Introduction - The characterization of soil hydraulic properties such as the soil water storage capacity (SWC) is essential in hydrology or agronomy to establish the soil water balance and thus represent the hydrological functioning of a territory and/or the dynamics of a crop. SWC spatial variability is often strong resulting from heterogeneity in texture and structure as well as soil depth. ln situ measurement of SWC is expensive, destructive and cannot be considered over a large area as it requires very large sampling plans. This study aims to develop a method to characterize SWC based on sentinel 2 images, yield map and the STICS model. The challenge is then to analyze how a model such as STICS, which involves a very large number of parameters, can be used in an operational context. This leads to define an inversion strategy that takes the main factors of variation into account. Material and Methods - The study was conducted on durum wheat crops in the Avignon region (South-Eastern France). A set of 7 plots was monitored, 6 of which were cultivated by a farmer equipped with a yield monitoring device and 1 on the INRA research centre. Remote sensing data were acquired by sentinel 2 satellites. The LAI and FAPAR were calculated using a neural network applied to the 2, 4 and 8 bands at the resolution of 10 m. Field observations were made in pits (3 to 5 pits per plot) where soil depth and texture were systematically observed. The parameterization of the soil moisture initialization in STICS model was set up at the beginning of September depending on the previous crop. Prior to the inversion method design, a sensitivity analysis was made using the Morris method considering soil thickness, SWC in unit layer, sowing depth, sowing density, soil initialization (water and nitrogen) and organic nitrogen content. The inversion was done using the GLUE method a Bayesian approach, which allows exploring the parameter field within an a priori distribution. Results - The influe