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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #379751

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Sentinel-1 soil moisture at 1km resolution: a validation study

item BALENZANO, A. - Consiglio Nazionale Delle Ricerche
item MATTIA, F. - Consiglio Nazionale Delle Ricerche
item SATALINO, G. - Consiglio Nazionale Delle Ricerche
item LOVERGINE, F. - Consiglio Nazionale Delle Ricerche
item PALMISANO, D. - Consiglio Nazionale Delle Ricerche
item PENG, J. - University Of Munich
item MARZAHN, P. - University Of Munich
item WEGMULLER, U. - Collaborator
item CARTUS, O. - Collaborator
item DABROWSKA-ZIELINSKA, K. - Collaborator
item MUSIAL, J. - Collaborator
item DAVIDSON, M.W. - European Space Agency
item Cosh, Michael

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/3/2021
Publication Date: 6/24/2021
Citation: Balenzano, A., Mattia, F., Satalino, G., Lovergine, F., Palmisano, D., Peng, J., Marzahn, P., Wegmuller, U., Cartus, O., Dabrowska-Zielinska, K., Musial, J., Davidson, M.J., Cosh, M.H. 2021. Sentinel-1 soil moisture at 1km resolution: a validation study. Remote Sensing of Environment. 263:112554.

Interpretive Summary: The Sentinel-1 satellite collects C-band synthetic aperture radar information about the earth’s surface. This information is capable of informing soil moisture models and allows for the estimation of surface soil moisture at the surface with reasonable accuracy at a 1 km resolution. This study was conducted to determine the viability of a soil moisture product from the Sentinel-1 satellite. Using a three year study period, an algorithm was developed and validation conducted using a variety of in situ resources across the globe. Comparisons were also made with other satellite soil moisture products with a variety of resolutions. Reasonable correlations and errors were found between this product and in situ resources, and there was good agreement with other satellites. This type of product will be of value for high resolution landscape modelers and decision-makers at a regional scale.

Technical Abstract: This study presents a validation of a pre-operational soil moisture product at 1 km resolution derived from data acquired by the European Radar Observatory Sentinel-1 (S-1). The product consists of an estimate of volumetric soil moisture [m^3/m^3] and its uncertainty [m^3/m^3], both at 1 km. The retrieval algorithm is based on a short term change detection (STCD) approach, taking advantage of the short revisit of S-1 observations. Following the guidelines of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) for methodologies and metrics, the performance of the S-1 soil moisture product was estimated through a direct comparison between 552 S-1 soil moisture images against in situ soil moisture measurements, acquired by 167 ground stations located in Europe, America and Australia, over the period January 2015 to January 2018 (local validation). A cross-comparison was also conducted against NASA Soil Moisture Active Passive (SMAP), ESA Soil Moisture and Ocean Salinity (SMOS), and EUMETSAT Advanced Scatterometer (ASCAT) soil moisture products, with grid spacing 36 km, 25 km and 12.5 km respectively, over a large portion of the Mediterranean basin (~360 × 103 km^2), stretching from Southern Spain to Northern Italy (regional validation). At the local scale, the paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 soil moisture retrieved at 1 km resolution and the in situ point-scale soil moisture observations. The impact of SRE on standard validation metrics, i.e., root mean square error (rmse), Pearson correlation (R) and linear regression, is quantified and experimentally assessed using S-1 and ground soil moisture data collected over a dense hydrologic network (4 - 5 stations/km^2) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the rmse and correlation are ~0.06 m^3/m^3 and 0.72, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km^2, the SRE increases the rmse by ~0.02 m^3/m^3 (70% Confidence Level). Globally, the S-1 soil moisture product is characterized by an intrinsic (i.e., with SRE removed) rmse of ~0.07 m^3/m^3 over the soil moisture range [0.03, 0.60] m^3/m^3 and R of 0.54. A breakdown of the rmse per dry, medium and wet soil moisture ranges is derived and its implications for setting realistic requirements for SAR soil moisture retrieval are discussed. At the regional scale, a temporal correlation analysis between spatially and temporally collocated S-1 and SMOS, SMAP, and ASCAT products show the lowest unbiased rmse (ubrmse) and the highest correlation with SMAP 36 km (i.e, median ubrmse = 0.045 m^3/m^3) and the C-band ASCAT system (median R = 0.72), respectively. Finally, based on the main outcomes, this study identifies specific requirements for validating high-resolution soil moisture products.