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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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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