In a global warming and climate change context, populations all over the world are impacted by an increasing number of hydrological crisis (flood events, droughts, ...), mainly related to the lack of knowledge and monitoring of the surrounding water bodies. In Europe, flood risk accounts for 46% of the extreme hazards recorded over the last 5 years and current events confirm these figures. Although the main rivers are properly monitored, a wide set of small rivers contributing to flood events are not monitored at all. There is a clear lack of river basins monitoring regarding the rapid increase of extreme events. Moreover, hydrological surveys are currently insured by heterogeneous means from a country to another and even inside a country, from a region to another. It results in a high-cost level to deploy a robust, relevant, and efficient monitoring of all watercourses at risks. Therefore, there is a real need for affordable, flexible, and innovative solutions for measuring and monitoring hydrological areas to address climate change and flood risk within the water big cycle. 

 VorteX.io aims to provide an innovative and intelligent service for monitoring hydrological surfaces, using easy-to-install and fixed in-situ instruments, based on compact light-weight device inspired from satellite technology: the micro-stations. From a technical point of view, vorteX-io micro stations are designed like remote sensing nanosatellites that do not fly, but are installed above watercourses (i.e., under a bridge). Onboard remote sensing instruments (lidar, thermal and multispectral camera, GNSS) allow them to remotely and in real-time measure water temperature, provide contextual images and hydro-meteorological parameters (water surface height, water surface velocity, rain rates). Water parameters are transferred in real-time through GSM or SpaceIOT networks.  The technology has been entirely designed and patented by vorteX-io.

The combination of these in-situ data with satellite measurements is thus optimal for downstream services related to water resources management and assessment of flood/drought risks: calibration, validation and accuracy assessment of EO projects in space hydrology. The vorteX-io technology is selected by ESA for Sentinel-3 Altimetry CalVal for inland waters: installation of stations under the track and synchronization of in-situ acquisition with the passage of the satellite to operationally provide Fiducial Reference Measurements (FRMs). In addition, vorteX-io is involved in the definition of future inland water FRMs for the upcoming CRISTAL mission and also on the ESA DTE for hydrology. 

In June 2023, the European Innovation Council awarded the company to deploy 1000 micro-stations in France and Croatia

In June 2024, vorteX-io has completed a funding round, which notably includes Caisse des Dépôts (CdC represent a major public financial institution in France) and CNES as shareholders. This funding will specifically facilitate the continued deployment of the constellation across Europe.

The long-term vision is to cover river basins in Europe with an in-situ network, to be used at large scale as earth-observation in situ component either for monitoring water quality parameters or for extreme hazards monitoring such as floods and drought

How to cite: Gachelin, J. P. and Poisson, J. C.: Development of a new remote sensing device to be used at large scale as earth-observation in situ component, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12071, https://doi.org/10.5194/egusphere-egu25-12071, 2025.

Small water bodies (SWBs), such as ponds and on-farm reservoirs, play a crucial role in agriculture irrigation, carbon storage, and biogeochemical cycle. Medium-spatial-resolution satellite imagery such as Sentinel-2 imagery has been widely promoted to monitor SWBs, due to its relatively fine spatial and temporal resolution. However, the small size and diverse spectral characteristics of SWBs present significant challenges, particularly the mixed-pixel problem, where both water and land classes contribute to the observed spectral response of the image pixel. To address this issue, we propose a novel regression-based surface water fraction mapping method (RSWFM) that leverages a random forest regression model and a synthetic spectral library to generate 10 m spatial resolution surface water fraction maps from Sentinel-2 imagery. RSWFM incorporates a compact set of endmembers, representing water, vegetation, impervious surfaces, and soil, to simulate a spectral library using both linear and nonlinear mixture models, while accounting for spectral variability across diverse SWBs. Additionally, to enlarge the number of pure spectra and enhance their representativeness for training, RSWFM applies data augmentation based on Gaussian noise. The performance of RSWFM was assessed across ten study sites with hundreds to thousands of SWBs smaller than 1 ha and was compared with fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and random forest (RF) regression without data augmentation. Results indicated that RSWFM generates a low root mean square error (RMSE) of less than 0.09, reducing by approximately 30%, 15%, and 11% compared to FCLS, MESMA, and nonlinear RF regression without data augmentation, respectively. Furthermore, RSWFM achieves an R² of approximately 0.85 in estimating the area of SWBs smaller than 1 ha. This study demonstrates the potential of RSWFM for addressing the mixed pixel problem in SWB monitoring across large areas.

How to cite: Wang, Y., Foody, G., Li, X., Zhang, Y., Zhou, P., and Du, Y.: Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14387, https://doi.org/10.5194/egusphere-egu25-14387, 2025.

Lake thermal dynamics provide critical insights into regional and global climate change, and play a regulatory role in lake biogeochemical cycles. In situ measurements, remote sensing, and hydrodynamic modelling are key sources for monitoring lake temperature. In situ data are essential for calibration and validation (cal/val) of satellite products and numerical models, but are often scarce or irregular for many lakes. Additionally, data assimilation of lake surface water temperature (LSWT) products can improve numerical models. Satellite thermal imagery has been widely used for LSWT monitoring at regional and global scales. However, current operational LSWT services are limited to 1 km resolution, thereby excluding small lakes. High-resolution, high-revisit Earth observation missions, such as ECOSTRESS, LSTM, TRISHNA, and SBG, extend LSWT services to smaller lakes, but require dedicated cal/val efforts due to their unique radiometric and geometric properties. Collecting reliable skin temperature and ancillary datasets across diverse lakes and optimizing LSWT retrieval algorithms is thus urgently needed.

Our research, within the ESA-funded TRISHNA – Science and Electronics Contribution (T-SEC) project, focuses on validating and improving high-resolution LSWT products, and openly publishing final products for lakes in the Alpine region. We operate automated reference stations in four Swiss lakes: Lake Geneva, Lake Aegeri, Lago Bianco, and Greifensee. These lakes comprise a variety of morphological, bio-physical, and meteorological features, and are located along an elevation gradient in pre-, sub-, and high-alpine environments. Skin, sky, and bulk temperatures, as well as meteorological data are available for all sites. We evaluated Landsat 7/8/9 LSWT products from USGS Collection-2 Level-2 data and the single-band Acolite-TACT algorithm. Our matchup comparisons yielded a Mean Absolute Error (MAE) of < 1.2 °C, a Mean Bias Error (MBE) < 0.1 °C and a correlation coefficient (R²) of > 0.94. However, official Level-2 ECOSTRESS data showed weaker performance (MAE > 2.4 °C, MBE < -2 °C, and R² < 0.85), highlighting the need for further cal/val and algorithm refinements, particularly for emissivity corrections.

Landsat validated algorithms are used for operational monitoring via AlpLakes web platform (www.alplakes.eawag.ch), which integrates satellite data, in situ measurements, and hydrodynamic models. AlpLakes’ scalable design enables rapid integration of new lakes and products. For example, we aim to disseminate our tools across lakes in the Alpine region under the EU Interreg AlpineSpace project DiMark (https://www.alpine-space.eu/project/dimark/). This pipeline will also facilitate the adoption of upcoming missions and timely dissemination of validated products. Ultimately, our research and datasets will support lake monitoring and modelling activities in Switzerland and beyond. Moreover, integrating satellite data, hydrodynamic models, and in situ measurements (e.g., assimilating LSWT products into existing models) will enhance understanding of short-term events and long-term trends in lakes, fostering interdisciplinary research and providing deeper insights into underlying bio-physical processes.

How to cite: Irani Rahaghi, A., Odermatt, D., and Naegeli, K.: Advancing Alpine lake monitoring and modelling through calibration, validation, and dissemination of high-resolution thermal remote sensing products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18287, https://doi.org/10.5194/egusphere-egu25-18287, 2025.

Freshwater is a valuable and scarce resource under constant threat due to global climate uncertainties, population growth, and economic expansion. Mapping water bodies can be useful in effective water resources management. The present study was an effort towards mapping inland water body and identifying areas suitable for water conservation in the Pune city of the Upper Bhima River Basin. In this study, land cover classification was performed using machine learning in Google Earth Engine (GEE) to identify water and non-water pixels to delineate small water bodies. Three machine learning models, namely Support Vector Machine (SVM), Random Forest (RF) and Gradient Tree Boost (GTB), were compared for their efficacy in mapping the water bodies. An open-source high-resolution multi-spectral image (MSI) information from Copernicus Sentinel-2 Level 2A harmonised data was used to generate a water body map. The classification models were further compared with the Modified Normalized Difference Water Index (MNDWI) thresholding method, which distinguishes water regions based on the reflectance difference between the Short Wave Infra-Red (SWIR) band and the Green band. As the study area covered a diversified spectral signature of land use and land cover, the analysis was performed under three scenarios. In scenario 1, the ML model was trained and validated using hilly and built-up region data, in scenario 2 agricultural and built-up areas were considered and in scenario 3 all three regions were covered. Results showed that the SVM model performed more accurately and detected the maximum area of water bodies followed by RF, GTB and MNDWI threshold methods. Moreover, scenario 3 which considers the entire dataset ranging from hilly, built-up and agricultural regions is the most robust analysis to perform water body mapping. Finally, the SVM model considering scenario 3, was used to detect the small water bodies for the entire catchment. In total, 20,479 water bodies were identified by the SVM model covering 279.42 sq.km area. Furthermore, river networks were removed from the classification, which resulted in a total of 17,616 small water bodies with an area of 243.97 sq.km. As this analysis was performed using Sentinel-2A data which has spatial resolution of 10 meters, ML models and MNDWI method cannot estimate water bodies smaller than 100 sq. meters. The water body map can be useful for water resources planning in the study area.

Keywords: Google Earth Engine, Random Forest, Support Vector Machine, Gradient Tree Boost and Modified Normalized Difference Water Index.

How to cite: Choubey, S., Mohanty, S., and Chatterjee, C.: Delineating small water bodies in Pune City India using Machine Leaning in Google Earth Engine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15594, https://doi.org/10.5194/egusphere-egu25-15594, 2025.

The water quality of lakes and reservoirs is influenced by atmospheric and land-use pressures, requiring actionable insights for effective management. Given the unique nature of each water body, data-driven modelling provides a practical solution for identifying sensitivities to these pressures, circumventing the complexity of hydrological-biogeochemical models. Remote sensing technologies offer consistent, multi-temporal, and multi-scale water quality monitoring, while global weather forecasting models enable predictions of key environmental parameters. Integrating these datasets facilitates a systematic examination of catchment-to-lake dynamics.

This study introduces a unified approach to modelling relationships between satellite-derived water quality metrics, such as Chlorophyll-a (Chl-a) concentration and turbidity, and meteorological drivers influencing catchments. Using multivariate autoregressive models, we aim to identify the influence of environmental factors on water quality variations, and  determine which sub-basins exert the greatest influence on lake dynamics. This approach supports short-term predictions of water quality changes. Ultimately, we anticipate that the data-driven models can be used to predict short-term water quality changes

The study focuses on small and medium-sized lakes and reservoirs in the United Kingdom, using Sentinel-2 MSI observations for high-resolution water quality datasets. ERA5-Land hourly reanalysis data provided meteorological variables influencing water quality, including wind, lake mixed-layer temperature, solar radiation, precipitation, and runoff. Both datasets were aggregated into five-day time series to address observation intervals caused by orbital patterns and cloud cover. Aggregated data were normalized and stabilized to account for variable magnitudes before being input into autoregressive models.

Vector Autoregression (VAR) was used to assess long-term environmental influences on water quality, leveraging Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). The reliance of VAR models on historical data enabled analysis of prolonged effects, with optimal four-time lags. In contrast, Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) incorporated contemporary meteorological inputs, allowing for short-term impact analysis. ARIMAX models also enabled near-term water quality predictions using forecasted meteorological variables. At the sub-basin level, models were evaluated using the Fréchet distance, which quantifies the similarity between time-series curves. By comparing Fréchet distances across sub-basins, the relative contributions of each sub-basin to lake water quality variations were determined.

Our findings suggest that: 1) VAR models explained the temporal variability in lake water quality variables with a strong fitness (R² > 0.82 for Chl-a and R² > 0.69 for turbidity); 2) VAR models relied heavily on the lake water quality inputs from priors with optimal four time lags. The first lag contributed the most, with a mean weight of 0.61 (σ = 0.45) for Chl-a concentration and 0.71 (σ = 0.46) for turbidity; 3) Catchment drivers exhibited weights up to 2.3% at the second time horizon, with their influence increasing over time, while the contribution from water quality observations decreased; 4) ARIMAX models demonstrated high accuracy in simulating lake water quality variations (R² > 0.83 for Chl-a and R² > 0.68 for turbidity), showing promise for future water quality predictions.

How to cite: Tan, Z., Simis, S., and Warren, M.: Data-Driven Modelling of Lake Water Quality Response to Catchment Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11981, https://doi.org/10.5194/egusphere-egu25-11981, 2025.

Iron concentrations in inland waters play an important role in nutrient cycling and water quality, particularly through their interaction with phosphorus, a key driver of eutrophication. Both excessive and insufficient ferric iron (Fe³⁺) levels can disrupt aquatic ecosystems. Insufficient Fe³⁺ availability hampers primary productivity, nutrient cycling, and ecosystem structure. Conversely, elevated levels of Fe³⁺ in water can pose risks to human and ecosystem health.

In Dutch agricultural-dominated lowland catchments, ferric iron-bound phosphorus is the main form of phosphorus in suspended particulate matter, potentially driving rapid transformation of dissolved phosphorus in groundwater to phosphorus in surface water. Groundwater seepage, rich in ferrous iron (Fe²⁺), further contributes to these dynamics, with Fe²⁺ oxidizing to Fe³⁺ upon exposure to oxygen, forming hydroxides that bind phosphorus. Seasonal hydrological changes also influence these interactions, with distinct red colouring observed in Dutch waters during winter attributed to iron oxidation under reduced biological activity.

A novel method using Sentinel-2 MSI data and machine learning has been developed to estimate and monitor the optically active Fe³⁺ concentration levels across Dutch surface waters in autumn and winter with a high spatial resolution (10 m). The model incorporates predictors including spectral band ratios, spectral band slopes, spectral band derivatives, and environmental variables such as air temperature and cumulative rainfall, derived from in situ data. It was trained on ~2,000 in situ iron measurements collected between 2015 and 2023.

Until now, research on Fe³⁺ in water using satellite data has been limited. This study provides a detailed spatiotemporal perspective on iron dynamics in the Netherlands, advancing the monitoring and management possibilities of water quality and ecosystem health.

How to cite: van Oostende, M. and van der Velde, Y.: Remote sensing of ferric iron in inland surface waters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15643, https://doi.org/10.5194/egusphere-egu25-15643, 2025.

Agricultural Water scarcity, amplified by climate change, poses a great challenge to global agricultural productivity and sustainability. This study explores a new indicator to monitor regional crop water productivity in agricultural systems. Using a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and ground observations, we assess spatiotemporal trends in water productivity across an agricultural production regional scale .The water productivity indicator (CWP_SM ) was computed as a ratio of normalized difference vegetation index (NDVI) to volumetric soil moisture content at 30cm and 60cm soil depths respectively and compared against a benchmark water productivity indictor ( CWP_EC) computed as a ratio of Gross primary productivity (GPP)  to Evapotranspiration (ET). Our research findings highlight a consistent strong positive correlation and alignment of CWP_SM at 30 cm, CWP_SM at 60 cm and CWP_EC trends over time with however CWP_SM at 60 cm demonstrating superior accuracy and reliability compared to CWP_SM at 30 cm as a proxy for CWP_EC. The results highlight the importance of ensuring that water reaches deeper layers to at least 60 cm depth during irrigation due to the stability of soil moisture, observed at this depth.

By providing actionable insights, the study contributes to achieving sustainable development goals of climate action, ending hunger and underscoring the importance of monitoring crop water productivity in addressing water management challenges in agricultural production

This research was funded by Szechenyi Plan Plus Program under the RRF 2.3.1 21 2022 00008 project. We gratefully acknowledge their tremendous support and contributions to the research.

How to cite: Louis, A., Zsolt, F., János, T., and Attila, N.: Regional Agricultural water productivity Monitoring for Climate Change Adaptation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8550, https://doi.org/10.5194/egusphere-egu25-8550, 2025.

Meteorological unpredictability, exacerbated by severe events caused by climate change, poses significant problems for water resource management (IPCC, 2023). Climate change has increased the frequency and severity of droughts, especially in mid-latitude regions, where reduced precipitation coupled with rising temperatures is expected to exacerbate water scarcity (https://doi.org/10.1007/s40641-018-0093-2). In this regard, Small Agricultural Reservoirs (SmARs) offer a strategic response, as they are designed to collect and store water for use in irrigation and other agricultural applications. This is the context in which the research activity described here is developed, contributing to the research project CASTLE - Creating Agricultural reSilience Through smaLl rEservoirs.

Despite their importance, the lack of comprehensive national databases for SmARs remains a major obstacle to their efficient management. Prior to this study, for example only eight of Italy's twenty regions had SmARs inventories, often based on non-standardised and incomparable approaches (https://indicatoriambientali.isprambiente.it/it/pericolosita-sismica/invasi-artificiali). This fragmentation of information makes the analysis and management of SmARs challenging. A possible option to overcome this problem is represented by satellite data, which provides accurate and continuous information over large geographical areas. Sentinel-2 satellite imagery - part of the European Space Agency's Copernicus programme - was particularly well suited to this study.

The objective of this research was to develop a methodology for detecting Small Agricultural Reservoirs from satellite imagery with integration of OpenStreetMap (OSM) and the ESA World Cover 2021 dataset and creating a comprehensive inventory of the existing reservoirs in Italy. The system was validated in Tuscany with the use of the ground truth database of LaMMA - CNR IBIMET (https://geoportale.lamma.rete.toscana.it/difesa_suolo/#/viewer/372).

Integration with OSM helped eliminate false positives such as ponds, glaciers, large dams, rivers, and canals, which spectral indices alone cannot distinguish from SmARs due to their similar reflectance characteristics, as they are also water surfaces. The ESA World Cover data were used to exclude urbanized areas, which were irrelevant to this study. 

The combined use of open-source data has enabled the development of a replicable methodology adaptable to various spatial scales, considerably enhancing the identification and mapping of SmARs. This strategy will help to manage agricultural water resources more efficiently and increase resilience to climate change.

ACKNOWLEDGMENTS

This study was carried out within the CASTLE project and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.1 – D.D. n. 104 02/02/2022 PRIN 2022 project code MUR 2022XSERL4 - CUP  B53D23007590006).

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How to cite: Mannucci, N., Bertoli, G., Lompi, M., Pacetti, T., Goodarzi, M. S., Ebel, P., Chiarelli, D. D., Azzari, M., and Caporali, E.: Small agricultural reservoirs detection with satellite data and OpenStreetMap integration for sustainable water management: a contribution to the CASTLE project., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12476, https://doi.org/10.5194/egusphere-egu25-12476, 2025.

The quantification of crop water stress is very crucial for efficient irrigation water management and sustainable agriculture. The empirically derived crop water stress index (CWSI) is a widely used method for quantifying the crop water status. However, developing lower baseline is a prerequisite for estimating the crop water stress using the empirical approach. Traditionally, the lower baseline is formulated by taking in-situ observations of a well-watered crop canopy using infrared radiometers. In this study, a novel methodology is formulated for estimating the lower baseline using land surface temperature (LST) and normalized difference vegetation index (NDVI) for the wheat crops using Landsat-8, Landsat-9 and Sentinel-2 satellite data. This study is conducted during the 2021-22 and 2022-23 wheat crop seasons, covering approximately 630 acres of agricultural fields, managed by local farmers in the western part of Uttar Pradesh, India. The entire analysis is conducted on Google Earth Engine.  Initially, multi-temporal image classification is performed, employing the synergetic use of Sentinel-2 and machine learning algorithms, to distinguish the wheat and non-wheat fields. The manually collected ground truth data are used to train and test the random forest model. Subsequently, the candidate pixels are selected based on the maximum NDVI range, from (NDVI_max - 0.1) to NDVI_max, which represents dense and healthy wheat patches. These candidate pixels are further refined by selecting the pixels having less than 10^th percentile of the LST values, which account for relatively higher evapotranspiration. The lower baseline is derived using LST values of the refined candidate pixels along with concurrent air temperature (T_a) and relative humidity measurements recorded by an automatic weather station.  Finally, CWSI is mapped for the study area using the empirical approach.

Classification accuracy of 96% and 95% was achieved for the classification of wheat and non-wheat fields during the 2021-22 and 2022-23 seasons, respectively, with corresponding Kappa coefficients of 0.85 and 0.80. For the classified wheat pixels, the lower baseline equation formulated by the proposed methodology are (LST – T_a) = -1.864VPD + 1.325 for 2021-22 season and (LST – T_a) = -4.92VPD + 3.14 for 2022-23 season, where VPD is vapour pressure deficit. The fixed upper baseline of (LST – T_a) = 4°C is taken for empirically deriving and mapping CWSI for both seasons. The minimum and maximum values of the CWSI ranged from 0 to 0.89 during the 2021-22 season and from 0 to 0.78 during the 2022-23 season. The 2021-22 cropping season observed increased CWSI values as compared to 2022-23, primarily due to the heatwave that occurred in the study area from during the latter part of the 2021-22crop season. Significant spatial and temporal variability is obtained in the CWSI values within the study area.  The results suggest that the proposed methodology can be effectively used for mapping crop water stress at field scale without the requirement of tedious in-situ canopy temperature observations.

How to cite: Yadav, M., Narakala, L. M., Chinthamaneni, S., and Upreti, H.: Mapping field-scale crop water stress for wheat using satellite remote sensing data by formulating lower baseline using a novel approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-810, https://doi.org/10.5194/egusphere-egu25-810, 2025.

Agricultural Drought presents a significant risk to food security, particularly in arid and semi-arid regions where crop production is highly dependent on irrigation and annual rainfall. Therefore, this research was conducted in Souss Massa region, a semi-arid region relying on agricultural production for its economy to develop drought early warning studies in this region. This study aims to assess the agricultural drought and its associated impacts, as accurate identification is crucial for effectively minimizing its negative impacts. In this work, we evaluate various Remote Sensing-based indices to create a composite drought index with distinct severity classes (No Drought, Moderate Drought, Severe Drought, Extreme Drought). This approach enables the identification and mapping of drought-affected areas. These findings provide valuable insights into the potential of remote sensing for drought monitoring and contribute to development of effective drought management strategies in Souss Massa region.

How to cite: Merzoug, S., El morjani, Z. E. A., Es-saady, Y., and El hajji, M.: Agricultural Drought Assessment Using Remote Sensing Technologies: A Case Study in Souss Massa Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18138, https://doi.org/10.5194/egusphere-egu25-18138, 2025.