How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture?

In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series.

While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts.