Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often high percolation, requiring the separate identification of two sources percolated water. An integrated methodology was developed estimate spatio-temporal dynamics with actual crop evapotranspiration (ETc act) being derived from satellite images data processed on Google Earth Engine (GEE) platform. GEE allowed extract time series vegetation indices Sentinel-2 enabling define coefficient (Kc curves based observed lengths growth stages. The stage were then used feed soil water balance model ISAREG, standard Kc values literature; thus, allowing estimation requirements drainage for independent Homogeneous Units Analysis (HUA) at Irrigation Scheme. HUA are defined according crop, type, system. ISAREG previously validated diverse crops plot level showing a good accuracy using measurements farmers’ calendars. Results show that during season, caused 11 ± 3% total percolation. When hotspots associated events corresponded soils low suitability irrigation, cultivated had no influence. However, maize spring vegetables stood out when irrigation. On average, off-season period, averaged 54 6% annual precipitation. spatial aggregation into Scheme scale provided method earth-observation-based accounting requirements, interest user’s association manager, same detection losses by within scheme.