Not Available

Not AvailableHailstorms cause enormous physical damage in agriculture, often result in disasters leading to widespread, sudden loss in harvestable produce, and at times entire loss to grownup orchards. Accurate area-wide crop damage assessment is a challenging task to provide timely relief to farmers. This study demonstrates the feasibility of using multispectral satellite data for mapping crop-damaged area, identification of hailstreaks, their ground track attributes using NDVI difference of pre and post-hailstorm events. Crop classification within hailstreak was performed using a multispectral, high resolution LISS-IV satellite data from IRS-Resourcesat-2. Six hailstorm-damaged streaks were examined in the study area, varying in width ranging from 3 to 8 km, and length ranging from 6 to 33 km. Maximum area damaged was in grapes, followed by sugarcane and papaya. Changes in NDVI profile of different crops in the study area was recorded, and a model was developed for estimating changes in NDVI due to hail damage. The crop classification error matrix indicated Kappa Coefficient (0.55) with an overall classification accuracy of 69.6%. This study discusses the potential of high spectral, spatial and temporal resolution remote sensing data for crop damage assessment in the aftermath of the hailstorms. (C) 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V.Not Availabl