Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km${}^2$ in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information.

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather forecasting framework. This paper presents an improved and fully automated solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. Main findings showed that the use of radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information, provides good soil moisture estimations. The accuracy is even slightly better comparatively to the accuracy obtained using a priori weather information.

Estimating water flows and stocks in surface hydrology is crucial for addressing important socio-economic issues, such as managing water resources and predicting extreme events like floods and droughts. These challenges become more significant with the ongoing global climate change, which may intensify the hydrological cycle. Advanced modeling tools are necessary for making precise and reliable local forecasts. However, hydrological models, regardless of their complexity and status, encounter difficulties in accurately and reliably predicting quantities of interest such as river flows or soil moisture states, and in accounting for meteorological-climatic effects on hydrology. Given the complexity of the physical processes involved and their heterogeneous and limited observability, hydrological modeling is a challenging task, and internal flows often have significant uncertainties. These uncertainties could be reduced by integrating new observations from remote sensing applied to continental surfaces, which is rapidly evolving. A variety of satellites and sensors now allow the observation of watershed surface characteristics and hydrological responses with increasing spatial-temporal resolutions. In particular, products of soil moisture, evaporation, and land use are now available at relatively high spatial-temporal resolution. This work focuses on improving the integration of satellite and in-situ land surface data into spatially distributed hydrological models. The Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on neural networks into the differentiable hydrological model SMASH, is modified to account for satellite moisture maps in addition to discharge at gauging stations and basins physical descriptors maps. Regional optimizations are performed on flash-flood-prone areas located in the South of France and their accuracy and robustness is evaluated in terms of simulated discharge and moisture against observations.

The integration of remote sensing observations into hydrological modeling frameworks presents a significant opportunity for improving spatial and temporal predictive capabilities across continental domains. This research introduces a novel hybrid distributed hydrological model that addresses key challenges in computational efficiency, by using a GPU-enabled computational infrastructure, and in predictive accuracy by assimilating multi-source remote sensing datasets, specifically satellite-based soil moisture and evapotranspiration, at a high spatial resolution (1km×1km) and temporal scale (hourly). The model addresses critical challenges in regional hydrological forecasting by leveraging advanced data assimilation techniques and machine learning methodologies.The proposed hybrid modeling framework synthesizes physically-based distributed hydrologic modeling principles with data-driven machine learning approaches, facilitating a more comprehensive representation of land surface hydrological processes. A key innovation is the GPU-enabled cell-to-cell routing algorithm, which enables fast and efficient computational processing of complex hydrological connectivity and water movement across large spatial domains. By integrating remote sensing observations, the methodology enables enhanced initial condition specification and improved parameter estimation, particularly in regions characterized by sparse ground-based measurement networks.Preliminary analytical results demonstrate significant improvements in model performance, particularly in capturing spatial and temporal variability of hydrological states and fluxes. The approach substantively advances current methodological capabilities in hydrological forecasting, offering a promising framework for developping enriched tensorial numerical solvers, addressing complex hydroclimatic prediction challenges in data-limited environments.