Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery

高光谱成像 遥感 环境科学 植被(病理学) 归一化差异植被指数 精准农业 光谱带 光化学反射率指数 叶绿素 反射率 多光谱图像 叶面积指数 农学 农业 地理 植物 生物 生态学 光学 物理 病理 医学
作者
‪Rama Rao Nidamanuri,Kuldeep Chaurasia,Supriyo Ghosh,V. K. Dadhwal
出处
期刊:The Journal of Agricultural Science [Cambridge University Press]
卷期号:146 (1): 65-75 被引量:52
标识
DOI:10.1017/s0021859607007514
摘要

SUMMARY Remotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.
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