A Radar vegetation index for crop monitoring using compact polarimetric sar data

Citation

Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y.S., Frery, A.C. (2020). A Radar vegetation index for crop monitoring using compact polarimetric sar data. IEEE Transactions on Geoscience and Remote Sensing, [online] 58(9), 6321-6335. http://dx.doi.org/10.1109/TGRS.2020.2976661

Plain language summary

The Canadian RADARSAT Constellation Mission (RCM) satellites implement a new radar imaging mode called Compact Polarimetry (CP). This mode offers an ability to synthesize many Synthetic Aperture Radar (SAR) parameters, over large geographical extents. CP will be an important imaging mode to monitor Canadian agriculture. This study develops an radar scattering index for CP data (compact-pol radar vegetation index or CpRVI). The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix (from the radar imagery) and the Kennaugh matrix of an ideal depolarizer from a vegetation canopy. The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. The CpRVI was tested using RADARSAT-2 data, simulated to CP, for a site in Manitoba (Canada). Wheat and soybean crops were the focus of this study, given that these crops have very different crop structures. The CpRVI was calculated over time and compared to crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages. The CpRVI tracks the plant growth for both wheat and soybean canopies. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. A linear regression analysis confirms that the CpRVI values are significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. Given the importance of CP imagery expected from RCM, this study provides a method to estimate crop growth using space based radar imagery.

Abstract

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI ( r = 0.72 and 0.85) and VWC ( r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

Publication date

2020-09-01

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