# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 1 Introduction  
Active and passive satellite-based microwave observations provide valuable tools for monitoring soil moisture, with the potential to provide global coverage at a high spatial and temporal resolution. Over the past decades, several satellite missions have been launched (e.g., ASCAT (Advanced Scatterometer) [1] and SMOS (Soil Moisture and Ocean Salinity) [2]), resulting in the development of a number of soil moisture products with different temporal and accuracy characteristics. These products have low spatial resolution, typically no better than 10 km, and are used for large-scale applications, including drought monitoring, flood forecasting, climate monitoring, and agricultural management [3, 4, 5, 6]. The accuracy and reliability of satellite-based soil moisture products depend on several factors, including the satellite sensor design, retrieval algorithms, validation techniques, and vegetation structure [7]. There is a need to improve the spatial resolution of satellite-derived surface soil moisture (SSM) products and to evaluate their assimilation into land surface models (LSMs) [8]. One way to achieve this goal is to downscale already existing low-resolution SSM products to the kilometer scale by integrating local information from land cover or in situ measurements through machine learning [9]. Another way is to take advantage of recently developed satellite-based SSM products based on high-resolution Sentinel-1 (S1) C-band backscatter in conjunction with Sentinel-2 (S2) vegetation indices [10, 11, 12, 13]. LSMs are used to simulate the diurnal and seasonal cycles of energy, water, and carbon fluxes at the soil-plant interface with the atmosphere at different spatial scales (from 0.1 to 100 km) worldwide. LSMs are capable of simulating physical and biological processes, such as photosynthesis and vegetation phenology, as well as soil moisture and soil temperature, for multiple soil layers [14; 15]. In LSMs, sub-grid heterogeneity can be represented by patches, where each patch represents a different land cover type (such as forest, grassland, or cropland) and a range of physical characteristics (such as the plant rooting depth). The ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model [16] is designed to be used in a variety of applications, either coupled with the atmosphere in climate and numerical weather prediction atmospheric models or offline (without interactions with the atmosphere). In the most advanced versions of ISBA, the leaf area index (LAI), a key vegetation state variable controlling carbon and water fluxes, is computed together with photosynthesis. The \"A-gs\" ISBA-A-gs configuration of ISBA [17; 18; 19], hereafter referred to as ISBA-A-gs, is used in this study. Accurate simulation of SSM and root zone soil moisture (RZSM) in addition to LAI is critical, as these variables control the partitioning of energy and water fluxes between the land surface and the atmosphere [20; 21; 22]. The measurement of soil moisture is challenging, especially at regional and global scales [23; 24; 25], leading to difficulties in evaluating RZSM. The assimilation of satellite-derived SSM data together with LAI into LSMs aims to improve the accuracy and predictive ability of LSMs [26; 27; 28]. Kalman filtering techniques can be used to integrate these satellite-based observations into model simulations [29; 30; 31; 32]. In the case of ISBA-A-gs, the joint assimilation of SSM and LAI products can be performed within the SURFEX (SURface Externalisee [33]) modeling platform, over France and on a global scale. The Land Data Assimilation System (LDAS) integrating satellite products in ISBA-A-gs is called LDAS-Monde [34] and produces modeled and analyzed variables (such as SSM, RZSM, LAI, evapotranspiration, and gross primary production (GPP)). Since ISBA-A-gs is not a crop model, it has shortcomings over agricultural areas, and the assimilation has a marked positive impact on the simulations [34].