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

REMOTE SENSING OF ENVIRONMENT

卷:263

Article

Balenzano, Anna¹  Mattia, Francesco¹  Satalino, Giuseppe¹  Lovergine, Francesco P.¹  Palmisano, Davide¹  Peng, Jian^2,13,14  Marzahn, Philip²  Wegmuller, Urs³  Cartus, Oliver³  Dabrowska-Zielinska, Katarzyna⁴  Musial, Jan P.⁴  Davidson, Malcolm W. J.⁵  Pauwels, Valentijn R. N.⁶  Cosh, Michael H.⁷  McNairn, Heather⁸  Johnson, Joel T.⁹  Walker, Jeffrey P.⁶  Yueh, Simon H.¹⁰  Entekhabi, Dara¹¹  Kerr, Yann H.¹²  Jackson, Thomas J.⁷ 

[1] CNR, Natl Res Council Italy, Inst Electromagnet Sensing Environm IREA, Uos Bari, Italy

[2] Ludwig Maximilians Univ Munchen LMU, Dept Geog, Munich, Germany

[3] Gamma Remote Sensing Res & Consulting AG GAMMA, Gumlingen, Switzerland

[4] Inst Geodesy & Cartog IGiK, Remote Sensing Ctr, Warsaw, Poland

[5] European Space Agcy, Mission Sci Div, Noordwijk, Netherlands

[6] Monash Univ, Dept Civil Engn, Clayton, Vic, Australia

[7] USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD USA

[8] Agr & Agri Food Canada AAFC, Ottawa, ON, Canada

[9] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA

[10] CALTECH, Jet Prop Lab JPL, Pasadena, CA USA

[11] MIT, Civil & Environm Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[12] Ctr Etud Spatiales BIOsphere CESBIO, Toulouse, France

[13] UFZ Helmholtz Ctr Environm Res, Dept Remote Sensing, Leipzig, Germany

[14] Univ Leipzig, Remote Sensing Ctr Earth Syst Res, Leipzig, Germany

关键词: Soil moisture;    High resolution;    Sentinel-1;    Synthetic Aperture Radar (SAR);    Spatial representativeness error (SRE);    Validation;   

DOI  :  10.1016/j.rse.2021.112554

来源: Elsevier

【 摘 要 】

This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. The product consists of an estimate of surface soil volumetric water content Theta [m(3)/m(3)] and its uncertainty [m(3)/m(3)], both at 1 km. The retrieval algorithm relies on a time series based Short Term Change Detection (STCD) approach, taking advantage of the frequent revisit of the S-1 constellation that performs C-band Synthetic Aperture Radar (SAR) imaging. The performance of the S-1 Theta product is estimated through a direct comparison between 1068 S-1 Theta images against in situ Theta measurements acquired by 167 ground stations located in Europe, America and Australia, over 4 years between January 2015 and December 2020, depending on the site. The paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 Theta retrieved at 1 km resolution and the in situ point-scale Theta 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 Theta 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 similar to 0.06 m(3)/m(3) and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km(2), the SRE increases the RMSE by similar to 0.02 m(3)/m(3) (70% Confidence Level). Globally, the S-1 Theta product is characterized by an intrinsic (i.e., with SRE removed) RMSE of similar to 0.07 m(3)/m(3) over the Theta range [0.03, 0.60] m(3)/m(3) and R of 0.54. A breakdown of the RMSE per dry, medium and wet Theta ranges is also derived and its implications for setting realistic requirements for SAR-based Theta retrieval are discussed together with recommendations for the density of in situ Theta observations.

【 授权许可】

Free