Prabhakar, M.; Gopinath, K.A.; Ravi Kumar, N.; Thirupathi, M.; Sai Sravan, U.; Srasvan Kumar, G.; Samba Siva, G.; Chandana, P.; Singh, V.K. Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing. Remote Sens.2024, 16, 954.
Prabhakar, M.; Gopinath, K.A.; Ravi Kumar, N.; Thirupathi, M.; Sai Sravan, U.; Srasvan Kumar, G.; Samba Siva, G.; Chandana, P.; Singh, V.K. Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing. Remote Sens. 2024, 16, 954.
Prabhakar, M.; Gopinath, K.A.; Ravi Kumar, N.; Thirupathi, M.; Sai Sravan, U.; Srasvan Kumar, G.; Samba Siva, G.; Chandana, P.; Singh, V.K. Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing. Remote Sens.2024, 16, 954.
Prabhakar, M.; Gopinath, K.A.; Ravi Kumar, N.; Thirupathi, M.; Sai Sravan, U.; Srasvan Kumar, G.; Samba Siva, G.; Chandana, P.; Singh, V.K. Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing. Remote Sens. 2024, 16, 954.
Abstract
Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.
Keywords
hyperspectral remote sensing; vegetation indices; canopy reflectance; leaf area
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
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