Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation

被引：6

作者：

Fang, Shenghui ^{[1
]}

Le, Yuan ^{[1
]}

Liang, Qi ^{[1
]}

Liu, Xiaojun ^{[1
]}

机构：

[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China

来源：

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING | 2014年 / 42卷 / 04期

基金：

国家高技术研究发展计划(863计划);

关键词：

LAI; Time-series MODIS data; Vegetation index; Neural network; NEURAL-NETWORK; CHLOROPHYLL CONTENT; BIOPHYSICAL PARAMETERS; LAI PRODUCT; REFLECTANCE; VALIDATION; RED; ALGORITHM; LEAVES; PHOTOSYNTHESIS;

D O I：

10.1007/s12524-013-0349-1

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square = 0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.

引用

页码：733 / 743

页数：11

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