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Amity University, Noida (2019)

Crop Water Stress Monitoring Through Hyper Spectral Remote Sensing

Krishna, Gopal

Titre : Crop Water Stress Monitoring Through Hyper Spectral Remote Sensing

Auteur : Krishna, Gopal

Université de soutenance : Amity University, Noida

Grade : Doctor of Philosophy (PhD) Geoinformatics and Remote Sensing 2019

Résumé
The timely detection of water deficit stress is very crucial which has been challenge through conventional approach being destructive, tedious and time consuming. Hyperspectral remote sensing and thermal remote sensing have been found to best alternate option for rapid, non-invasive assessment of water stress in plants. Present study aims to explore potential use of hyperspectral remote sensing for water deficit stress assessment in rice and wheat crops. This study also attempts to understand the behaviour of ten rice genotypes for different water deficit stress levels. Crops were grown in the experimental field of ICAR-Indian Agricultural Research Institute, New Delhi, India. The geo-location of experimental site was 2838 28.59 N and 7709 28.09 E and altitude was 22.16m above the mean sea level. Ten Rice genotypes, five drought sensitive and five drought tolerant were grown with three replications. Similarly ten wheat genotypes were also grown with three replications of each. The study was mainly focused on rice crop but for regional scale evaluation of predictive model, wheat crop was also analyzed. This study successfully employed and evaluated both uni-variate and multivariate modelling approaches to predict relative water content (RWC) under water deficit stress condition of rice genotypes. This research makes a substantial contribution towards understanding, monitoring, mapping and modelling of water deficit stress in agricultural crops using thermal and hyperspectral sensors on ground, airborne and satellite-borne platforms which may potentially be employed in upcoming hyperspectral satellite data. For quantification of the water deficit induced effects in rice crop, the identified optimum drought sensitive bands can be efficiently used for timely detection of water deficit stress in crop over a large area using the hyperspectral satellite/airborne data and may help in developing low cost sensor for water deficit stress monitoring of crops

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