Identification and Classification of Water Stressed Crops Using Hyperspectral Data: A Case Study of Paithan Tehsil

Abstract

Globally, agricultural drought is the heterogeneous issue which causes the reduction of food production. The conventional methods have many limitations. Moreover, the use of multispectral remote sensing in drought condition monitoring possesses a limited spectral resolution which is insignificant for an understanding of water stress in the vegetation. In this regard, the study has been examined the agricultural droughts using ground observation, meteorological data and hyperspectral remote sensing (HRS) for assessment of crop water stress. The objective of this research was to: (a) examine the meteorological and hyperspectral data set for drought assessment (b) examine the agricultural stress tool for agricultural crop stress classification. The experimental results were evaluated and validated. The overall accuracy was obtained 86.66% with kappa coefficient 0.80. The research study has investigated the severe drought in the study area due to scanty rainfall during the Kharif season of year 2014. The present work is beneficial for identifying and monitoring the agricultural drought for better planning and management of crops.