Double-scale analysis on the detectability of irrigation signals from remote sensing soil moisture over an area with complex topography in central Italy

Despite a detailed knowledge of the spatial-temporal dynamics of irrigation being necessary to optimize the agricultural production without exacerbating the pressure exercised on the water resource, such information is still often lacking worldwide. In this study, a double-scale analysis on the detectability of the irrigation occurrence over an area in central Italy through remote sensing soil moisture is proposed; the period of interest is a 3-year time span from 2017 to 2019. The detectability of district- or sub-district-scale irrigation signals through remotely sensed soil moisture data is investigated at two different spatial resolutions: 1 km and plot scale. Three soil moisture products sampled at 1 km resolution are evaluated: a DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled version of SMAP (Soil Moisture Active Passive) and two Sentinel-1-derived products, namely the 1 km version delivered by Copernicus and a plot-scale-born version developed by THEIA and aggregated at 1 km. The THEIA Sentinel-1 product aggregated at 100 m is used in the plot-scale analysis. Over the study area, the irrigation extent is determined by the fragmentation of the agricultural fields and the complex topography, making the adoption of plot-scale data necessary. Satisfactory results are obtained by comparing maps of irrigated areas at 100 m spatial resolution produced through the k-means clustering algorithm with ground-truth data, since the method fails only once out of seven in properly reproducing the irrigated or non-irrigated conditions occurred over four pilot agricultural fields.

Automated summary

This study proposes a double-scale analysis on the detectability of irrigation occurrence in central Italy using remote sensing soil moisture. The study finds that despite a lack of detailed information on irrigation worldwide, irrigation signals can be detected through remotely sensed soil moisture data at different spatial resolutions, with plot-scale data being necessary.