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Crop LAI and Biomass Estimation from Different Polarization Modes of Simulated NISAR Data

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Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries

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Abstract

This study investigates the performances of three radar vegetation indices derived from full (HH-VH-VV), compact (RH-RV), and dual (VV-VH or HH-HV) polarimetric Synthetic Aperture Radar (SAR) data for Leaf Area Index (LAI) and biomass estimation. We use the notion of a geodesic distance between the incoherent representation of radar measurements and suitable volume scattering models to derive the full and compact polarimetric indices, i.e., the Generalized Radar Vegetation Index (GRVI) and Compact-pol Radar Vegetation Index (CpRVI). Independently, we have also introduced a radar vegetation index (DpRVI) for measurements in dual-pol utilizing the degree of polarization (DoP) of scattered waves. We evaluate these indices with particular attention for the NASA-ISRO SAR (NISAR) L-band SAR system. For this purpose, we simulate NISAR data from the L-band full polarimetric UAVSAR data, which were acquired over the test site at Winnipeg in Canada during the SMAPVEX12 campaign. The radar-derived vegetation indices at different phenological stages of canola affirm a good correlation with biophysical variables. Subsequently, we employed linear and non-linear regression models to estimate LAI and biomass. The correlation analysis indicates that GRVI derived LAI and biomass have the lowest RMSE and high R2 values compared to CpRVI and DpRVI. The DpRVI in VV-VH mode also provided promising accuracy for these biophysical parameter retrievals.

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Notes

  1. 1.

    https://uavsar.jpl.nasa.gov/science/documents/nisar-sample-products.html

  2. 2.

    During the simulation of 2 × 2 covariance matrix C2, the ellipticity of transmitted wave y = 45°, and the right-hand circular condition is considered (Kumar et al., 2017)

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Acknowledgments

The authors would like to thank the NASA/JPL for providing simulated NISAR data products. The SMAPVEX12 team members are also appreciated for proving the in-situ measurements through NSIDC User Services. Also, the authors acknowledge the GEO-AWS Earth Observation Cloud Credits Program, which supported the computation on the AWS cloud platform through the project: "AWS4AgriSAR-Crop inventory mapping from SAR data on the cloud computing platform."

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Authors and Affiliations

  1. Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India

    Dipankar Mandal, Avik Bhattacharya & Y. S. Rao

  2. Department of Water Resources, Delft University of Technology, Delft, The Netherlands

    Vineet Kumar

Authors
  1. Dipankar Mandal
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  2. Vineet Kumar
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  3. Avik Bhattacharya
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  4. Y. S. Rao
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Editor information

Editors and Affiliations

  1. Remote Sensing Scientist, NASA Marshall Space Flight Center, Hunstville, Alabama, USA, University of Maryland, College Park, MD, USA

    Krishna Prasad Vadrevu

  2. Scientist, Centre d’Etudes Spatiales de la Biosphere (CESBIO), Toulouse, France

    Thuy Le Toan

  3. Scientist, Mahalanobis National Crop Forecast Centre, New Delhi, India

    Shibendu Shankar Ray

  4. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Chris Justice

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Mandal, D., Kumar, V., Bhattacharya, A., Rao, Y.S. (2022). Crop LAI and Biomass Estimation from Different Polarization Modes of Simulated NISAR Data. In: Vadrevu, K.P., Le Toan, T., Ray, S.S., Justice, C. (eds) Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries. Springer, Cham. https://doi.org/10.1007/978-3-030-92365-5_13

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