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Updated: Aug 20, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Crop classification by using dual-pol SAR vegetation indices derived from Sentinel-1 SAR-C data.

Deeksha Mishra1, Gunjan Pathak2, Bhanu Pratap Singh2

  • 1GIS Lab - Gurugram Node, Haryana Space Applications Centre (HARSAC), Hisar, Haryana, India. dmishra583@gmail.com.

Environmental Monitoring and Assessment
|November 17, 2022
PubMed
Summary

This study uses Sentinel-1 SAR data for rainy season crop classification in India. The developed SVIDP index accurately distinguishes crop types, achieving over 93% accuracy with RF and SVM classifiers.

Keywords:
PaddyRFSVIDPSVMSentinel-1 SAR

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Area of Science:

  • Earth Observation
  • Agricultural Science
  • Remote Sensing

Background:

  • Accurate crop classification is vital for agricultural monitoring and food security.
  • Time-series Sentinel-1 Synthetic Aperture Radar (SAR) data offers consistent monitoring capabilities, even through cloud cover.

Purpose of the Study:

  • To classify rainy season crops using time-series Sentinel-1 SAR data.
  • To evaluate the effectiveness of the SVIDP index for differentiating crop types.

Main Methods:

  • Analysis of Sentinel-1 SAR data (dual-pol VV and VH bands) from May to September 2020.
  • Utilization of the SVIDP index, incorporating NRPB, DPDD, IDPDD, and VDDPI ratios.
  • Application of Random Forest (RF) and Support Vector Machine (SVM) classifiers for crop classification.

Main Results:

  • High classification accuracies were achieved: 93.77% for RF and 93.50% for SVM.
  • The IDPDD index demonstrated high sensitivity to crop variations, showing strong correlations with SAR bands and other indices.
  • The IDPDD index showed minimal correlation with water bodies, aiding in their differentiation.

Conclusions:

  • Time-series Sentinel-1 SAR data combined with the SVIDP index is effective for rainy season crop classification.
  • The IDPDD index is a valuable component for distinguishing between crop types and water bodies.