Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Analysis of the Radar Vegetation Index and Potential Improvements

View through CrossRef
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.
Title: Analysis of the Radar Vegetation Index and Potential Improvements
Description:
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.

Related Results

Housing Improvements for Health and Associated Socio‐Economic Outcomes: A Systematic Review
Housing Improvements for Health and Associated Socio‐Economic Outcomes: A Systematic Review
Poor housing is associated with poor health. This suggests that improving housing conditions might lead to improved health for residents. This review searched widely for studies fr...
Impact of vegetation control measures on the bedform of braided gravel-bed river
Impact of vegetation control measures on the bedform of braided gravel-bed river
<p>Braiding is among the most dynamic landscape on Earth. It provides diverse habitats for freshwater creatures. Unfortunately, the number of braided rivers is reduci...
Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang
Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang
Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused o...
Open areas in patchy ecosystems: key spaces for vegetation survival.
Open areas in patchy ecosystems: key spaces for vegetation survival.
<p>Drylands are one of the largest biomes over the Earth, covering around 40% of land surface. These are water limited ecosystems where vegetation occupies the most f...
Metallized Plastic Waveguide Antenna Solutions for Next-Generation Automotive Radar Systems
Metallized Plastic Waveguide Antenna Solutions for Next-Generation Automotive Radar Systems
The automotive industry has significantly focused on developing reliable driving assistance systems, with radar sensors emerging as key components for autonomous driving, thanks to...
Realization and Prediction of Ecological Restoration Potential of Vegetation in Karst Areas
Realization and Prediction of Ecological Restoration Potential of Vegetation in Karst Areas
Based on the vegetation ecological quality index retrieved by satellite remote sensing in the karst areas of Guangxi in 2000–2019, the status of the ecological restoration of the v...
A vegetation classi?cation and map: Guadalupe Mountains National Park
A vegetation classi?cation and map: Guadalupe Mountains National Park
A vegetation classi?cation and map for Guadalupe Mountains National Park (NP) is presented as part of the National Park Service Inventory & Monitoring - Vegetation Inventory Pr...
RADAR-Pipeline: Scalable Feature Generation for Mobile Health Data
RADAR-Pipeline: Scalable Feature Generation for Mobile Health Data
Introduction & BackgroundRADAR-Pipeline is an open-source Python framework designed to simplify and enhance mobile health data analysis. It has been designed to efficiently rea...

Back to Top