Comparing 1-km Sentinel-1 surface soil moisture with coarser-resolution satellite data for agricultural drought monitoring in Mediterranean regions

Agricultural drought is an extreme event that threatens crop production, particularly in the Mediterranean region. To manage its cascading impacts sustainably, this study aims to evaluate the potential of a 1-km surface soil moisture (SSM) product and to intercompare it with SSM products at coarser spatial resolutions in order to identify periods of drought. The study covered two Mediterranean sites: the Occitanie region in France and several bioclimatic regions in Tunisia. The SSM dataset used in this paper is produced from Sentinel-1 and Sentinel-2 data based on a 100 m resolution land cover map (CGLS-LC100), then aggregated to 1 km (hereafter referred to as HRSM). First, the performance of the HRSM product was compared to that of two other SSM products: the Soil Moisture Active Passive (SMAP) and the European Space Agency Climate Change Initiative (ESA CCI). The results show an overall good coherence between the different time series of the HRSM and the other two SSM products over France and Tunisia. However, HRSM and SMAP were more correlated compared to HRSM and CCI SSM. The intercomparison of products based on different bioclimatic regions in Tunisia shows that over the humid and subhumid regions, the best agreement was observed between the CCI SSM and HRSM time series. Second, to identify drought events, the ESA CCI root zone soil moisture (RZSM) at a depth of 1 m was also used as confirmation of agricultural drought. The monthly averages of SSM and RZSM were used to compute a drought index. The results show that the drought events identified by the SSM drought index are also observed with the RZSM drought index. Among the SSM products, HRSM has the advantage of identifying drought events only in grasslands and agricultural fields. This is ensured by its native high spatial resolution, which allows non-agricultural and non-grassland fields to be masked before being aggregated to 1 km. However, thanks to their high revisit frequency, the CCI SSM and SMAP are better able than HRSM to capture the dynamics of SSM in response to rainfall. They therefore make it more efficient to identify all drought events. Consequently, the proposed identification method in this study is sensitive to the availability of SSM products and the revisit time required to sufficiently cover rainfall events and different hydrological processes.