Crop RS-Met: A soil water content model for crop fields driven by high temporal satellite vegetation index

Yield production of rainfed crops in dryland regions is mostly determined by the available water in the soil, which may vary greatly across the field area and along the season. Thus, continuous spatial and temporal estimations of soil water content are essential for irrigation management and proper decision making in these fields. In-situ measurements of soil water content lack this spatial dimension, while interpolating techniques may result in large uncertainties. Here, we use a biophysical model combining remote sensing and meteorological information (Crop RS-Met) to assess root-zone soil water content across wheat fields. The model, which is based on the dual FAO56 formulation uses a water deficit factor calculated from rainfall and atmospheric demand information to constrain actual evapotranspiration and soil water content in crops growing under dry conditions. Crop RS-Met spatial assessment is driven by the spatial resolution of the remote sensing spectral-based information. In this study, we test Crop RS-Met with spectral-based vegetation index derived from different satellite platforms, including the recently lunched Israeli-French Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) and Sentinel2. Results are compared with Crop RS-Met using proximal sensing and in-situ soil water content measurements in a dryland experimental wheat field. Correlations with grain yield will be tested after harvest, followed by a comparison between the spatial and temporal advantages of the different platforms.