A simple framework to calibrate a soil water balance model with Sentinel-1 and Sentinel-2 observations over irrigated fields

This study presents a framework to calibrate a combined soil water balance (SWB) model and Water Cloud Model (WCM) with Sentinel-1 backscatter observations. The SWB is coupled with WCM, which can simulate backscatter from soil moisture (SM) and Normalized Difference Vegetation Index (NDVI). The combined model, namely SWB(WCM), is calibrated by maximizing the Kling-Gupta Efficiency (KGE) between simulated backscattering values and observations from Sentinel-1. The procedure is carried out over data collected during a field campaign in 2017 at an experimental site in Budrio (BO), Italy, cultivated with tomato. The calibration scheme involves 7 parameters and presents good results in terms of backscatter calibration (KGE = 0.69). To evaluate the overall performance of the model, SM estimates from the SWB model are compared with in-situ SM measurements from a Proximal Gamma Ray Station (PGRS), showing promising results (KGE = 0.58) in the estimation of soil moisture, without requiring any in-situ soil moisture measurements for calibration.