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<p><strong>Abstract.</strong> Bacterial wilt disease (pathogen: <i>Pseudomonas solancearum</i>) is a major problem affecting brinjal crop. Infected leaves show yellowing, loss in turgidity, drying and ultimately the entire plant collapses. The study aims to examine the potential of hyperspectral remote sensing for detection of biotic stress caused due to bacterial wilt disease and identify best spectral band widths and hyperspectral indices indicative of disease infestation. This study was conducted in a farmer’s plot at Alampur in Baruipur block, South 24 Pargana district, West Bengal. Canopy spectra (using ASD Fieldspec 2 Spectroradiometer), chlorophyll content (by Chlorophyll meter) and Leaf Area Index (LAI) (by plant canopy imager) were collected. The healthy plants had green and fully turgid leaves whereas diseased plants had lower chlorophyll content and LAI. The reduction in chlorophyll content lowered reflectance in green region and internal leaf damage in near-infrared region. A correlation analysis was carried out between reflectance at specific bandwidths and hyperspectral indices with chlorophyll content and LAI of healthy and stressed plants. Bandwidths of 528&amp;ndash;531&amp;thinsp;nm, 550&amp;ndash;570&amp;thinsp;nm, 710&amp;ndash;760&amp;thinsp;nm, and single bands such as 800&amp;thinsp;nm and 920&amp;thinsp;nm and indices viz. Greenness index, Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Triangular Vegetation Index (TVI), Simple Ratio Pigment Index (SRPI), Photochemical Reflectance Index (PRI 2), Lichtenthaler Indices (LIC1, LIC2), Structure Intensive Pigment Index (SIPI) etc. were found to have strong positive correlation (R²&amp;thinsp;&amp;gt;&amp;thinsp;0.9) with plant parameters. These specific bandwidths and indices can be helpful in biophysical parameter estimation and early detection of crop stress, crop growth and disease monitoring.</p>

Crop production forecasting is essential for various economic policy and decision making. There is a very successful operational programme in the country, called FASAL, which uses multiple approaches for pre-harvest production forecasting.  With the increase in the frequency of extreme events and their large-scale impact on agriculture, there is a strong need to use remote sensing technology for assessing the impact.  Various works have been done in this direction. This article provides three such case studies, where remote sensing along with other data have been used for assessment of flood inundation of rice crop post Phailin cyclone, period operational district/sub-district level drought assessment and understanding the impact of recent hailstorm/unseasonal rainfall on wheat crop. The case studies highlight the great scope of remote sensing data for assessment of the impact of extreme weather events on crop production.

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