Monitoring Anthropogenic Irrigation Water Use by assimilating satellite land surface temperature and soil moisture

The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in Northern Africa, which is already strongly impacted by climate change with prolonged drought periods, imposing limitation to irrigation water availability. The objective of this study was to develop a procedure for the monitoring of anthropogenic irrigation water use for the irrigation districts of Doukkala in Morocco, from 2013 to 2022.

The system is based on the energy-water balance model FEST-EWB, which is an agro-hydrologic pixel wise model that computes continuously in time the main processes of the hydrological cycle where evapotranspiration and soil moisture behaviour in agricultural soil layer are modelled solving the energy and water mass balance model (EWB).

Firstly, the model has been calibrated and validated over non-irrigated areas, against satellite land surface temperature from LANDSAT and downscaled Sentinel3 data at 30m of spatial resolution, and evapotranspiration from MOD16, GLEAM and FAOWapor. The model has been run using as input the past observed meteorological forcings (ECMWF ERA5-Land) and vegetation data. From the pixel-by-pixel comparison between modelled and observed LST, a mean absolute difference of 3.5 °C is obtained over the period 2017-2022 for the whole Doukkala area.

The second step refers to the historical estimates of the actual irrigation volumes through the calibrated model implementing three different irrigation strategy, at hourly scale and at 30m of spatial resolution: the FAO approach based on soil moisture (SM) and crop stress thresholds (Allen et al., 1998), the separate and joint assimilation of satellite land surface temperature (downscaled Sentinel3 data) and of satellite soil moisture (1km SMAP-Sentinel1) to update the modeled fluxes and estimates irrigation volume. Overall, the results suggested that the yearly total irrigation volumes modeled with the FAO approach are quite in agreement with the observed water allocations; and similar outcomes are obtained when the joint assimilation of satellite LST and SM is implemented which allows to overcome the problems related to the number of available satellite images, which could lead to missing irrigation events.