Red Edge Position (REP), an Indicator for Crop Stress Detection: Implication on Rice (Oryza sativa L)

Crop stresses due to both biotic and abiotic are the major factors affecting crop productivity. The need of the hour is to minimize the yield losses due to these stresses. Early detection can help to reduce the impact of stresses on crop growth and yield. Remote sensing techniques have been shown to be timely, non-destructive and provide spatial estimates for quantifying and monitoring crop stress as compared to direct field techniques. In this study we tested the possibility of detecting impact of abiotic stresses, mainly Nitrogen (N) and elevated CO2 and Temperature on growth and yield of rice crops based on the spectral reflectance data in the red edge position (REP). Spectral reflectances of crop canopyi from 350 to 2500 nm acquired using SVC spectroradiometer under clear sky condition between 11:00 and 13:00 IST. The results thus obtained indicate that REP is a good indicator of crop stress detection as healthy crops always are at longer wavelength as compared to crop under stress. The research work done also elucidates that REP can lead to the development of real-time management tool for crop stress detection, thereby reducing the yield losses due these stresses.

Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO2

Chlorophyll content is an important indicator of winter wheat health status. It is valuable to investigate whether the relationship between spectral reflectance and the chlorophyll content differs under elevated CO2 condition. In this open-top chamber experiment, the CO2 treatments were categorized into ambient (aCO2; about 400 μmol⋅mol–1) or elevated (eCO2; ambient + 200 μmol⋅mol–1) levels. The correlation between the spectral reflectance and the chlorophyll content of the winter wheat were analyzed by constructing the estimation model based on red edge position, sensitive band and spectral index methods, respectively. The results showed that there was a close relationship between chlorophyll content and the canopy spectral curve characteristics of winter wheat. Chlorophyll content was better estimated based on sensitive spectral bands and difference vegetation index (DVI) under both aCO2 and eCO2 conditions, though the accuracy of the models varied under different CO2 conditions. The results suggested that the hyperspectral measurement can be effectively used to estimate the chlorophyll content under both aCO2 and eCO2 conditionsand could provide a useful tool for monitoring plants physiology and growth.

Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China

The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value). We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves.