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Title (Primary) Sentinel-1 soil moisture at 1 km resolution: a validation study
Author Balenzano, A.; Mattia, F.; Satalino, G.; Lovergine, F.P.; Palmisano, D.; Peng, J.; Marzahn, P.; Wegmüller, U.; Cartus, O.; Dąbrowska-Zielińska, K.; Musial, J.P.; Davidson, M.W.J.; Pauwels, V.R.N.; Cosh, M.H.; McNairn, H.; Johnson, J.T.; Walker, J.P.; Yueh, S.H.; Entekhabi, D.; Kerr, Y.H.; Jackson, T.J.;
Journal Remote Sensing of Environment
Year 2021
Department RS;
Volume 263
Language englisch;
Keywords Soil moisture; High resolution; Sentinel-1; Synthetic Aperture Radar (SAR); Spatial representativeness error (SRE); Validation
Abstract 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 Θ [m3/m3] and its uncertainty [m3/m3], 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 Θ product is estimated through a direct comparison between 1068 S-1 Θ images against in situ Θ 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 Θ retrieved at 1 km resolution and the in situ point-scale Θ 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 Θ data collected over a dense hydrologic network (4 − 5 stations/km2) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the RMSE and correlation are ~0.06 m3/m3 and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km2, the SRE increases the RMSE by ~0.02 m3/m3 (70% Confidence Level). Globally, the S-1 Θ product is characterized by an intrinsic (i.e., with SRE removed) RMSE of ~0.07 m3/m3 over the Θ range [0.03, 0.60] m3/m3 and R of 0.54. A breakdown of the RMSE per dry, medium and wet Θ ranges is also derived and its implications for setting realistic requirements for SAR-based Θ retrieval are discussed together with recommendations for the density of in situ Θ observations.
ID 24825
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=24825
Balenzano, A., Mattia, F., Satalino, G., Lovergine, F.P., Palmisano, D., Peng, J., Marzahn, P., Wegmüller, U., Cartus, O., Dąbrowska-Zielińska, K., Musial, J.P., Davidson, M.W.J., Pauwels, V.R.N., Cosh, M.H., McNairn, H., Johnson, J.T., Walker, J.P., Yueh, S.H., Entekhabi, D., Kerr, Y.H., Jackson, T.J. (2021):
Sentinel-1 soil moisture at 1 km resolution: a validation study
Remote Sens. Environ. 263 , art. 112554