Presentation type:

Session 1 – Innovative geophysical sensing methods in hydrological and critical zone research

GC8-Hydro-7 | Orals | Session 1

TEMBO Africa: New sensors and geo-services for water management and agriculture  

Nick van de Giesen, Hessel Winsemius, Frank Annor, Tomáš Fico, Eugenio Realini, Remko Uilenhoet, and Salvador Peña-Haro

TEMBO Africa is a project funded by the European Commission that seeks to fill some of the many geo-data gaps in Africa. Specifically, TEMBO Africa will produce operational data products for rainfall, river flow, soil moisture, bathymetry, and open water. With these products, new services will be developed for reservoir management, germination insurance, and flood early warnings. The products will be the result of the combination of innovative in situ sensors, satellite observations, and environmental models. There will be at least seven innovative in situ sensing methods involved, namely X-band rainfall radars, neutron counting for soil moisture based on natural boron, commercial microwave links, camera-based velocimetry, bathymetry with fish finders, raindrop intervalometers, and GNSS level sensors. TEMBO Africa is transformative in that it aims to reduce the total costs of ownership of the geo-services to less than 10% of present costs. We do not only look at the capital costs of the sensors but also at reduction of maintenance cost and the availability and development of human resources. For this reason, co-development is essential to ensure that context specific challenges are addressed. In this presentation, we highlight the general design approach and early results.

The work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement n° 101086209. The opinions expressed in the document are of the authors only and no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

    TEMBO Africa 


How to cite: van de Giesen, N., Winsemius, H., Annor, F., Fico, T., Realini, E., Uilenhoet, R., and Peña-Haro, S.: TEMBO Africa: New sensors and geo-services for water management and agriculture , A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-7,, 2023.

There is a large discrepancy between the spatial extent of a catchment and the volume or area covered by a single sensor, particularly for sensors operating below the soil surface. Especially in the unsaturated zone, spatial heterogeneity combined with the very small soil volume represented by a data point (often 1 cubic decimeter or less), this contrast necessitates vast sensor networks that are costly to maintain and generate large quantities of data that require extensive processing to provide information useful at scales relevant for land and water management.

Over the past years, we developed a technology to measure the concentration of selected gases in soils by burying gas-permeable, flexible tubes of up to tens of meters of length in the soil at desired depths and flushing them with a gas of known composition (e.g., dry air). Pressure changes observed during short intervals during which the gas flow is stopped can be used to derive the difference in partial pressures of a target gas inside the tube and in the soil surrounding the tube. After processing, this gives the average concentration of the target gas in the soil surrounding the entire length of the tube. The technology is operational for CO2, and will be employed in a forest ecosystem to measure soil respiration in real time.

By specific choices of the tube material, the composition of the flushing gas, and the reference system, the measurement system can be adapted to other gases. If the target gas is water vapor, the relative humidity (RH) of soil air can be measured. According to first laboratory results this results in a measure of the area-averaged soil water content assuming local phase equilibrium between water vapor and liquid soil water. In very dry soil, e.g., in arid and hyper-arid regions, the RH of the soil air drops measurably. In this case the averaged matric potential of the soil water can be monitored in situ in a range far beyond that of water-filled tensiometers.

The presentation will explain the set-up of the system, showcase completed trials and elaborate on on-going plans for CO2-concentration measurements in a forest soil. 

How to cite: Lazik, D., de Rooij, G., and Hashar, M.: An innovative membrane-based sensor technology for large-scale measurements of gas concentrations in the subsurface, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-31,, 2023.

GC8-Hydro-33 | Orals | Session 1

Monitoring wet stream dynamics in ephemeral streams: stage-cam system experimental evidence 

Simone Noto, Andrea Petroselli, Flavia Tauro, Ciro Apollonio, and Salvatore Grimaldi

Scientific interest in ephemeral streams increased in the last decades, but monitoring their dynamics remains a major challenge in hydrology. Motivated by the last advancements in computer vision techniques, we propose an optical-based and non-invasive low-cost approach to provide a continuous estimation of the water level fluctuations. The system comprises a consumer grade wildlife camera with near infrared (NIR) night vision capabilities and a target pole set in the thalweg. The water level estimated through a simple white pole is compared to estimations obtained through different types of targets, such as broader coloured bars, with the aim to identify the optimal stage-cam setup. The feasibility of the approach is demonstrated through a set of benchmark experiments performed in natural settings with different illumination conditions and during rainfall events. Our findings show that broader bars enhance the visibility of the target but also increase the reflection effect of the water. Therefore, using the stage-cam configuration comprising the narrow target and optimizing the parameters involved in the image analysis procedure may be sufficient to monitor water level dynamics.

How to cite: Noto, S., Petroselli, A., Tauro, F., Apollonio, C., and Grimaldi, S.: Monitoring wet stream dynamics in ephemeral streams: stage-cam system experimental evidence, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-33,, 2023.

To understand and predict soil moisture dynamics it is essential to take the role of the vegetation into account. For example, hydrological processes in agricultural soils are strongly affected by seasonal vegetation dynamics in terms of rooting depth and root distribution. Here we present a new approach to monitor and model root dynamics and its influence on soil moisture in the critical zone using mini-rhizotrons combined with phenological observations.

The setup in the field observatory consists of a portable root scanner connected to a tablet computer and a number of acrylic glass tubes with a diameter of two inches that are inserted into the soil at the start of the growing season of selected crops. 360-degrees-scans of soil and roots are taken regularly at different depths in the tubes. Root parameters such as length, diameter, surface and density are identified automatically from the data for each soil layer. Complementary observations of aboveground plant phenology, obtained either by visual inspection in-situ or by remote sensing techniques, are related to the root parameters.

Results from mini-rhizotron data collected at two observatories in Germany show that vertical root distribution and maximum rooting depth in agricultural soils, which varies with plant species and phenology, weather patterns, soil type and management, irrigation etc., are crucial parameters to explain the observed temporal variability and vertical gradients in soil moisture satisfactorily. Deriving these parameters from above-ground phenology and incorporating them into a soil water model led to a significant improvement when compared to a model version based on reference rooting depths from the literature. Thus, we argue that mini-rhizotrons constitute a useful supplement to hydrological observatories and can help understand and predict soil moisture dynamics in the critical zone.

How to cite: Herbst, M., Böske, L., and Falge, E.: Observing spatio-temporal variations in rooting depth and density as a control factor for soil moisture dynamics, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-46,, 2023.

GC8-Hydro-56 | Orals | Session 1

Improved extraction of hydrologic information from geophysical data during an artificial hillslope infiltration

Benjamin Mary, Konstantinos Kaffas, Matteo Censini, Francesca Sofia Manca di Villahermosa, Andrea Dani, Matteo Verdone, Federico Preti, Paolo Trucchi, Daniele Penna, and Giorgio Cassiani

GC8-Hydro-91 | Orals | Session 1

First multi-year cosmic-ray neutron sensing cluster: insights from three years of soil water storage observations across depths and scales at an agricultural research site  in North-East Germany 

Lena Scheiffele, Katya Dimitrova-Petrova, Maik Heistermann, Till Francke, Daniel Altdorff, and Sascha Oswald

Cosmic ray neutron sensing (CRNS) allows for the estimation of root-zone soil water storage at the hectare scale. Therefore, CRNS can be valuable assets of long-term hydrological observatories aimed at unravelling key hydrological processes beyond the point scale. One such observatory is the cluster established within the Cosmic Sense project, situated within the ATB research site in Marquardt, NE Germany, and probably the best-equipped CRNS field laboratory so far. Here we present an overview of datasets which uniquely combined three years of observations (2019-2022) from a dense CRNS cluster with a wealth and variety of supplementary measurements. The long-term operating CRNS cluster (8 permanently installed sensors) was complemented with (i) short-term measurements of additional stationary CRNS, expanding the cluster footprint, (ii) rover CRNS campaigns as well as (iii) a dedicated irrigation experiment which was monitored by a cross-scale combination of sensors, including UAV and CRNS roving. Alongside the CRNS, insights on soil water storage states and fluxes were gained by long-term measurements of profile soil moisture (at 27 locations, up to 105 cm depth), soil water tension (up to 200 cm depth), groundwater and surface water levels (3 locations along the hillslope) and GNSS-R (Global Navigation Satellite Systems reflectometry). Snapshot information of near-surface water storage dynamics were obtained by UAV-based remote sensing. Furthermore, Electrical Resistivity Tomography profiles along the hillslope supplied a 3D view of water storage distribution in depth. Ground truthing campaigns, ancillary measurements of biomass and soil properties helped  capture the spatial distribution of these properties  and made the interpretation of the soil water content data more robust. Overall, the Marquardt cluster is unique in its combination of a dense CRNS cluster along with the long ongoing operational period of more than three years and the wealth of additional hydrometerological data. Additionally, the 3-year data-set captures a wide range of wetness conditions, from prolonged dry spells to heavy rainfall events and snow episodes. Therefore, such a comprehensive dataset, combining innovative techniques with traditional hydrometeorological measurements gives the opportunity to investigate a range of research questions. Those could be related, but not limited to, the study of dominant flow paths and hydrological connectivity during heavy rainfall; the suitability of sensor combinations to best study water storage dynamics in a heterogeneous landscape and the retrieval of spatial soil water storage patterns using a CRNS cluster and ancillary data.

Cosmic Sense Official University of Potsdam Webpage

How to cite: Scheiffele, L., Dimitrova-Petrova, K., Heistermann, M., Francke, T., Altdorff, D., and Oswald, S.: First multi-year cosmic-ray neutron sensing cluster: insights from three years of soil water storage observations across depths and scales at an agricultural research site  in North-East Germany, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-91,, 2023.

GC8-Hydro-73 | Orals | Session 1

A novel combined approach for bridging scales in spatiotemporal soil moisture monitoring applying metrological principles 

Sascha E. Oswald, Sebastian Rothermel, Gabriele Baroni, Anna Balenzano, Henrik Kjeldsen, Martin Schrön, and Miroslav Zboril

One of the key environmental variables and essential climate variable is soil moisture, with its high relevance for applications such as agriculture, forestry, water management including hydrometeorological extreme events or hydrological modelling. Yet accurate measurement of soil moisture is limited by its high natural spatiotemporal variability, given spatially (vertically and horizontally) variable hydraulic properties of soil, and events that are highly variable themselves (in time, extension and intensity).

One geophysical method to close the gap between point-scale measurements and satellite-based remote sensing is Cosmic Ray Neutron Sensing (CRNS). Its integration area of about 0.1 km² is above the coverage of wireless sensor networks and at least when combined to CRNS clusters can cover several pixels of high resolution satellite remote sensing, e.g. by the ESA Sentinel-1 Earth Observation mission. We combine these three methods to bridge the scales in monitoring of soil moisture, and this within a novel metrological framework on validation and standardization.

The basis for that is an EU-wide collaboration project of 18 institutions called SoMMet[1]. Its approach is to thoroughly establish CRNS as a bridging method at intermediate scale by linking it to point-scale soil moisture sensors with certification according to newly established metrological standards while testing a range of CRNS detector designs in facilities for neutron metrology. The aim is to achieve an improved comparability and reliable estimates of uncertainty and provide recommendations on network design and validation practices, which shall result in a more widespread transfer into remote sensing applications and hydrological modelling.

A central component is to conduct field comparison and testing campaigns covering the different scales at three high-level field sites across Europe. One of the candidate sites is located close to Potsdam, Northern Germany. Having evolved from temporary soil moisture field campaigns it hosts the sole long-term CRNS cluster (currently 15 CRNS probes) that covers a conjoined area. This is somewhat similar to wireless in-situ sensor networks, but working non-invasively, with partially overlapping footprints and last not least on larger scale, here about 0.6 km² altogether. We will present examples of SoMMet field test sites, and especially first results of this CRNS cluster from 2023 in its recently extended coverage set-up that now brings it further up to satellite remote sensing resolution.

[1] Acknowledgment: The project 21GRD08 SoMMet has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.

How to cite: Oswald, S. E., Rothermel, S., Baroni, G., Balenzano, A., Kjeldsen, H., Schrön, M., and Zboril, M.: A novel combined approach for bridging scales in spatiotemporal soil moisture monitoring applying metrological principles, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-73,, 2023.

GC8-Hydro-90 | ECS | Orals | Session 1

CRNS-based monitoring technologies as solutions for climate-resilient agriculture 

Markus Köhli, Patrick Stowell, Jannis Weimar, Patrizia Ney, Felix Nieberding, Ulrich Schmidt, Heye Bogena, and Klaus Görgen

Accurate soil moisture (SM) measurements are key in hydrological observations and subsequent applications as it can greatly improve our understanding of soil processes. Recently, Cosmic-Ray Neutron Sensors (CRNS) have been recognized as a promising tool in SM monitoring due to its large footprint of several hectares and half a meter in depth. The key characteristic feature of the method is the exceptionally high moderation strength of hydrogen, which makes it nearly independent of the soil chemistry. CRNS has a great potential for irrigation and monitoring applications as to the non-invasive nature of the method and the low-maintenance, independently operating sensors. From the initial focus on hydrological research. CRNS are increasingly used in agriculture, e.g. irrigation management and soil moisture mapping, and have been integrated LoRa or NB-IoT networks for fast data transmission. Two projects are discussed which advance CRNS technologies into monitoring networks.

COSMIC-SWAMP aims to provide an open-source water monitoring platform that integrates cosmic ray sensing data with FiWare Smart Application compliant analysis routines. Extending the existing Smart Water Management Platform (, COSMIC-SWAMP supports dynamic processing of multiple co-located cosmic ray sensor streams to support automated and continuous growth forecasting using Wageningen/WOFOST crop models.

ADAPTER involves the development and provision of innovative simulation-based information products. Addressing weather- and climate-resilient agriculture, daily and comprehensive long-term weather and soil information are made available to the agricultural community and all interested parties as easy-to-use analyses, data products, and information interfaces ( The hydrological model ParFlow coupled to its Common Land Model (CLM) module provides a nationwide water balance prediction with 600 m spatial resolution. The data assimilation within the product platform is supported by an independent network of CRNS stations (12 agricultural locations in North Rhine-Westphalia).

This contribution provides an overview about the current state of the art in CRNS methodological integration, neutron detection technology and development of IoT interfaces with measurements and forecasts focusing on the water balance, including groundwater.

How to cite: Köhli, M., Stowell, P., Weimar, J., Ney, P., Nieberding, F., Schmidt, U., Bogena, H., and Görgen, K.: CRNS-based monitoring technologies as solutions for climate-resilient agriculture, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-90,, 2023.

Session 2 – Environmental monitoring and modeling with the support of UAS and satellites

Water resources and the ecosystems depending on them are under growing pressure due climate change and human activities. Managers and governments need to take effective mitigation and adaptation measures, but decisions are often based on incomplete information as in situ observations are declining, and impacts on ecosystem functioning are not entirely clear. Earth Observation data can help by improving our understanding on the joint regulation of water and carbon fluxes and the links among terrestrial, aquatic and atmospheric processes across whole watersheds and even precipitation-sheds.  

Currently, there is a wide range of satellite data streams that can be used in synergy to estimate ecohydrological variables, but this requires a redesign of methods. Additionally, remote sensing in the optical domain is mostly used to estimate structural characteristics of vegetation such as biomass, greenness or leaf area index, while estimation of rapidly changing ecophysiological variables, such as stomatal conductance, transpiration or photosynthesis, the distinction between metabolic pathways (C3 or C4) or water use strategies (iso/anisohydric) is still a challenge.  

Drones or UAS can provide information complementary to satellites by acquiring in (quasi) real time environmental variables under clouds. They can bridge the scale mismatch with in situ datasets, augment them, and allow monitoring of small farms or narrow headwater streams among others. An emerging technology are hyperspectral miniaturized sensors on drones but the data quality is affected by lower signal to noise ratios compared to airborne sensors, flying under intermittent clouds and turbulences, and in several cases a lack of thorough radiometric and spectral calibrations.

At the conference, I will present some examples of research that jointly exploit hyper/multispectral and thermal data with proximal, drones, or satellite sensors to estimate ecohydrological variables and evaluate interventions using data-driven or process based models. For example, how biochar affects water use efficiency of rice, understanding better leave thermoregulation under heatwaves or drought or develop indicators of the ecological state of freshwater systems. In addition, some of the current limitations and future perspectives of this technology will be discussed.

How to cite: Garcia, M.: Environmental monitoring and modeling with the support of UAS and satellites, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-59,, 2023.

GC8-Hydro-58 | ECS | Orals | Session 2

Estimating evapotranspiration by using canopy conductance models with Sentinel-2 data in irrigated crops in California and Australia 

Oscar Rosario Belfiore, William P. Kustas, Guido D'Urso, Kyle Knipper, Nicolas Bambach-Ortiz, Andrew J. McElrone, Dongryeol Ryu, Sebastian Castro, John H. Prueger, Nishan Bhattarai, Joseph G. Alfieri, Lawrence E. Hipps, Maria M. Alsina, Carlo De Michele, Francesco Vuolo, and Qotada Alali

Deriving evapotranspiration is crucial for determining the water requirements of crops and for efficiently allocating water resources for irrigation. Various experiments and methods have proven that earth observation (EO) is a useful tool for estimating evapotranspiration and supporting irrigation and water resource management at different scales.

This study presents a framework based on the Penman-Monteith big leaf model and Shuttleworth-Wallace sparse canopy model for estimating the evapotranspiration in irrigated crops with partial and full-canopy conditions.

The approach fully utilizes the high-resolution and multi-spectral capabilities of the Sentinel-2 (S2) sensors for the derivation of surface parameters such as hemispherical shortwave albedo(α), Leaf Area Index (LAI), and the water status of the soil-canopy ensemble by using the OPTRAM model. Proposed by Sadeghi [1], the OPTRAM model uses the pixel distribution in the Shortwave Infrared Transformed Reflectance (STR)-NDVI space, where the water content of the soil-canopy system is linearly correlated to the STR index.

In detail, the proposed approach estimates the contributions of soil and canopy to the total evapotranspiration by incorporating the OPRAM model to assess the water status of the surface and adjust the resistance terms in the combination equation [2]

The results are validated by using Eddy Covariance data collected during the GRAPEX (Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment) project [3], T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) project, and COALA (COpernicus Applications and services for Low impact agriculture in Australia) project [4]. These projects are conducted respectively in irrigated vineyards and almond orchards in California, and in irrigated maize and alfalfa in Australia.

[1] Sadeghi, Morteza, Scott B. Jones, and William D. Philpot.: A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164, 66-76 (2015).

[2] D’Urso, G., Bolognesi, S. F., Kustas, W. P., Knipper, K. R., Anderson, M. C., Alsina, M. M., ... & Belfiore, O. R.: Determining evapotranspiration by using combination equation models with sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sensing, 13(18), 3720 (2021).

[3] Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 2018, 99, 1791-1812.

[4] COALA project.

How to cite: Belfiore, O. R., Kustas, W. P., D'Urso, G., Knipper, K., Bambach-Ortiz, N., McElrone, A. J., Ryu, D., Castro, S., Prueger, J. H., Bhattarai, N., Alfieri, J. G., Hipps, L. E., Alsina, M. M., De Michele, C., Vuolo, F., and Alali, Q.: Estimating evapotranspiration by using canopy conductance models with Sentinel-2 data in irrigated crops in California and Australia, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-58,, 2023.

GC8-Hydro-72 | Orals | Session 2

Remote sensing of Land Surface Temperature for precision irrigation modelling: experience for different case studies. 

Marco Mancini, Nicola Paciolla, Chiara Corbari, Carmelo Cammalleri, Giovanni Ravazzani, and Alessandro Ceppi

Agriculture will progressively require more and more attention due to changing climatic conditions and increasing population, consequently threatening food security worldwide. Improving irrigation efficiency and its control on large agricultural areas has become a must for the present and next future.

Satellite data coupled with pixel-wise energy and water balance plays a relevant role in the soil moisture assessment and relative irrigation water needs for different crop and soil types.

In this framework, data from remote sensing is a potential source of information and in particular land surface temperature is nowadays extensively used in agricultural monitoring as input of energy balance models (residuals) that provide evapotranspiration estimates and so irrigation water needs.

Two main issues hinder the quality of the results from these models: (a) sub-pixel heterogeneity, in particular related to mixed crops (e.g. row and tree crops), which can be captured only partially by the available LST spatial resolution and (b) temporal frequency of the information, which for most freely-available products is usually in contrast with spatial resolution (e.g., 1 km data from MODIS is available daily, whereas 90 m data from Landsat only once every 7-8 days).

This work discusses the use of land surface temperature for calibration and validation of a pixel-wise soil-energy-water balance model and its impact on irrigation volumes for different case studies.

The discussion is carried out on several case studies characterized by different land use heterogeneity due to arboreal or crop cover and comparing satellite and ground data. LST data at different grid resolutions (10^0 to 10^2 m) are available and have been used with a corresponding spatial scheme to model the pixel soil, energy and water balances.

How to cite: Mancini, M., Paciolla, N., Corbari, C., Cammalleri, C., Ravazzani, G., and Ceppi, A.: Remote sensing of Land Surface Temperature for precision irrigation modelling: experience for different case studies., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-72,, 2023.

Evapotranspiration (ET) is the largest loss term in the terrestrial water balance and plays a key role in the energy and carbon cycles. Accurate and timely measurements of ET are critical for understanding the ecosystem responses to climate change and managing water resources. Traditional methods for measuring ET are either highly individualistic leaf- or stem-scale approaches or large-scale tools that aggregate across entire landscapes. Unmanned aerial vehicles (UAVs) constitute a new frontier in measurement of ET that bridges the gap between in situ measurements and remotely sensed observations of water and energy fluxes. With advances in sensor technology and data processing algorithms, UAV-based remote sensing of ET provides both an avenue to refine satellite-based algorithms for retrieving water use and an improved understanding of the fundamental exchange processes between vegetation and the atmosphere.

We present an approach for estimating ET at leaf to landscape scales using thermal imagery, structural data, and a suite of environmental sensors mounted on a UAV platform. Our approach derives ET solely from UAV-acquired data using a combined atmospheric profiling and surface energy balance algorithm. Centimeter-scale leaf position and orientation information derived from Structure-from-Motion (SfM) are integrated with the functional data to constrain available energy, allowing for multi-scale estimation of plant water use within and across canopies.

Using thermal imagery and a suite of environmental sensors mounted on a UAV platform, we calculated ET of a Mediterranean grassland in Southern California at <1-m spatial resolution for 16 flights across the 2021 and 2022 growing seasons. We compare UAV-derived fluxes using four different formulations of aerodynamic resistance to measurements from an eddy covariance tower at the site. We then discuss the relative importance of surface temperature, aerodynamic terms, and meteorological variables for calculating ET from surface energy balance, highlighting the limitations of current approaches and the potential opportunities for future studies.

How to cite: Morgan, B. and Caylor, K.: A novel surface energy balance algorithm for estimating evapotranspiration from UAV-acquired data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-95,, 2023.

Soil moisture is a key variable to our understanding of heat and water fluxes at the land-atmosphere interface. Satellite-based remote sensing instruments offer soil moisture data sets with global coverage that help advancing climate modeling and weather forecast models. However, the development of algorithms to estimate soil moisture from these satellite missions is complex and depends on several other parameters such as vegetation cover and surface roughness.

This demands for comprehensive reference data sets to validate and calibrate satellite products against but faces a challenge in spatial resolution: Traditional in situ methods measure on a representative horizontal scale of few meters while satellite instrumentation offers a much coarser resolution of hundreds of meters to tens of kilometers. Cosmic-ray neutron sensing (CRNS) fills this measurement gap by averaging over the moisture content of the upper soil layers within a footprint of approximately ten hectares. Mobile applications of CRNS extend the method’s coverage to up to a square kilometer. In the recent years the interest was set to understanding neutron transport by Monte-Carlo simulations for complex environmental topographies. As a conclusion, its remarkable performance in signal interpretation allows for a promising prospect of more comprehensive data quality.

With snapshot measurements of soil moisture with a spatial resolution of 50 m and a coverage of up to 1 km2 using mobile CRNS, high-quality data sets can be obtained as ground truthing for remote sensing products. These on demand campaigns can cover different land types and may be combined with existing sensor networks in order to improve soil moisture retrieval algorithms for satellite-based instruments.

How to cite: Weimar, J. and Köhli, M.: Validation potential for Remote Sensing soil moisture products using Cosmic-Ray Neutron Sensing, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-49,, 2023.

GC8-Hydro-79 | ECS | Orals | Session 2

Prediction of soil properties by lab and airborne spectral data 

Bar Efrati and Eyal Ben Dor

Over the preceding 50 years, issues of soil degradation, food insecurity, water scarcity, and loss of ecosystem services are at the center of environmental studies and global concern. These environmental and social issues have intensified the need for sustainable land management and higher-quality global-scale information on soil. The use of soil spectroscopy and hyperspectral remote sensing (HRS) has advanced the soil science discipline by providing an accurate, rapid, and inexpensive estimation of the Earth’s soil composition. In this study, we created field and lab soil spectral libraries (SSL) and predicted elementary soil properties such as water infiltration rate (WIR), soil texture content, and organic carbon (OC), essential for agricultural management. These models were applied in a case study of Campania,  Italy,  to the high-end HRS AVIRIS NG NASA sensor with a ground resolution of 3 meters, and 224 spectral bands along the VNIR-SWIR range (400-2500 nm). In addition, we discuss the differences between field, lab, airborne, and satellite spectra and emphasize the need for separating the models.

How to cite: Efrati, B. and Ben Dor, E.: Prediction of soil properties by lab and airborne spectral data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-79,, 2023.

GC8-Hydro-63 | ECS | Orals | Session 2

Integrating soil structure in hydrologic models of the unsaturated zone 

Efthymios Chrysanthopoulos, Christos Pouliaris, Kostas Markantonis, Ioannis Tsirogiannis, and Andreas Kallioras

Most hydrologic models of the unsaturated zone predict soil hydraulic parameters using empirical equations (Pedotransfer functions - PTFs) driven by a limited set of soil characteristics, predominantly soil texture. Despite the increasing capabilities of PTFs, due to the advancement of machine learning algorithms and neural networks over the course of the years, several researchers have pointed out that PTFs integrate limited or any information for soil structure (Novick et al., 2022).  Soil structure, which is not correlated with soil texture, is affected by several factors, such as climate variations, biophysical activity, clay minerals, and the growth of roots, which determines the process of water movement in the unsaturated zone.

In this study, two laboratory methods were implemented (centrifuge and salt solution method) in order to define the water–retention curve of 1 m. undisturbed soil sample from an experimental kiwi field, located in the Epirus region (NW Greece). The water–retention curve correlates volumetric soil water content (θ) with matric potential, which becomes more negative when soils dry and is recognized as the fundamental driver of water flow in the unsaturated zone. Furthermore, a new permeability cell was constructed to conduct the falling head permeability method on undisturbed soil samples to determine saturated hydraulic conductivity (Ksat). The combination of all these methods led to a complete characterization of the undisturbed samples' hydraulic properties.

Subsequently, Hydrus-1D model was chosen to simulate the water movement in the soil–crop system within the experimental kiwi field, both by integrating predicted soil hydraulic properties from soil texture data and by embedding those measured from laboratory methods.  The results generated by the different approaches were compared and an inverse modeling process was followed to improve the efficiency of the model’s predictions based on observations.


This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call SUPPORT OF REGIONAL EXCELLENCE (project code MIS: 5047059).

Novick, K. A., Ficklin, D. L., Baldocchi, D., Davis, K. J., Ghezzehei, T. A., Konings, A. G., MacBean, N., Raoult, N., Scott, R. L., Shi, Y., Sulman, B. N., & Wood, J. D. (2022). Confronting the water potential information gap. Nature Geoscience, 15(3), Article 3.

How to cite: Chrysanthopoulos, E., Pouliaris, C., Markantonis, K., Tsirogiannis, I., and Kallioras, A.: Integrating soil structure in hydrologic models of the unsaturated zone, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-63,, 2023.

GC8-Hydro-27 | ECS | Orals | Session 2

Combining UAS LiDAR, sonar and radar altimetry for river hydraulic characterization 

Monica Coppo Frias, Alexander Rietz Vesterhauge, Daniel Haugård Olesen, Filippo Bandini, Henrik Grosen, Sune Nielsen, and Peter Bauer-Gottwein

Hydraulic characterization of river reaches is fundamental for flood risk assessment and flood forecasting. Hydraulic models can translate discharge into water levels, with measurements of river topography, water level and hydraulic roughness. Traditionally, these measurements are taken with in-situ surveys that are normally costly and time consuming when large or spatially distributed datasets are required, and difficult to retrieve in some locations. Remote sensing solutions have been widely used in the last years to measure inland water topography and water levels, reducing the time and cost of traditional surveys. Satellite earth observations can measure inland water bodies with high temporal and spatial frequency, but they only work in large rivers, can have limited accuracy and cannot measure the submerged portion of the river. UAS techniques offer high-resolution measurements of river topography, including river bathymetry and water level in medium-sized streams that are too big to wade through, offering a good opportunity to recover a full river hydraulic characterization.

UAS techniques have been widely used in hydrologic surveys, especially in smaller streams where satellite-based solutions are unfeasible due to the measurement sparsity. In addition, these techniques are very versatile, offering different types of measurements depending on the payload attached to the airborne system. River bathymetry can be retrieved using sonar or water penetrating radar (WPR), which provides depth measurements that, combined with water level measurements, can be used to calculate bathymetry. Moreover, land elevation can be measured with a LiDAR payload, providing topographic information on the river edges and adjacent floodplains. Radar altimeters can also provide water level measurements at a very high spatial resolution and accuracy. These data-sets can be used together to calibrate hydraulic roughness, which cannot be observed at the scale needed.

In this study, we propose a new UAS data acquisition technique for full hydraulic characterization of a river reach combining sonar bathymetry, LiDAR elevation of the land adjacent to the river and radar water level measurements to calibrate a hydraulic model. The method is demonstrated in a reach of Ryå stream in Jammerbugt, Denmark. This stream has a river width of around 10 meters and is characterized by dense vegetation in the surroundings, with deep areas where it is not possible to wade through. The bathymetry is observed using a sonar payload that measures depth in contact with water and water level measurements from RTK. The sonar depth is acquired in 1 day with a quasi-continuous UAS flight that measured 54 cross-sections separated by 100 meters. The land elevation is measured using a LiDAR in scanning mode, which gives measurements for 8 km of the river reach in less than half a day. The water level measurements were taken with a radar altimeter payload for 8 km of the river reach. The topographic and water level measurements are used in a hydraulic model to calibrate hydraulic roughness by estimating water levels that are compared with observed water level from radar altimetry.

How to cite: Coppo Frias, M., Vesterhauge, A. R., Olesen, D. H., Bandini, F., Grosen, H., Nielsen, S., and Bauer-Gottwein, P.: Combining UAS LiDAR, sonar and radar altimetry for river hydraulic characterization, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-27,, 2023.

GC8-Hydro-48 | Orals | Session 2

Mapping and quantification of groundwater–surface water exchange along a headwater stream using Distributed Temperature Sensing: First findings from the Wüstebach Catchment, Germany 

Jochen Wenninger, Konstantina Katsanou, Alessandro Cattapan, Raymond Venneker, Heye Bogena, and Roland Bol

Groundwater-Dependent Ecosystems (GDEs) are valuable as they support ecosystem services at local and regional scales. Although they are closely related, surface- and groundwater bodies have traditionally been studied and managed separately. Since local hydrogeology and climate conditions affect GDEs, detailed spatial and temporal studies on their chemical and quantitative interactions are required.

A multi-disciplinary approach was used to investigate the interactions between surface- and groundwater in the Wüstebach test site, a 38.5 ha headwater catchment located in the Eifel National Park, Germany. This catchment is operated by the Forschungszentrum Jülich and is part of the TERENO Eifel Lower Rhine Valley Observatory. Along the streambed, a Fibre Optic Distributed Temperature Sensing (FO-DTS) experiment was set up in October 2022 to monitor the temperature changes of surface water along a 293 m long transect. The FO cable was connected to a Silixa XT-DTS instrument and temperature measurements were collected at 25 cm and 15 min sampling intervals. Moreover, a series of conservative tracer measurements were carried out using slug and continuous injection tests along the stream to quantify the amounts of groundwater exfiltration. In addition, spatially detailed electrical conductivity readings along the stream together with groundwater level measurements were carried out and water samples were collected for chemical determinations. Results of the salt dilution injections revealed that the headwater stream receives a significant contribution from groundwater along the transect, while the initial DTS recordings pinpointed several distinct locations where groundwater inputs occur along the stream; which were also identified on the field. An improved understanding of the catchment's quantitative and qualitative water flows is anticipated as a result of the mapping and subsequent quantification of the groundwater input.

How to cite: Wenninger, J., Katsanou, K., Cattapan, A., Venneker, R., Bogena, H., and Bol, R.: Mapping and quantification of groundwater–surface water exchange along a headwater stream using Distributed Temperature Sensing: First findings from the Wüstebach Catchment, Germany, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-48,, 2023.

GC8-Hydro-37 | Poster | Session 2

Bridging structural and functional hydrological connectivity in dryland ecosystems 

Octavia Crompton, Gabriel Katul, Sally Thompson, and Dana Lapides

On dryland hillslopes, vegetation water availability is often subsidized by the redistribution of rainfall runoff from bare soil (sources) to vegetation patches (sinks). In regions where rainfall volumes are too low to support spatially continuous plant growth, such functional connectivity between bare soil and vegetated areas enables the establishment and persistence of dryland ecosystems.  Increasing the connectivity within bare soil areas can intensify runoff and increase water losses from hillslopes, disrupting this redistribution and reducing the water available to sustain ecosystem function.  Inferring functional connectivity (from bare to vegetated, or within bare areas) from structural landscape features is an attractive approach to enable rapid, scalable characterization of dryland ecosystem function from remote observations. Such inference, however, would rely on metrics of structural connectivity, which describe the contiguity of bare soil areas.  Several studies have observed non-stationarity in the relations between functional and structural connectivity metrics as rainfall conditions vary. Consequently, the suitability of using structural connectivity to provide a reliable proxy for functional connectivity remains uncertain and motivates the work here. 

Rainfall-runoff simulations across a wide range of dryland hillslopes, under varying soil and rainfall conditions, are used to establish relations between structural and functional connectivity metrics.  The model results identify that the relations vary between two hydrologic limits -- a `local' limit, in which functional connectivity is related to structural connectivity, and a ‘global’ limit, in which functional connectivity is most related to the hillslope vegetation fraction regardless of the structural connectivity of bare soil areas. The transition between these limits within the simulations depends on rainfall intensity and duration, and soil permeability. While the local limit may strengthen positive feedbacks between vegetation and water availability, the implications of these limits for dryland functioning need further exploration, particularly considering the timescale separation between storm runoff production and vegetation growth.

How to cite: Crompton, O., Katul, G., Thompson, S., and Lapides, D.: Bridging structural and functional hydrological connectivity in dryland ecosystems, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-37,, 2023.

GC8-Hydro-38 | Poster | Session 2

Influence of soil hydrological conditions on the accumulation of heavy metals in tree species in the post-mining landscape of Freiberg, Germany (Saxony) 

Viktoriia Lovynska, Oliver Wiche, Carsten Montzka, Svitlana Syntyk, Visakh Sivaprasad, Alla Samarska, and David Mengen

The post-mining landscape of Freiberg, Saxony, Germany is characterized by soils with elevated concentrations of non-essential elements, particularly arsenic (As), cadmium (Cd) and lead (Pb). While literature on soil mineralization and factors influencing the soil-plant transfer in managed agroecosystems is extensive, information on the accumulation in native woody plant species, including soil-associated factors influencing their accumulation, is still lacking. In this study, we evaluated the concentrations of 24 elements, including As, Cd and Pb in leaves and branches of three taxa of tree species (Betula pendula, Populus tremula and Salix spec.) throughout the study area and compared the results with data on potentially plant available element concentrations in soil (sequential extraction), total element concentrations as well as with remote sensing data on surface soil moisture and water availability in the root-zone. Populus tremula and Salix spec. were identified as plant species that are suitable for bioindication of soil pollution. Leaf concentrations were substantially higher compared to branches. The concentrations in leaves largely reflected the availability of elements in soil. Concomitantly, higher soil-plant-transfer of elements correlated with remote sensing data, onsurface water accumulation and water content in the root-zone. This suggests that higher soil water contents increase the availability of the toxic elements to plants and/or impacts the translocation of elements to aboveground plant parts. The contribution of soil-associated factors and plant-associated factors to the hyperaccumulation observed remains a field for further research. Nevertheless, we could demonstrate that coupling leaf analysis with remote sensing data on soil moisture could be a promising approach in environmental assessments as well as in phytoremediation and phytomining approaches.  

How to cite: Lovynska, V., Wiche, O., Montzka, C., Syntyk, S., Sivaprasad, V., Samarska, A., and Mengen, D.: Influence of soil hydrological conditions on the accumulation of heavy metals in tree species in the post-mining landscape of Freiberg, Germany (Saxony), A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-38,, 2023.

GC8-Hydro-16 | ECS | Poster | Session 2

morphometric analysis of the Soummam basin using remote sensing and GIS technologies

khemmal hichem yakoub, Hani Azzedine, and Benmarce Kaddour

GC8-Hydro-32 | Poster | Session 2

Improving water resource management through the development of a flux tower network and remote sensing modeling of evapotranspiration and water stress of woody perennial crops in California 

William Kustas, Nicholas Bambach, Andrew McElrone, Kyle Knipper, Alfonso Torres-Rua, Matthew Roby, Maria Mar Alsina, John Prueger, Joseph Alfieri, Sebastian Castro, Lawrence Hipps, Lynn McKee, Oscar Belfiore, and Guido D'Urso

Improving water resource management in the western United States is critically needed to achieve sustainability between the competing demands of water supplies for cities and towns, for agriculture, particularly irrigated regions, by industries principally for generating electricity, and for the environment (i.e., providing adequate ecosystem services). The last decade marked by historically severe droughts revealed the need for new water management policies and environmental regulations. Moreover, the impact of climate change not only has exacerbated droughts but also may be causing episodic extreme wet periods requiring a new paradigm on water management strategies for surface water reservoirs and groundwater aquifers. The Sustainable Groundwater Management Act (SGMA) is an example of developing a water management policy for sustainability of water resources in California. California produces 80% of the world’s almonds, is the 4th largest wine producer worldwide while also providing three-quarters of the fruits and nuts in the U.S. Much of the production requires reliable water sources for irrigation.  This has motivated research into designing networks of evapotranspiration (ET) flux tower measurements in grape and tree crop systems in conjunction with developing new remote sensing tools for mapping crop water use, ET, to efficiently use and conserve water resources across the over 1.5 million acres of woody perennial crop production fields.  This acreage is largely irrigated and uses approximately 70% of freshwater resources in the region.  The two projects, GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) and T-REX (Tree-crop Remote sensing of Evapotranspiration eXperiment) have the overall goal to identify management opportunities to maximize water use efficiency in vineyard, almond and other woody perennial crops.  This presentation will describe the measurement network used for understanding the water and energy flux exchange of these complex cropping systems and in validating and refining remote sensing modeling tools from UAS and satellite platforms for estimating ET and crop water stress from plant and sub field to regional scales required for improving water management strategies of these agricultural systems.

How to cite: Kustas, W., Bambach, N., McElrone, A., Knipper, K., Torres-Rua, A., Roby, M., Alsina, M. M., Prueger, J., Alfieri, J., Castro, S., Hipps, L., McKee, L., Belfiore, O., and D'Urso, G.: Improving water resource management through the development of a flux tower network and remote sensing modeling of evapotranspiration and water stress of woody perennial crops in California, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-32,, 2023.

GC8-Hydro-54 | Poster | Session 2

Monitoring Environmental Variables to Promote Precision Agriculture 

Viviana Maggioni, Christian Massari, Janani Kandasamy, Yuan Xue, Sara Modanesi, Domenico De Santis, Francesca Sofia Manca di Villahermosa, Daniele Penna, Paolo Benettin, and Marco Dionigi

Precision agriculture is a modern approach based on farm and irrigation management to improve the efficiency in the use of water resources. Precision agriculture, therefore, maximizes crop productivity and yield through technologies that identify, analyze, and monitor variability within a field and optimize profitability, sustainability, and land protection. This study proposes a combination of approaches to monitor a suite of environmental variables with the goal of improving agricultural management. We selected the experimental vineyard of Grignanello (Tuscany, Italy), located on a mild slope at 350 m.a.s.l in the famous Chianti wine region, where extensive ecohydrological data are available. In combination with this set of ground-based observations, the Environmental Policy Integrated Climate Model (EPIC) is adopted to model key variables for crop production, including soil temperature and soil water content. Using the EPIC model, we generate three sets of simulations based on three different parameterizations (i.e., original cosine, enhanced cosine, and pseudo heat transfer). By comparing model output against ground-based measurements and UAV-based soil temperature, we assess what model set-up is more accurate and for which environmental variable of interest. Furthermore, a new set of soil temperature and soil moisture estimates is obtained by taking the mean of the three EPIC simulations. Thus, we assess the possibility to improve the performance of the single models, as shown in previous studies across the Central Valley in California. Outcomes from this work will provide a solid basis towards developing a decision guidance system for precision agriculture management. 

How to cite: Maggioni, V., Massari, C., Kandasamy, J., Xue, Y., Modanesi, S., De Santis, D., Manca di Villahermosa, F. S., Penna, D., Benettin, P., and Dionigi, M.: Monitoring Environmental Variables to Promote Precision Agriculture, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-54,, 2023.

GC8-Hydro-18 | ECS | Poster | Session 2

Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data 

Mathilde de FLEURY, Laurent Kergoat, and Manuela Grippa

Sahelian small water bodies are critical resources that people use for multiple purposes: irrigation, fishing, bathing, drinking water, livestock watering. Better understanding their hydrological regimes and quantify water inflows and outflows is necessary to achieve better management of these water bodies. Thanks to satellite technological advances allowing regular monitoring in space and time, remote sensing techniques provide a major tool to do this. In this work we develop a remote sensing based method to quantify water fluxes in Sahelian small water bodies.

Water heights from Sentinel-3 SAR Radar Altimeter (SRAL) are combined with water areas estimated through MNDWI thresholding on Sentinel-2 Multispectral Instrument (MSI) images (using Google Earth Engine) to create a height-area curve for each studied lake. Dense water height time series are then obtained by pooling water height from both altimetry and optical data. Water height variations are compared to evaporative losses, estimated by the Penman–Monteith method with data from ECMWF ERA5, to analyse water fluxes during the dry season, when precipitation is null.

This method is applied on 37 lakes in the Central Sahel (Mali, Burkina Faso and Niger), whose areas range from 0.04 km2 to 37.91 km2, over the five year period 2016-2020. The five-year averaged dry-season difference between water height decrease and evaporation varies from –12.45 mm.d-1 to 9.71 mm.d-1. Lakes display three different regimes: a net water loss (i.e. water height decrease greater than evaporation), a net water supply, and a balanced behaviour (i.e. water losses correspond to evaporation).

Water supply is mainly observed in lakes in the the Inner Niger Delta and it is likely due to connections to the Niger River hydrographic network. The main flood in the Inner Niger Delta occurs indeed after the end of the rainy season. Water loss is mainly found in the centre of Burkina Faso and correspond to water withdrawal for small-scale irrigation. Interannual variability is related to changes in rainfall, in the length of the dry season, and in anthropogenic actions. Only 6 out of 37 lakes show a change in regime from positive to negative or vice versa within the study period. One of these lakes is a reservoir whose infrastructure was damaged by attacks during conflicts which caused leaks.

The remote sensing method developed allows to better understand the regime of small Sahelian water bodies and assessing water fluxes and anthropogenic water withdrawals. Oncoming SWOT data will allow to apply this approach to a much larger number of water bodies in this region and more generally in semi-arid areas.

How to cite: de FLEURY, M., Kergoat, L., and Grippa, M.: Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-18,, 2023.

GC8-Hydro-60 | Poster | Session 2

Earth Observation data for the monitoring of irrigation water use in Italy: The case study of the INCIPIT project. 

Guido D'Urso, Oscar Rosario Belfiore, Antonio Coppola, Alessandro Comegna, Attilio Toscano, Gabriele Baroni, Simona Consoli, Daniela Vanella, Giuseppe Longo Minnolo, Matteo Ippolito, Dario De Caro, Alessandro Castagna, and Claudio Gandolfi

Agriculture is the main source of pressure on water resources, so accurate estimates of irrigation demands play a key role in sustainable water management. The INCIPIT (INtegrated Computer modeling and monitoring for Irrigation Planning in Italy) project (funded by Italian Min. Univ. and Research) aims to address the gaps between research and practical application in monitoring irrigation water use in six Italian regions [1].

It is designed to meet the requirements of sustainable water policies, such as the Water Framework Directive and the MIPAAF Ministry Decree, by providing accurate measurements and estimations of irrigated areas and water volumes. The project uses the ESA Sentinel-2 (S2) satellites as a valuable source of information to map irrigated areas and estimate distributed irrigation water requirements.

This study presents the results of the IRRISAT methodology, the first Italian satellite-based irrigation advisory service [2], which was applied in the Campania region. The methodology uses a one-step approach, based on the Penman-Monteith equation, and is adjusted with canopy parameters from S2 data, to quantify irrigation water abstraction. Effective irrigated areas were assessed by using pre-existing maps, unsupervised clustering, and supervised machine learning algorithms applied to vegetation index data [3].

The results of the methodology for the irrigation seasons of 2019 and 2020 will be presented for seven Irrigation and Land Reclamation Consortiums, which vary in size, irrigation scheme, farm delivery, irrigation methods, and crop types.

[1] INCIPIT Project,

[2] Vuolo F., D’Urso G., De Michele C., Bianchi B., Cutting M.: Satellite-based irrigation advisory services: a common tool for different experiences from Europe to Australia”. Agricultural Water Management, Elsevier, vol. 147: 82-95; (2015).

[3] Falanga Bolognesi S., Pasolli E., Belfiore O. R., De Michele C., D’Urso G.: Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy". Remote Sensing, MDPI, vol.12, no. 8: 1275; (2020).

How to cite: D'Urso, G., Belfiore, O. R., Coppola, A., Comegna, A., Toscano, A., Baroni, G., Consoli, S., Vanella, D., Longo Minnolo, G., Ippolito, M., De Caro, D., Castagna, A., and Gandolfi, C.: Earth Observation data for the monitoring of irrigation water use in Italy: The case study of the INCIPIT project., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-60,, 2023.

Groundwater resources are biodiversity hotspots, and provide crucial ecosystems services. Yet, groundwater dependent ecosystems (GDEs) are exposed to several anthropogenic threats, including climate and land use change. Tackling these threats requires improving the on-the-ground identification of GDEs at the global scale and especially in vulnerable areas such as the Mediterranean biome where water is scarce.

In order to identify the location of groundwater dependent vegetation (GDV) in the landscape and create a harmonized global map of GDV a novel multi-instrument and multi-scale approach was developed. The approach combines a geodata-based potential GDV zones-index (pGDVZ) together with high-resolution vegetative, hydrogeological and topographic remote sensing parameters.

The pGDVZ integrates global and openly available datasets to combine groundwater vegetation interaction, land use, soil characteristics and landscape wetness potential. The index is currently tested for the whole Mediterranean. Results can help to pinpoint areas of high GDVZ potential where regional high-resolution identification of GDV is necessary.

The regional GDV-mapping concept implements different criteria aiming at: 1) high vitality and wetness during dry periods (e.g., Enhanced Vegetation Index, Normalized Difference Vegetation Index, Normalized Difference Water Index), 2) low seasonal changes in vitality, 3) low interannual changes in vitality, 4) high topographic potential of water accumulation and low water table depth and 5) general topography (elevation, slope). Processing of different remote sensing data (e.g., Sentinel 1;2, Digital Elevation Models, MODIS) is performed using the Google Earth Engine. Botanical mapping as well as integration of several geodata is used for validation and calibration of derived GDV-likelihoods. Furthermore, the integration of vegetation plots extracted from sPlot, the global vegetation database introduces a novel methodology to train machine learning algorithm for classification and modelling of GDV.

After successfully testing the mapping approach at local scale in the ‘Cilento, Vallo di Diano and Alburni National Park’ in Campania (Italy), a two-step upscaling methodology is currently designed to implement the concept on regional (county) and global (biome) scale.

How to cite: El-Hokayem, L., De Vita, P., and Conrad, C.: Mapping of potential groundwater dependent vegetation zones in the Mediterranean using a simple index based on global-available geodata and high-resolution remote sensing, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-61,, 2023.

GC8-Hydro-97 | Poster | Session 2

Downscaling and validation of 1km ESA Soil Water Index for soil moisture monitoring on agricultural land in temperate climate conditions 

Christopher Conrad, Johannes Schmelzer, Julian Schlaak, and Johannes Löw

Near-surface soil moisture is an important parameter for estimating the water balance of ecosystems, especially for the exchange of water between atmosphere and soil. In the past decades, global and continental products, e.g. the Soil Water Index (SWI) of ESA, have been developed for large-scale monitoring from passive and active microwave data. ESA SWI was developed for Europe based on the surface soil moisture product derived from Sentinel-1 C-band SAR data (1km resolution) and Metop ASCAT surface soil moisture (12.5km). However, for practical, small-scale applications on the local level, there is currently little data available. This contribution elaborates on the validation of the SWI for temperate agricultural regions at the example of the agrometeorological network of the JECAM test site DEMMIN in Northeast Germany. For this purpose, two resampling methods, a bilinear interpolation and a statistical downscaling approach using Random Forest were tested on SWI data from 2019. The statistical downscaling approach integrates Sentinel satellite data and the Topographical Wetness Index based on a 10m resolved elevation model. Both resampling methods showed similar results. Over time, the SWI significantly overestimates the in situ data before and after the crop growing season. A higher agreement is observed in the summer months. For 19 of the 29 investigated agrometeorological stations a statistically positive correlation with R>0.5 was found. The remaining stations showed little to no correlation, most likely due influences of various crops types on the remote sensing data. The results suggest a temporally limited applicability of the SWI 1km data for local assessments of soil moisture.

How to cite: Conrad, C., Schmelzer, J., Schlaak, J., and Löw, J.: Downscaling and validation of 1km ESA Soil Water Index for soil moisture monitoring on agricultural land in temperate climate conditions, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-97,, 2023.

Hydrology relies on the measurement of various variables, among which snow water equivalent (SWE) plays a crucial role in predicting runoff, particularly for catchments with high levels of snow accumulation. However, SWE measurements are rare and limited to point scales, making it difficult to obtain accurate spatialized estimates. While current satellite missions do not directly measure SWE, they offer valuable proxy information that can be used to reconstruct SWE. We propose using optical and radar sensors from MODIS, Sentinel-2, and Landsat missions to extract data on snow persistence on the ground and merge this multi-scale information to obtain accurate estimates of SWE. The technique involves observing snow patterns at high spatial resolutions from Landsat and Sentinel-2 missions and using this information to reconstruct a low-resolution image from MODIS. Additionally, information on the duration of melting can be obtained using Synthetic Aperture Radar (SAR) from Sentinel-1. In-situ air temperature data is used to estimate potential melting, and snow depth observations are used to determine if accumulation is occurring. The final output is daily high-resolution SWE maps. This approach has the advantage of not relying on precipitation observations, which are often uncertain in high-elevation catchments. We investigate the effectiveness of this approach in estimating peak snowmelt discharge for two monitored catchments in South Tyrol (Italy), comparing the results to those obtained using state-of-the-art hydrological models such as GEOtop and New Age. These results have the potential to significantly improve snowmelt estimation in poorly monitored basins.

How to cite: Bertoldi, G., Premier, V., Bozzoli, M., and Marin, C.: A comparison of a new remote sensing-based SWE estimation method  with physical models for improving snow melt estimation in alpine catchments, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-100,, 2023.

GC8-Hydro-69 | ECS | Poster | Session 2

Identifying controls on throughfall variability at the hillslope scale through satellite data and UAV-assisted techniques 

Matteo Verdone, Pilar Llorens, Christian Massari, Ilja van Meerveld, and Daniele Penna

Spatio-temporal variability of throughfall (TF) in forested catchments depends on climatic forcing, forest stand parameters, and rainfall characteristics. Identifying and quantifying these controls is fundamental for a correct analysis and modelling of interception and catchment hydrological response. Despite many studies carried out at the stand and hillslope scale focused on the analysis of controls on TF variability, very little is known about the role of hillslope topography and the associated tree population characteristics in shaping throughfall spatio-temporal variability. In addition to ground-based measurements, satellite and unmanned aerial vehicle (UAV)-data can be explored to obtain a more robust assessment of the main drivers of TF variability. Therefore, this work aimed at i) identifying the dominant factors on throughfall variability in a European beech stand along a steep hillslope; and ii) quantifying forest interception at the hillslope scale and upscaling it at the small catchment scale using ground-level measurements and UAV-derived and satellite observations.

The experimental activities were carried out in the upper part of the Re della Pietra catchment, Tuscany Apennines, Central Italy. The hillslope is roughly 110 m long and 60 m wide, has a mean slope of 30°, and is dominantly covered by European beech trees. The TF experimental plot consists of 126 throughfall collectors divided in two square grids of 144 m2 with 49 collectors at the bottom and the top of the hillslope, and a transect of 28 collectors from the bottom to the top grid. TF was manually measured from the collectors approximately monthly and compared with gross precipitation. Moreover, five automatic gauges were installed along the hillslope to increase the temporal resolution. Topographic surveys were conducted to measure the main physiological characteristics (diameter, height and age) of the trees in the TF plot. Leaf Area Index (LAI) was estimated using a ceptometer above each sampler in four dates in the dormant and in the growing season.

The 40 manual measurements revealed a large spatial and temporal variability of the TF/precipitation ratio (mean 68%, standard deviation 37%). In the growing period, the TF/precipitation ratio showed higher spatial variability compared to the dormant season (66±29% and 85±31%, respectively), suggesting that the crown expansion can be an important control on TF variability. Moreover, TF was consistently lower in the bottom plot, characterized by larger tree size compared to the top grid indicating a control by trees size. Event-scale data from the gutter gauges show a rainfall intensity control on TF but showed no correlation between TF and hillslope position.

To corroborate the preliminary observations on crown and tree size, in early March 2023 a UAV survey will be conducted to determine the crown architecture and lateral expansion, and relate this parameters to the observed TF. Furthermore, LAI ground measurements will be compared with LAI data derived from Sentinel-2 data to establish a relation between ground measurements and satellite observations. This relationship will be then use to upscale LAI and its possible associate control on TF variability from the hillslope to the small (0.3 km2) catchment scale.

How to cite: Verdone, M., Llorens, P., Massari, C., van Meerveld, I., and Penna, D.: Identifying controls on throughfall variability at the hillslope scale through satellite data and UAV-assisted techniques, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-69,, 2023.

GC8-Hydro-103 | Poster | Session 2

Assessment of UAV-based LiDAR and photogrammetry data in crop morphology monitoring for advanced irrigation 

Attila Nagy, Erika Buday-Bódi, Andrea Szabó, Zsolt Zoltán Fehér, and János Tamás

Most of the climate scenarios forecast increased water scarcity in semi-arid and arid areas, such as Hungary. Since only 2% of Hungary’s agricultural land is irrigated where mostly outdated irrigation technology is applied, there is a huge need for act to enhance advanced irrigation. The general aim of the present research was to develop the basis of variable rate irrigation for a water-saving precision sprinkler irrigation system on an maize site (85 ha) located in the reference area of the Tisza River Basin. There are limited available water resources at the site, therefore alternative water sources utilization system was set up for irrigation to adapt to climate change and reduce fertilizers. As the alternative water resource for irrigation, inland excess water, treated wastewater, and biogas fermentation sludge are collected in a water reservoir with a capacity of 114,000 m3. For proper irrigation scheduling, heterogeneity of topography, hydrological, soil and crop conditions have to be explored and monitored. For this reason, UAV-based surveys were carried out with high spatial and temporal resolution by which DEM, DSM, multispectral vegetation data was conducted both on irrigated and non-irrigated parts of a maize field in Hungary this site. Supplementing these data with physically based modelling of the soil and crop status, and the water balance surveying is tested to use for accurate irrigation scheduling.

In the surveys we used a DJI Matrice 300 RTK UAV drone equipped with a Zenmuse L1 LiDAR payload with integrated RGB Surveying Solution and a DJI Zenmuse H20T thermal camera, which measures in the spectral range from 8000 to 14,000 nm. A DJI Mavic 2 Zoom drone equipped with a Sentera Double 4K sensor that can calculate NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red-Edge Index) by filtering out red and NIR wavelengths was used for the multispectral research. These data enables the monitoring of crop height and biomass and the assessment of the thermal properties and the photosynthetic activity of the crop, respectively. A crucial work phase is the data management of these remotely sensed data by which we gain point-cloud and raster from semi-raw formats and photogrammetry analysis can commence. For validation, the results of field measurements for crop height, general status and chlorophyll content were applied by virtue of the high spatial resolution provided by the sensors. Based on the results, the considerable relation was found between the field and RS based data to survey the surface, the height and health status of the maize, which contributes to the mapping of a proper vegetation patterns fostering variable rate irrigation prescription maps.

The abstract was funded by European Union’s Horizon 2020 “WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production” research and innovation programme under Grant Agreement No. 858375. This research was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences

How to cite: Nagy, A., Buday-Bódi, E., Szabó, A., Fehér, Z. Z., and Tamás, J.: Assessment of UAV-based LiDAR and photogrammetry data in crop morphology monitoring for advanced irrigation, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-103,, 2023.

GC8-Hydro-107 | ECS | Poster | Session 2

Monitoring water turbidity with camera: a real scale experiment 

Domenico Miglino, Khim Cathleen Saddi, Francesco Isgrò, Seifeddine Jomaa, Michael Rode, and Salvatore Manfreda

Turbidity is one of the most critical metrics in water quality monitoring.  High turbidity in river basins can be an indicator of both organic and inorganic material presence. Improving existing river monitoring techniques is essential, given the growing presence of critical factors, such as climate change, population growth, and pollution in recent years.

In this study, a real scale experiment has been conducted in Selke River within the Bode catchment in Germany. The Bode basin is one of the best-instrumented catchments in Central Germany, managed by UFZ Helmholtz Centre for Environmental Research. In this experiment, the level of turbidity has been artificially increased by adding kaolin clay into the river, upstream enough from the monitored river cross-section to ensure the complete mixing between clay and water.  Kaolin is usually exploited to prepare turbidity standard solutions. In addition, it is a harmless, easy to handle, and low-cost clay mineral, which is also an abundant silicate in soils and sediments.

The monitoring field campaign has been conducted using different instruments, such as an optical camera, a multispectral camera mounted on fixed positions and a drone, which have been used to describe, from different points of view, the synthetic turbidity event generated. Different types of camera and installation settings have been investigated to understand the full potential of this technology for water quality monitoring. The gathered optical data was compared to the recorded turbidity of the UFZ sensors, which has been currently installed in the Selke river cross-section.

The final goal of this work is to build a reliable image processing procedure for the development of a camera system that could support existing monitoring techniques and increase the temporal and spatial resolution in  river monitoring.

Keywords: camera, UAS, river monitoring, sediment transport, image processing, spectral indices, remote sensing, drones, water quality assessment

How to cite: Miglino, D., Saddi, K. C., Isgrò, F., Jomaa, S., Rode, M., and Manfreda, S.: Monitoring water turbidity with camera: a real scale experiment, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-107,, 2023.

GC8-Hydro-106 | ECS | Poster | Session 2

Utilizing Unmanned Aerial Systems in River Water Quality Variability Monitoring 

Khim Cathleen Saddi, Domenico Miglino, Seifeddine Jomaa, Michael Rode, Francesco Isgro, and Salvatore Manfreda

Traditional water quality monitoring in River Systems is both labor intensive and expensive. However, in order to better understand the different phenomena occurring in river systems, it is vital to have robust data available. Satellite observations have been successful in monitoring different environmental systems, but generally, current available spatial resolutions and cloud cover in inland waters limit the monitoring of rivers. Recent developments in the use of Unmanned Aerial Systems (UAS) highlighted the potential to address this gap in environmental monitoring. 

In this study, UAS and image processing techniques were utilized to gather an overview of the water quality variability, specific to turbidity level and pollutant transport, along the Sarno River, which is the most polluted river in Europe, and the river pollution has long been subject to disputes between many sectors. Preliminary findings highlighted the potential of image processing and allowed to identify the  variability in river water quality along the main river by adopting a sampling protocol in several points of the Sarno River. While there were few observations of plastic in river banks, organic transport was mostly observed and interestingly, there is a water quality spatial mixing in the river mouth, which is difficult to observe using traditional in situ point measurements. This study only covers the initial phase of the river monitoring activities. 

Keywords: UAS river monitoring, sediment transport, image processing, spectral indices, remote sensing, drones, water quality assessment



Miglino, D., Jomaa, S., Rode, M., Isgro, F., & Manfreda, S. (2022). Monitoring Water Turbidity Using Remote Sensing Techniques. Environmental Sciences Proceedings, 21(1), 63.

How to cite: Saddi, K. C., Miglino, D., Jomaa, S., Rode, M., Isgro, F., and Manfreda, S.: Utilizing Unmanned Aerial Systems in River Water Quality Variability Monitoring, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-106,, 2023.

GC8-Hydro-11 | ECS | Poster | Session 2

Using Sentinel-2 multispectral imagery to assess flow-intermittency in non‑perennial rivers 

Carmela Cavallo, Maria Nicolina Papa, Giovanni Negro, Giuseppe Ruello, Massimiliano Gargiulo, and Paolo Vezza

Non-perennial rivers are characterized by periods with dry bed or chains of isolated ponds. Given the extremely high biodiversity and various ecosystem services, these environments require careful management. The main obstacle to the implementation of correct management practices is related to the lack of information about the duration and frequency of zero-flow periods, that are the primary determinants of ecosystem processes in this kind of streams.

In many cases, the presence of non-perennial reaches within the river network is unknown. Given the high extension of the network of non-perennial rivers and their strong spatial inhomogeneity, traditional gauging systems are not adequate to provide measures with adequate spatial coverage. Moreover, point measures cannot capture the space-pattern of presence/absence of water. On the other hand, field surveys of water patterns have a limited temporal resolution and therefore lack in capturing the regime’s time-patterns. In this context, satellite data can make a key contribution thanks to the possibility of monitoring large areas with high temporal resolutions. Their use for monitoring the regime of non-perennial rivers has so far been limited by the availability of images with adequate resolution and accessible costs.

In this work, we explored the potential of medium-resolution multispectral Sentinel-2 data to identify non-perennial rivers and to assess their degree of intermittency. Examining the spectral signatures of water, sediment and vegetation covers, the bands in which these classes are most differentiated were identified. Exploiting these bands, we generated false-color image in which the pixels covered by water stand out from the background. From the false-color composite images, it was possible to identify the three distinct flowing status of non-perennial rivers: “flowing”, “ponding” and “dry” . The classification of flowing status was checked against ground truth, showing very good agreement. To enable a wider audience to identify flowing status along non-perennial rivers, we have developed and made freely available a code on the Google Earth Engine platform. For all the archive images (since 2015) we identified one of the three possible flowing status: flowing, ponding and dry bed. The obtained dataset allowed to train a random forest (RF) model able to predict the daily occurrence of a specific flowing status using as predictors spatially interpolated rainfall and air temperature data. The analysis was performed for 5 reaches of the streams Sciarapotamo, Mingardo and Lambro (Campania region, Italy), for which a RF model was calibrated. Classification RF models performed well in terms of accuracy (ranging from 82% to 97%) and true skill statistic (ranging from 0.65 to 0.95). All the studied reaches showed a no-flow condition during the observation period. Three of the five reaches resulted to have a dry bed condition each year while the other two reaches never dry up completely. With its ability to monitor the presence and absence of water in a cost-effective manner, this method has the potential to significantly improve the management and the conversation of non-perennial rivers, enabling a better understanding of their ecological status, as required by the European Water Framework Directive 2000/60/EC.

How to cite: Cavallo, C., Papa, M. N., Negro, G., Ruello, G., Gargiulo, M., and Vezza, P.: Using Sentinel-2 multispectral imagery to assess flow-intermittency in non‑perennial rivers, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-11,, 2023.

GC8-Hydro-52 | Poster | Session 2

UAS Hydrometry – Contactless airborne measurements of water level, depth, flow velocity and discharge in rivers and streams 

Peter Bauer-Gottwein, Daniel Olesen, Karina Nielsen, Alexander Rietz, Monica Coppo Frías, Alexey Dobrovolskiy, Alexey Kadek, Niksa Orlic, Tomislav Grubesa, Tom Hiller, Henrik Grosen, Sune Nielsen, Angelica Tarpanelli, Daniele Giordan, Silvia Barbetta, David Gustafsson, Daniel Wennerberg, Markus Disse, Fabian Merk, and Laia Romero and the UAWOS project team

High-resolution monitoring of rivers is important because rivers are severely affected by climate change and both frequency and magnitude of extreme events are changing rapidly. Advanced in-situ monitoring technologies need to be combined with satellite Earth Observation (EO) to provide accurate, reliable, and spatio-temporally resolved information for effective decision support, risk assessment, investment analysis for climate change adaptation, and operational forecasting/surveillance.

Traditional hydrometric monitoring of rivers is in-situ and station-based. In-situ monitoring networks lack spatial resolution, have been declining in many regions, and data accessibility is increasingly restricted because of growing conflicts between countries over water resources allocation. To solve this problem, hydrometric monitoring using satellite earth observation needs to be combined with drone-borne hydrometric monitoring technology for validation, deployment in remote and inaccessible regions, and for reliable and accurate estimation of river discharge.

The Horizon Europe UAWOS project develops an Unmanned Airborne Water Observing System to provide key hydrometric variables (bathymetry, velocimetry, water surface elevation) at high spatial resolution/coverage, and data-based products/services to support management and decision making. UAWOS integrates airborne data streams with Copernicus water bodies and water level services for cross validation and to estimate river discharge from satellite EO data.

This contribution outlines the UAWOS work programme and reports first results of airborne surveys using (i) radar altimetry for water surface elevation mapping, (ii) water penetrating radar and sonar for bathymetric mapping and Doppler radar for surface velocity monitoring. The combination of these datasets for river discharge estimation as well as for validation and enhancement of satellite radar altimetry datasets will be discussed.

How to cite: Bauer-Gottwein, P., Olesen, D., Nielsen, K., Rietz, A., Coppo Frías, M., Dobrovolskiy, A., Kadek, A., Orlic, N., Grubesa, T., Hiller, T., Grosen, H., Nielsen, S., Tarpanelli, A., Giordan, D., Barbetta, S., Gustafsson, D., Wennerberg, D., Disse, M., Merk, F., and Romero, L. and the UAWOS project team: UAS Hydrometry – Contactless airborne measurements of water level, depth, flow velocity and discharge in rivers and streams, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-52,, 2023.

Over the last decades, numerous models for sediment transport prediction have been proposed with application to littoral transport. However, the morpho-dynamic interactions that occur at the river mouth are still largely unexplored given different concurring phenomena, deriving from both river hydraulics and marine hydrodynamics. Against the high technical-scientific interest, the calibration of the hydrodynamic models of coast-mouth interaction presents,a lack of possible observations, due to both the spatial extension of domains and to their strong two-dimensional pattern.

To overcome this, the present work investigates the possibility of using satellite images, as tools for calibrating and validating hydrodynamic numerical models, an approach already successfully used over the years in the field of hydrological modelling.

For this purpose, hydrodynamic model (TELEMAC2D) were used to simulate the hydrodynamic components and the relative sediment transport  components (SISYPHE), and the pattern of the superficial velocities fields is compered with the remote sensing images.

In this study two different cases study in Adriatic Basin ware analysed. The River Piave (220 km), which flows from the eastern Italian Alps to the North Adriatic Sea, and a river in the southern part of Italy, Ofanto River (134km). The configuration of the Adriatic basin has such a shape as to generate particular tidal and wave conditions, which is why it is important to carry out a hydrodynamic study upstream, using the various drivers both on the river side and on the seaside, such as water discharge, tide, wind, etc. Then, depending on the results obtained from the hydrodynamic model, the dispersion of sediment during a flood event is analysed.

Early results show potential for using satellite images of suspended sediment plumes as calibration targets for numerical models.

How to cite: Menzione, A. and Mancini, M.: Satellite images potentiality for calibration of hydrodynamic model in estuaries and coastal areas, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-78,, 2023.

Session 3 – Data assimilation, artificial intelligence, and hydrological observations

Over the last 40 years remote sensing has significantly changed the way we observe and predict the Earth system, particularly in the oceanographic and meteorological sciences. Today, every General Circulation model (GCM) relies upon advanced and well-established data assimilation (DA) techniques, and Land Surface Models (LSMs) – which are integral components of GCM – have been increasingly using DA to constrain the LSM model predictions with available remote sensing data of hydrological, carbon and energy cycles.

Despite this, the use of DA into hydrological models (HMs) is still operationally limited and the reasons for that lie in 1) the considerable variability between different HMs, with much uncertainty in their respective representations of processes (often conceptual) and their sensitivity to changes in key variables, 2) the contrast between the scale of application of HMs (often smaller than LSMs) and the coarse-scale information provided by remote sensing along with their associated accuracy and, 3) the variety of the data assimilation setups, specificity of the study areas, and pre-processing used by a plethora of studies on the topic which provide a blurred picture on the real benefit of DA of relevant hydrological variables into HMs (e.g. soil moisture, precipitation, snow, terrestrial water storage anomalies).

The recent exponential grown of high-resolution remote sensing data (the European Union's Earth observation Programme Copernicus with the constellation of the Sentinel satellites is a notable example) has potentially opened new opportunities for improving our HMs also for small scale applications.  However, their usefulness is still limited by our ability to analyse and integrate efficiently a large volume of observations with current hydrologic models. In other words, most of the issues mentioned above have been not overcome with a consequent under-exploitation of potentially useful information to constrain HMs.

This contribution aims to summarize the main challenges and opportunities of DA into HMs from a hydrological perspective in light of the availaiblity of new and more skillful Earth observations. It identifies and explains critical challenges by using published literature by the author on European catchments as well as on-going studies, and offers insights for a productive research based on new available models and observations as to build a comprehensive hydrologic data assimilation framework that is a critical component of future hydrologic observation and modelling systems.

How to cite: Massari, C.: Data assimilation of remote sensing observations into hydrological models: challenges and perspectives in light of a new era of Earth observations, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-6,, 2023.

GC8-Hydro-21 | Orals | Session 3

Assimilation of Sentinel-1 backscatter data into AquaCrop v7 for soil moisture and biomass updating over Europe 

Shannon de Roos, Louise Busschaert, Michel Bechtold, Zdenko heyvaert, Sujay Kumar, Hans Lievens, Jonas Mortelmans, Dirk Raes, Samuel Scherrer, Maxime Van den Bossche, Elias Fereres, Margarita Garcia-Vila, Pasquale Steduto, Theodore Hsiao, Lee Heng, Maher Salman, and Gabrielle De Lannoy

Recent advances in gridded crop modeling and satellite observations help to improve the monitoring of crop growth and water requirements. In this contribution, we use AquaCrop v7 within the NASA Land Information System (i) to produce spatially distributed estimates of soil moisture, biomass and backscatter, and their uncertainty, and (ii) to assimilate backscatter observations from the Sentinel-1 satellite mission to improve soil moisture and biomass via state updating, at 1 km resolution over Europe. The results are evaluated against in situ observations of soil moisture and satellite-based vegetation products. We will discuss the opportunities and challenges of high-resolution gridded crop models and satellite-based active microwave data for agricultural applications. 

How to cite: de Roos, S., Busschaert, L., Bechtold, M., heyvaert, Z., Kumar, S., Lievens, H., Mortelmans, J., Raes, D., Scherrer, S., Van den Bossche, M., Fereres, E., Garcia-Vila, M., Steduto, P., Hsiao, T., Heng, L., Salman, M., and De Lannoy, G.: Assimilation of Sentinel-1 backscatter data into AquaCrop v7 for soil moisture and biomass updating over Europe, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-21,, 2023.

GC8-Hydro-64 | Orals | Session 3

Landslide Early Warning System based on Machine learning and radar data 

Giovanni Francesco Santonastaso, Pasquale Marino, Daniel Camilo Roman Quintero, and Roberto Greco

In the area of Casamicciola, on the island of Ischia, in the Gulf of Naples, on November 26, 2022, heavy rain triggered landslides that killed people and caused great damage to buildings and roads. Rain gauges on the island recorded heavy rainfall starting at midnight on November 25. The 6-hour cumulative rainfall (between 00:00 on 25/11 and 06:00 on 26/11) resulted 126 mm. The peak hourly rainfall at the two nearest rain gauges was 51.6 mm in Forio and 50.4 mm in Monte Epomeo, attained just before the triggering of the major landslide. The attainment of critical rainfall depth was so sudden, that rain gauges recordings did not allow deploying timely risk mitigation measures. In this context, an effective Landslide Early Warning System (LEWS), based not only on rain gauges, would be an important tool to mitigate the impact of landslides. The goal of a LEWS is to provide timely information to individuals and organizations, so that they can take appropriate actions to reduce the risk. These systems typically use a combination of monitoring networks and modeling techniques, to issue real-time warnings when the probability of a landslide becomes high. A well-designed LEWS can save lives, reduce property damage, and minimize the economic impact of the events.

In this paper, a novel approach to LEWS, based on machine learning and radar data, is proposed. Specifically, a random forest model is trained to define pre-alarm thresholds based on radar measurements available on the portal MISTRAL (Mistral portal Meteo Italian SupercompuTing poRtAL), and on rainfall measurements from four rain gauges on the island of Ischia. Two concentric monitoring areas around the island of Ischia are divided into 16 sectors, and the model evaluates every five minutes the percentage of nodes in each sector where the rainfall height detected by the radar exceeds assigned thresholds, corresponding to pre-alarm stages. Preliminary results show the prospects of using machine learning in LEWS.

How to cite: Santonastaso, G. F., Marino, P., Roman Quintero, D. C., and Greco, R.: Landslide Early Warning System based on Machine learning and radar data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-64,, 2023.

GC8-Hydro-42 | ECS | Orals | Session 3

Optimizing irrigation parameters to improve land surface model irrigation simulations: an example over the Po Valley, Italy 

Sara Modanesi, Gabriëlle J. M. De Lannoy, Michel Bechtold, Louise Busschaert, and Christian Massari

Improving the knowledge of agricultural water uses is in the spotlight of hydrologic sciences and water management authorities due to an increasing amount of water used for irrigation. An efficient water management system has a crucial role also considering the climate change projections scenario and the large increase in the frequency, duration, and severity of droughts, especially over the Mediterranean basin, which has been recognized as a hotspot of extreme weather events. However, simulating irrigation through large scale land surface models is not trivial, because the simplistic model parameterization do not necessarily resolve field scale conditions. In particular, the main challenge is to reproduce the amount and timing of irrigation applications by farmers, because these are often not physically-based and effectively driven by water policies instead of root zone soil moisture conditions.

Some recent approaches have demonstrated the utility of remote sensing observations to either derive irrigation directly, or indirectly via their assimilation into land surface and hydrological models. Indeed, high resolution remote sensing offers an unprecedented opportunity to observe the soil/vegetation system and to consequently detect irrigation. However, although both methods seem promising, irrigation quantification and detection are still at their infancy due to limitations of both satellite data and models. In particular, recent data assimilation experiments have shown the crucial role of an accurate land surface model parameterization to optimally integrate models and satellite observations.

The aim of this study is to test the benefit of directly optimizing the irrigation parameters of a sprinkler irrigation module embodied in the Noah MP land surface model running within the NASA Land Information System framework. The experiment was conducted over a highly irrigated area in the Po Valley (Italy) using synthetic irrigation benchmark data and at a spatial resolution of 0.01°. The improvement of the poorly-parameterized sprinkler irrigation scheme through a proper calibration is intended to be a valid alternative to quantify agricultural water uses.

How to cite: Modanesi, S., De Lannoy, G. J. M., Bechtold, M., Busschaert, L., and Massari, C.: Optimizing irrigation parameters to improve land surface model irrigation simulations: an example over the Po Valley, Italy, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-42,, 2023.

Due to soil moisture and vegetation's critical role in controlling land-atmosphere interactions, detailed and accurate hydrological and ecological information is essential to understand, monitor, and predict hydroclimate extremes (e.g., droughts and floods), natural hazards (e.g., wildfires and landslides), irrigation demands, weather, and climate dynamics. While in-situ soil moisture and vegetation biomass measurements can provide detailed information, their representativeness is limited, and networks of sensors are not widely available. Multispectral satellite observations offer global coverage, but retrievals can be infrequent or too coarse to capture the local extremes. This observation data gap limits the use of such information to adequately represent land surface processes and their initialization conditions for seasonal to sub-seasonal (S2S) prediction models. To bridge this gap, the assimilation of remote sensing observations into land surface models at hyper-resolution spatial scales (< 100 meters) provides a pathway forward to (i) reconcile model and observation scales and (ii) enhance S2S hydroclimate predictability in Earth System Models.

To this aim, we introduce a scalable approach that leverages advances in machine learning, radiative transfer modeling, and in-situ observations to assimilate satellite observations into unstructured tile-based land surface models. In this approach, a machine learning model is trained to harness information from big environmental datasets and in-situ observations to learn how the physical model and satellite biases are related to specific hydrologic conditions and landscape characteristics and how these biases evolve over time. We demonstrate the added value of this approach for improving soil moisture and vegetation dynamics at the hyper-resolution scales by assimilating MODIS Leaf Area Index and NASA’s SMAP brightness temperature observations into the LM4.0 – the land model component of the NOAA-GFDL Earth System Model. To this end, we performed stand-alone LM4.0 simulations between 2000 to 2021 over the Continental United States, with the MODIS and SMAP assimilation performed from 2002 and 2015, respectively, until the present day. Soil moisture estimates are evaluated against independent in-situ observations. To quantify the approach added value for S2S predictability, we compare the impact of soil moisture and vegetation data assimilation on root zone soil moisture, runoff, vegetation biomass, surface temperature, and evapotranspiration.

How to cite: Vergopolan, N.: Leveraging advances in hyper-resolution soil moisture and vegetation land data assimilation for S2S hydroclimate applications, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-82,, 2023.

GC8-Hydro-44 | ECS | Orals | Session 3

From observations towards operational site-specific soil moisture ensemble forecasting 

Richard Hoffmann, Klaus Görgen, Heye Bogena, and Harrie-Jan Hendricks-Franssen

The use of numerical models for real-time management of water resources is becoming increasingly popular as the increasing frequency and intensity of extreme weather events negatively affect society, agriculture and crop yields. Prolonged droughts are becoming the new normal, which, among other things, increase the need for operational, site-specific soil moisture forecasting. A model that provides accurate site-specific soil moisture forecasts can support agriculture by contributing to precision irrigation and the provision of important information for crop planning, yield maximization and the coordination of field operations. Soil moisture assimilation has proven potential to provide appropriate initial conditions for such a forecast model. However, the operational estimation of an initial condition requires model-specific protocols for continuously incorporating new observational data into models for hydrological, crop, land surface, vadose zone, or subsurface processes that are not yet widely available. In this study, we present an automated data pipeline for operational, site-specific soil moisture ensemble forecasting based on the Community Land Model Version 5.0 (CLM5) taking the TERENO agricultural research station "Selhausen" in western Germany as an example. CLM5 simulates vegetation states, carbon and nitrogen pools prognostically. We compare land surface model prediction quality (e.g., soil moisture, crop yield) with and without weather forecasts and with and without near real-time soil moisture data assimilation. Climatological mean time series and 10-day ensemble weather forecasts from the German Weather Service, aggregated to the grid cell, are the atmospheric forcings in simulating future states. Forecasts start from the states of the last simulation time step with on-site measurements of precipitation, wind speed, air temperature, air pressure, relative humidity, and global radiation as the atmospheric forcings. In parallel with forward simulations from 2011-2021 (open loop experiment), soil moisture assimilation is being performed for 2018-2021 to generate site-specific initial conditions for the land surface model with reduced uncertainty. Forecasts starting from initial conditions based on soil moisture assimilation are more reliable as model bias is reduced. Preliminary results show that the inclusion of site-specific weather forecast uncertainties in the model improves the simulation of soil moisture dynamics at the plot scale and is thus important for optimizing irrigation schedules while keeping crop productivity stable.

How to cite: Hoffmann, R., Görgen, K., Bogena, H., and Hendricks-Franssen, H.-J.: From observations towards operational site-specific soil moisture ensemble forecasting, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-44,, 2023.

GC8-Hydro-53 | ECS | Orals | Session 3

Identification of hydrological controls of slope response to precipitations using machine learning techniques 

Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco

The assessment of the response of slopes to precipitations is important for several applications: from drought associated problems to the evaluation of the occurrence of threatening events such as floods and landslides (Bogaard & Greco, 2016). This study aims at identifying the most important variables, that can be monitored in the field, suitable to describe the initial conditions that control the capability of a slope to store infiltrating water at the end of precipitation events. The case study of the slopes near the town of Cervinara, southern Italy, is presented, where field observations and laboratory experiments allowed the understanding of the water processes at different scales (Marino et al., 2020). A synthetic dataset, simulating the major hydraulic processes observed in the field, was generated to enrich the available data. It was built by simulating the response of the slope to a 1000-year long synthetic rainfall series, generated with the NSRP model, with a physically based model coupling the unsaturated flow in the coarse granular soil cover with the shallow aquifer hosted by the uppermost part of the underlying fractured limestone bedrock (Marino et al., 2021). The hydraulic behavior of the soil cover is modelled with the 1D Richards’ equation, while the aquifer, connected to the soil cover through its lower boundary condition, is modelled as a simple linear reservoir.

Two variables expressing underground antecedent conditions, one hour before any rainfall event, were analyzed: mean water content in the uppermost meter of the soil cover and aquifer water level. The slope response was quantified as the fraction of rainwater remaining stored in the soil cover at the end of any rainfall event. The non-linear relationships linking the three variables were studied with clustering and random forest techniques, allowing the identification of three major hydrological conditions. The first one is linked to dry seasons, when the lowest aquifer water level coincides with soil water content below field capacity: in this condition, rainwater tends to remain completely stored in the soil at the end of rain events. Once the soil cover overcomes the field capacity, two different conditions are found. When the aquifer water level is high, active drainage through the soil-bedrock interface limits the increase of water stored in the soil cover. Conversely, when the aquifer water level is low, it corresponds to impeded drainage, i.e., there is little leakage from the soil cover to the bedrock. In this condition, most rainwater tends to remain stored in the soil cover even when it is already wet at the beginning of the rain event.




Bogaard, T., & Greco, R. (2016). Landslide hydrology: from hydrology to pore pressure. Wiley Interdisciplinary Reviews: Water, 3(3), 439–459.

Marino, P., Comegna, L., Damiano, E., Olivares, L., & Greco, R. (2020). Monitoring the hydrological balance of a landslide-prone slope covered by pyroclastic deposits over limestone fractured bedrock. Water (Switzerland), 12(12).

Marino, P., Santonastaso, G. F., Fan, X., & Greco, R. (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides, 18(1), 31–41.

How to cite: Roman Quintero, D. C., Marino, P., Santonastaso, G. F., and Greco, R.: Identification of hydrological controls of slope response to precipitations using machine learning techniques, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-53,, 2023.