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

Abstract

While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects, such as groundwater depletion and soil salinization. As detailed knowledge about location, timing and amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved to be a convenient means to fill this gap. However, the spatial resolution and/or the revisit time of satellite products represent a major limitation to accurately estimating irrigation. In this work, we systematically and quantitatively assess the impact of the spatio-temporal resolution of soil moisture observations on the reliability of the retrieved irrigation information, i.e., timing and water amounts. Through a synthetic experiment based on soil moisture timeseries simulated by a hydrological model, we evaluate first the individual and then the combined impact of varying spatial and temporal resolution on both the detection and quantification accuracy. Furthermore, we investigate the effect of instrument noise typical of current satellite sensors, i.e., retrieval error, and irrigation rate, i.e., irrigation system and/or farmer’s decision on how much to irrigate. Satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson’s R > 0.8), are found with soil moisture temporal samplings up to 3 days, or irrigated fractions as low as 30%, i.e., at least one-third of the pixel covers the irrigated field(s). Although lower spatial and temporal resolutions lead to a decrease in detection and quantification accuracy, the presence of random noise in the soil moisture timeseries has a more significant negative impact. As expected, better performances are found when higher volumes of irrigation water reach the soil. Finally, we show that current high-resolution satellite soil moisture products (e.g., from Sentinel-1) agree significantly better with model simulations forced with irrigation compared to rainfed simulations. On the other hand, coarse-scale products achieve higher correlations with soil moisture simulated without irrigation. Hence, our analysis highlights the potential for employing Sentinel-1 derived soil moisture for field-scale irrigation monitoring.