EOmaps is a free and open-source python package specifically tailored for geographic data visualization and analysis.The main goals of the package are twofold:Speed up and simplify the daily struggle of geographic data visualization Directly use the figures as fully customisable interactive data-analysis widgets EOmaps is built on top of matplotlib and cartopy and integrates well with the scientific python infrastructure (numpy, pandas, xarray, geopandas, datashader, etc.). It provides a flexible and well-documented API to create publication-ready figures and it can be used to visualize (potentially large) structured (e.g. raster) or unstructured (e.g. unordered lists) datasets provided in arbitrary projections. In addition, EOmaps comes with many useful features to help with scientific geo-data analysis:Maps can have multiple layers to interactively compare and (transparently) overlay datasets, web-maps etc. Once a dataset is plotted, you can assign arbitrary callback functions to interactively run your analysis-workflow on selected datapoints (e.g. load data from a database, plot underlying timeseries, histograms etc.) Figures created with EOmaps can be exported as images (png, jpeg, ...), vector-graphics (svg) or embedded in Jupyter Notebooks, web-pages (html) or in GUI frameworks such as Qt or tkinter.In this presentation we will highlight the capabilities of EOmaps and show how it can be used in a variety of different situations to aid your scientific data analysis workflow.EOmaps source-code: https://github.com/raphaelquast/EOmaps EOmaps documentation: https://eomaps.readthedocs.io/

Study region The study focus on five catchments in the Mediterranean Region distributed over Spain, France, Italy, Tunisia and Algeria. Study focus The study runs four lumped parameter hydrological models combining eight precipitation products and four soil moisture products. A Bayesian inference scheme is built to estimate posterior parameter distribution for any combination of hydrological model, precipitation and soil moisture. The simulated streamflows are evaluated using both Nash-Sutcliffe Efficiency criteria and multi-resolution analysis. The results from Bayesian inference combining streamflow and soil moisture is compared to the results when considering only streamflow as a benchmark. New hydrological insights for the region The results indicates that hydrological model performance through the Nash-Sutcliffe Efficiency criteria is more sensitive to the forcing precipitation product than the model structure. Also, forcing hydrological models with merged precipitation product brings better streamflow predictions than using satellite precipitation products. Regarding soil moisture accounting in hydrological modeling, the results show that including soil moisture in the parameter estimation can improve the predictive performance of hydrological models when the model is forced with satellite precipitation product. Also, soil moisture datasets derived from Sentinel-1 offer better consistency in hydrological modeling of river streamflow simulation.

Radiative transfer models have been extensively applied in soil moisture studies; however, their application to L-band synthetic aperture radar (SAR) data has not been fully explored. This research introduces a comprehensive approach for soil moisture retrieval using SAOCOM L-band SAR dual-polarization data (VV-VH). The novel bistatic radiative transfer modeling framework (RT1) is used, validated previously with Sentinel-1 C-band SAR and advanced scatterometer (ASCAT) data. For the first time, the RT1 model is applied to SAOCOM L-band data over the Petacciato landslide area in Italy, covering the period from January 2021 to December 2023. A statistical comparison of soil moisture estimates derived from L-band SAR data (lambda=23 cm) is conducted, with the model's performance evaluated against multiple regional-scale soil moisture datasets, including ASCAT, ERA-5 Land, and soil moisture active passive. Validation is performed using soil moisture time series and advanced statistical methods. The study incorporates the antecedent precipitation index (API), calculated from precipitation in the days leading up to an event, as an indicator of soil moisture, helping assess retained moisture from prior rainfall. The proposed methodology exhibits high accuracy, as evidenced by a strong correlation (r >= 0.67, RMSE = 0.0936 m(3)/m(3), MSE = 0.088 m(3)/m(3), and Bias = -0.0603 m(3)/m(3)) between the RT1 soil moisture retrieval and reference datasets, such as ASCAT data. This approach provides a reliable tool for continuous soil moisture monitoring in landslide-prone regions, with SAOCOM L-band SAR and radiative transfer modeling enhancing retrieval in complex and agricultural terrains for improved landslide monitoring.

In remote sensing applications, the ability to efficiently fit models to vast amounts of observational data is vital for deriving high-quality data products, as well as accelerating research and development. Addressing this challenge, we developed a high-performance non-linear Trust Region Reflectance solver specialised for datacubes, by integrating Python's interoperability with C++ and Dask's distributed computing capabilities. Our solution achieves high throughput both locally and potentially on any Dask-compatible backend, such as EODC's Dask Gateway. The Dask framework takes care of chunking the datacube, and streaming each chunk efficiently to available workers where our specialised solver is applied. Introducing Dask for distributed computing enables our algorithm to run on different compatible backends. This approach not only broadens operational flexibility, but also allows us to focus on enhancing the algorithm's efficiency, free from concerns about concurrency. This enabled us to implement a highly efficient solver in C++, which is optimised to run on a single core, but still utilise all available resources effectively. For the heavy lifting, such as performing singular value decompositions and matrix operations we rely on Eigen, a powerful open-source C++ library specialized on linear algebra. To describe the spatial reference and other auxiliary data associated with our datacube, we employ the Xarray framework. Importantly, Xarray integrates seamlessly with Dask. Finally, to ensure robustness and extensibility of our framework, we applied state-of-the-art software engineering practices, including Continuous Integration and Test-Driven Development. In our work we demonstrate the significant performance gains achievable by effectively utilising available open-source frameworks, and adhering to best engineering practices. This is exemplified by our practical workflow demonstration to fit a soil moisture estimation model.

Vegetation is a key part of the water and carbon cycle and the interaction between Earth's surface and atmosphere. Understanding water dynamics within vegetation is crucial for improving models that represent vegetation processes. Previous studies have investigated exploiting ASCAT scatterometer data from the METOP satellites to evaluate dynamics in vegetation water content. ASCAT has been operational since 2007 and captures microwave backscatter from multiple angles, revealing the relation between backscatter and the incidence angle. This relation reflects the relative contributions of volume and surface scattering—the former affected by water on and within vegetation, and the latter influenced by water in the top soil layer. Currently, a weighted regression using ASCAT observations from 42 days is used to estimate the parameters representing this relation: the slope and curvature, or the first and second order derivative of a second order Taylor approximation, respectively. This estimation method is implemented in the Soil Water Retrieval Retrieval Package developed by TU Wien. Adverse artefacts of this estimation method are the aggregation of observations corresponding to varying states of the earth surface, e.g. before and after a forest fire. Here, we present results from a study to improve the estimation method for ASCAT's slope and curvature parameters, tailored to quantification of vegetation processes. Goals include: representing parameters at briefer temporal scales, reducing the impact of interception, and restricting temporal aggregation around instantaneous events of change such as storms. In addition to analysing real ASCAT observations, synthetic ASCAT observations are simulated using a radiative transfer model, enabling a thorough comparison of estimated slope against simulated ground truth values. Preliminary results show that simulated ASCAT slope time series represent the dynamics of real ASCAT slope, indicating that synthetic observations can be used to quantify improvement of the slope estimation method.

Groundwater is a massive portion of the total freshwater available, representing a primary source of water for domestic and agricultural uses, often threatened by climate change and human pressure. Its monitoring is thus a priority challenge for which satellite sensors can help in overcoming common issues related to in situ networks. This study proposes a novel approach for estimating groundwater recharge through satellite soil moisture, consisting in calibrating soil parameters commonly used in analytical formulations of the downward water flux due to gravitational forces through the inversion of the soil water balance and using rainfall rates as a benchmark. To do this, a soil moisture-based inversion approach is implemented over the Umbria region, central Italy, disposing of a dense network of piezometric stations for validation purposes. After a first step aimed at identifying the best performing combination of input data, 5 years (2017-2021) of groundwater recharge rates are estimated by exploiting 1 km Sentinel-1 soil moisture, 1 km potential evaporation, and in situ rainfall. Without any information on groundwater dynamics for calibration purposes, a good agreement between anomalies of estimated and ground-based monthly recharge is found. Over half of the pilot piezometric stations, monthly Pearson correlation is higher than 0.5 and the root mean square error is lower than 1.72. Performances increase the more the reference groundwater level records are correlated with rainfall, i.e., for undisturbed aquifers. Further analyses partially exploiting groundwater depth records for scaling estimated recharge rates to the observed ones show that yearly measured amounts can be quantified from satellite with a median percentage error of -6%. The proposed approach is a promising tool for remotely mapping and monitoring groundwater recharge, which is essential in the assessment of freshwater availability trends.

Climate change is already causing suffering and damage, representing the greatest current challenge and threat to our planet. As global temperatures increase, widespread shifts in weather systems occur, making events such as droughts and floods more intense and unpredictable. Both have a direct connection to the variability of Soil Moisture (SM), which therefore needs to be provided at adequate spatiotemporal resolutions and with good accuracy along the soil profile. Currently, there are no satellite SM products that can offer information at high temporal and spatial resolutions, particularly when investigating root zone and large spatial scales. Blending satellite products with similar characteristics but different features in terms of resolution may allow us to face such a gap. In this light, the 25 km Metop ASCAT Surface Soil Moisture (SSM) product, with a sub-daily temporal resolution (2-6 measurements per day), and the weekly improved SSM S-1 data at 1 km spatial resolution are based on satellite acquisitions in the same microwave spectral region (i.e., the C-band) processed with the RT1 algorithm (Quast et al., 2023)In this work, we fused, firstly, these products to obtain a daily 1 km soil moisture product, named SCAT- SAR SWI, following the method of Bauer-Marschallinger et al. (2018). As inputs, we used the ASCAT H119 - H120 (Climate Data Record v7 Extension 12.5 km sampling) and an optimized version of SENTINEL 1 SM products made available by the Technological University of Wien for the January 2017 - July 2022 period. Subsequently, we applied the Soil Moisture Analytical Relationship (SMAR) model (Manfreda et al., 2014) to the SCAT-SAR SWI surface product to obtain RZSM information. This made it possible to depict the Basilicata region (southern Italy) test case in four dimensions (time t plus x, y, and z) at high spatiotemporal resolutions. The performance of the developed SCAT- SAR SWI SMAR product, as well as that of the SCAT- SAR SWI, was evaluated for comparison with the 1 km ERA5-Land downscaled SM data (i.e., volumetric_soil_water_layer_1; volumetric_soil_water_layer_2). The results are encouraging, demonstrating the capability of the product to discriminate the behaviour of areas characterized by different SM contents based on their orography and precipitation regimes. The western part of the region, more affected by precipitation and more mountainous than the other sections of the region, shows indeed a positive correlation (R≃0.8) with the ERA 5 LAND 1 km product, higher than that obtained for the flatter western subset (R≃0.6-0.7). This is likely due to the more consistent precipitation patterns in the western part.ReferenceBauer-Marschallinger, B. et al., 2018. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing 10, 1030. Manfreda, S., et al. 2014. A physically based approach for the estimation of root-zone soil moisture from surface measurements. HESS 18, 1199–1212. Quast, R., et al., 2023. Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley. Rem. Sens. Of Env., 295, 113651

Despite the key role of irrigation in the Earth system, we lack fundamental information regarding the distribution of irrigated fields, irrigation timing and the amount of water utilized. In the past years, the SM_Delta and SM_Inversion approaches have been independently developed to provide estimates of irrigation timing and water amounts based on satellite soil moisture data. The SM_Delta approach retrieves irrigation from variations in soil moisture between an individual pixel and the surrounding rainfed area, while the SM_Inversion approach estimates the total amount of water entering the soil, then irrigation is derived by subtracting precipitation. In this study, we perform a comprehensive assessment of irrigation estimates from the SM_Delta and SM_Inversion algorithms based on Sentinel-1 surface soil moisture retrievals at 1 km resolution. Our analysis focuses on the Ebro basin, an irrigated region in Spain covering 83000 km2, during the period 2017–2019. We assess the ability of the two methods to discriminate irrigated and rainfed pixels, then we quantify the agreement of irrigation timing and water volumes with reference irrigation data. An inter-comparison between estimates from the SM_Delta and SM_Inversion methods is carried out considering both temporal and spatial features, i.e., monthly irrigation peaks and spatial irrigation patterns. Finally, we explore two potential applications of satellite-derived irrigation estimates: attributing irrigation water volumes to specific irrigation systems and to individual crops. We observe that both methods erroneously retrieve irrigation over rainfed pixels, and are therefore not suitable to map irrigated and rainfed fields. However, when auxiliary information on irrigated fields is available, we find a satisfactory agreement between district-scale reference data and satellite-retrieved irrigation, using both the SM_Delta and SM_Inversion approaches (Pearson R equal to 0.67 and 0.71, bias equal to −4.99 and −4.75 mm/15 days, respectively). When aggregated in space or time, the irrigation estimates exhibit coherent temporal dynamics and spatial patterns. For instance, estimates from both SM_Delta and SM_Inversion capture the delayed irrigation that occurred in 2018 due to wetter than usual conditions in spring. However, at the pixel-scale, limited consistency exists between irrigation estimates from the two methods due to different assumptions and parameterizations, e.g., use of constant vs pixel-specific soil water capacity (in the SM_Delta and SM_Inversion, respectively). Overall, the study demonstrates the reliability of irrigation estimates derived from the SM_Delta and SM_Inversion approaches, especially when shifting from small spatial and short temporal scales (pixel level, sub-weekly) to larger and longer scales (district level, seasonal). Hence, satellite-based irrigation estimates could inform water resources managers and basin authorities, as well as serve the modelling community by providing reliable information on the timing and the amounts of water employed at the basin level.

Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed.

Irrigation water use represents the primary source of freshwater consumption by humans. The amount of water withdrawals for agricultural purposes is expected to further increase in the upcoming years to face the rising world population and higher living standards. Hence, effective plans for enacting a rational management of agricultural water use are urgent, but they are limited by knowledge gaps about irrigation. Detailed information on irrigation dynamics (i.e., extents, timing, and amounts) is generally lacking worldwide, but satellite observations can be used to fill this gap. This paper describes the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from satellite observations. The products are developed over three major river basins characterized by varying irrigation extents and methodologies, as well as by different climatic conditions. The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project. The irrigation amounts have been estimated through the SM-based (soil-moisture-based) inversion approach over the Ebro river basin (northeastern Spain), the Po valley (northern Italy), and the Murray–Darling basin (southeastern Australia). The satellite-derived irrigation products referring to the case studies in Europe have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. All the irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray–Darling basin are available for the time span April 2017–July 2020. The retrieved irrigation amounts have been compared with benchmark rates collected over selected agricultural districts. Results highlight satisfactory performances over the major part of the pilot sites falling within the two regions characterized by a semiarid climate, namely, the Ebro and the Murray–Darling basins, quantified by median values of RMSE, Pearson correlation r, and bias equal to 12.4 mm/14 d, 0.66, and −4.62 mm/14 d, respectively, for the Ebro basin and to 10.54 mm/month, 0.77, and −3.07 mm/month, respectively, for the Murray–Darling basin. The assessment of the performances over the Po valley is affected by the limited availability of in situ reference data for irrigation. The developed products are made available to the scientific community for use and further validation at https://doi.org/10.5281/zenodo.7341284 (Dari et al., 2022a).

<p>Irrigation is widely recognized as the human activity that alters the natural circulation of water on the Earth’s surface the most. It greatly contributes to making the canonical conceptualization of the hydrological cycle incomplete. Nevertheless, irrigation dynamics are still generally unmonitored worldwide, but satellite capabilities have recently proved their suitability for such a purpose.</p><p>In this contribution, the first regional-scale and high-resolution data sets of irrigation water use retrieved from satellite data are presented. The products, obtained through the SM-based (Soil-Moisture-based) inversion approach, are an outcome of the Irrigation+ project (https://esairrigationplus.org/) funded by the European Space Agency (ESA). The data have been produced over the Ebro basin (Spain), the Po valley (Italy), and the Murray-Darling basin (Australia) and they are available at: https://zenodo.org/record/7341284#.Y7WHsHbMKUm. The irrigation estimates referring to the Spanish and the Italian pilot areas rely on Sentinel-1 soil moisture obtained through the RT1 (first-order Radiative Transfer) model and are characterized by a spatial resolution of 1 km. A 6 km spatial sampling has been adopted for the Murray-Darling basin; in this case, irrigation water amounts have been retrieved from CYGNSS (CYclone Global Navigation Satellite System) soil moisture. The data sets referring to the European sites cover a time span ranging from January 2016 to July 2020, while irrigation amounts over the Murray-Darling basin are available for the period April 2017 – July 2020. The reliability of the retrieved irrigation estimates has been assessed through comparison against benchmark amounts. Satisfactory performances have been found over the Ebro and the Murray-Darling basins. More in detail, a median value of RMSE, Pearson correlation, r, and BIAS equal to 12.4 mm/14-day, 0.66, and -4.62 mm/14-day, respectively, is found across pilot districts located within the Ebro basin. The analogous results obtained over the Murray-Darling basin are equal to10.54 mm/month, 0.77, and -3.07 mm/month. The evaluation over the Po valley is affected by the limited availability of in-situ reference data for irrigation. This study sheds light on the perspective of building operational systems aimed at monitoring agricultural water use relying on satellite data.</p>

The products are the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (https://esairrigationplus.org/). The irrigation amounts have been estimated through the SM-based (Soil-Moisture-based) inversion approach. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and on their performance assessment can be found in: Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space. Earth System Science Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023. Novelties in v1.1 with respect to v1.0: v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021. Acknowledgements: ESA Irrigation+ project, https://esairrigationplus.org/, (contract n. 4000129870/20/I-NB). ESA 4DMED-Hydrology project, https://www.4dmed-hydrology.org/, (contract n. 4000136272/21/I-EF).

Most scientific studies dealing with the retrieval of soil moisture data from Synthetic Aperture Radar (SAR) data focus on the formulation, training, and validation of the models used to convert the backscatter measurements into soil moisture data, while paying little attention to how the backscatter data are preprocessed. This is insofar surprising given that the topography of the Earth surface in combination with the variable SAR imaging geometry may introduce strong orbit-related geometric effects that obscure the soil moisture signal in backscatter time series. Furthermore, backscatter mechanisms are characterized by a very high spatial variability, leading to variable sensitivity to soil moisture. Differences in backscatter mechanisms and soil moisture sensitivity are hardly ever accounted for except for masking some obvious soil-moisture-insensitive areas such as water bodies, dense forest and urban areas. In this contribution we give an overview of the ongoing efforts at TU Wien to develop Sentinel-1 preprocessing workflows to produce 1 km backscatter time series that are optimized to the task of retrieving soil moisture data at the same spatial resolution. The following topics are addressed: (i) the use of radiometric terrain corrected backscatter data instead of the standard ground range detected products, (ii) the masking of subsurface scattering areas, dense forest and other soil-moisture-insensitive areas, and (iii) the standardization of the backscatter data to a reference incidence angle using machine learning techniques. Our preliminary results over Europe and the Mediterranean region show a substantial improvement of the Sentinel-1 soil moisture retrievals that would be impossible to achieve by a sole focus on the scientific retrieval algorithm.AcknowledgementsWe acknowledge funding by the European Space Agency (DTE Hydrology and 4DMED), the Copernicus Land Monitoring Service, and the Austrian Space Applications Programme (ROSSHINI and GHG-KIT). The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).

Soil moisture is an important variable controlling many land surface processes and is used to quantify precipitation, drought, flooding, irrigation and other factors that influence decision making and risk-assessment. This paper presents the retrieval of high resolution (∼1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. The model is applied over a diverse set of landcover types across the entire Po-Valley in Italy over a 4-year time-period from 2016 to 2019. The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The model parameterisation and retrieval method are chosen such as to constitute a trade-off between a physically plausible and a computationally feasible modeling approach. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.

Abstract. Satellite Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based Earth observation data in hydrological modelling. In a set of experiments, the distributed hydrological model Continuum is set up for the Po River Basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean = 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite dataset on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.

The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to two soil moisture products over the Po River basin: a high-resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and a 25 (12.5 km spacing) product derived from ASCAT, resampled to the same grid as S1-RT1. In order to overcome the need for calibration and to allow for its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 d, since the low temporal resolution of this sensor (from 1.5 to 4 d over Europe) prevents its application for infer daily rainfall. On average, the ASCAT-derived rainfall product performs better than S1-RT1, even if the performances are equally good when 30 d accumulated rainfall is considered (resulting in a mean Pearson correlation for the parameterized SM2RAIN product of 0.74 and 0.73, respectively). Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high-spatial-resolution information in obtaining spatially detailed rainfall. Finally, the performances of the parameterized products are similar to those obtained with the calibrated SM2RAIN algorithm, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide, even without the availability of a rainfall benchmark product.

The incidence angle dependence of C-band backscatter is strongly affected by the presence of vegetation in the sensor footprint. Many studies have shown the suitability of this dependence for studying and monitoring vegetation dynamics. However, short-term dynamics in the backscatter-incidence angle dependence remain unexplained and indicate that secondary effects might be superimposed on the vegetation component. In this study, we hypothesize that the observed short-term dynamics are caused by soil moisture. We investigate the effect by exploring relationships between the slope of the backscatter-incidence angle dependence (σ′) from the Advanced Scatterometer (ASCAT) and soil moisture, rainfall, temperature, and leaf area index. We carry out the analysis over six study regions in Portugal, Austria, and Russia with different climate, land cover, and vegetation cycles. Our results indicate that soil moisture has an effect on σ′. Spearman correlations of σ′ anomalies with soil moisture anomalies are stronger than with any other variable in most study regions and range from −0.38 to −0.70. Even when accounting for effects of water on canopy, correlations between σ′ and soil moisture remain relatively strong, ranging from −0.14 to −0.46. These results confirm the presence of secondary effects in the dynamic σ′, which need to be corrected for when applying σ′ in studies of vegetation dynamics. A correction may be achieved by the application of a suitable smoothing on σ′ (i.e., removing high frequency signal components), by masking observations taken under wet conditions, or by the use of models that explicitly account for the effect of soil moisture on σ′.

Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on C-Band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments.

<p>Active microwave remote sensing satellites allow to retrieve surface soil moisture (SSM) consistently and independently from sun illumination or cloud cover. The current generation of Synthetic Aperture Radars (SAR) on-board of the Sentinel-1A and 1B satellites, launched in 2014 and 2016 respectively, provide backscatter observations in their interferometric wide swath mode at 20 x 22 m resolution. These data are being used by the Copernicus Global Land Service (CGLS) for generating SSM data at kilometre-scale resolution using a change detection approach. The data are operationally and freely available from https://land.copernicus.eu/global/. The goal of this study was to assess the quality of the CGLS SSM retrieval algorithm over different land cover types and crop species. For this purpose, we compared the satellite retrievals against in-situ SSM from the International Soil Moisture Network (ISMN). The stations analyzed are located in France and Austria (SMOSMANIA and HOAL) and cover a wide range of land cover types, from cropland and grassland to forested areas. For each station, backscatter at 20m resolution was averaged over fields containing the ISMN station using Land Parcel Identification System (LPIS) data. The resampled field backscatter, which covers one specific land cover or crop type, was then used as input for the change detection model and compared to the in-situ SSM from ISMN. The study shows that the temporal correspondence of the resulting SSM with in-situ data is strongly varying between crop species and land cover type. The results suggest that crops with seasonal variations in vegetation structure (e.g. winter wheat stem elongation and heading), have a negative impact on the performance of the model. In comparison, the retrieved SSM is better correlated to in-situ data over land cover such as grasslands or maize fields with more homogeneous vegetation development. This study explores the potential and challenges posed by the high resolution of Sentinel-1 backscatter data for SSM retrieval. It demonstrates the effect changes in vegetation structure can have on S1 backscatter, which is important information to all retrieval algorithms for S1 SSM retrieval.  It also provides a first path forward to improve SSM using the TUWien change detection from Sentinel-1. </p>

Irrigation is the most impacting anthropogenic activity on the natural water cycle. Despite this, the current knowledge of irrigation extents and of the amounts of water used for such purpose is often not sufficient. Models and remote sensing technologies proved to be useful tools for monitoring irrigation dynamics. This study presents a comparison between two different approaches (one satellite-based and one model-based) aimed at estimating irrigation water amounts developed within the European Space Agency (ESA) Irrigation+ project. The project targets are: (i) the development of methods and algorithms to detect, map, and quantify irrigation at different spatial scales; (ii) the production of satellite-derived irrigation products; and (iii) the assessment of the impacts of irrigation on society and science. In particular, analyses carried out over two test sites in Italy where irrigation is practiced at different spatial scales are presented and discussed. The results highlight the capability of both approaches in reproducing the irrigation occurrence, and show a better performance of the satellite-based method in reproducing the actually occurred irrigation amounts.