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Analysis of the Radar Vegetation Index and Potential Improvements

Szigarski, Christoph and Jagdhuber, Thomas and Baur, Martin and Thiel, Christian and Parrens, Marie and Wigneron, Jean-Pierre and Piles, Maria and Entekhabi, Dara (2018) Analysis of the Radar Vegetation Index and Potential Improvements. Remote Sensing, 10 (11), p. 1776. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs10111776 ISSN 2072-4292

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Official URL: http://dx.doi.org/10.3390/rs10111776

Abstract

The Radar Vegetation Index (RVI) is a well-established microwave metric of vegetation cover. The index utilizes measured linear scattering intensities from co- and cross-polarization and is normalized to ideally range from 0 to 1, increasing with vegetation cover. At long wavelengths (L-band) microwave scattering does not only contain information coming from vegetation scattering, but also from soil scattering (moisture & roughness) and therefore the standard formulation of RVI needs to be revised. Using global level SMAP L-band radar data, we illustrate that RVI runs up to 1.2, due to the pre-factor in the standard formulation not being adjusted to the scattering mechanisms at these low frequencies. Improvements on the RVI are subsequently proposed to obtain a normalized value range, to remove soil scattering influences as well as to mask out regions with dominant soil scattering at L-band (sparse or no vegetation cover). Two purely vegetation-based RVIs (called RVII and RVIII), are obtained by subtracting a forward modeled, attenuated soil scattering contribution from the measured backscattering intensities. Active and passive microwave information is used jointly to obtain the scattering contribution of the soil, using a physics-based multi-sensor approach; simulations from a particle model for polarimetric vegetation backscattering are utilized to calculate vegetation-based RVI-values without any soil scattering contribution. Results show that, due to the pre-factor in the standard formulation of RVI the index runs up to 1.2, atypical for an index normally ranging between zero and one. Correlation analysis between the improved radar vegetation indices (standard RVI and the indices with potential improvements RVII and RVIII) are used to evaluate the degree of independence of the indices from surface roughness and soil moisture contributions. The improved indices RVII and RVIII show reduced dependence on soil roughness and soil moisture. All RVI-indices examined indicate a coupled correlation to vegetation water content (plant moisture) as well as leaf area index (plant structure) and no single dependency, as often assumed. These results might improve the use of polarimetric radar signatures for mapping global vegetation.

Item URL in elib:https://elib.dlr.de/131221/
Document Type:Article
Title:Analysis of the Radar Vegetation Index and Potential Improvements
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Szigarski, ChristophUNSPECIFIEDUNSPECIFIED
Jagdhuber, ThomasUNSPECIFIEDUNSPECIFIED
Baur, MartinUNSPECIFIEDUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Parrens, MarieUNSPECIFIEDUNSPECIFIED
Wigneron, Jean-PierreUNSPECIFIEDUNSPECIFIED
Piles, MariaUNSPECIFIEDUNSPECIFIED
Entekhabi, DaraUNSPECIFIEDUNSPECIFIED
Date:9 November 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.3390/rs10111776
Page Range:p. 1776
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:microwaves; radiometer; radar; vegetation index; soil scattering; roughness; soil moisture; SMAP; SMOS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Thiel, Christian
Deposited On:11 Dec 2019 10:22
Last Modified:14 Dec 2019 04:27

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