Abstract
Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.
Highlights
Vegetation indices are often used as a proxy for plant growth
We propose a novel Compact-pol Radar Vegetation Index is defined as
The vegetation indices for different sampling sites are generated from the RADARSAT-2 quad-pol data set
Summary
Vegetation indices are often used as a proxy for plant growth. Recognizing the potential of vegetation indices derived from optical sensors, regional to global products are advocated for operational uses. Similar to the spectral indices that are well established in optical remote sensing, a vegetation index derived from synthetic aperture radar (SAR) data could provide complementary information for crop growth monitoring (van Zyl, 2011; Li, Wang). This information from SAR data is essential when the optical measurements are not practicable considering the cloud cover. In radar remote sensing application, the Radar Vegetation Index (RVI) (Kim, van Zyl) was introduced as a proxy for plant growth. The investigations by Kim et al (2012) by a comparative analysis of RVI with optical-sensor based indices i.e., Normalized Difference Vegetation Index (NDVI) results in a good correlation between these indices
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