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Retrieving Leaf Area Index and Extinction Coefficient of Dominant Vegetation Canopy in Meijiang Watershed of China Using ETM+ Data

机译:ETM +数据检索梅江流域梅江流域占优势植被冠层的叶区指数和消光系数

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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.
机译:用于源源污染建模研究的SWAT(土壤水分评估工具)模型的改变,需要流域中主要植被冠层的叶区域指数(LAI)和消光系数(EC)作为输入应用的各种方程和过程模型。遥感提供了有效估计LAI和EC的空间变异的解决方案。为了通过远程感测数据检索LAI和EC,通过欧特尔Lai-2000植物冠层分析仪和光量子仪,测量赖斯和光合作用辐射测量的Lai和光合作用辐射,图像融合方法,四个植被指数,即NDVI,Savi,RVI和Tsavi。用测量的Lai计算EC的现场数据,并根据Beer-Lambert方程的标准。在本研究中使用的四个植被指数中,发现NDVI是最强大的指标,R2值为0.793,用于估计赖,但估计EC的R2值为0.581。

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