Title: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles
Language: English
Authors: Bauer-Marschallinger, Bernhard  
Paulik, Christoph 
Modanesi, Sara 
Schaufler, Stefan 
Freeman, Vahid 
Cao, Senmao 
Stachl, Tobias 
Massari, Christian  
Wagner, Wolfgang  
Ciabatta, Luca  
Brocca, Luca  
Category: Original Research Article
Issue Date: 2019
Citation: 
Bauer-Marschallinger, B., Paulik, C., Modanesi, S., Schaufler, S., Freeman, V., Cao, S., Stachl, T., Massari, C., Wagner, W., Ciabatta, L., & Brocca, L. (2019). Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2858004
Journal: IEEE Transactions on Geoscience and Remote Sensing 
ISSN: 0196-2892
Abstract: 
Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.
Keywords: Change detection algorithms; Copernicus; image sampling; Sentinel-1; soil moisture
DOI: 10.1109/TGRS.2018.2858004
Library ID: AC15135580
URN: urn:nbn:at:at-ubtuw:3-3721
Organisation: E120 - Department für Geodäsie und Geoinformation 
Publication Type: Article
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