2021.

Data preparation process generally consumes up to 80% of the Data Scientists time, with 60% of that being attributed to cleaning and labeling data.[1]  Our solution is to use automated pipelines to prepare, annotate, and catalog data. The first step upon ingestion, especially in the case of real world—unstructured and unlabeled datasets—is to leverage Snorkel, a tool specifically designed around a paradigm to rapidly create, manage, and model training data. Configured properly, Snorkel can be leveraged to temper this labeling bottle-neck through a process called weak supervision. Weak supervision uses programmatic labeling functions—heuristics, distant supervision, SME or knowledge base—scripted in python to generate “noisy labels”. The function traverses the entirety of the dataset and feeds the labeled data into a generative—conditionally probabilistic—model. The function of this model is to output the distribution of each response variable and predict the conditional probability based on a joint probability distribution algorithm. This is done by comparing the various labeling functions and the degree to which their outputs are congruent to each other. A single labeling function that has a high degree of congruence with other labeling functions will have a high degree of learned accuracy, that is, the fraction of predictions that the model got right. Conversely, single labeling functions that have a low degree of congruence with other functions will have low learned accuracy. Each prediction is then combined by the estimated weighted accuracy, whereby the predictions of the higher learned functions are counted multiple times. The result yields a transformation from a binary classification of 0 or 1 to a fuzzy label between 0 and 1— there is “x” probability that based on heuristic “n”, the response variable is “y”. The addition of data to this generative model multi-class inference will be made on the response variables positive, negative, or abstain, assigning probabilistic labels to potentially millions of data points. Thus, we have generated a discriminative ground truth for all further labeling efforts and have improved the scalability of our models. Labeling functions can be applied to unlabeled data to further machine learning efforts.
 
Once our datasets are labeled and a ground truth is established, we need to persist the data into our delta lake since it combines the most performant aspects of a warehouse with the low-cost storage for data lakes. In addition, the lake can accept unstructured, semi structured, or structured data sources, and those sources can be further aggregated into raw ingestion, cleaned, and feature engineered data layers.  By sectioning off the data sources into these “layers”, the data engineering portion is abstracted away from the data scientist, who can access model ready data at any time.  Data can be ingested via batch or stream. 
 
The design of the entire ecosystem is to eliminate as much technical debt in machine learning paradigms as possible in terms of configuration, data collection, verification, governance, extraction, analytics, process management, resource management, infrastructure, monitoring, and post verification. 

How to cite: Meil, J.: Programmatic Labeling of Dark Data for Artificial Intelligence in Spatial Informatics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16326, https://doi.org/10.5194/egusphere-egu21-16326, 2021.

Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.

In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.

In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.

How to cite: Lesiv, M., Schepaschenko, D., Dürauer, M., Buchhorn, M., Georgieva, I., and Fritz, S.: Collecting training data to map forest management at global scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15297, https://doi.org/10.5194/egusphere-egu21-15297, 2021.

Lack of well-labelled and coherent training data is the main reason why machine learning (ML) and data-driven interpretations are not established in the field of Ground-Penetrating Radar (GPR). Non-representative and limited datasets lead to non-reliable ML-schemes that overfit, and are unable to compete with traditional deterministic approaches. To that extent, numerical data can potentially complement insufficient measured datasets and overcome this lack of data, even in the presence of large feature spaces.

Using synthetic data in ML is not new and it has been extensively applied to computer vision. Applying numerical data in ML requires a numerical framework capable of generating synthetic but nonetheless realistic datasets. Regarding GPR, such a framework is possible using gprMax, an open source electromagnetic solver, fine-tuned for GPR applications [1], [2], [3]. gprMax is fully parallelised and can be run using multiple CPU’s and GPU’s. In addition, it has a flexible scriptable format that makes it easy to generate big data in a trivial manner. Stochastic geometries, realistic soils, vegetation, targets [3] and models of commercial antennas [4], [5] are some of the features that can be easily incorporated in the training data.

The capability of gprMax to generate realistic numerical datasets is demonstrated in [6], [7]. The investigated problem is assessing the depth and the diameter of rebars in reinforced concrete. Estimating the diameter of rebars using GPR is particularly challenging with no conclusive solution. Using a synthetic training set, generated using gprMax, we managed to effectively train ML-schemes capable of estimating the diameter of rebar in an accurate and efficient manner [6], [7]. The aforementioned case studies support the premise that gprMax has the potential to provide realistic training data to applications where well-labelled data are not available, such as landmine detection, non-destructive testing and planetary sciences.

References

[1] Warren, C., Giannopoulos, A. & Giannakis, I., (2016). gprMax: Open Source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, Computer Physics Communications, 209, 163-170.

[2] Warren, C., Giannopoulos, A., Gray, A., Giannakis, I., Patterson, A., Wetter, L. & Hamrah, A., (2018). A CUDA-based GPU engine for gprMax: Open source FDTD, electromagnetic simulation software. Computer Physics Communications, 237, 208-218.

[3] Giannakis, I., Giannopoulos, A. & Warren, C. (2016). A realistic FDTD numerical modeling framework of Ground Penetrating Radar for landmine detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(1), 37-51.

[4] Giannakis, I., Giannopoulos, A. & Warren, C., (2018). Realistic FDTD GPR antenna models optimized using a novel linear/non-linear full waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 207(3), 1768-1778.

[5] Warren, C., Giannopoulos, A. (2011). Creating finite-difference time-domain models of commercial ground-penetrating radar antennas using Taguchi’s optimization method. Geophysics, 76(2), G37-G47

[6] Giannakis, I., Giannopoulos, A. & Warren, C. (2021). A Machine Learning Scheme for  Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar. IEEE Geoscience and Remote Sensing Letters.

[7] Giannakis, I., Giannopoulos, A., & Warren, C. (2019). A machine learning-based fast-forward solver for ground penetrating radar with application to full-waveform inversion. IEEE Transactions on Geoscience and Remote Sensing. 57(7), 4417-4426.

How to cite: Warren, C., Giannakis, I., and Giannopoulos, A.: gprMax: An Open Source Electromagnetic Simulator for Generating Big Data for Ground Penetrating Radar Applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10347, https://doi.org/10.5194/egusphere-egu21-10347, 2021.

The ESA-funded AIREO project [1] sets out to produce AI-ready training dataset specifications and best practices to support the training and development of machine learning models on Earth Observation (EO) data. While the quality and quantity of EO data has increased drastically over the past decades, availability of training data for machine learning applications is considered a major bottleneck. The goal is to move towards implementing FAIR data principles for training data in EO, enhancing especially the finability, interoperability and reusability aspects.  To achieve this goal, AIREO sets out to provide a training data specification and to develop best practices for the use of training datasets in EO. An additional goal is to make training data sets self-explanatory (“AI-ready) in order to expose challenging problems to a wider audience that does not have expert geospatial knowledge. 

Key elements that are addressed in the AIREO specification are granular and interoperable metadata (based on STAC), innovative Quality Assurance metrics, data provenance and processing history as well as integrated feature engineering recipes that optimize platform independence. Several initial pilot datasets are being developed following the AIREO data specifications. These pilot applications include for example  forest biomass, sea ice detection and the estimation of atmospheric parameters.An API for the easy exploitation of these datasets will be provided.to allow the Training Datasets (TDS) to work against EO catalogs (based on OGC STAC catalogs and best practises from ML community) to allow updating and updated model training over time.

This presentation will present the first version of the AIREO training dataset specification and will showcase some elements of the best-practices that were developed. The AIREO compliant pilot datasets will be presented which are openly accessible and community feedback is explicitly encouraged. 

[1] https://aireo.net/

How to cite: McKinstry, A., Boydell, O., Le, Q., Preet, I., Hanafin, J., Fernandez, M., Warde, A., Kannan, V., and Griffiths, P.: AI-Ready Training Datasets for Earth Observation: Enabling FAIR data principles for EO training data., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12384, https://doi.org/10.5194/egusphere-egu21-12384, 2021.

Understanding human dynamics is of crucial importance for managing human activities for sustainable development. According to the United Nations, 68% of people will live in cities by 2050. Therefore, it is important to understand human footprints in order to develop policies that will improve the lives in urban and suburban areas. Our study aims at detecting spatial-temporal activity patterns from mobile phone data provided by a telecom service provider. To be more precise we used the activity data set which contains the amount of sent/received SMS, calls, as well as internet usage per radio-base station in defined time-stamps. The case study focus is on the capital city of Serbia, Belgrade, which has have nearly 2 million inhabitants and included the month of February 2020 in the analysis. We applied the biclustering (spectral co-clustering) algorithm on the telecom data to detect locations in the city that behave similarly in the specific time windows. Biclustering is a data mining technique that is being used for finding homogeneous submatrices among rows and columns of a matrix, widely used in text mining and gene expression data analysis.  Although, there are no examples in the literature of the algorithm usage on location-based data for urban application, we have seen the potential due to its ability to detect clusters in a more refined way, during a specific period of time that could not otherwise be detected with global clustering approach. To prepare the data for the algorithm appliance, we normalized each type of activity (SMS/Call In/Out and Internet activity) and aggregated the total activity on each antenna per hour. We transformed the data into the matrix, where rows were presenting the antennas, and columns the hours. The algorithm was applied for each day separately. On average number of discovered biclusters was 5, usually corresponding to regular based activities, such as work, home, commuting, and free time, but also to the city’s nightlife. Our results confirmed that urban spaces are the function of space and time. They revealed different functionalities of the urban and suburban parts in the city. We observed the presence of patterned behavior across the analyzed days. The type of day dictated the spatial-temporal activities that occurred. We distinguished different types of days, such as working days (Monday to Thursday), Fridays, weekends, and holidays. These findings showed the promising potential of the biclustering algorithm and could be utilized by policymakers for precisely detecting activity clusters across space and time that correspond to specific functions of the city.

How to cite: Grujić, N., Brdar, S., Novović, O., Obrenović, N., Govedarica, M., and Crnojević, V.: Biclustering for uncovering spatial-temporal patterns in telecom data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14423, https://doi.org/10.5194/egusphere-egu21-14423, 2021.

The SE-Carpathians produce significant geodynamic activity due to the current subduction process. The strong seismicity in the Vrancea-zone is its most important indicator. The focus area of these seismic events is relatively small, around 80*100 km and the distribution of their locations is quiet dense.

The authors have carried out cluster analyses of the focal mechanism solutions estimated from local and tele-seismic measurements and stress inversions to support the recent and previously published studies in this region. They have applied different pre-existing clustering methods – e.g. HDBSCAN (hierarchical density-based clustering for applications with noise) and agglomerative hierarchical analysis – considering to the geographical coordinates, focal depths and parameters of the focal mechanism solutions of the used seismic events, as well. Moreover, they have attempted to improve a fully-automated algorithm for the classification of the earthquakes for the estimations. This algorithm does not call for the setting of hyper-parameters, thus the affection of the subjectivity can be reduced significantly and the running time can be also decreased. In all cases, the resulted stress tensors are in close agreement with the earlier presented results.

How to cite: Czirok, L., Kuslits, L., and Gribovszki, K.: Cluster analysis in the studying of stress relation in the Vrancea-zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9749, https://doi.org/10.5194/egusphere-egu21-9749, 2021.

How to cite: Pacher, C., Schicker, I., DeWit, R., and Plant, C.: AWT - Clustering using an Aggregated Wavelet Tree: A novel automatic unsupervised clustering and outlier detection algorithm for time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12324, https://doi.org/10.5194/egusphere-egu21-12324, 2021.

Wildfires constitute a complex environmental disaster triggered by several interacting natural and human factors that can affect the biodiversity, species composition and ecosystems, but also human lives, regional economies and environmental health. Therefore, wildfires have become the focus on forestry and ecological research and are receiving considerable attention in forest management. Current advances in automated learning and simulation methods, like machine learning (ML) algorithms, recently aroused great interest in wildfires risk assessment and mapping. This quantitative evaluation is carried out by taking into account two factors: the location and spatial extension of past wildfires events and the geo-environmental and anthropogenic predisposing factors that favored their ignition and spreading. When dealing with risk assessment and predictive mapping for natural phenomena, it is crucial to ascertain the reliability and validity of collected data, as well as the prediction capability of the obtained results. In a previous study (Tonini et al. 2020) authors applied Random Forest (RF) to elaborate wildfire susceptibility mapping for Liguria region (Italy). In the present study, we address to the following outstanding issues, which are still unsolved: (1) the vegetation map included a class labeled “burned area” that masked to true burned vegetation; (2) the implemented model based on RF gave good results, but it needs to be compared with other ML based approaches; (3) to test the predictive capabilities of the model, the last three years of observations were taken, but these are not fully representative of different wildfires regimes, characterizing non-consecutives years. Thus, by improving the analyses, the following results were finally achieved. 1) the class “burned areas” has been reclassified based on expert knowledge, and the type of vegetation correctly assigned. This allowed correctly estimating the relative importance of each vegetation class belonging to this variable. (2) Two additional ML based approach, namely Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM), were tested besides RF and the performance of each model was assessed, as well as the resulting variable ranking and the predicting outputs. This allowed comparing the three ML based approaches and evaluating the pros and cons of each one. (3) The training and testing dataset were selected by extracting the yearly-observations based on a clustering procedure, allowing accounting for the temporal variability of the burning seasons. As result, our models can perform on average better prediction in different situations, by taking into considering years experiencing more or less wildfires than usual. The three ML-based models (RF, SVM and MLP) were finally validated by means of two metrics: i) the Area Under the ROC Curve, selecting the validation dataset by using a 5-folds cross validation procedure; ii) the RMS errors, computed by evaluating the difference between the predicted probability outputs and the presence/absence of an observed event in the testing dataset.

Bibliography:

Tonini, M.; D’Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences 2020, 10, 105. https://doi.org/10.3390/geosciences10030105

How to cite: Trucchia, A., Isnardi, S., D'Andrea, M., Biondi, G., Fiorucci, P., and Tonini, M.: Wildfire susceptibility assessment: evaluation of the performance of different machine learning algorithms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7162, https://doi.org/10.5194/egusphere-egu21-7162, 2021.

The Earth System Model Evaluation Tool (ESMValTool) is a free and open-source community diagnostic and performance metrics tool for the evaluation of Earth system models such as those participating in the Coupled Model Intercomparison Project (CMIP). Version 2 of the tool (Righi et al. 2020, www.esmvaltool.org) features a brand new design composed of a core that finds and processes data according to a ‘recipe’ and an extensive collection of ready-to-use recipes and associated diagnostic codes for reproducing results from published papers. Development and discussion of the tool (mostly) takes place in public on https://github.com/esmvalgroup and anyone with an interest in climate model evaluation is welcome to join there.

Since the initial release of version 2 in the summer of 2020, many improvements have been made to the tool. It is now more user friendly with extensive documentation available on docs.esmvaltool.org and a step by step online tutorial. Regular releases, currently planned three times a year, ensure that recent contributions become available quickly while still ensuring a high level of quality control. The tool can be installed from conda, but portable docker and singularity containers are also available.

Recent new features include a more user-friendly command-line interface, citation information per figure including CMIP6 data citation using ES-DOC, more and faster preprocessor functions that require less memory, automatic corrections for a larger number of CMIP6 datasets, support for more observational and reanalysis datasets, and more recipes and diagnostics.

The tool is now also more reliable, with improved automated testing through more unit tests for the core, as well as a recipe testing service running at DKRZ for testing the scientific recipes and diagnostics that are bundled into the tool. The community maintaining and developing the tool is growing, making the project less dependent on individual contributors. There are now technical and scientific review teams that review new contributions for technical quality and scientific correctness and relevance respectively, two new principal investigators for generating a larger support base in the community, and a newly created user engagement team that is taking care of improving the overall user experience.

How to cite: Andela, B., Alidoost, F., Brunner, L., Camphuijsen, J., Crezee, B., Drost, N., Gier, B., Hassler, B., Kalverla, P., Lauer, A., Loosveldt-Tomas, S., Lorenz, R., Predoi, V., Righi, M., Schlund, M., Smeets, S., Vegas-Regidor, J., Von Hardenberg, J., Weigel, K., and Zimmermann, K.: Recent developments on the Earth System Model Evaluation Tool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3476, https://doi.org/10.5194/egusphere-egu21-3476, 2021.

Ease of use can easily become a limiting factor to scientific quality and progress. In order to verify and build upon previous results, the ability to effortlessly access and process increasing data volumes is crucial.

To level the playing field for all researchers, a shared infrastructure had to be developed. In Europe, this effort is coordinated mainly through the IS-ENES projects. The current infrastructure provides access to the data as well as compute resources. This leaves the tools to easily work with the data as the main obstacle for a smooth scientific process. Interestingly, not the scarcity of tools, but rather their abundance can lead to diverging workflows that hamper reproducibility.

The Earth System Model eValuation Tool (ESMValTool) was originally developed as a command line tool for routine evaluation of important analytics workflows. This tool encourages some degree of standardization by factoring out common operations, while allowing for custom analytics of the pre-processed data. All scripts are bundled with the tool. Over time this has grown into a library of so-called ‘recipes’.

In the EUCP project, we are now developing a Python API for the ESMValTool. This allows for interactive exploration, modification, and execution of existing recipes, as well as creation of new analytics. Concomitantly, partners in IS-ENES3 are making their infrastructure accessible through JupyterLab. Through the combination of these technologies, researchers can easily access the data and compute, but also the workflows or methods used by their colleagues - all through the web browser. During the vEGU, we will show how this extended infrastructure can be used to easily reproduce, and build upon, previous results.

How to cite: Kalverla, P. C., Smeets, S., Drost, N., Andela, B., Alidoost, F., and Camphuijsen, J.: Bringing ESMValTool to the Jupyter Lab, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4805, https://doi.org/10.5194/egusphere-egu21-4805, 2021.

The Earth System Model Evaluation Tool (ESMValTool) has been developed with the aim of taking model evaluation to the next level by facilitating analysis of many different ESM components, providing well-documented source code and scientific background of implemented diagnostics and metrics and allowing for traceability and reproducibility of results (provenance). This has been made possible by a lively and growing development community continuously improving the tool supported by multiple national and European projects. The latest major release (v2.0) of the ESMValTool has been officially introduced in August 2020 as a large community effort, and since then several additional smaller releases have followed.

The diagnostic part of the ESMValTool includes a large collection of standard “recipes” for reproducing peer-reviewed analyses of many variables across ESM compartments including atmosphere, ocean, and land domains, with diagnostics and performance metrics focusing on the mean-state, trends, variability and important processes, phenomena, as well as emergent constraints. While most of the diagnostics use observational data sets (in particular satellite and ground-based observations) or reanalysis products for model evaluation some are also based on model-to-model comparisons. This presentation gives an overview on the latest scientific diagnostics and metrics added during the last year including examples of applications of these diagnostics to CMIP6 model data.

How to cite: Bock, L., Hassler, B., and Lauer, A. and the ESMValTool Develpoment Team: New scientific diagnostics in the ESMValTool – an overview, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7724, https://doi.org/10.5194/egusphere-egu21-7724, 2021.

Developing an Earth system model evaluation tool for a broad user community is a real challenge, as the potential users do not necessarily have the same needs or expectations. While many evaluation tasks across user communities include common steps, significant differences are also apparent, not least the investment by institutions and individuals in bespoke tools. A key question is whether there is sufficient common ground to pursue a community tool with broad appeal and application.

We present the main results of a survey carried out by Assimila for the H2020 IS-ENES3 project to review the model evaluation needs of European Earth System Modelling communities. Interviewing approximately 30 participants among several European institutions, the survey targeted a broad range of users, including model developers, model users, evaluation data providers, and infrastructure providers. The output of the study provides an analysis of  requirements focusing on key technical, standards, and governance aspects.

The study used ESMValTool as a  current benchmark in terms of European evaluation tools. It is a community diagnostics and performance metrics tool for the evaluation of Earth System Models that allows for comparison of single or multiple models, either against predecessor versions or against observations. The tool is being developed in such a way that additional analyses can be added. As a community effort open to both users and developers, it encourages open exchange of diagnostic source code and evaluation results. It is currently used in Coupled Model Intercomparison Projects as well as for the development and testing of “new” models.

A key result of the survey is the widespread support for ESMValTool amongst users, developers, and even those who have taken or promote other approaches. The results of the survey identify priorities and opportunities in the further development of the ESMValTool to ensure long-term adoption of the tool by a broad community.

How to cite: Servonnat, J., Guilyardi, E., Stott, Z., Serradell, K., Lauer, A., Zimmerman, K., Adloff, F., Greening, M.-C., Kazeroni, R., and Vegas, J.: Model evaluation expectations of European ESM communities: results from a survey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15681, https://doi.org/10.5194/egusphere-egu21-15681, 2021.

An breadboard for end-to-end (E2E) Marine Litter Optical Performance Simulations (ML-OPSI) is being designed in the frame of the ESA Open Space Innovation Platform (OSIP) Campaign to support Earth Observation (EO) scientists with the design of computational experiments for Operations Research. The ML-OPSI breadboard will estimate Marine Litter signal at Top-Of-Atmosphere (TOA) from a set of Bottom-Of-Atmosphere (BOA) scenarios representing the various case studies by the community (e.g., windrows, frontal areas, river mouths, sub-tropical gyres), coming from synthetic data (computer-simulated) or from real observations. It is a modular, pluggable and extensible framework, promoting re-use and be adapted for different missions, sensors and scenarios.

The breadboard consists of (a) the OPSI components for the simulation i.e. the process of using a model to study the characteristics of the system by manipulating variables and by studying the properties of the model allowing an evaluation to optimise performance and make predictions about the real system; and (b) the Marine Litter model components for the detection of marine litter. It shall consider the changes caused in the water reflectance and properties due to marine litter, exploiting gathered information of plastic polymers, different viewing geometries, and atmospheric conditions as naturally occurring. The modules of the breadboard include a Scenario Builder Module (SB) with maximum spatial resolution and best modelling as possible of the relevant physical properties, which for spectral sensors could include high spatial resolution and high spectral density/resolution BOA radiance simulations in the optical to SWIR bands; a Radiative Transfer Module (RTM) transforming water-leaving to TOA reflectance for varying atmospheric conditions and observational geometries; a Scene Generator Module (SGM) which could use Sentinel-2, Landsat, or PRISMA data as reference or any other instrument as pertinent; a Performance Assessment Module (PAM) for ML detection that takes into account the variability of the atmosphere, the sunlight & skylight at BOA, the sea-surface roughness with trains of wind waves & swells, sea-spray (whitecaps), air bubbles in the mixed layer, marine litter dynamics as well as instrumental noise to assess marine litter detection feasibility.

Marine Litter scenarios of reference shall be built based on in-situ campaigns, to reflect the true littering conditions at each case, both in spatial distribution and composition. The breadboard shall be validated over artificial targets at sea in field campaigns as relevant. This might include spectral measurements from ASD, on-field radiometers, and cameras on UAVs, concomitant with Copernicus Sentinel-2 acquisitions. Combined, they can be used to estimate atmospheric contribution and assess performance of the testes processing chain.

This activity collaborates on the ““Remote Sensing of Marine Litter and Debris” IOCCG taskforce.

How to cite: Emsley, S., Arias, M., Papadopoulou, T., and Martin-Lauzer, F.-R.: Simulating Marine Litter observations from space to support Operations Research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15661, https://doi.org/10.5194/egusphere-egu21-15661, 2021.

Automatically extracting buildings from remote sensing images (RSI) plays important roles in urban planning, population estimation, disaster emergency response, etc. With the development of deep learning technology, convolutional neural networks (CNN) with better performance than traditional methods have been widely used in extracting buildings from remote sensing imagery (RSI). But it still faces some problems. First of all, low-level features extracted by shallow layers and abstract features extracted by deep layers of the artificial neural network could not be fully fused. it makes building extraction is often inaccurate, especially for buildings with complex structures, irregular shapes and small sizes. Secondly, there are so many parameters that need to be trained in a network, which occupies a lot of computing resources and consumes a lot of time in the training process. By analyzing the structure of the CNN, we found that abstract features extracted by deep layers with low geospatial resolution contain more semantic information. These abstract features are conducive to determine the category of pixels while not sensitive to the boundaries of the buildings. We found the stride of the convolution kernel and pooling operation reduced the geospatial resolution of feature maps, so, this paper proposed a simple and effective strategy—reduce the stride of convolution kernel contains in one of the layers and reduced the number of convolutional kernels to alleviate the above two bottlenecks. This strategy was used to deeplabv3+net and the experimental results for both the WHU Building Dataset and Massachusetts Building Dataset. Compared with the original deeplabv3+net the result showed that this strategy has a better performance. In terms of WHU building data set, the Intersection over Union (IoU) increased by 1.4% and F1 score increased by 0.9%; in terms of Massachusetts Building Dataset, IoU increased by 3.31% and F1 score increased by 2.3%.

How to cite: Chen, M., Wu, J., and Tian, F.: Reducing the stride of the convolution kernel: a simple and effective strategy to increase the performance of CNN in building extraction from remote sensing image, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10783, https://doi.org/10.5194/egusphere-egu21-10783, 2021.

Long time series of essential climate variables (ECVs) derived from satellite data are key to climate research. SemantiX is a research project to establish, complement and expand Advanced Very High Resolution Radiometer (AVHRR) time series using Copernicus Sentinel-3 A/B imagery, making them and derived ECVs accessible using a semantic Earth observation (EO) data cube. The Remote Sensing Research Group at the University of Bern has one of the longest European times series of AVHRR imagery (1981-now). Data cube technologies are a game changer for how EO imagery are stored, accessed, and processed. They also establish reproducible analytical environments for queries and information production and are able to better represent multi-dimensional systems. A semantic EO data cube is a newly coined concept by researchers at the University of Salzburg referring to a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance (Augustin et al. 2019). Offering analysis ready data (i.e., calibrated and orthorectified AVHRR Level 1c data) in a data cube along with semantic enrichment reduces barriers to conducting spatial analysis through time based on user-defined AOIs.

This contribution presents a semantic EO data cube containing selected ECV time series (i.e., snow cover extent, lake surface water temperature, vegetation dynamics) derived from AVHRR imagery (1981-2019), a temporal and spatial subset of AVHRR Level 1c imagery (updated after Hüsler et al. 2011) from 2016 until 2019, and, for the later, semantic enrichment derived using the Satellite Image Automatic Mapper (SIAM). SIAM applies a fully automated, spectral rule-based routine based on a physical-model to assign spectral profiles to colour names with known semantic associations; no user parameters are required, and the result is application-independent (Baraldi et al. 2010). Existing probabilistic cloud masks (Musial et al. 2014) generated by the Remote Sensing Research Group at the University of Bern are also included as additional data-derived information to support spatio-temporal semantic queries. This implementation is a foundational step towards the overall objective of combining climate-relevant AVHRR time series with Sentinel-3 imagery for the Austrian-Swiss alpine region, a European region that is currently experiencing serious changes due to climate change that will continue to create challenges well into the future.

Going forward, this semantic EO data cube will be linked to a mobile citizen science smartphone application. For the first time, scientists in disciplines unrelated to remote sensing, students, as well as interested members of the public will have direct and location-based access to these long EO data time series and derived information. SemantiX runs from August 2020-2022 funded by the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP 16) (project #878939) in collaboration with the Swiss Space Office (SSO).

How to cite: Augustin, H., Sudmanns, M., Weber, H., Baraldi, A., Wunderle, S., Neuhaus, C., Reichel, S., van der Meer, L., Hummer, P., and Tiede, D.: SemantiX: a cross-sensor semantic EO data cube to open and leverage AVHRR time-series and essential climate variables with scientists and the public , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12722, https://doi.org/10.5194/egusphere-egu21-12722, 2021.

Nitrogen dioxide (NO²) is one of the main air quality pollutants of concern in many urban and industrial areas worldwide. Being emitted by fossil fuel burning activities including mainly road traffic, the NO² pollution is responsible for population health degradation and secondary pollutants formation as nitric acid and ozone. In the European region, almost 20 countries exceeded in 2017 the NO² annual limit values imposed by European Commission Directive 2008/50/EC (EEA, 2019). Therefore, NO² pollution monitoring and regulation is a necessary task to help decision makers to search for a sustainable solution for environmental quality and population health status improvement. In this study, we propose a comparative analysis of the tropospheric NO² column density spatial configuration over Europe between similar periods from 2019 and 2020, based on ESA Copernicus Sentinel-5P products. Our results highlight the NO² pollution dynamics over the abrupt transition from a normal condition situation to the COVID-19 outbreak context, characterized by short-time decrease of traffic intensities and industrial activities, this situation being also reflected by the national level statistics referring to COVID-19 cases and economic indicatiors. The validation approach provides high correlation between TROPOMI derived data and independent data from ground-based observations with encouraging values of the R² ranging between 0.5 and 0.75 in different locations.  

How to cite: Vîrghileanu, M., Săvulescu, I., Mihai, B.-A., Nistor, C., and Dobre, R.: Using Sentinel-5P time-series products for Nitrogen Dioxide (NO2) Spatio-Temporal Analysis over Europe During the Coronavirus Pandemic Lockdown, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10882, https://doi.org/10.5194/egusphere-egu21-10882, 2021.

The climate is strongly affected by interaction with clouds. To reduce major errors in climate predictions, this interaction requires a much finer understanding of cloud physics than current knowledge. Current knowledge is based on empirical remote sensing data that is analyzed under the assumption that the atmosphere and clouds are made of very broad and uniform layers. To help to overcome this problem, 3D scattering computed tomography (CT) has been suggested as a way to study clouds. 

CT is a powerful way to recover the inner structure of three dimensional (3D) volumetric heterogeneous objects. CT has extensive use in many research and operational domains. Aside from its common usage in medicine, CT is used for sensing geophysical terrestrial structures, atmospheric pollution and fluid dynamics. CT requires imaging from multiple directions and in nearly all CT approaches, the object is considered static during image acquisition. However, in many cases, the object changes while multi-view images are acquired sequentially. Thus, an effort has been invested to expand 3D CT to four-dimensional (4D) spatiotemporal CT. This effort has been directed at linear CT modalities. Since linear CT is computationally easier to handle, it has been a popular method for medical imaging. However, these linear CT modalities do not apply to clouds: clouds constitute a scattering medium, and therefore radiative transfer is non-linear in the clouds’ content.

This work focuses on the challenge of 4D scattering CT of clouds. Scattering CT of clouds requires high-resolution multi-view images from space. There are spaceborne and high-altitude systems that may provide such data, for example AirMSPI, MAIA, HARP and AirHARP. An additional planned system is the CloudCT formation, funded by the ERC. However, these systems are costly. Deploying them in large numbers to simultaneously acquire images of the same clouds from many angles can be impractical. Therefore, the platforms are planned to move above the clouds: a sequence of images is taken, in order to span and sample a wide angular breadth. However, the clouds evolve while the angular span is sampled.

We pose conditions under which this task can be performed. These regard temporal sampling and angular breadth, in relation to the correlation time of the evolving cloud. Then, we generalize scattering CT. The generalization seeks spatiotemporal recovery of the cloud extinction field in high resolution (10m), using data taken by a small number of moving cameras. We present an optimization-based method to reach this, and then demonstrate the method both in rigorous simulations and on real data.

How to cite: Ronen, R., Schechner, Y. Y., and Eytan, E.: Spatiotemporal tomography based on scattered multiangular signals and its use for resolving evolving clouds using moving platforms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10892, https://doi.org/10.5194/egusphere-egu21-10892, 2021.

Urbanization and the trend of people moving to cities often leads to problematic traffic conditions, which can be very challenging for traffic management. It can hamper the flow of people and goods, negatively affecting businesses through delays and the inability to estimate travel times and thus plan, as well as the environment and health of population due to increased fuel consumption and subsequent air pollution. Many cities have a policy and rules to manage traffic, ranging from standard traffic lights to more dynamic and adaptable solutions involving in-road sensors or cameras to actively modify the duration of traffic lights, or even more sophisticated IoT solutions to monitor and manage the conditions on a city-wide scale. The core to these technologies and to decision making processes is the availability of reliable data on traffic conditions, and better yet real-time data. Thus, a lot of cities are still coping with the lack of good spatial and temporal data coverage, as many of these solutions are requiring not only changes to the infrastructure, but also large investments.

One approach is to exploit the current and the forthcoming advancements made available by Earth Observation (EO) satellite technologies. The biggest advantage is EOs great spatial coverage ranging from a few km² to 100 km² per image on a spatial resolution down to 0.3m, thus allowing for a quick, city-spanning data collection. Furthermore, the availability of imaging sensors covering specific bands allows the constituent information within an image to be separated and the information to be leveraged.

In this respect, we present the findings of our work on multispectral image sets collected on three occasions in 2019 using very high resolution WorldView-3 satellite. We apply a combination of machine learning and PCA methods to detect vehicles and devise their kinematic properties (e.g., movement, direction, speed), only possible with satellites with a specific design allowing for short time lags between imaging in different spectral bands. As these data basically constitute a time-series, we will discuss how the results presented fully apply to the forthcoming WorldView-Legion constellation of satellites providing up to 15 revisits per day, and thus near-real time traffic monitoring and its impact on the environment.

How to cite: Duro, R., Neubauer, G., and Bojor, A.-I.: The potential of monitoring traffic conditions up to 15 times a day using sub-meter resolution EO images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12831, https://doi.org/10.5194/egusphere-egu21-12831, 2021.

Processing, handling and visualising the large data volume produced by satellite altimetry missions is a challenging task. A reference tool for the visualisation of satellite laser altimetry data is the OpenAltimetry platform, a tool that provides altimetry-specific data from the Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 satellite missions through a web-based interactive interface. However, by focusing only on altimetry data, that tool leaves out access to many other equally important information existing in the data products from both missions.

The main objective of the work reported here was the development of a new web-based tool, called ICEComb, that offers end users the ability to access all the available data from both satellite missions, visualise and interact with them on a geographic map, store the data records locally, and process and explore data in an efficient, detailed and meaningful way, thus providing an easy-to-use software environment for satellite laser altimetry data analysis and interpretation.

The proposed tool is intended to be mainly used by researchers and scientists to aid their work using ICESat and ICESat-2 data, offering users a ready-to-use system to rapidly access the raw collected data in a visually engaging way, without the need to have prior understanding of the format, structure and parameters of the data products. In addition, the architecture of the ICEComb tool was developed with possible future expansion in mind, for which well-documented and standard languages were used in its implementation. This allows, e.g., to extend its applicability to data from other satellite laser altimetry missions and integrate models that can be coupled with ICESat and ICESat-2 data, thus expanding and enriching the context of studies carried out with such data.

The use of the ICEComb tool is illustrated and demonstrated by its application to ICESat/GLAS measurements over Lake Mai-Ndombe, a large and shallow freshwater lake located within the Ngiri-Tumba-Maindombe area, one of the largest Ramsar wetlands of international importance, situated in the Cuvette Centrale region of the Congo Basin.

Keywords: Laser altimetry, ICESat/GLAS, software tool design, data visualization, Congo Basin.

Acknowledgement. This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) through LARSyS − FCT Pluriannual funding 2020−2023.

How to cite: Silva, B., Guerreiro Lopes, L., and Campos, P.: ICEComb − A New Software Tool for Satellite Laser Altimetry Data Processing and Visualisation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13727, https://doi.org/10.5194/egusphere-egu21-13727, 2021.

The use of remote sensing in mineral detection and lithological mapping has become a generally accepted augmentative tool in exploration. With the advent of multispectral sensors (e.g. ASTER, Landsat, Sentinel and PlanetScope) having suitable wavelength coverage and bands in the Shortwave Infrared (SWIR) and Thermal Infrared (TIR) regions, multispectral sensors have become increasingly efficient at routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this project sought to evaluate and discuss the detection and mapping of vanadium bearing magnetite, found in discordant bodies and magnetite layers, on the Eastern Limb of the Bushveld Complex. The Bushveld Complex hosts the world’s largest resource of high-grade primary vanadium in magnetitite layers, so the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes warranted the delineation of magnetite.

The detection and mapping of the vanadium bearing magnetite was evaluated using specialized traditional, and advanced machine learning algorithms. Prior to this study, few studies had looked at the detection and exploration of magnetite using remote sensing, despite remote sensing tools having been regularly applied to diverse aspects of geosciences. Maximum Likelihood, Minimum Distance to Means, Artificial Neural Networks, Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope data in ENVI, QGIS, and Python. For each classification algorithm, a thematic landcover map was attained and the accuracy assessed using an error matrix, depicting the user's and producer's accuracies, as well as kappa statistics.

The Maximum Likelihood Classifier significantly outperformed the other techniques, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The erroneous classification of some mining activity pixels as magnetite in the Maximum Likelihood was inherent to all classification algorithms. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially in iron oxide mineralization in the Eastern Limb of Bushveld Complex. 

How to cite: Twala, M., Roberts, J., and Munghemezulu, C.: Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7932, https://doi.org/10.5194/egusphere-egu21-7932, 2021.

We have developed a set of geometric standards for assessing earth observing data products derived from space-borne remote sensors.  We have worked with the European Space Agency (ESA) Earthnet Data Assessment Pilot (EDAP) project to provide a set of guidelines to assess geometric performance in data products from commercial electronic-optical remote sensors aboard satellites such as those from Planet Labs. The guidelines, or the standards, are based on performance from a few NASA procured sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors and the Advanced Baseline Imager (ABI) sensors. The standards include sensor spatial response, absolute positional accuracy, and band-to-band co-registration. They are tiered in “basic”, “intermediate” and “goal” criteria. These are important geometric factors affecting scientific use of remote sensing data products. We also discuss possible approaches achieving the highest goal in geometric performance standards.

How to cite: Lin, G. (., Wolfe, R., Tan, B., and Nickeson, J.: Community geometric standards for remote sensing products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1327, https://doi.org/10.5194/egusphere-egu21-1327, 2021.

Serendipitous observations of airless bodies of the inner solar system provide a unique means to the calibration of instruments on meteorological research satellites, because the physical properties of their surfaces change very little, even on large time scales. We investigated how certain instrumental effects can be characterised with observations of the Moon and Mercury. For this we identified and analysed intrusions of the Moon in the deep space views of HIRS/2, /3, and /4 (High-resolution Infrared Sounder) on various satellites in polar orbits and as well some images obtained with SEVIRI (Spinning Enhanced Visible Infra-Red Imager) on MSG-3 and -4 (Meteosat Second Generation), which had Mercury standing close to the Earth in the rectangular field of view.

A full-disk, infrared Moon model was developed that describes how the lunar flux density depends on phase angle and wavelength. It is particularly helpful for inter-calibration, checks of the photometric consistency of the sounding channels, and the calculation of an upper limit on the non-linearity of the shortwave channels of HIRS. In addition, we used the Moon to determine the co-registration of the different spectral channels.

Studies of the channel alignment are also presented for SEVIRI, an infrared sounder with an angular resolution about a hundred times better than HIRS. As we wanted to check the image quality of this instrument with a quasi-point source as well, we replaced here the Moon with Mercury. We found the typical smearing of the point spread function in the scan direction and occasionally a nearby ghost image, which is three to four times fainter than the main image of the planet. Both effects cause additional uncertainties of the photometric calibration.  

How to cite: Burgdorf, M., Buehler, S. A., John, V., Müller, T., and Prange, M.: Calibration and Validation of Infrared Sounders with Moon and Mercury, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7919, https://doi.org/10.5194/egusphere-egu21-7919, 2021.

The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument installed on the International Space Station (ISS) has completed over three and a half years of data collection and production of science data products. The SAGE III/ISS is a solar and lunar occultation instrument that scans the light from the Sun and Moon through the limb of the Earth’s atmosphere to produce vertical profiles of aerosol, ozone, water vapor, and other trace gases. It continues the legacy of previous SAGE instruments dating back to the 1970s to provide data continuity of stratospheric constituents critical for assessing trends in the ozone layer. This presentation shows the validation results of comparing SAGE III/ISS ozone and water vapor vertical profiles from the newly released v5.2 science product with those of in situ and satellite data .

How to cite: Kizer, S., Flittner, D., Roell, M., Damadeo, R., Roller, C., Hurst, D., Hall, E., Jordan, A., Cullis, P., Johnson, B., and Querel, R.: Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) Newly Released V5.2 Validation of Ozone and Water Vapor Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8481, https://doi.org/10.5194/egusphere-egu21-8481, 2021.

The atmospheric correction inter-comparison exercise (ACIX) is an international initiative to benchmark various state-of-the-art atmospheric correction (AC) processors. The first inter-comparison exercise initiated in 2016 with the collaboration of European Space Agency (ESA) and National Aeronautics and Space Administration (NASA) in the frame of the CEOS WGCV (Committee on Earth Observation Satellites, Working Group on Calibration & Validation). The evolution of the participating processors and the increasing interest of AC community to repeat and improve such experiment stimulated the continuation of ACIX and its second implementation (ACIX-II). In particular, 12 AC developer teams from Europe and USA participated in ACIX-II over land sites. In this presentation the benchmarking protocol, i.e. test sites, input data, inter-comparison metrics, etc. will be briefly described and some representative results of ACIX-II will be presented. The inter-comparison outputs varied depending on the sensors, products and sites, demonstrating the strengths and weaknesses of the corresponding processors. In continuation of ACIX-I achievements, the outcomes of the second one are expected to provide an enhanced standardised approach to inter-compare AC processing products, i.e. Aerosol Optical Thickness (AOT), Water Vapour (WV) and Surface Reflectance (SR), and quantitively assessed their quality when in situ measurements are available.

How to cite: Doxani, G., Vermote, E. F., Skakun, S., Gascon, F., and Roger, J.-C.: Atmospheric Correction Inter-comparison eXercise: the second implementation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8702, https://doi.org/10.5194/egusphere-egu21-8702, 2021.

Absolute calibration of Earth observation sensors is key to ensuring long term stability and interoperability, essential for long term global climate records and forecasts. The Moon provides a photometrically stable calibration source, within the range of the Earth radiometric levels, and is free from atmospheric interference. However, to use this ideal calibration source, one must model the variation of its disk integrated irradiance resulting from changes in Sun-Earth-Moon geometries.

LIME, the Lunar Irradiance Model of the European Space Agency, is a new lunar irradiance model developed from ground-based observations acquired using a lunar photometer operating from the Izaña Atmospheric Observatory and Teide Peak, Tenerife. Approximately 300 lunar observations acquired between March 2018 and October 2020 currently contribute to the model, which builds on the widely-used ROLO (Robotic Lunar Observatory) model.

This presentation will outline the strategy used to derive LIME. First, the instrument was calibrated traceably to SI and characterised to determine its thermal sensitivity and its linearity over the wide dynamic range required. Second, the instrument was installed at the observatory, and nightly observations over a two-hour time window were extrapolated to provide top-of-atmosphere lunar irradiance using the Langley plot method. Third, these observations were combined to derive the model. Each of these steps includes a metrologically rigorous uncertainty analysis.

Comparisons to several EO sensors will be presented including Proba-V, Pleiades and Sentinel 3A and 3B, as well as a comparison to GIRO, the GSICS implementation   of the ROLO model. Initial results indicate LIME predicts 3% - 5% higher disk integrated lunar irradiance than the GIRO/ROLO model for the visible and near-infrared channels. The model has an expanded (k = 2) absolute radiometric uncertainty of ~2%, and it is expected that planned observations until at least 2024 will further constrain the model in subsequent updates.

How to cite: Taylor, S., Adriaensen, S., Toledano, C., Barreto, Á., Woolliams, E., and Bouvet, M.: LIME: the Lunar Irradiance Model of the European Space Agency, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10066, https://doi.org/10.5194/egusphere-egu21-10066, 2021.

The worldwide operating Pandonia Global Network (PGN) is measuring atmospheric trace gases at high temporal resolution with the purpose of air quality monitoring and satellite validation. It is an activity carried out jointly by NASA and ESA as part of their “Joint Program Planning Group Subgroup” on calibration and validation and field activities, with additional collaboration from other institutions, most notably a strongly growing participation of the US Environmental Protection Agency (EPA). The more than 50 official PGN instruments are homogeneously calibrated and their data are centrally processed in real-time. Since 2019, total NO2 column amounts from the PGN are uploaded daily to the ESA Atmospheric Validation Data Centre (EVDC), where they are used for operational validation of Sentinel 5P (S5P) retrievals. During 2020, a new processor version 1.8 has been developed, which produces improved total NO2 column amounts and also the following new PGN products: total columns of O3, SO2 and HCHO based on direct sun observations and tropospheric columns, surface concentrations and tropospheric profiles of NO2 and HCHO based on sky observations. In this presentation we show some first examples of comparisons of the new PGN products with S5P data. Compared to the total NO2 columns from the previous processor version 1.7, the 1.8 data use better estimations for the effective NO2 temperature and the air mass factor. The effect of this improvement on the comparison with S5P retrievals is shown for some remote and high-altitude PGN sites. The new PGN total O3 column algorithm also retrieves the effective O3 temperature, which is a rather unique feature for ground-based direct sun retrievals. This allows us to analyze whether potential differences to satellite O3 columns might be influenced by the O3 temperature. Including the O3 temperature in the spectral fitting has also allowed the retrieval of accurate total SO2 columns. This PGN data product is of particular interest for satellite validation, as ground-based total SO2 column amounts are hardly measured by other instrumentation. An initial comparison of the PGN SO2 columns with S5P retrievals at selected PGN sites around the world is shown. PGN total HCHO columns from direct sun measurements are now possible for those PGN instruments, where the hardware parts made of Delrin, which outgasses HCHO, have been replaced by Nylon pieces. An initial comparison to HCHO retrievals from S5P is shown for locations with these upgraded instruments. Another new feature in the 1.8 PGN data is that they come with comprehensive uncertainty estimations, separated in the output files as independent, structured, common and total uncertainty.

How to cite: Cede, A., Tiefengraber, M., Gebetsberger, M., Van Roozendael, M., Eskes, H., Lerot, C., Loyola, D., Theys, N., De Smedt, I., Abuhassan, N., Hanisco, T., Dehn, A., Von Bismarck, J., Casadio, S., Valin, L., and Lefer, B.: New and improved data from the Pandonia Global Network for satellite validation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13302, https://doi.org/10.5194/egusphere-egu21-13302, 2021.

The number, range and criticality of applications of Earth viewing optical sensors is increasing rapidly.  Not only from national/international space agencies but also through the launch of commercial constellations such as those of planet and the concept of Analysis Ready Data (ARD) reducing the skill needed for utilisation of the data.  However, no one organisation can provide all the tools necessary, and the need for a coordinated holistic earth observing system has never been greater. Achieving this vision has led to international initiatives coordinated by bodies such as the Committee on Earth Observation Satellites (CEOS and Global Space Inter-Calibration System (GISCS) of WMO to establish strategies to facilitate interoperability and the understanding and removal of bias through post-launch Calibration and Validation. 

In parallel, the societal challenge resulting from climate change has been a major stimulus for significantly improved accuracy and trust of satellite data. Instrumental biases and uncertainty must be sufficiently small to minimise the multi-decadal timescales needed to detect small trends and attribute their cause, enabling them to become unequivocally accepted as evidence. 

Although there have been many advances in the pre-flight SI-traceable calibration of optical sensors, in the last decade, unpredictable degradation in performance from both launch and operational environment remains a major difficulty.  Even with on-board calibration systems, uncertainties of less than a few percent are rarely achieved and maintained and the evidential link to SI-traceability is weak. For many climate observations the target uncertainty needs to be improved ten-fold. 

However, this decade will hopefully see the launch of two missions providing spectrally resolved observations of the Earth at optical wavelengths, CLARREO Pathfinder on the International Space Station from NASA [1] and TRUTHS from ESA [2] to change this paradigm.  Both payloads are explicitly designed to achieve uncertainties close to the ideal observing system, commensurate with the needs of climate, with robust SI-Traceability evidenced in space.  Not only can they make high accuracy climate quality observations of the Earth and in the case of TRUTHS also the Sun, but they will also transfer their SI-traceable uncertainty to other sensors.  In this way creating the concept of a ‘metrology laboratory in space’, providing a ‘gold standard’ reference to anchor and improve the calibration of other sensors. The two missions achieve their traceability in orbit through differing methods but will use synergistic approaches for establishing in-flight cross-calibrations.  This paper will describe these strategies and illustrate the benefit through examples where improved accuracy has the most impact on the Earth observing system.

The complementarity and international value of these missions has ensured a strong partnership during early development phases of the full CLARREO mission and that of the NPL conceived TRUTHS. Following a proposal by the UK Space Agency  and subsequent adoption into the ESA EarthWatch program this partnership is further strengthened with the ESA team and a vision that together the two missions can lay the foundation of a framework for a future sustainable international climate and calibration observatory to the benefit of the global Earth Observing community.

References

[1]  https://clarreo-pathfinder.larc.nasa.gov/

[2] https://www.npl.co.uk/earth-observation/truths

How to cite: Fox, N., Shea, Y., Fehr, T., Gary, F., Lukashin, C., Pilewskie, P., Remedios, J., and Smith, P.: Toward a Climate and Calibration Observatory in space: NASA CLARREO Pathfinder and ESA TRUTHS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14656, https://doi.org/10.5194/egusphere-egu21-14656, 2021.

The Tropics are covering around 40% of the globe and are home to approximately 40% of the world population. However, numerical weather prediction (NWP) for this region still remains challenging due to the lack of basic observations and incomplete understanding of atmospheric processes, also affecting extratropical storm developments. As a result, the largest impact of the ESA’s Aeolus satellite observations on NWP is expected in the Tropics where only a very limited number of wind profile observations from the ground can be performed.

An especially important case relating to the predictability of tropical weather system is the outflow of Saharan dust, its interaction with cloud micro-physics and the overall impact on the development of tropical storms over the Atlantic Ocean. The region of the coast of West Africa uniquely allows the study of the Saharan Aerosol layer, African Easterly Waves and Jets, Tropical Easterly Jet, as well as the deep convection in ITCZ and their relation to the formation of convective systems and the transport of dust.

Together with international partners, ESA and NASA are currently implementing a joint Tropical campaign from July to August 2021 with its base in Cape Verde. The campaign objective is to provide information on the validation and preparation of the ESA missions Aeolus and EarthCARE, respectively, as well as supporting a range of related science objectives for the investigation in the interactions between African Easterly and other tropical waves with the mean flow, dust and their impact on the development of convective systems; the structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems within and across the ITCZ; and impact of wind, aerosol, clouds, and precipitation effects on long range dust transport and air quality over the western Atlantic.

The campaign comprises a unique combination of both strong airborne and ground-based elements collocated on Cape Verde. The airborne component with wind and aerosol lidars, cloud radars, in-situ instrumentation and additional observations includes the NASA DC-8 with science activities coordinated by the U. of Washington, the German DLR Falcon-20, the French Safire Falcon-20 with activities led by LATMOS, and the Slovenian Aerovizija Advantic WT-10 light aircraft in cooperation with the U. Novo Gorica. The ground-based component led by the National Observatory of Athens is a collaboration of more than 25 European teams providing in-situ and remote sensing aerosol and cloud measurements with a wide range of lidar, radar and radiometer systems, as well as drone observatins by the Cyprus Institute.

In preparation for the field campaign, the NASA and ESA management and science teams are closely collaborating with regular coordination meetings, in particular in coordinating the shift of the activity by one year due to the COVID-19 pandemic. The time gained has been used to further consolidate the planning, and in particular with a dry-run campaign organized by NASA with European participation where six virtual flights were conducted in July 2020.

 This paper will present a summary of the campaign preparation activities and the consolidated plan for the 2021 Tropical campaign.

How to cite: Fehr, T., Skofronick-Jackson, G., Amiridis, V., von Bismarck, J., Chen, S., Flamant, C., Koopman, R., Lemmerz, C., Močnik, G., Parrinello, T., Piña, A., and Straume, A. G.: The Joint ESA-NASA Tropical Campaign Activity – Aeolus Calibration/Validation and Science in the Topics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15144, https://doi.org/10.5194/egusphere-egu21-15144, 2021.

The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.

Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates.

Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA Joint Program Planning Group (JPPG).

The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature). 

Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.

The Platform is also meant to monitor salinity in selected oceanographic “case studies”, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins.

The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform.

A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.

How to cite: Sabia, R., Guimbard, S., Reul, N., Lee, T., Schanze, J., Vinogradova, N., Le Vine, D., Bingham, F., Collard, F., Scipal, K., and Laur, H.: Pi-MEP Salinity – an ESA-NASA Platform for sustained satellite surface salinity validation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15161, https://doi.org/10.5194/egusphere-egu21-15161, 2021.

In this paper, the authors propose to describe the methodologies developed for the validation of Very High-Resolution (VHR) optical missions within the Earthnet Data Assessment Pilot (EDAP) Framework.  The use of surface-based, drone, airborne, and/or space-based observations to build calibration reference is playing a fundamental role in the validation process. A rigorous validation process must compare mission data products with independent reference data suitable for the satellite measurements. As a consequence, one background activity within EDAP is the collection, the consolidation of reference data of various nature depending on the validation methodology.

The validation methodologies are conventionally divided into three categories; i.e. validations of the measurement, the geometry and the image quality. The validation of the measurement requires an absolute calibration reference. This latter on is built up by using either in situ measurements collected with RadCalNet[1] stations or by using space based observations performed with “gold” mission (Sentinel-2, Landsat-8) over Pseudo Invariant Calibration Site (PICS). For the geometric validation, several test sites have been set up. A test site is equipped with data from different reference sources. The full usability of a test site is not systematic. It depends on the validation metrics and the specifications of the sensor, particularly the spatial resolution and image acquisition geometry. Some existing geometric sites are equipped with Ground Control Point (GCP) set surveyed by using Global Navigation Satellite System (GNSS) devices in the field.  In some cases, the GCP set comes in support to the refinement of an image observed with drones in order to produce a raster reference, subsequently used to validate the internal geometry of images under assessment. Besides, a limiting factor in the usage of VHR optical ortho-rectified data is the accuracy of the Digital Surface Model (DSM) / Digital Terrain Model (DTM). In order to separate errors due to terrain elevation and error due to the sensor itself, some test sites are also equipped with very accurate Light Detection and Ranging (LIDAR) data.

The validation of image quality address all aspect related to the spatial resolution and is strongly linked to both the measurement and the geometry. The image quality assessments are performed with both qualitative and quantitative approaches. The quantitative approach relies on the analysis of artificial ground target images and lead to the estimate of Modulation Transfer Function (MTF) together with additional image quality parameters such as Signal to Noise Ratio (SNR). On the other hand, the qualitative approach assesses the interpretability of input images and leads to a rating scaling[2] which is strongly related to the sensor Ground Resolution Distance (GRD). This visual inspection task required a database including very detailed image of man-made objects. This database is considered within EDAP as a reference.

How to cite: Saunier, S.: Reference Data and Methods for Validation of Very High Resolution Optical Data Within ESA / EDAP Project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15501, https://doi.org/10.5194/egusphere-egu21-15501, 2021.

The Integrated Carbon Observation System (ICOS) provides long term, high quality observations that follow (and cooperatively set) the global standards for the best possible quality data on the atmospheric composition for greenhouse gases (GHG), greenhouse gas exchange fluxes measured by eddy covariance and CO₂ partial pressure at water surfaces. The ICOS observational data feeds into a wide area of science that covers for example plant physiology, agriculture, biology, ecology, energy & fuels, forestry, hydrology, (micro)meteorology, environmental, oceanography, geochemistry, physical geography, remote sensing, earth-, climate-, soil- science and combinations of these in multi-disciplinary projects.
As ICOS is committed to provide all data and methods in an open and transparent way as free data, a dedicated system is needed to secure the long term archiving and availability of the data together with the descriptive metadata that belongs to the data and is needed to find, identify, understand and properly use the data, also in the far future, following the FAIR data principles. An added requirement is that the full data lifecycle should be completely reproducible to enable full trust in the observations and the derived data products.

In this presentation we will introduce the ICOS operational data repository named ICOS Carbon Portal that is based on the linked open data approach. All metadata is modelled in an ontology coded in OWL and based on a RDF triple store that is available through an open SparQL endpoint. The repository supports versioning, collections and models provenance through a simplified Prov-O ontology. All data objects are ingested under strict control for the identified data types on provision of the correct and sufficient (provenance) metadata, data format and data integrity. All data, including raw data, is stored in the long term trusted repository  B2SAFE with two replicates. On top of the triple store and SparQL endpoint we have built a series of services, APIs and graphical interfaces that allow machines to machine and user interaction with the data and metadata. Examples are a full faceted search with connected data cart and download facility, preview of higher level data products (time series of  point observations and spatial data), and cloud computing services like eddy covariance data processing and on demand atmospheric footprint calculations, all connected to the observational data from ICOS.  Another interesting development is the community support for scientific workflows using Jupyter notebook services that connect to our repository through a dedicated python library for direct metadata and data access.

How to cite: Vermeulen, A., Hellström, M., Mirzov, O., Karstens, U., D'Onofrio, C., and Lankreijer, H.: The ICOS Carbon Portal as example of a  FAIR community data repository supporting scientific workflows, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8458, https://doi.org/10.5194/egusphere-egu21-8458, 2021.

The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e‑infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data – University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.

In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes.  

The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.

How to cite: Michalek, J., Atakan, K., Rønnevik, C., Indrøy, H., Ottemøller, L., Natvik, Ø., Langeland, T., Lampe, O. D., Fonnes, G., Cook, J., Christensen, J. M., Baadshaug, U., Kierulf, H. P., Grøtan, B.-O., Olesen, O., Dehls, J., and Maupin, V.: EPOS-Norway Portal, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15394, https://doi.org/10.5194/egusphere-egu21-15394, 2021.

Svalbard Integrated Arctic Earth Observing System (SIOS) is an international consortium to develop and maintain a regional observing system in Svalbard and the associated waters. SIOS brings together the existing infrastructure and data of its members into a multidisciplinary network dedicated to answering Earth System Science (ESS) questions related to global change. The Observing System is built around “SIOS core data” – long-term data series collected by SIOS partners. SIOS Data Management System (SDMS) is dedicated to harvesting information on historical and current datasets from collaborating thematic and institutional data centres and making them available to users. A central data access portal is linked to the data repositories maintained by SIOS partners, which manage and distribute data sets and their associated metadata. The integrity of the information and harmonisation of data is based on internationally accepted protocols assuring interoperability of data, standardised documentation of data through the use of metadata and standardised interfaces by data systems through the discovery of metadata. By these means, SDMS is working towards FAIR data compliance (making data findable, accessible, interoperable and reusable), among other initiatives through the H2020 funded ENVRI-FAIR project (http://envri.eu/envri-fair/).

How to cite: Ignatiuk, D., Godøy, Ø., Ferrighi, L., Jennings, I., Hübner, C., Jawak, S., and Lihavainen, H.: SIOS Data Management System: distributed data system for Earth System Science, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15205, https://doi.org/10.5194/egusphere-egu21-15205, 2021.

The WMO Commission of Hydrology (CHy) is realizing the WMO Hydrological Observing System (WHOS), a software (and human) framework with the aim of improving sharing of hydrological data and knowledge worldwide.

National Hydrological Services (NHS) are already sharing on the web (both archived and near real time) data collected in each country, using disparate publication services. WHOS is leveraging the Discovery and Access Broker (DAB) technology developed and operated in its cloud infrastructure by CNR-IIA to realize WHOS-broker, a key component of WHOS architecture. WHOS-broker is in charge of harmonizing the available and heterogeneous metadata, data and services making the already published information more accessible to scientists (e.g. modelers), decision makers and general public worldwide. 

WHOS-broker supports many service interfaces and API that hydrological application builders already can leverage, example given OGC SOS, OGC CSW, OGC WMS, ESRI Feature Service, CUAHSI WaterOneFlow, DAB REST API, USGS RDB, OAI-PMH/WIGOS, THREDDS. New API and service protocols are continuously added to support new applications, being WHOS-broker a modular and flexible framework with the aim of enabling interoperability and assuring it as the standards will change/evolve through time. 

Three target programmes have already benefited from WHOS:

• La Plata river basin: hydro and meteo data from Argentina, Bolivia, Brazil, Paraguay, Uruguay are harmonized and shared by WHOS-broker to the benefit of different applications, one of them is the Plata Basin Hydrometeorological Forecasting and Early Warning System (PROHMSAT-Plata model, developed by HRC), based on CUAHSI WaterOneFlow and experts from the five countries.
• Arctic-HYCOS: hydro data from Canada, Finland, Greenland, Iceland, Norway, Russia, United States are harmonized and shared by WHOS-broker to the benefit of different applications, one of them is the WMO HydroHub Arctic portal, based on ESRI technologies.
• Dominican Republic: hydro and meteo data of Dominican Republic published by different originators is being harmonized by WHOS-broker to the benefit of different applications, one of them is the Met data explorer application developed by BYU based on THREDDS catalog service.

The three programmes should act as a driving force for more to follow, by demonstrating possible applications that can be built on top of WHOS.

The public launch of WHOS official homepage at WMO is expected by mid 2021, will include:

• A dedicated web portal based on Water Data Explorer application developed by BYU
• Results from the three programs
• Detailed information on how to access WHOS data by using one of the many WHOS-broker service interfaces
• An online training course for data providers interested in WHOS
• The WHOS Hydro Ontology, leveraged by WHOS-broker in order to both semantically augment user queries and harmonize results (e.g. in case of synonyms of the same concept in different languages).

How to cite: Boldrini, E., Mazzetti, P., Papeschi, F., Roncella, R., Santoro, M., Olivieri, M., Nativi, S., Pecora, S., Chernov, I., and Caponi, C.: The brokering framework empowering WMO Hydrological Observing System (WHOS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9400, https://doi.org/10.5194/egusphere-egu21-9400, 2021.

Collaboration requires some minimum of common understanding, in the case of Earth data in particular common principles making data interchangeable, comparable, and combinable. Open standards help here; in case of Big Earth Data specifically the OGC/ISO Coverages standard. This unifying framework establishes a common framework in particular for regular and irregular spatio-temporal datacubes. Services grounding on such common understanding have proven more uniform to access and handle, implementing a principle of "minimal surprise" for users visiting different portals while using their favourite clients. Data combination and fusion benefits from canonical metadata allowing automatic alignment, e.g, between 2D DEMs, 3D satellite image time series, 4D atmospheric data, etc.

The EarthServer datacube federation s showing the way towards unleashing in full the potential of pixels for supporting the UN Sustainable Development Goals, local governance, and also businesses. EarthServer is an open, free, transparent, and democratic network of data centers offering dozens of Petabytes of a critical variety, such as radar and optical Copernicus data, atmospheric data, elevation data, and thematic cubes like global sea ice. Data centers like DIASs and CODE-DE, research organizations, companies, and agencies have teamed up in EarthServer. Strictly based on the open OGC standards, an ecosystem of data has been established that is available to users as a single pool, without the need for any coding skills (such as python). A specific unique capability is location-transparency: clients can fire their query against any of the mebers, and the federation nodes will figure out the optimal work distribution irrespective of data location.

The underlying datacube engine, rasdaman, enables all datacube access, analytics, and federation. Query evaluation is optimized automatically applying highly efficient intelligent, rule-based methods in homogeneous and heterogeneous mashups, up to satellite on-board deployments as done in the ORBiDANSe project. Users perceive one single, common information space accessible through a wide spectrum of open-source and proprietary clients.

In our talk we present technology, services, and governance of this unique line-up of data centers. A demo will show distributed datacube fusion live.

How to cite: Baumann, P.: Teams Win: The European Datacube Federation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15148, https://doi.org/10.5194/egusphere-egu21-15148, 2021.

The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.

Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).

Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.

This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.

How to cite: Lacagnina, C., Peng, G., Downs, R. R., Ramapriyan, H., Ivanova, I., Moroni, D. F., Wei, Y., Bastin, L., Ritchey, N. A., Larnicol, G., Wyborn, L. A., Shie, C.-L., Habermann, T., Ganske, A., Champion, S. M., Wu, M., Bastrakova, I., Jones, D., and Berg-Cross, G.: Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-23, https://doi.org/10.5194/egusphere-egu21-23, 2021.

Earth system cyberinfrastructures include three types of data services: repositories, collections, and federations. These services arrange data by their purpose, level of integration, and governance.  For instance, registered data of uniform measurements fulfill the goal of publication but do not necessarily flow in an integrated data system. The data repository provides the first and high level of integration that strongly depends on the standardization of incoming data. One example here is the framework Observation to Archive and Analysis (O2A) that is operational and continuously developed at the Alfred-Wegener-Institute, Bremerhaven. A data repository is one of the components of the O2A framework and much of its functionality depends on the standardization of the incoming data. In this context, we focus on the development of a modular approach to provide the standardization and quality control for the monitoring of the near real-time data. Two modules are under development. First, the driver module transforms different tabular data to a common format. Second, the quality control module that runs the quality tests on the ingested data. Both modules rely on the sensor operator and on the data scientist, two actors that interact with both ends of the ingest component of the O2A framework (http://data.awi.de/o2a-doc). We demonstrate the driver and the quality control modules in the data flow within Digital Earth showcases that also connect repositories and federated databases to the end-user. The end-user is the scientist, who works closely in the development approach to ensure applicability. The result is the proven benefit of harmonizing data and metadata of multiple sources, easy integration and rapid assessment of the ingested data. Further, we discuss concepts and current development that aim at the enhanced monitoring and scientific workflow.

How to cite: Silva, B., Fischer, P., Immoor, S., Denkmann, R., Maturilli, M., Weidinger, P., Rehmcke, S., Düde, T., Anselm, N., Gerchow, P., Haas, A., Schäfer-Neth, C., Schäfer, A., Frickenhaus, S., and Koppe, R. and the Computing and Data Centre of the Alfred-Wegener-Institute: Data flow, harmonization, and quality control, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10547, https://doi.org/10.5194/egusphere-egu21-10547, 2021.

Environmental research data from the Swiss Federal Research Institute WSL, an Institute of the ETH Domain, is published through the environmental data portal EnviDat (https://www.envidat.ch). EnviDat actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles and offers guidance and support to researchers throughout the research data publication process.

WSL strives to increase the fraction of environmental data easily available for reuse in the public domain. At the same time, WSL facilitates the publication of high-quality environmental research datasets by providing an appropriate infrastructure, a formal publication process and by assigning Document Object Identifiers (DOIs) and appropriate citation information.

Within EnviDat, we conceptualize and implement data publishing workflows that include automatic validation, interactive quality checks, and iterative improvement of metadata quality. The data publication workflow encompasses a number of steps, starting from the request for a DOI, to an approval process with a double-checking principle, and the submission of the metadata-record to DataCite for the final data publication. This workflow can be viewed as a decentralized peer-review and quality improvement process for safeguarding the quality of published environmental datasets. The workflow is being further developed and refined together with partner institutions within the ETH Domain.

We have defined and implemented additional features in EnviDat, such as (i) in-depth tracing of data provenance through related datasets; (ii) the ability to augment published research data with additional resources which support open science such as model codes and software; and (iii) a DataCRediT mechanism designed for specifying data authorship (Collection, Validation, Curation, Software, Publication, Supervision).

We foresee that these developments will help to further improve approaches targeted at modern documentation and exchange of scientific information. This is timely given the increasing expectations that institutions and researchers have towards capabilities of research data portals and repositories in the environmental domain.

How to cite: Iosifescu Enescu, I., Plattner, G.-K., Espona Pernas, L., Haas-Artho, D., and Buchholz, R.: Improved FAIR Data Publication Quality in Specialized Environmental Data Portals, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5663, https://doi.org/10.5194/egusphere-egu21-5663, 2021.

As in many scientific disciplines, there are a variety of activities in Earth system sciences that address the important aspects of good research data management. What has not been sufficiently investigated and dealt with so far is the easy discoverability and re-use of quality-checked data. This aspect is taken up by the EASYDAB label.

EASYDAB¹ is a currently developed branding for FAIR and open data from the Earth System Sciences. The branding can be adopted by institutions running a data repository which stores data from the Earth System Sciences. EASYDAB is always connected to a research data publication with DataCite DOIs. Data published under EASYDAB are characterized by a high maturity, extensive metadata information and compliance with a comprehensive discipline-specific standard. For these datasets, the EASYDAB logo is added to the landing page of the data repository. Thereby, repositories can indicate their efforts to publish data with high maturity.

The first standard made for EASYDAB is the ATMODAT standard², which has been developed within the AtMoDat³ project (Atmospheric Model Data). It incorporates concrete recommendations and requirements related to the maturity, publication and enhanced FAIRness of atmospheric model data. The requirements are for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages are a core element of the ATMODAT standard and should hold and present discipline-specific metadata on simulation and variable level. 

The ATMODAT standard includes checklists for the data producer and the data curator so that the compliance with the standard can easily be obtained by both sides. To facilitate automatic checking of the netCDF files headers, a checker program will also be provided and published with DOI. Moreover, a checker for the compliance with the requirements for the DOI Metadata will be developed and made openly available. 

The integration of standards from other disciplines in the Earth System Sciences, such as oceanography, into EASYDAB is helpful and desirable to improve the re-use of reviewed, high-quality data. 

 ¹www.easydab.de

²https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_0

³www.atmodat.de

How to cite: Ganske, A., Kaiser, A., Kraft, A., Heydebreck, D., Lammert, A., and Thiemann, H.: EASYDAB (Earth System Data Branding) for FAIR and Open Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2139, https://doi.org/10.5194/egusphere-egu21-2139, 2021.

Large scale transient climate simulations and their intercomparison with paleo data within the German initiative PalMod (www.palmod.de, currently in phase II) provides an exclusive example of applying a Data Management Plan (DMP) to conceptualise  data workflows within and outside a large multidisciplinary project. PalMod-II data products include output of three state-of-the-art climate models with various coupling complexities and spatial resolutions  simulating the climate of the past 130,000 years. Additional to the long time series of model data, a comprehensive compilation of paleo-observation data (including a model-observation-comparison toolbox, Baudouin et al, 2021 EGU-CL1.2) is envisaged for validation. 

Owing to the enormous amount of data coming from models and observations, produced and handled by different groups of scientists spread across various institutions, a dedicated DMP as a living document provides a data-workflow framework for exchange and sharing of data within and outside the PalMod community. The DMP covers the data life cycle within the project starting from its generation (data formats and standards), analysis (intercomparison with models and observations), publication (usage, licences), dissemination (standardised, via ESGF) and finally archiving after the project lifetime. As an active and continually updated document, the DMP ensures the ownership and responsibilities of data subsets of various working groups along with their data sharing/reuse regulations within the working groups in order to ensure a sustained progress towards the project goals. 

This contribution discusses the current status and challenges of the DMP for PalMod-II which covers the details of data produced within various working groups, project-wide workflow strategy for sharing and exchange of data, as well as a definition of a PalMod-II variable list for ESGF standard publication. The FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles play a central role and are proposed for the entire life cycle of PalMod-II data products (model and proxy paleo data) for sharing/reuse during and after the project lifetime.

How to cite: Gehlot, S., Peters-von Gehlen, K., and Lammert, A.: PalMod-II Data Management Plan: A FAIR-inspired conceptual framework for data simulation, inter-comparison, sharing and publication  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5965, https://doi.org/10.5194/egusphere-egu21-5965, 2021.

Global environmental challenges like climate change, pollution, and biodiversity loss are complex. To understand environmental patterns and processes and address these challenges, scientists require the observations of natural phenomena at various temporal and spatial scales and across many domains. The research infrastructures and scientific communities involved in these activities are often following their own data management practices which inevitably leads to a high degree of variability and incompatibility of approaches. Consequently, a variety of metadata standards and vocabularies have been proposed to describe observations and are actively used in different communities. However, this diversity in approaches now causes severe issues regarding the interoperability across datasets and hampers their exploitation as a common data source.

Projects like ENVRI-FAIR, FAIRsFAIR, FAIRplus are addressing this difficulty by working on the full integration of services across research infrastructures based on FAIR Guiding Principles supporting the EOSC vision towards an open research culture. Beyond these projects, we need collaboration and community consensus across domains to build a common framework for representing observable properties. The Research Data Alliance InteroperAble Descriptions of Observable Property Terminology Working Group (RDA I-ADOPT WG) was formed in October 2019 to address this need. Its membership covers an international representation of terminology users and terminology providers, including terminology developers, scientists, and data centre managers. The group’s overall objective is to deliver a common interoperability framework for observable property variables within its 18-month work plan. Starting with the collection of user stories from research scientists, terminology managers, and data managers or aggregators, we drafted a set of technical and content-related requirements. A survey of terminology resources and annotation practices provided us with information about almost one hundred terminologies, a subset of which was then analysed to identify existing conceptualisation practices, commonalities, gaps, and overlaps. This was then used to derive a conceptual framework to support their alignment. 

In this presentation, we will introduce the I-ADOPT Interoperability Framework highlighting its semantic components. These represent the building blocks for specific ontology design patterns addressing different use cases and varying degrees of complexity in describing observed properties. We will demonstrate the proposed design patterns using a number of essential climate and essential biodiversity variables. We will also show examples of how the I-ADOPT framework will support interoperability between existing representations. This work will provide the semantic foundation for the development of more user-friendly data annotation tools capable of suggesting appropriate FAIR terminologies for observable properties.

How to cite: Magagna, B., Moncoiffe, G., Stoica, M., Devaraju, A., Pamment, A., Schindler, S., and Huber, R.: The I-ADOPT Interoperability Framework: a proposal for FAIRer observable property descriptions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13155, https://doi.org/10.5194/egusphere-egu21-13155, 2021.

Making research data FAIR (Findable, Accessible, Interoperable, and Reusable) is critical to maximizing its impact. However, since the FAIR principles are designed as guidelines and do not specify implementation rules, it is difficult to verify the practice of these principles. Therefore, metrics and associated tools need to be developed to enable the assessment of FAIR compliance of services and datasets. Such practical solutions are important for many stakeholders to assess the quality of data-related services. They are important for selecting such services, but can also be used to iteratively improve data offerings, e.g., as part of FAIR advisory processes. With the increasing number of published datasets and the need to test them repeatedly, there is a growing body of literature that recognizes this importance of automated FAIR assessment tools. Our goal is to contribute to this area of FAIR through the development of an open source tool called F-UJI.  F-UJI supports programmatic FAIR assessment of research data based on a set of core metrics against which the implementation of FAIR principles can be assessed. This paper presents the development and application of F-UJI and the underlying metrics. For each of the metrics, we have designed and implemented practical tests based on existing standards and best practices for research data. The tests are important to our expanded understanding of how to test FAIR metrics in practice that have not been fully addressed in previous work on FAIR data assessment. We demonstrate the use of the tool by assessing several multidisciplinary datasets from selected trusted digital repositories, followed by recommendations for improving the FAIRness of these datasets. We summarize the experience and lessons learned from the development and testing.

How to cite: Huber, R. and Devaraju, A.: F-UJI : An Automated Tool for the Assessment and Improvement of the FAIRness of Research Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15922, https://doi.org/10.5194/egusphere-egu21-15922, 2021.

The the amount of work and resources invested by the modelling centers to provide CMIP6 (Coupled Model Intercomparison Project Phase 6) experiments and climate projection datasets is huge, and therefore it is extremely important that the teams receive proper credit for their work. The Citation Service makes CMIP6 data citable with DOI references for the evolving CMIP6 model data published in the Earth System Grid Federation (ESGF). The Citation Service as a new piece of the CMIP6 infrastructure was developed upon the request from the CMIP Panel.

CMIP6 provides new global climate model data assessed in the IPCC's (Intergovernmental Panel on Climate Change) Sixth Assessment Report (AR6). Led by the Technical Support Unit of IPCC Working Group I (WGI TSU), the IPCC Task Group on Data Support for Climate Change Assessment (TG-Data) developed FAIR data guidelines, for implementation by the TSUs of the three IPCC WGs and the IPCC Data Distribution Centre (DDC) Partners. A central part of the FAIR data guidelines are the documentation and citation of data used in the report.

The contribution will show how CMIP6 data usage is documented in IPCC WGI AR6 from three angles: technical implementation, collection of CMIP6 data usage information from the IPCC authors, and a report users’ perspective.

Links:

• CMIP6 Citation Service: http://cmip6cite.wdc-climate.de
• CMIP6: https://pcmdi.llnl.gov/CMIP6/
• IPCC AR6: https://www.ipcc.ch/assessment-report/ar6/
• IPCC AR6 WGI report: https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/
• IPCC TG-Data: https://www.ipcc.ch/data/

How to cite: Stockhause, M., Matthews, R., Pirani, A., Treguier, A. M., and Yelekci, O.: CMIP6 data documentation and citation in IPCC's Sixth Assessment Report (AR6), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2886, https://doi.org/10.5194/egusphere-egu21-2886, 2021.

We report on the current status of the software repository of the Map Overlay and Statistical System (MOSS) and upcoming actions to ensure long term preservation of the codebase as a historic geospatial source. MOSS is the earliest known open source Geographic Information System (GIS). Active development of the vector-based interactive GIS by the U.S. Department of Interior began in 1977 on a CDC mainframe computer located at Colorado State University. Development continued until 1985 with MOSS being ported to multiple platforms, including DG-AOS, UNIX, VMS and Microsoft DOS. Many geospatial programming techniques and functionalities were first implemented in MOSS, including a fully interactive user interface and integrated vector and raster processing. The public availability of the WWW in the early 1990s sparked a growth of new Open Source GIS projects, which led to the formation of the Open Source Geospatial Foundation (OSGeo). The goal of OSGeo is to support and promote the collaborative development of open geospatial technologies and data. This includes best practices for project management and repositories for codebases. From its start, OSGeo recognised MOSS as the original forerunner project. After the decline of active use of MOSS since the 1990s, the U.S. Bureau of Land Management (BLM) continued to provide the open source MOSS codebase on an FTP-Server, which allowed use, analysis and reference by URL. This service was discontinued at some point before 2018, which was eventually discovered due to a broken URL link. This led to a global search and rescue effort among the OSGeo communities to track down remaining offline copies of the codebase. In mid 2020 a surviving copy of the MOSS codebase was discovered at the University of Latvia, which is temporarily preserved at the German Institute of Economic Research (DIW Berlin). OSGeo has agreed to make MOSS the first OSGeo Heritage Project to ensure long term preservation in a OSGeo code repository. This is a significant first step to enable MOSS-related research based on the FAIR (Findable, Accessible, Interoperable, Reusable) paradigm. Follow up actions will be required to enable scientific citation and credit by persistent identifiers for code and persons, such as Digital Object Identifiers (DOI) and Open Researcher Contributor Identification Initiative-ID (ORCID-ID) within the OSGeo repository environment. This will advance the OSGeo portfolio of best practices also for other open geospatial projects.

How to cite: Löwe, P., Nartišs, M., and Reed, C. N.: Raiders of the Lost Code: Preserving the MOSS Codebase - Significance, Status, Challenges and Opportunities , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5492, https://doi.org/10.5194/egusphere-egu21-5492, 2021.

The European Open Science Cloud-Synergy (EOSC-Synergy) project delivers services that serve to expand the use of EOSC. One of these services, O3as, is being developed for scientists using chemistry-climate models to determine time series and eventually ozone trends for potential use in the quadrennial Global Assessment of Ozone Depletion, which will be published in 2022. A unified approach from a service like ours, which analyses results from a large number of different climate models, helps to harmonise the calculation of ozone trends efficiently and consistently. With O3as, publication-quality figures can be reproduced quickly and in a coherent way. This is done via a web application where users configure their queries to perform simple analyses. These queries are passed to the O3as service via an O3as REST API call. There, the O3as service processes the query and accesses the reduced dataset. To create a reduced dataset, regular tasks are executed on a high performance computer (HPC) to copy the primary data and perform data preparation (e.g. data reduction, standardisation and parameter unification). O3as uses EGI check-in (OIDC) to identify users and grant access to certain functionalities of the service, udocker (a tool to run Docker containers in multi-user space without root privileges) to perform data reduction in the HPC environment, and the Universitat Politècnica de València (UPV)  Infrastructure Manager to provision service resources (Kubernetes).

How to cite: Kerzenmacher, T., Kozlov, V., Sanchis, B., Cayoglu, U., Hardt, M., and Braesicke, P.: An online service for analysing ozone trends within EOSC-synergy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8418, https://doi.org/10.5194/egusphere-egu21-8418, 2021.

The Natural Environment Research Council’s (NERC) Vocabulary Server (NVS¹) has been serving the marine and wider community with controlled vocabularies for over a decade. NVS provides access to standardised lists of terms which are used for data mark-up, facilitating interoperability and discovery in the marine and associated earth science domains. The NVS controlled vocabularies are published as Linked Data on the web using the data model of the Simple Knowledge Organisation System (SKOS). They can also be accessed as web services (RESTFul, SOAP) or through a sparql endpoint. NVS is an operational semantic repository, which underpins data systems like SeaDataNet, the pan-European infrastructure of marine data management, and is embedded in SeaDataNet-specific tools like MIKADO. Its services are being constantly monitored by the SeaDataNet Argo monitoring system, ensuring a guarantee of reliability and availability. In this presentation we will discuss the pathway of challenges we encountered while enhancing an operational semantic repository like NVS with VocPrez, a read-only web delivery system for Simple Knowledge Organization System (SKOS)-formulated RDF vocabularies. We will also present our approach on implementing CI/CD delivery and the added value of VocPrez to NVS in terms of FAIRness. Finally we will discuss the lessons learnt during the lifecycle of this development. 

VocPrez² is an open-source, pure Python, application that reads vocabularies from one or more sources and presents them online (HTTP) in several different ways: as human-readable web pages, using simple HTML templates for different SKOS objects and as machine-readable RDF or other formats, using mapping code. The different information model views supported by VocPrez are defined by profiles, that is, by formal specifications. VocPrez supports both different profiles and different formats (Media Types) for each profile.

VocPrez enhanced the publication of NVS both for human users and machines. Humans accessing NVS are presented with a new look and feel that is more user friendly, providing filtering of collections, concepts and thesauri, and sorting of results using different options. For machine-to-machine communication, VocPrez presents NVS content in machine-readable formats which Internet clients can request directly using the Content Negotiation by Profile standard³. The profiles and formats available are also listed on an “Alternate Profiles” web page which is automatically generated per resource thus allowing for discovery of options. As a result, human or machine end users can access NVS collections, thesauri and concepts according to different information models such as DCAT, NVS’ own vocabulary model or pure SKOS and also in different serializations like JSON-LD , turtle, etc. using content negotiation.

¹http://vocab.nerc.ac.uk/

²https://github.com/RDFLib/VocPrez

³https://www.w3.org/TR/dx-prof-conneg/

How to cite: Kokkinaki, A., Luong, Q., Thompson, C., Car, N., and Moncoiffe, G.: Applying VocPrez to operational semantic repositories: the NVS experience, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15669, https://doi.org/10.5194/egusphere-egu21-15669, 2021.

Since few years Research Data Management is becoming an increasingly important part of scientific projects regardless of the number of topics or subjects, researchers or institutions involved. The bigger the project, the more are the data organization and data management requirements in order to assure the best outcome of the project. Despite this, projects rarely have clear structures or responsibilities for data management. The importance of clearly defining data management and also budgeting for it is often underestimated and/or neglected. A rather scarce number of reports and documentations explaining the research data management in certain projects and detailing best practice examples can be found in the current literature.  Additionally, these are often mixed up with topics of the general project management. Furthermore, these examples are very focused on the certain issues of the described projects and thus, a transferability (or general application) of provided methods is very difficult.

This contribution presents generic concepts of research data management with an effort to separate them from general project management tasks. Project size, details among the diversity of topics and the involved researcher, play an important role in shaping data management and determining which methods of data management can add value to the outcome of a project. We especially focus on different organisation types, including roles and responsibilities for data management in projects of different sizes. Additionally, we show how and when also education should be included, but also how important agreements in a project are.

How to cite: Anders, I., Gehlot, S., Lammert, A., and Peters-von Gehlen, K.: Generic concepts for organising data management in research projects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8903, https://doi.org/10.5194/egusphere-egu21-8903, 2021.

The term SMART Monitoring was also defined by the project Digital Earth (DE) , a central joint project of eight Helmholtz centers in Earth and Environment. SMART Monitoring in the sense of DE means that measured environmental parameters and values need to be specific/scalable, measurable/modular, accepted/adaptive, relevant/robust, and trackable/transferable (SMART) for sustainable use as data and improved real data acquisition. SMART Monitoring can be defined as a reliable monitoring approach with machine-learning, and artificial intelligence (A.I.) supported procedures for an “as automated as possible” data flow from individual sensors to databases. SMART Monitoring Tools must include various standardized data flows within the entire data lifecycle, e.g., specific sensor solutions, novel approaches for sampling designs, and defined standardized metadata descriptions. One of the SMART Monitoring workflows' essential components is enhancing metadata with comprehensive information on data quality. On the other hand, SMART Monitoring must be highly modular and adaptive to apply to different monitoring approaches and disciplines in the sciences.

In SMART monitoring, data quality is crucial, not only with respect to data FAIRness. It is essential to ensure data reliability and representativeness. Hence, comprehensively documented data quality is essential and required to enable meaningful data selection for specific data blending, integration, and joint interpretation. Data integration from different sources represents a prerequisite for parameterization and validation of predictive tools or models. This data integration demonstrates the importance of implementing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) for sustainable data management (Wilkinson et al. 2016). So far, the principle of FAIRdata does not include a detailed description of data quality and does not cover content-related quality aspects. Even though data may be FAIR in terms of availability, it is not necessarily “good" in accuracy and precision. Unfortunately, there is still considerable confusion in science about the definition of good or trustworthy data.

An assessment of data quality and data origin is essential to preclude the possibility of inaccurate, incomplete, or even unsatisfactory data analysis applying, e.g., machine learning methods, and avoid poorly derived, misleading or incorrect conclusions. The terms trustworthiness and representativeness summarise all aspects related to these issues. The central pillars of trustworthiness/representativeness are validity, provenience/provenance, and reliability, which are fundamental features in assessing any data collection or processing step for transparent research. For all kinds of secondary data usage and analysis, a detailed description and assessment of reliability and validity involve an appraisal of applied data collection methods.

The presentation will give exemplary examples to show the importance of data trustworthiness and representativeness evaluation and description, allowing scientists to find appropriate tools and methods for FAIR data handling and more accurate data interpretation.

How to cite: Koedel, U., Dietrich, P., Fischer, P., and Schuetze, C.: Ensuring data trustworthiness within SMART Monitoring of environmental processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8361, https://doi.org/10.5194/egusphere-egu21-8361, 2021.

Numerous applications, including generating future predictions via numerical modelling, establishing appropriate policy instruments, and effectively tracking progress against them, require the multitude of complex processes and interactions operating in rapidly changing mountainous environmental systems to be well monitored and understood. At present, however, not only are environmental available data pertaining to mountains often severely limited, but interdisciplinary consensus regarding which variables should be considered absolute observation priorities remains lacking. In this context,  the concept of so-called Essential Mountain Climate Variables (EMCVs) is introduced as a potential means to identify critical observation priorities and thereby ameliorate the situation. Following a brief overview of the most critical aspects of ongoing and expected future climate-driven change in various key mountain system components (i.e. the atmosphere, cryosphere, biosphere and hydrosphere), a preliminary list of corresponding potential EMCVs – ranked according to perceived importance – is proposed. Interestingly, several of these variables do not currently feature amongst the globally relevant Essential Climate Variables (ECVs) curated by GCOS, suggesting this mountain-specific approach is indeed well justified. Thereafter, both established and emerging possibilities to measure, generate, and apply EMCVs are summarised. Finally, future activities that must be undertaken if the concept is eventually to be formalized and widely applied are recommended.

How to cite: Thornton, J., Palazzi, E., Pepin, N., Cristofanelli, P., Essery, R., Kotlarski, S., Giuliani, G., Guigoz, Y., Kulonen, A., Li, X., Pritchard, D., Fowler, H., Randin, C., Shahgedanova, M., Steinbacher, M., Zebisch, M., and Adler, C.: Towards a definition of Essential Mountain Climate Variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7747, https://doi.org/10.5194/egusphere-egu21-7747, 2021.

The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.

Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.

Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.

In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.

How to cite: Buck, V., Stäbler, F., Gonzalez, E., and Greinert, J.: The Digital Earth Viewer: A new visualization approach for geospatial time series data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15623, https://doi.org/10.5194/egusphere-egu21-15623, 2021.

How to cite: Darroch, L., Thomas, G., Margaret, Y., Christopher, C., Emma, S., Elizabeth, B., Justin, B., Robert, J., Andrew, H., and Jennifer, B.: The use of ERDDAP in a self-monitoring and nowcast hazard alerting coastal flood system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14621, https://doi.org/10.5194/egusphere-egu21-14621, 2021.

How to cite: Boog, J. and Kalbacher, T.: Point to Space: Data-driven Soil Moisture Spatial Mapping using Machine-Learning for Small Catchments in Western Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16308, https://doi.org/10.5194/egusphere-egu21-16308, 2021.

Japan Aerospace Exploration Agency (JAXA) launched its first L-band SAR mission - Japanese Earth Resources Satellite (JERS-1) in 1992. Though the design life of JERS-1 was 2 years, the satellite had obtained observational data for more than 6 years and ended the mission in 1998. Following to JERS-1, Advanced Land Observing Satellite (ALOS) was launched in 2006. ALOS was equipped with three sensors: the Phased Array type L-band SAR (PALSAR), the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2). ALOS's observation data has been used in various areas including disaster mitigation through observing regions damaged by earthquakes, tsunami, or typhoons, as well as carrying out forest monitoring, natural environment maintenance, agriculture, and compiling a 1/25,000 topographical map. When the Great East Japan Earthquake hit Japan in 2011, ALOS took some 400 images over disaster-stricken areas to provide information to all parties concerned.

Technologies acquired from the ALOS are succeeded to the second Advanced Land Observing Satellite “ALOS-2.”, which was successfully launched on 24th May 2014. The mission sensor of ALOS-2 is the Phased Array type L-band Synthetic Aperture Radar-2 called PALSAR-2 which is the state-of-the-art L-band SAR system. Until now after the successful completion of initial checkout after launching, ALOS-2 has been contributed to a lot of emergency observations for natural disasters, not only in Japan but also in the world. Furthermore, based on the Basic Observation Scenario (BOS) of ALOS-2, 10m global map data and other mode data are routinely collected and archived. This paper describes the results of ALOS-2 operation in nominal operation phase and outline of future ALOS series missions, especially ALOS-4 launched JFY2022.

How to cite: Sobue, S., Tadono, T., Miura, S., Noda, A., Motooka, T., and Ohki, M.: Japan's L-SAR missions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-534, https://doi.org/10.5194/egusphere-egu21-534, 2021.

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).

In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.

The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.

References

Balenzano. A., et al., “Sentinel-1 soil moisture at 1km resolution: a validation study”, submitted to Remote Sensing of Environment (2020).

Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., “ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,” final report, contract number 4000118762/16/I-NB, 2020.

How to cite: Balenzano, A., Satalino, G., Lovergine, F., Palmisano, D., Mattia, F., Rinaldi, M., and Montzka, C.: Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8660, https://doi.org/10.5194/egusphere-egu21-8660, 2021.

Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (T_B) and backscatter (σ⁰), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated T_B (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ⁰ were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.

How to cite: Zhao, H., Zeng, Y., Su, B., and Hofste, J.: Modelling of Microwave Multi-Frequency Emission and Backscatter by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6659, https://doi.org/10.5194/egusphere-egu21-6659, 2021.

The monitoring of inundation phenomena through synthetic aperture radar (SAR) data on vegetated areas can be improved through an integrated analysis of different spectral bands. The combination of data with different penetration depths beneath the vegetated canopy can help determine the response of flooded areas with distinct types of vegetation cover to the microwave signal. This is useful especially in cases, which actually constitute the majority, where ground data are scarce or not available.

The present study concerns the application of multi-temporal, multi-frequency, and multi-polarization SAR images, specifically data from the Sentinel-1 and PALSAR 2 SAR sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively, in synergy with globally-available land cover data, for improving flood mapping in densely vegetated areas, such as the Zambezi-Shire basin, Mozambique [1], characterized by wetlands, open and closed forest, cropland, grassland (herbaceous and shrubs), and a few urban areas.

We show how the combination of various data processing techniques and the simultaneous availability of data with different frequencies and polarizations can help to monitor floodwater evolution over various land cover classes. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site.

This kind of studies are expected to assume increasing importance as the availability of multi-frequency data from SAR satellite constellations will increase in the future, thanks to initiatives such as the EU Copernicus program L-band satellite mission ROSE-L [2], and their tight integration with Sentinel-1 as well as with other national constellations such as ALOS 2, or SAOCOM.

References

[1] Refice, A.; Zingaro, M.; D’Addabbo, A.; Chini, M. Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas. Water 2020, 12, 2745, doi:10.3390/w12102745.

[2] Pierdicca, N.; Davidson, M.; Chini, M.; Dierking, W.; Djavidnia, S.; Haarpaintner, J.; Hajduch, G.; Laurin, G.V.; Lavalle, M.; López-Martínez, C.; et al. The Copernicus L-band SAR mission ROSE-L (Radar Observing System for Europe). In Active and Passive Microwave Remote Sensing for Environmental Monitoring III; SPIE: Washington, DC, USA, 2019; Volume 11154, p. 13.

How to cite: Refice, A., D'Addabbo, A., Chini, M., and Zingaro, M.: Flood monitoring in remote areas: integration of multi-frequency SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4452, https://doi.org/10.5194/egusphere-egu21-4452, 2021.

Approximately 90% of the world total paddies area and annual output of the rice production are concentrated in monsoon Asia, which has no more land/water resources for further expansion of cultivation. Most rice grows under lowland conditions where currently facing to the fresh water scarcity due to sea-water intrusion accelerated by sea-level rise and land-subsidence, and decelerating freshwater supply by upstream-dam construction. Since the rice production also requires large amount of water (3,000-5,000 L kg^-1 rice), water-saving irrigation practice (e.g., Alternate Wetting and Drying, a.k.a., AWD) is desirable to be implemented in this region to save the water-demand sustainably, and irrigation status need to be evaluated for the decision making on sustainable food security. In addition to the significance of AWD’s role as an adaptation to drought risks, AWD also has a potential to act as an important mitigation-measure by reducing methane emission from paddy soils. This function is very important since rice cropping is responsible for approximately 11% of global anthropogenic CH₄ emissions, and rice has the highest greenhouse gas intensity among the main food crops.

In order to implement AWD in Asian rice paddies as a mitigation-measure based on a carbon pricing scheme, it is important to evaluate the spatial distribution of AWD paddy fields in the target region. For the detection of AWD-fields versus continuously-flooding fields, it is essential to develop method using EO data to detect soil inundation under rice plants at various growth stages.  In this study, ALOS-2/PALSAR-2 and Sentinel-1 data were used to combine the penetration capacity of L-band SAR data with C-band data capacity to monitor rice growth status with their high temporal resolution.

The study was conducted in triple rice cropping systems in the Vietnamese Mekong delta (5 sites: Thot Not in Can Tho city; Chau Thanh, Cho Moi, Thoai Son and Tri Ton in An Giang Province, where AWD field campaign was conducted from 2012 to 2017. EO data consisted of ALOS-2/PALSAR-2 every 14 days in 2017/2018 in An Giang province at high resolution observation mode (3-6m resolution) and ScanSAR observation mode (25-100m resolution) every 42 days over the Mekong delta.

As the result of the classification using the dual-polarization ALOS-2/PALSAR-2 data, soil inundation status could be detected during various rice growth stages. To evaluate rice productivity and GHG emissions from rice fields, we developed a simulation system based on the DeNitrification-Decomposition (DNDC) model which can assimilate PALSAR-2 inundation map and ground observed GHG -flux and rice growth status data on a pixel basis. For spatial extension, rice map, together with rice calendar (sowing date, rice growth status), required as inputs by DNDC are provided by the GeoRice project, based on the use of Sentinel-1 6-day time series. This paper presents the performance of multi-sensor data fusion to realize sustainable agricultural management by mitigating the GHGs emission while maintaining or improving regional fresh water use efficiency for stable food production under climate change pressure.

How to cite: Arai, H., Le Toan, T., Takeuchi, W., Oyoshi, K., Phan, H., Nguyen, L. D., Fumoto, T., and Inubushi, K.:   Detecting  rice inundation status for water saving and methane emission mitigation measures using Sentinel-1 & ALOS-2/PALSAR-2 Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7831, https://doi.org/10.5194/egusphere-egu21-7831, 2021.

The split-spectrum method (SSM) can largely isolate and correct for the ionospheric contribution in the L-band interferometric synthetic aperture radar (InSAR). The standard SSM is performed on the assumption of only the first-order ionospheric dispersive effect, which is proportional to the total electron content (TEC). It is also known that during extreme atmospheric events, either originated from the ionosphere or in the troposphere, other dispersive effects do exist and potentially provide new insights into the dynamics of the atmosphere, but there have been few detection reports of such signals by InSAR. We apply L-band InSAR into heavy rain cases and examine the applicability and limitation of the standard SSM. Since no events such as earthquakes to cause surface deformation took place, the non-dispersive component is apparently attributable to the large amount of water vapor associated with heavy rain, whereas there are spotty anomalies in the dispersive component that are closely correlated with the heavy rain area. The ionosonde and Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) map both show little anomalies during the heavy rain, which suggests few ionospheric disturbances. Therefore, we interpret that the spotty anomalies in the dispersive component of the standard SSM during heavy rain are originated not in the ionosphere but the troposphere. While we can consider two physical mechanisms, one is runaway electron avalanche and the other is the scattering due to rain, comparison with the observations from the ground-based lightning detection network and rain gauge data, we conclude that the rain scattering interpretation is spatiotemporally favorable. We further propose a formulation to examine if another dispersive phase than the first-order TEC effect is present and apply it to the heavy rain cases as well as two extreme ionospheric sporadic-E events. Our formulation successfully isolates the presence of another dispersive phase during heavy rain that is in positive correlation with the local rain rate. Furthermore, our formulation is also able to detect the occurrence of higher-order ionospheric effects during Sporadic-E cases.

How to cite: Setiawan, N. and Furuya, M.: Tropospheric Dispersive Phase Anomalies during Heavy Rain Detected by L-band InSAR and Their Interpretation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13944, https://doi.org/10.5194/egusphere-egu21-13944, 2021.

This research is related to the JAXA 6th Research Announcement for the Advanced Land
Observing Satellite-2 (ALOS-2) project "Improved Sea Ice Parameter Estimation with L-Band SAR (ISIPELS)".
In the study ALOS-2/PALSAR-2 dual-polarized Horizontal-transmit-Horizontal-receive/
Horizontal-transmit-Vertical-receive (HH/HV) ScanSAR mode L-band  Synthetic Aperture Radar (SAR) imagery
over an Arctic study area were evaluated for their suitability for operational sea ice monitoring.
The SAR data consisting of about 140 HH/HV ScanSAR ALOS-2/PALSAR-2 images were acquired during the winter 2017.
These L-band SAR data were studied for estimation of different sea ice parameters:
sea ice concentration, sea ice thickness, sea ice type, sea ice drift. Also some comparisons with nearly
coincident C-band data over the same study area have been made. The results indicate that L-band
SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied sea ice parameters and equally good
or better than using the conventional operational dual-polarized C-band SAR satellite data.

How to cite: Karvonen, J.: ALOS-2/PALSAR-2 dual-polarized L-band data for sea ice parameter estimation and sea ice classification , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3920, https://doi.org/10.5194/egusphere-egu21-3920, 2021.

Synthetic aperture radar (SAR) has become an essential component in ocean remote sensing due it’s high sensitivity to sea surface dynamics and its high spatial resolution. The ALOS-2 SAR data are underutilized for ocean surface wind and current retrieval. Although the primary goals of the ALOS-2 mission are focused on land applications, the extension of the satellite scenes over the coastal areas offers an opportunity for ocean applications. The underutilization of ALOS-2 data is mainly due to the fact that at low radar frequencies, e.g. L-band, the sensitivity of the radar scattering coefficient to wind speed and the sensitivity of the Doppler frequency shift to sea surface velocity is lower than at higher frequencies, e.g. C- and X-band. This is also due to the fact that most of ALOS-2 images are acquired in HH or HV polarization while the VV polarization is often preferred in ocean applications due the higher signal to noise ratio.

The wind speed is retrieved from Sentinel-1 and ALOS-2 using the existing empirical C- and L-band geophysical model functions. For Sentinel-1, the Doppler frequency shift provided in the OCN product is used. For ALOS-2, the Doppler frequency shift is estimated from the single look complex data using the pulse-pair processing method. The estimated Doppler shift converted to the surface radial velocity and the velocity is calibrated using land as a reference. The estimated L-band Doppler shift and surface velocity is compared to the C-band Doppler shift provided in the Sentinel-1 OCN product. Due the difference in the local time of ascending node (about 6 hours at the equator) of the two satellites, a direct pixel-by-pixel comparison is not possible, i.e. the wind and surface current can not be assumed to be constant during such a large time difference. Thus, the retrieved wind from each sensor is compared separately to model data and in-situ observations.

In this paper, the quality of the wind speed retrieved from the L-band SAR (ALOS-2) in coastal areas is assessed and compared to the C-band SAR (Sentinel-1). In addition, the feasibility of the surface current retrieval from the L-band Doppler frequency shift is investigated and also compared to Sentinel-1. Examples will be shown and discussed. This opens an opportunity for synergy between L-band and C-band SAR missions to increase the spatial and temporal coverage, which is one of the main limitations of SAR application in ocean remote sensing.

How to cite: Elyouncha, A. and Eriksson, L. E. B.: Assessment of the sea surface wind and current retrieval from ALOS-2 and Sentinel-1 SAR data over coastal areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7490, https://doi.org/10.5194/egusphere-egu21-7490, 2021.