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

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...

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Main Authors: Mengyu Liang, Marion Pause, Nikolas Prechtel, Matthias Schramm
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/3/551
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spelling doaj-9957e72a42294b049f23a344674eed592020-11-25T02:36:04ZengMDPI AGRemote Sensing2072-42922020-02-0112355110.3390/rs12030551rs12030551Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case StudyMengyu Liang0Marion Pause1Nikolas Prechtel2Matthias Schramm3Department of Geographical Sciences, University of Maryland–College Park, College Park, MD 20740, USAFaculty of Environmental Sciences, TU Dresden, 01062 Dresden, GermanyFaculty of Environmental Sciences, TU Dresden, 01062 Dresden, GermanyDepartment of Geodesy and Geoinformation, TU Wien, 1040 Vienna, AustriaSurface 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.https://www.mdpi.com/2072-4292/12/3/551surface soil moistureregional scalevegetation traitsmulti-sensor approachwetlandenvironmental monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Mengyu Liang
Marion Pause
Nikolas Prechtel
Matthias Schramm
spellingShingle Mengyu Liang
Marion Pause
Nikolas Prechtel
Matthias Schramm
Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
Remote Sensing
surface soil moisture
regional scale
vegetation traits
multi-sensor approach
wetland
environmental monitoring
author_facet Mengyu Liang
Marion Pause
Nikolas Prechtel
Matthias Schramm
author_sort Mengyu Liang
title Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
title_short Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
title_full Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
title_fullStr Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
title_full_unstemmed Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study
title_sort regionalization of coarse scale soil moisture products using fine-scale vegetation indices—prospects and case study
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-02-01
description 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.
topic surface soil moisture
regional scale
vegetation traits
multi-sensor approach
wetland
environmental monitoring
url https://www.mdpi.com/2072-4292/12/3/551
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