Assessing lodging damage of jute crop due to super cyclone Amphan using multi-temporal Sentinel-1 and Sentinel-2 data over parts of West Bengal, India

Environ Monit Assess. 2021 Jul 4;193(8):464. doi: 10.1007/s10661-021-09220-w.

Abstract

The present study is a maiden attempt to assess jute crop lodging due to super cyclone Amphan (20 May 2020) by synergistic use of Sentinel-2 (optical) and Sentinel-1 (SAR) data over part of West Bengal, India. Pre-event Sentinel-2 data (9 April, 14 May) along with the ground information were used to map the jute crop of the affected districts with accuracy of 85%. The cross-polarized backscatter (σ0VH) of Sentinel-1 was found to be sensitive to the sudden change in the canopy structure due to lodging and partial flooding. [Formula: see text](σ0VH_22 May - σ0VH_16 May) indicating post-event damage was > 2.5 dB over the affected jute crop and [Formula: see text] (σ0VH_22 May - σ0VH_28 May) representing post-event recovery showed > 1.5 dB for recovered crop, depending on the crop vigor/height. Decision matrix was prepared combining [Formula: see text] and [Formula: see text] for NDVI-based crop vigor strata (low, medium, and high) to classify the area into affected, marginally affected and normal. Overall accuracy of the classified map was found to be 84.12% with kappa coefficient of 0.74. Nearly, 12.5% of the jute area, i.e., 38,119 ha was found to be either affected or marginally affected due to Amphan and distributed in the southern part of Murshidabad, north-eastern Nadia, northern 24 Paraganas (N), and middle region of Hooghli district. Geospatial map of block-wise affected jute area was prepared to facilitate informed decision making. The study demonstrated an operational methodology for assessing crop lodging due to natural calamities to support relief management and crop insurance.

Keywords: Corchorus; Crop lodging; Crop mapping; Cross-polarized backscatter; Cyclone damage; SAR.

MeSH terms

  • Corchorus*
  • Cyclonic Storms*
  • Environmental Monitoring
  • Floods
  • India