Analysis of the Radar Vegetation Index and Potential Improvements

Christoph Szigarski,Thomas Jagdhuber,Marie Parrens,Christian Thiel,Dara Entekhabi,Maria Piles,Martin Baur,Jean-Pierre Wigneron

doi: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.

Highlights

Mapping the density of woody vegetation cover at large scales is required in applications such as estimating terrestrial carbon stocks
Remote sensing of vegetation cover has mainly been based on measurements in visible, near infrared and shortwave bands [1,5,6]
Active and passive microwave remote sensing operates in a wavelength range between 1 mm and 1 m

Summary

Introduction

Mapping the density of woody vegetation cover at large scales is required in applications such as estimating terrestrial carbon stocks. Microwave remote sensing is independent from solar illumination and penetrates areas covered by clouds, haze and rain (study frequency: L-band at about 1.3 GHz [7]) [6,8]. They allow gap-free data streams for vegetation mapping. While optical methods mostly sense surface and top of canopy conditions, microwaves can penetrate into the vegetation canopy especially at longer wavelengths (e.g., L-band at about 21 cm wavelength) In this regard, observations in the microwave spectrum provide information related to plant moisture content and structure and offer a distinct view on plant conditions [5]. We analyze these characteristics of the RVI index and introduce two improved formulations using a multi-sensor (active-passive microwave) approach

Test Sites

Modelling and Retrieval of Standard and Improved RVI