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A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

文献类型： 外文期刊

作者： Cui, Bei 1 ; Zhao, Qianjun 4 ; Huang, Wenjiang 1 ; Song, Xiaoyu 5 ; Ye, Huichun 1 ; Zhou, Xianfeng 6 ;

作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China

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

3.Key Lab Earth Observat, Sanya 572029, Hainan, Peoples R China

4.Chinese Acad Sci, Bur Sci & Technol Dev, Beijing 100864, Peoples R China

5.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

6.Hangzhou Dianzi Univ, Coll Life Informat Sci & Instrument Engn, Hangzhou 310018, Peoples R China

关键词： leaf chlorophyll content; red-edge reflectance; spectral index; winter wheat

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2019 年 11 卷 8 期

页码：

收录情况： SCI

摘要： Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R-2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** r 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 r 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

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