PICO
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We welcome research aimed at improving the quality of remote sensing products, such as higher spatial and/or temporal resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impacts on water-stressed vegetation, as well as on irrigation volumes monitoring and modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (e.g., physically based models or artificial intelligence techniques) or an integration of multiple sources of data (remote sensing and in situ) across various imagery types (satellite, airborne, drone).
Finally, we wish to attract presentations on developments of user-friendly platforms (following FAIR principles), providing smooth access to remote sensing data for water applications. We are particularly interested in applications of remote sensing to determine the human-water interactions and the climate change impacts on the whole water cycle (including the inland and coastal links).
PICO: Tue, 5 May, 10:45–12:30 | PICO spot 4
Agricultural drought is a growing concern in Morocco, especially in the Souss-Massa basin, where the economy is heavily reliant on increasingly limited water resources. As climate As climate variability intensifies and groundwater levels decline, traditional drought monitoring tools (mainly based on rainfall alone) no longer provide a comprehensive representation of the evolution of water stress in crops and soils. Based on remote sensing, we developed a framework to facilitate understanding of these intricate interactions.
We combine four satellite indicators to reflect different aspects of drought stress: vegetation greenness (VCI), land surface temperature (TCI), soil moisture availability (SMCI), and photosynthetic activity (GPP anomaly). These datasets, derived from MODIS and ESA-CCI products, were processed into a consistent time series from 2000 to 2023. Utilising the seasonal Standardized Precipitation Evapotranspiration Index (SPEI-6) as a reference, we trained a Random Forest model to generate a Remote Sensing Drought Index (RSDI) specifically tailored to the wheat-growing season in the Souss-Massa basin.
The developed index demonstrates robust performances across the region, effectively capturing both rapid shifts in meteorological conditions and the slower cumulative effects of water stress on vegetation.
The model exhibits strong predictive accuracy (R² ≈ 0.75) and remains stable even when applied to stations not utilized during the training process.
Importantly, the RSDI aligns closely with observed wheat yield anomalies (r ≈ 0.9), indicating its relevance for agricultural decision-making. The framework also reproduces major drought years, such as 2015–2016 and 2023–2024, revealing clear spatial contrasts linked to topography and irrigation patterns.
The combined use of multiple remote-sensing indicators provides a reliable measure of drought evolution and supports regional actors in planning and managing water and agricultural activities under growing climatic pressure.
How to cite: Gouahi, S., Hssaisoune, M., Bouras, E. H., Ait Brahim, Y., and Bouchaou, L.: A Multi-Sensor Remote Sensing Framework to Track Agricultural Drought in the Souss-Massa Basin, Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1327, https://doi.org/10.5194/egusphere-egu26-1327, 2026.
Growing demand for dates—driven by Morocco’s rising population and expanding international markets for well-branded, high-value (“noble”) varieties—is accelerating the expansion of date-palm cultivation in the country’s arid and semi-arid frontiers. Much of this growth relies on intensified groundwater pumping, increasing pressure on shared aquifers that have long sustained oasis agroecosystems. Historically, these aquifers were managed through locally embedded water-sharing institutions that supported efficient allocation and long-term use; the rapid spread of pumped irrigation is nowadays reshaping this balance by amplifying competition for the same resource.
We investigate these dynamics in the Figuig oasis (eastern Morocco) and its watershed by linking agricultural expansion to water demand and comparing this demand with watershed-scale water availability. We hypothesize that once recently established plantations reach full productive age—beyond the relatively low-demand establishment phase—total water demand will exceed the catchment’s available supply.
We develop a date-palm water demand model that combines evapotranspiration-based water requirements with high-resolution mapping of fields and palm abundance. Field boundaries are delineated using a U-Net + watershed segmentation workflow, and palm trees are detected and counted using a YOLO object-detection model applied to drone imagery (small, heterogeneous oasis parcels) and satellite imagery (newer, larger, more homogeneous plantations). These tools are applied within a remote-sensing time series to quantify agricultural expansion and the associated increase in demand over time. Field surveys provide key parameters to translate mapped plantations into water demand, including irrigation method and efficiency, tree age classes, irrigation frequency, and planting density.
Our results indicate an approximately threefold expansion of agricultural land relative to the historically stable oasis area. About 65% of farms remain in early production stages (0–5 and 6–12 years), when water needs are relatively low, yet estimated demand already nearly matches watershed-scale availability. As plantations mature, projected demand is likely to surpass catchment-scale availability within the next decade, increasing the risk of irreversible impacts. Consistent with this trend, we observe drying of traditional springs and deteriorating water quality, underscoring the need to prioritize surface-water use and water-harvesting measures and to strictly regulate groundwater pumping.
How to cite: Boubou, Y.: From Oasis Water Commons to Expanding Date-Palm Plantations: Deep Learning Mapping and Evapotranspiration-Based Water Demand in the Figuig Oasis, Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21477, https://doi.org/10.5194/egusphere-egu26-21477, 2026.
Accurate quantification of irrigation water use efficiency (IWUE) is essential for sustainable oasis agriculture in arid regions with multi-source water supply. Examining the Yongji Irrigation Area of the Hetao Irrigation District, this study developed a Surface Energy Balance System (SEBS)-based evapotranspiration (ET) model for the area using Landsat-8 imagery and 2023 meteorological data. Model performance was evaluated against ground-based observations. SEBS-derived ET was coupled with a regional water balance approach to estimate IWUE under multi-source irrigation and compared with the conventional canal water balance method. The main findings are as follows: (1) in 2023, remotely sensed ET totaled 5.4×108m3, corresponding to an IWUE of 0.426; (2) SEBS-retrieved daily ET agreed well with in situ observations at the Linhe Meteorological Station on seven dates between April and October 2023, with a coefficient of determination R2 = 0.816 , root-mean-square error (RMSE) of 0.714 mm/day, mean absolute error (MAE) of 0.703mm/day, and bias of −0.337mm/day, confirming that SEBS reliably captures daily ET dynamics in the Yongji Irrigation District; (3) the SEBS-based IWUE differed by 6.33% from the traditional canal water balance estimate (0.455), suggesting good consistency between the two approaches. These findings indicate that SEBS-based remote sensing can provide spatially explicit, operational assessments of irrigation efficiency and support precision water resources management in arid and semi-arid agricultural regions.
How to cite: Xu, Y. and Feng, S.: Estimation and Analysis of Irrigation Water Use Efficiency in Multi-Source Irrigation Areas of Arid Regions Based on the SEBS Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6814, https://doi.org/10.5194/egusphere-egu26-6814, 2026.
Reliable crop mapping is essential in land and water management studies to understand the spatial distribution and dynamics of agricultural practices, to model resource use and production, and to propose sustainable scenarios for agricultural and water management. This work presents high-resolution crop mapping for the Mediterranean area, which is particularly interesting due to the limited data availability and the high level of land use heterogeneity. The main European land use dataset, Corine Land Cover (CLC), lacks the specificity required for accurate agricultural classification, especially for crop differentiation, and does not provide frequent or timely updates, which are crucial for many applications. Other more recent EU-wide crop mapping efforts (d’Andrimont et al, 2021) still lack regional accuracy due to widely scattered training data. To overcome these limitations, a large-scale crop mapping initiative was implemented in Sardinia to test and validate an artificial intelligence–based approach for Mediterranean environments. In this context, irrigated agriculture is a key sector for the sustainable management of limited water resources. The method uses Sentinel‑2 time series and survey data from the LPIS (Land Parcel Identification System). The study relies on IOTA2, a land‑use map production chain first developed and tested at the French level, producing maps with 24 land‑use classes. The originality of the approach lies in the use of open‑source satellite images and an automated processing workflow based on supervised classifiers, making crop mapping faster and easily reproducible across years.
Learning samples are derived from 2018 LPIS data, supplemented by CLC and CLCplus Backbone datasets for natural areas and the Urban Atlas for urban areas. Two nomenclatures are tested: a detailed versus a simplified one, with 32 and 26 thematic classes, respectively, both focusing on Mediterranean-relevant crop typologies. The two nomenclatures are evaluated with sampling rates of 10%, 50%, and 100% of training pixels. Results show that the simplified nomenclature achieves higher accuracy, with an Overall Accuracy (OA) of 0.77 compared to 0.61 for the detailed nomenclature, using 100% training pixels. Increasing the training sample rate improves classification quality in both nomenclatures: in the short nomenclature, OA values are 0.596, 0.613, and 0.774 for 10%, 50%, and 100% sampling rates. In the detailed nomenclature, the improvement is weaker, with OA values of 0.596, 0.601, and 0.610, indicating that increasing sample size does not resolve class confusion. Among agricultural classes, rice, citrus, vegetables, and grapevine achieve the highest classification scores, which are among the crops with the largest irrigation requirements. Nuts, cereals, and fruit trees perform poorly, mainly due to insufficient training samples. Overall, the proposed nomenclature significantly improves the crop classes available in the CLC by increasing crop specificity and differentiation. This study presents a framework for fully automatic crop‑map production in Mediterranean environments, ensuring fast reproducibility over the years thanks to the use of openly accessible satellite imagery and an automated processing chain. This can improve the accuracy and reliability of water accounting for the agricultural sector and help promote sustainable use of limited water resources in the Mediterranean areas.
How to cite: Borgo, A., Thierion, V., Trabucco, A., Lupia, F., and Debolini, M.: High-resolution crop map generation in Mediterranean environments using IOTA2 chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11687, https://doi.org/10.5194/egusphere-egu26-11687, 2026.
Eutrophication in inland lakes has become increasingly prominent, often accompanied by frequent algal blooms and risks of degraded aquatic ecosystem functions. Therefore, broad and dynamic monitoring of lake trophic status is crucial for aquatic ecosystem protection and refined water-resources management. Satellite remote sensing enables rapid, large-area monitoring of lakes. Previous studies have developed large-scale trophic status assessment methods based on the visible-band water color index, the Forel–Ule Index (FUI), to retrieve a chlorophyll-a-referenced trophic state index (TSI(Chl-a)). However, inland waters are optically complex; high concentrations of chromophoric dissolved organic matter (CDOM) or suspended matter can inflate FUI values. Consequently, the single-index approach using FUI alone to assess TSI(Chl-a) (Model1) tends to misclassify mesotrophic waters as eutrophic. To mitigate this interference, most studies have adopted an improved strategy in which FUI serves as the primary indicator and specific spectral bands provide auxiliary discrimination. In this study, we incorporate two medium-to-high resolution satellites with red-edge bands, GF-6 and Sentinel-2, and design Red-edge Band Hue Angle α’(RHA α’) based on bands Red（630nm-690nm/650nm-680nm）,Red-edge1（690nm-730nm/698nm-713nm）,Red-edge2（730nm-770nm/733nm-748nm）. We then develop a coupled lake trophic status assessment method integrating FUI and RHA α’ (Model2).
The results indicate that: (1) RHA α’ can characterize the reflectance-peak feature of chlorophyll-a near 700 nm. For waters with FUI ≥ 11, if elevated FUI is primarily driven by high chlorophyll-a concentrations, RHA α’ tends to be high; conversely, if elevated FUI is mainly caused by high suspended matter concentrations, RHA α’ tends to be low. Thus, Model2 can effectively distinguish high-chlorophyll waters from highly turbid waters by leveraging RHA α’. (2) Using the IOCCG Hydrolight simulated dataset (including 500 synthetic water spectra under varying concentrations of phytoplankton pigments, CDOM, and non-pigmented suspended matter across 400–800 nm). For simulated Gaofen-6 data, the eutrophic-state monitoring assessment accuracies of Model1 and Model2 were respectively 84.1% and 95.8%, and the overall accuracies were respectively 88.6% and 90.4%; for simulated Sentinel-2 data, the corresponding eutrophic-state monitoring assessment accuracies were respectively 84.9% and 99.1%, and the overall accuracies were respectively 88.0% and 89.8%. Overall, Model2 markedly improves the accuracy of eutrophic-state assessment. (3) Taking 252 spatially representative lakes across China as monitoring targets, we produced lake trophic status products for 2021–2022 using Model2 and validated them against the National Surface Water Quality Report released by the Ministry of Ecology and Environment of the People’s Republic of China, achieving an overall accuracy of 79.84%.
In the next step, we will extend this method to long-term spatiotemporal analysis of TSI(Chl-a) for Chinese lakes with an area of 1 km² and above. The data preparation has been largely completed, and the related analyses are currently underway.
How to cite: Zhao, H., Cao, Y., Zhao, H., Zhang, H., Hua, W., Gan, Y., and Li, H.: A Method for Assessing Trophic Status of Inland Lakes Based on the Forel–Ule Index and Red-edge Band Hue Angle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16751, https://doi.org/10.5194/egusphere-egu26-16751, 2026.
A widespread decline in dissolved oxygen (DO) has been observed in rivers, temperate lakes and oceans, yet the impacts of climatic warming on global lake deoxygenation remain unclear. Here, we train data-driven models using climatic data, satellite images and geographic factors to reconstruct surface DO and quantify the climatic contribution to DO variations in 15,535 lakes from 2003 to 2023. Our analysis indicates a continuous deoxygenation in 83% of the studied lakes. The mean deoxygenation rate in global lakes (-0.049 mg/L/decade) is faster than that observed in the oceans (-0.022 mg/L/decade) and in rivers (-0.038 mg/L/decade). By decreasing solubility, climatic warming contributes 55% of global lake deoxygenation. Meanwhile, heatwaves exert rapid influences on DO decline, resulting in a 7.7% deoxygenation compared to that observed under climatological mean temperatures. By the end of the century, global lake DO is projected to decrease by 0.41 mg/L (4.3%) under SSP2–4.5 and 0.86 mg/L (8.8%) under SSP5–8.5 scenarios.
How to cite: Zhang, Y.: Climate Warming and Heatwaves Accelerate Global Lake Deoxygenation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2496, https://doi.org/10.5194/egusphere-egu26-2496, 2026.
Monitoring water quality in inland and coastal waters is essential for understanding biogeochemical cycles and the impacts of anthropogenic pressures such as land use and land cover change, mining, deforestation, dam construction, and climate change. Traditional field surveys, conducted bimonthly or quarterly at limited sampling stations, are valuable but do not offer the spatial and temporal coverage necessary to fully support public policies for sustainable aquatic system management. In this context, remote sensing plays a key role in enabling large-scale water quality monitoring across extensive and remote regions, such as Brazilian Amazon. Despite recent advances, there remains a lack of accessible platforms that deliver validated remote sensing products and algorithms to researchers, stakeholders, and decision-makers. To address this gap, the Instrumentational Laboratory for Aquatic Ecosystems (LabISA) at the Brazilian National Institute for Space Research (INPE) is developing MAPAQUALI, a semi-automatic cloud-based platform designed to generate and distribute water quality products at high spatial and temporal resolution for aquatic ecosystems in Brazil. MAPAQUALI integrates a set of semi-analytical and machine-learning algorithms developed and validated by INPE’s research team. These algorithms retrieve key water quality parameters, including chlorophyll-a, phycocyanin, Secchi disk depth, and total suspended solids, using observations from ESA and NASA multispectral sensors (Sentinel-2 MSI, Sentinel-3 OLCI, and Landsat-8/9 OLI) with a focus on specific reservoirs and lakes in Brazil. In addition to the MAPAQUALI, a new project named MAPAQUALI-IA is leveraging large-scale mapping of water quality in Brazil using artificial intelligence (i.e., machine learning and deep learning methods) to provide these water quality parameters using a single large-scale algorithm. The project will develop algorithms with the help of newly released open datasets, such as BRAZA and GLORIA. The MAPAQUALI/MAPAQUALI-IA processing pipeline incorporates advanced aquatic atmospheric correction techniques, specifically ACOLITE and 6SV, as well as corrections for glint and adjacency effects. A STAC-compliant data cube environment (Brazil Data Cube platform) allows to generate and store data enabling rapid access, visualization, and analysis. This publication introduces the current MAPAQUALI/MAPAQUALI-IA prototype, a modular and continuous monitoring system implemented for representative Brazilian aquatic environments, including Amazonian lakes, eutrophic cascade reservoir system, and coastal waters. Future developments will expand sensor compatibility, include new water-quality algorithms, and extend coverage to additional inland and coastal environments. Ultimately, MAPAQUALI aims to bridge the gap between scientific data and operational application, supporting more informed decision-making to improve aquatic ecosystem conservation and management in Brazil.
How to cite: Maciel, D., Barbosa, C., Novo, E., Flores Júnior, R., Ciotti, A., Lobo, F., Lopes, F., Ribeiro, G., Noernberg, M., Marinho, R., and Martins, V.: Advancing near-real-time water-quality monitoring in Brazil through remote sensing: the MAPAQUALI and MAPAQUALI-IA platforms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-884, https://doi.org/10.5194/egusphere-egu26-884, 2026.
River discharge estimation is critical for flood forecasting and water resources management, yet traditional gauging methods are often limited in spatial coverage. Accurate estimation of river discharge from satellite observations remains challenging in large rivers where hydraulic controls and anthropogenic disturbances induce non-stationary width–discharge relationships. In this study, multi-decadal river width time series derived from multi-sensor satellite imagery (Landsat-5/7/8 and Sentinel-1/2) were employed to estimate discharge in the Ganjiang River Basin, China, using the Google Earth Engine (GEE) platform. Particular emphasis was placed on quantifying the impacts of backwater effects and channel morphological changes on inversion accuracy. Results indicate that: (1) satellite-based width–discharge scaling performs robustly in morphologically stable reaches, yielding high accuracy at the Ji’an, Xiajiang, and Zhangshu stations (R2 > 0.92$; NSE > 0.90); (2) in contrast, performance at the Waizhou station is strongly degraded by complex hydromorphological dynamics, where intensified backwater effects from Poyang Lake during the wet season weaken the functional coupling between river width and discharge (R2 decreases to 0.59), and pronounced channel incision associated with historical sand mining (mean bed lowering of 2.97 m) introduces additional non-stationarity into the rating relationship; and (3) to account for these time-varying controls, a segmented modeling framework was implemented to explicitly reflect periods of morphological adjustment, substantially improving discharge estimates at Waizhou and increasing both R2 and NSE to 0.90 from 2012 to 2019. These findings highlight the importance of considering morphodynamic evolution and hydraulic boundary conditions explicitly for reliable satellite-based discharge estimation in dynamically evolving river–lake systems.
How to cite: Wang, G. and Gu, P.: Satellite-Based Discharge Estimation in Morphologically Dynamic Rivers: A Segmented Modeling Approach for the Ganjiang River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6227, https://doi.org/10.5194/egusphere-egu26-6227, 2026.
The Ebro River Basin is one of the most intensively managed and climatically sensitive basins in the Mediterranean region, where increasing water demands, pronounced climate variability, and environmental constraints pose major challenges for sustainable water resources management. Addressing these challenges requires hydrological models capable of consistently representing both natural processes and anthropogenic water use. In this context, the INTERROGATION project, funded by the Italian Ministry of Universities and Research, examines the interactions between climatic and anthropogenic factors in the development and recovery of major hydrological droughts that have affected the Ebro River Basin in recent decades (1990-2023).
In this study, we present a comprehensive reconstruction of the water cycle in the Ebro River Basin, explicitly accounting for both natural processes and human water use. For this purpose, three different precipitation datasets are used as input data to the flexible conceptual hydrological model MISDc (Modello Idrologico Semistribuito in Continuo): long-term (2000-2023) daily in situ observations and two versions of a daily integrated dataset obtained by merging GPM and SM2RAIN products at low (10 km) and high (1 km) spatial resolutions.
The hydrological model is calibrated against observed river discharge and validated through a multi-variable comparison with satellite-based estimates of soil moisture, evapotranspiration, snow water equivalent, and irrigation, which were developed within the framework of the European Space Agency Digital Twin Earth (DTE) Hydrology Next project. The results of this work demonstrate the significance of employing a suitable hydrological model in conjunction with accurate satellite information for capturing the spatiotemporal evolution of the hydrological cycle within highly managed basins. These results will be the basis for developing a decision support system that will guide stakeholders toward an integrated management of water resources in the Ebro River Basin.
How to cite: Kalimisetty, S., Ceola, S., Palazzoli, I., Montanari, A., Stocchi, P., and Camici, S.: Reconstructing the Hydrological Cycle of the Ebro River Basin through Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10071, https://doi.org/10.5194/egusphere-egu26-10071, 2026.
Accurate and reliable delineation of surface water from optical satellite imagery is a pre-requisite for many hydrological applications. In inland and riverine environments, water masking is a major source of uncertainty due to optically complex waters, mixed land–water pixels and strong adjacency effects. Conventional water masks provide no explicit measure of classification confidence and do not account for uncertainty in pixel classification, particularly along riverbanks, under bridges, in building-shadows, and in highly dynamic systems, where small changes in reflectance or threshold parameters can lead to unstable water boundaries.
In this study, we present a generic Monte Carlo–based water detection framework that explicitly propagates Sentinel-2 ACOLITE remote sensing reflectance uncertainty through multiple spectral threshold-based water indices (NDWI, MNDWI, AWEI, and MBWI), resulting in per-pixel water occurrence probabilities. These indices are evaluated independently and combined using a deterministic voting-based fusion scheme. This decision logic is further constrained by physically motivated reflectance thresholds in the near-infrared and shortwave infrared bands, together with a low-signal filter, to suppress shadows and dark non-water surfaces that commonly generate false positives in index-based approaches.
The method is demonstrated as a proof of concept using a Sentinel-2 acquisition over the Seine River in Paris characterized by complex optical conditions. High-confidence water pixels dominate the main river channel, while intermediate probabilities are concentrated along riverbanks, bridges, and narrow tributaries. Within the final detected water mask, the mean water probability reaches 0.98, with more than 97% of water pixels classified with high confidence (P ≥ 0.9). Classification uncertainty is very low overall, indicating strong consistency across Monte Carlo realizations. Intermediate probabilities (0.3 < P < 0.7) represent less than 1% of detected water pixels and are spatially confined to water–land transition zones. Sensitivity experiments indicate that total water extent is weakly affected by increasing reflectance perturbation, whereas uncertainty increases systematically at water–land boundaries. By explicitly quantifying water-detection uncertainty, this Monte Carlo framework provides a statistically robust foundation for subsequent water-quality retrieval and uncertainty propagation.
How to cite: Amin, G., Pourret, O., Dupin, V., Guérin-Rechdaoui, S., and Dujany, A.: Monte Carlo–Based Uncertainty Propagation for Probabilistic Water Masking from Satellite Remote Sensing Reflectance Product, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5592, https://doi.org/10.5194/egusphere-egu26-5592, 2026.
The Landsat Collection 2 (C2) archive is vital for inland water monitoring, yet the Land Surface Reflectance Code (LaSRC) atmospheric correction for Landsat-8/9 introduces Dark Plume Over Water (DPOW) artifacts. These aerosol extrapolation errors cause severe negative biases in shortwave bands, disrupting long-term consistency. To address this, we developed a cloud-native Alternative Correction (AC) method on Google Earth Engine. This data-driven approach employs random forest regression, trained on spatiotemporally aggregated high-quality water pixels, to reconstruct reliable surface reflectance (SR) from Top-of-Atmosphere observations. Validation against a global in-situ hyperspectral dataset and benchmarking against the physics-based ACOLITE processor demonstrate the robustness of the proposed method. While ACOLITE effectively resolves the negative bias issue, the AC method achieves superior radiometric accuracy, reducing the ultra-blue Root Mean Square Error to 0.019 (compared to 0.029 for ACOLITE and 0.031 for C2 SR). Notably, under high-aerosol conditions, the AC method minimizes the residual spectral distortions often observed in physical inversions, effectively restoring the natural spectral shape. Spatially, the method eliminates DPOW artifacts; furthermore, it removes systematic biases between Landsat-8/9 and legacy sensors (Landsat-4/5/7). By restoring radiometric integrity, this automated solution secures the foundation for reliable long-term global limnology.
How to cite: Bi, S., Shi, K., and Xu, J.: A cloud-native alternative correction for Landsat-8/9 Collection 2 surface reflectance over inland waters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3651, https://doi.org/10.5194/egusphere-egu26-3651, 2026.
Net radiation (Rn) is a key control on land–atmosphere exchanges and a primary forcing for transpiration modelling. However, commonly used radiation products often lack the spatial resolution required to resolve soil–plant–atmosphere interactions in heterogeneous landscapes, limiting their applicability for water management studies. Here, we present a new daily net shortwave and net radiation dataset for Europe at 1-arcminute (~1.4 km) spatial resolution covering the period 2001–2020, developed to support high-resolution transpiration modelling using the Priestley–Taylor approach.
The dataset is generated through the integration of multiple complementary data sources, combining station-based downward shortwave radiation from the EMO-1 dataset, satellite-derived longwave radiation from the ELITE product, and a physically based estimation of blue-sky albedo derived from GLASS black and white-sky albedo products, together with information on photosynthetically active radiation from BESS.
Evaluation against FLUXNET observations reveals that the high-resolution net shortwave radiation product outperforms the coarser ERA5-Land reanalysis across 7 of 9 analyzed European countries, with particularly strong improvements in topographically complex regions, such as the Alps, and in heterogeneous land-use areas. However, the net radiation product shows larger uncertainties in semi-arid regions and during high-latitude winter conditions, reflecting known limitations in satellite-based radiation retrievals. Nevertheless, the high spatial resolution represents a valuable contribution to remote-sensing-based water cycle studies, drought assessment, and land-surface modeling.
How to cite: Mahajan, K., Tuo, Y., and Peng, J.: High-resolution net-shortwave and net-radiation products for Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21227, https://doi.org/10.5194/egusphere-egu26-21227, 2026.
