Detecting irrigation amount from the integration of remote sensing data in the soil water model

Agriculture is one of the primary consumers of freshwater globally. However, precise data on irrigation water use (IWU) at the regional scale is often lacking, which hampers the development of effective water management plans. This information gap is particularly crucial in water-stressed regions, resulting in significant resource waste. Remote sensing datasets offer a valuable opportunity to monitor irrigation patterns over extended periods at a regional scale. Since irrigation affects both soil moisture (SM) and actual evapotranspiration (ET), increases in SM and ET values following irrigation events can be leveraged to frequently retrieve IWU from remotely sensed data. In this regard, we first developed an irrigation-free soil water model in the root zone to simulate SM dynamics during non-growing periods. We then computed the residuals between the modeled SM and the 9 km root zone SM retrieved from SMAP L3, as well as the residuals between the modeled ET and both 30-m OpenET and 500-m PML, to estimate IWU. We used annual IWU data from Arizona State, USA, in 2017 to examine model performance. The simulated SM by our soil water model showed strong agreement with SMAP, evidenced by R² = 0.68 and RMSE = 0.015 [mm³/mm³]. The estimated IWU using OpenET closely aligned with benchmark data, showing a bias of -17%. However, IWU retrieved by PML led to a much higher bias of -56%, indicating the deficiency of course-resolution ET products in capturing irrigation signals. We further found that over 97 % of the estimated IWU was attributed to ET rather than SM residuals, which is due to SMAP’s low spatial resolution, which limits its ability to resolve farm-scale irrigation volumes.