Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHC year_I,pixel_j , corresponding to minimal difference between observed and synthetic LAX year_I,pixel_j , is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAX year_I . Results show that simulated and synthetic LAX year_I,allpixels obtained from SWHC year_I,allpixels are close and accurately fit the observed LAX year_I,allpixels (RMSE = 0.05 m 2 /m 2 to 0. SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m 2 /m 2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

35Agriculture generates important impacts on the environment, which can be evaluated with 36 agri-environmental indicators. A key element of environment protection in agriculture is the 37 maintenance of a dense soil cover for the longest possible period. Notably, soil cover is 38 known to diminish erosion risks and nitrate leaching. In this study, an agri-environmental 39 indicator for soil cover is presented, which integrates data from the crop model STICS to 40 quantify vegetation growth dynamics. Simulations were conducted with STICS for the major 41 crops cultivated in Switzerland across several contrasting pedoclimatic situations. They were 42 then integrated with data for crop residue cover to evaluate soil cover at the field and farm 43 levels in the framework of a farm network survey. At the field level, for the period from the 44 harvest of the previous crop through the harvest of the main crop, the highest soil cover was 45 achieved by silage maize and winter barley. A high variability between fields was observed, 46 due to the diversity of cultural practices during the period preceding the seeding of the main 47