Spectral Discrimination of Crop Types Based on Hyperspectral Sensor

Agriculture is the art of producing different crop types from the soil and plays an important role in our lives, sustaining and improving the economic sector. This study is mainly focused on the discrimination of crop types based on a space-borne hyperspectral (PRISMA) sensor over the Khanna, Amloh, and Bassi Pathanan blocks which lie in Punjab state, India. The hyperspectral sensor consists of narrow bands and provides a precise, continuous spectral signature which can significantly help obtain an unambiguous distinction among the crop types. A total of 135 individual points were surveyed during the paddy growing season (May and June months) and the main crop types over the study area were maize, sunflower, moong, sugarcane, and chilli. The collected end-member spectra of same crop types at different sites were averaged to produce reference spectra for various specimens. Each collected field data point was accompanied with a photo record. The results of this study will help improve the accuracy of crop mapping and crop condition assessment.