叶面积指数
环境科学
遥感
归一化差异植被指数
光合有效辐射
增强植被指数
分水岭
天蓬
植被(病理学)
水文学(农业)
地理
植被指数
生态学
计算机科学
地质学
机器学习
生物
病理
医学
考古
光合作用
岩土工程
植物
作者
Geying Lai,Lingling Zhang,Ying Liu,Fazhao Yi,Xianggui Zeng,Ruixin Pan
标识
DOI:10.1109/rsete.2012.6260715
摘要
The modification of the plant growth models of SWAT (Soil and Water Assessment Tool) model for a research on nonpoint source pollution modeling requires leaf area index (LAI) and extinction coefficient (EC) of dominant vegetation canopy in a watershed as an input into the various equations and process models that are applied. Remote sensing provides a solution to effectively estimate the spatial variability of LAI and EC. In order to retrieve the LAI and EC by remotely sensed data, this is illustrated using ETM+ imagery, measured LAI and photosynthetically active radiation by Licor LAI-2000 Plant Canopy Analyzer instrument and the instrument of light quantum, methods of image fusion, four vegetation indices, namely NDVI, SAVI, RVI and TSAVI . The field data of EC was calculated with measured LAI and PAR according to Beer-Lambert equation. Of the four vegetation indices used in this study, it was found that the NDVI was the most robust index with an R2 value of 0.793 for the estimating of LAI but with an R2 value of 0.581 for the estimating of EC.
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