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Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study

1
Department of Geographical Sciences, University of Maryland–College Park, College Park, MD 20740, USA
2
Faculty of Environmental Sciences, TU Dresden, 01062 Dresden, Germany
3
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 551; https://doi.org/10.3390/rs12030551
Received: 31 December 2019 / Revised: 27 January 2020 / Accepted: 4 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes. View Full-Text
Keywords: surface soil moisture; regional scale; vegetation traits; multi-sensor approach; wetland; environmental monitoring surface soil moisture; regional scale; vegetation traits; multi-sensor approach; wetland; environmental monitoring
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Liang, M.; Pause, M.; Prechtel, N.; Schramm, M. Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study. Remote Sens. 2020, 12, 551.

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