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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 were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a 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.

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), invasion endangered the maize production worldwide, including India. The objective of this study was to quantify the FAW damage severity and its impact on leaf area index (LAI), biomass and grain yield of maize and to detect the field damage using high-resolution multispectral spaceborne remote sensing data. Maize growing fields in the Kurnool District of Andhra Pradesh and the Gadwal District of Telangana, India, were randomly surveyed to collect detailed ground-truth information. Foliar damage due to FAW was recorded, and the fields were categorized into various severity grades (healthy, low, medium and severe). FAW infestation caused significant change in LAI between the severity grades, which formed the basis for its damage detection using multispectral spaceborne remote sensing. Severe FAW infestation caused significant reduction in LAI, biomass and grain yield ranging between 36.9 and 39.9% compared to healthy grade. The infestation at the leaf collar (LC) stage caused significant yield loss of up to 26.5% compared to the tassel initiation (TI) and tasselling and silking (TS) stages. Canopy spectral reflectance from healthy and FAW-infested plants showed significant differences in the visible and near infrared (NIR) regions. A reflectance peak was observed in the NIR region of healthy plants compared to infested plants. Among various spaceborne vegetation indices, the Soil Adjusted Vegetation Index (SAVI) performed better in identifying the FAW infestation (R2 = 0.61**), biomass (R2 = 0.70**) and yield loss (R2 = 0.82**). These findings indicate the feasibility of utilizing multispectral remote sensing data for monitoring FAW infestation on a spatial scale, thus enabling the site-specific management. [ABSTRACT FROM AUTHOR]