Irrigation detection with Sentinel-1 radar backscatter observations over an agricultural area in the Po River Valley (Italy)

Given the projected decrease in water availability due to climate change and anthropogenic processes, the quantification of water usage for agricultural purposes is of critical importance. However, an accurate quantification of irrigation and groundwater extraction remains a major challenge for the current generation of scientists. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but still suffers from simplified assumptions, such as the mostly unknown timing and quantity of irrigation, often for lack of enough ground-based data. Remote sensing observations offer an opportunity to fill this gap in our knowledge, as they will detect irrigation activities. Earlier studies have used satellite soil moisture products obtained from microwave sensors to detect irrigated areas, but only some studies have dealt with the quantification of irrigation using satellite soil moisture data.

The aim of this study is to investigate the ability of high-resolution Sentinel-1 observations to detect changes in soil moisture and vegetation caused by irrigation fluxes. The focus area is the Po river Valley, one of the most important agricultural areas in Northern Italy, where in situ data are available for evaluation at four pilot sites. A comparison of Level-2 Sentinel-1 soil moisture retrievals, in situ data and Noah-MP land surface model (LSM) estimates confirms the presence of irrigation at the pilot sites. However, we hypothesize that even more information on both the irrigated soil moisture and vegetation can be extracted from the Level-1 Sentinel-1 signal via backscatter data assimilation. To prepare for such an assimilation system, Level-1 Sentinel-1 backscatter observations, pre-processed to the 1 km EASE-v2 grid, are further compared to the total backscatter simulated by a Water Cloud Model, using the simulated soil moisture obtained by the Noah-MP LSM as part of the NASA Land Information System (LIS). Noah-MP is here selected for its ability to simulate dynamic vegetation. Our results will show that irrigation can indeed also be detected from the mismatch between simulated and observed backscatter values.