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Journal of Integrative Agriculture  2019, Vol. 18 Issue (6): 1230-1245    DOI: 10.1016/S2095-3119(18)62093-3
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Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data
CUI Bei1, 2, 3, ZHAO Qian-jun1, HUANG Wen-jiang1, 3, SONG Xiao-yu4, YE Hui-chun1, 3, ZHOU Xian-feng5
1 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, P.R.China
2 University of Chinese Academy of Sciences, Beijing 100094, P.R.China
3 Key Laboratory of Earth Observation, Hainan Province, Sanya 572029, P.R.China
4 Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China
5 College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, P.R.China
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Abstract  Leaf chlorophyll content (LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management. Red-edge information from hyperspectral data has been widely used to estimate crop LCC. However, after the advent of red-edge bands in satellite imagery, no systematic evaluation of the performance of satellite data has been conducted. Toward this end, we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites (RapidEye, Sentinel-2 and EnMAP) and their new red-edge bands by using partial least squares regression (PLSR) and a vegetation-indexbased approach. These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution. The results showed: 1) This study confirmed that RapidEye, Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval. For the PLSR approach, Sentinel-2 data provided more accurate estimates of LCC (R2=0.755, 0.844, 0.805 for 2002, 2010, and 2002+2010) than do RapidEye data (R2=0.689, 0.710, 0.707 for 2002, 2010, and 2002+2010) and EnMAP data (R2=0.735, 0.867, 0.771 for 2002, 2010, and 2002+2010). For index-based approaches, the MERIS terrestrial chlorophyll index, which is a vegetation index with two red-edge bands, was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data (R2≥0.628), and the indices (NDRE1, SRRE1 and CIRE1) with a single red-edge band were the most sensitive and robust indices for the RapidEye data (R2≥0.420); 2) According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval, the short-wavelength red-edge bands (from 699 to 734 nm) provided more accurate predictions when using the PLSR approach, whereas the long-wavelength red-edge bands (740 to 783 nm) gave more accurate predictions when using the vegetation indice (VI) approach. In addition, the prediction accuracy of RapidEye, Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution; VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands, but for normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like index, two red-edge bands index didn’t significantly improve the predictive accuracy of LCC than those indices with a single red-edge band. Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC, the level of this improvement remains insufficient because of higher spectral resolution, which results in a worse signal-to-noise ratio. The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.
Keywords:  leaf chlorophyll content        RapidEye        Sentinel-2        EnMAP        red-edge band  
Received: 12 February 2018   Accepted:
Fund: This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19080304), the Agricultural Science and Technology Innovation of Sanya, China (2015KJ04), the Natural Science Foundation of Hainan Province, China (20164179, 2016CXTD015), the Technology Research, Development and Promotion Program of Hainan Province, China (ZDXM2015102), the Hainan Provincial Department of Science and Technology, China (ZDKJ2016021), the National Natural Science Foundation of China (41601466), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2017085).
Corresponding Authors:  Correspondence HUANG Wen-jiang, Tel: +86-10-82178178, E-mail:   
About author:  CUI Bei, Tel: +86-898-88597739, E-mail:;

Cite this article: 

CUI Bei, ZHAO Qian-jun, HUANG Wen-jiang, SONG Xiao-yu, YE Hui-chun, ZHOU Xian-feng. 2019. Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data. Journal of Integrative Agriculture, 18(6): 1230-1245.

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