Abstract
Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data.
Highlights
The technological advancement has in recent years boosted the role of remote sensing in the applicative aspect across all geosciences fields
Data from Advanced Microwave Scanning Radiometer 2 (AMSR2) are not featured for Chiese, as they are already contained within the European Space Agency (ESA)-Climate Change Initiative (CCI) passive dataset
An inquiry into the hydrological consistency of different remotely sensed Surface Soil Moisture (SSM) datasets is presented in this work
Summary
The technological advancement has in recent years boosted the role of remote sensing in the applicative aspect across all geosciences fields. Other studies provide a comparison between different remotely sensed SSM datasets: Cui et al [27] tested SSM data from Soil Moisture Active Passive (SMAP), SMOS, Advanced Microwave Scanning Radiometer 2 (AMSR2), and European Space Agency Climate Change Initiative (ESA-CCI), among others, obtaining medium-to-high correlations with ground data (ranging from the 0.48 of AMSR2 to 0.89 of SMAP); El Hadjj et al [28] compared SMOS, SMAP, ASCAT, and Sentinel-1 SSM products, employing on-ground measurements and obtaining slightly better correlation results for SMAP (higher than 0.6) than ASCAT (around 0.5) and SMOS (lower than 0.5). Methodology are compared with standard simple statistical correlation indexes, Pearson and Spearman correlations, to verify the improvements in discerning the relationship between SSM and precipitation and irrigation
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