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Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

文献类型： 外文期刊

作者： Xie, Qiaoyun 1 ; Huang, Wenjiang 1 ; Dash, Jadunandan 3 ; Song, Xiaoyu 4 ; Huang, Linsheng 5 ; Zhao, Jinling 5 ; Wang, 1 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100049, Peoples R China

2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China

3.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England

4.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

5.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

关键词： Leaf area index;Hyperspectral remote sensing;Vegetation index;Nitrogen and water treatment

期刊名称：ADVANCES IN SPACE RESEARCH （ 影响因子：2.152； 五年影响因子：1.978 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices. (C) 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.

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