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Open AccessArticle

Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering

1
Remote Sensing Research Group, Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
2
Earth Observation Data Centre for Water Resources Monitoring (EODC), 1030 Vienna, Austria
3
Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (NRC), 06128 Perugia, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1030; https://doi.org/10.3390/rs10071030
Received: 17 May 2018 / Revised: 16 June 2018 / Accepted: 25 June 2018 / Published: 29 June 2018
Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT’s signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS). View Full-Text
Keywords: soil moisture; SAR; scatterometer; data fusion; scale gap soil moisture; SAR; scatterometer; data fusion; scale gap
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MDPI and ACS Style

Bauer-Marschallinger, B.; Paulik, C.; Hochstöger, S.; Mistelbauer, T.; Modanesi, S.; Ciabatta, L.; Massari, C.; Brocca, L.; Wagner, W. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sens. 2018, 10, 1030. https://doi.org/10.3390/rs10071030

AMA Style

Bauer-Marschallinger B, Paulik C, Hochstöger S, Mistelbauer T, Modanesi S, Ciabatta L, Massari C, Brocca L, Wagner W. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing. 2018; 10(7):1030. https://doi.org/10.3390/rs10071030

Chicago/Turabian Style

Bauer-Marschallinger, Bernhard; Paulik, Christoph; Hochstöger, Simon; Mistelbauer, Thomas; Modanesi, Sara; Ciabatta, Luca; Massari, Christian; Brocca, Luca; Wagner, Wolfgang. 2018. "Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering" Remote Sens. 10, no. 7: 1030. https://doi.org/10.3390/rs10071030

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