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Newly Combined Spectral Indices to Improve Estimation of Total Leaf Chlorophyll Content in Cotton

文献类型： 外文期刊

作者： Jin, Xiuliang 1 ; Li, Zhenhai 2 ; Feng, Haikuan 1 ; Xu, Xingang 1 ; Yang, Guijun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China

关键词： Chlorophyll content estimation;combined spectral indices;cotton;PROSAIL model

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2014 年 7 卷 11 期

页码：

收录情况： SCI

摘要： The total leaf chlorophyll content (TLCC) provides valuable information about the physiological status of crops. The objectives of this study were 1) to analyze the leaf area index (LAI) and soil factors that influences the estimation of TLCC using the PROSAIL model, which is a combination of the PROSPECT leaf model and the SAIL canopy model; 2) to propose newly combined spectral indices that reduce the influence of LAI and soil factors in order to improve the TLCC estimation; and 3) to test and validate the relationship between TLCC and the newly combined spectral indices. Ground-based hyperspectral data and concurrent TLCC parameters of samples were acquired at the Shihezi University Experiment Site, Xinjiang Province, China, during the 2009 and 2010 cotton growing seasons. The results showed that the newly combined spectral indices [double-peak canopy nitrogen index I (DCNI I), the ratio of the structure insensitive pigment index to the ratio vegetation index III (SIPI/RVI III), the ratio of the plant pigment ratio to the normalized difference vegetation index (PPR/NDVI), and the modified MERIS terrestrial chlorophyll index (MMTCI)] were more sensitive to chlorophyll and more resistant to LAI than the PPR, SIPI, and MERIS terrestrial chlorophyll index alone. In this study, DCNI I proved to be the best spectral index for estimating chlorophyll content, with determination coefficients (R-2) and root mean square error (RMSE) values of 0.80 and 8.31 mu g.cm(-2), respectively. PPR/NDVI was also strongly correlated with chlorophyll content, with corresponding R2 and RMSE values of 0.79 and 9.45 mu g.cm(-2), respectively. This study concluded that DCNI I and PPR/NDVI, in association with indices related to nitrogen, have good potential for assessing nitrogen content.

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