Estimates of Irrigation Water Volume by Assimilation of Satellite Land Surface Temperature or Soil Moisture Into a Water‐Energy Balance Model in Morocco

Abstract

Abstract The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in North Africa, which is already strongly impacted by climate change with prolonged drought periods, imposing limitations on irrigation water availability. The objective of this study was to estimate irrigation water use for the irrigation district of Doukkala in Morocco from 2017 to 2022 at daily resolution. The approach is based on the energy‐water balance model FEST‐EWB, which computes continuously in time on a pixel basis the main processes of the hydrological cycle and models evapotranspiration and soil moisture (SM) dynamics in the agricultural soil layer by solving the energy and water mass balance equations. Three different approaches were implemented to quantify actual irrigation volumes: (a) FAO‐approach with the irrigation scheduling based on soil moisture and crop stress thresholds, (b) assimilation of satellite land surface temperature (LST) (downscaled Sentinel‐3 data) and (c) assimilation of satellite soil moisture (SMAP‐Sentinel‐1 data). The model was first calibrated over non‐irrigated areas, against LST from LANDSAT and Sentinel‐3. The three irrigation approaches were then validated against soil moisture and evapotranspiration from reference models (MOD16 and WaPOR). The assimilation of LST gave the best estimates of total irrigation volumes compared to observed water allocation data (relative error = 1.5%). The FAO approach also performed well but slightly overestimated the observed data by 15%. On the other hand, coarse pixel resolution and low revisit time affected the performance of the satellite SM assimilation (relative error of −80%).