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Journal of Integrative Agriculture  2017, Vol. 16 Issue (02): 266-285    DOI: 10.1016/S2095-3119(15)61293-X
Section 2: Agricultural quantitative remote sensing Advanced Online Publication | Current Issue | Archive | Adv Search |
Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat
LI He1, CHEN Zhong-xin1, JIANG Zhi-wei1, 2, WU Wen-bin1, REN Jian-qiang1, LIU Bin1, Tuya Hasi1, 2

1 Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China

2  National Meteorological Information Center, China Meteorological Administration, Beijing 100081, P.R.China

 

 

 

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Abstract   Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 (GF-1) wide field of view (WFV) camera, environment and disaster monitoring and forecasting satellite (HJ-1) charge coupled device (CCD), and Landsat-8 operational land imager (OLI) data for estimating the leaf area index (LAI) of winter wheat via reflectance and vegetation indices (VIs).  The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model.  The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed.  The results yielded the following observations: (1) The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination (R2) between 0.375 to 0.818.  The differences in reflectance are ranging from 0.002 to 0.054.  The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933.  The differences in the VIs are ranging from 0.07 to 0.156.  These results show the three sensors’ images can all be used for cross calibration of the reflectance and VIs.  (2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI (R2 between 0.703 and 0.849).  The linear models associated with the 2-band enhanced vegetation index (EVI2), which feature the highest R2 (higher than 0.746) and the lowest root mean square errors (RMSE) (less than 0.21), were selected to estimate the winter wheat LAI.  The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors (RE) of 2.18% and an RMSE of 0.13, while the HJ-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15.  (3) The inversion errors in the different sensors’ LAI estimates using the PROSAIL model are small.  The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the HJ-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26.  (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.
Keywords:  GF-1 WFV      HJ-1 CCD      Landsat-8 OLI      leaf area index      PROSAIL      vegetation indices  
Received: 19 October 2015   Accepted:
Fund: 

This work was supported by the National Natural Science Foundation of China (41371396, 41401491 and 41471364), the Introduction of International Advanced Agricultural Science and Technology, Ministry of Agriculture, China (948 Program, 2011-G6), and the Agricultural Scientific Research Fund of Outstanding Talents and the Open Fund for the Key Laboratory of Agri-informatics, Ministry of Agriculture, China (2013009).

Corresponding Authors:  CHEN Zhong-xin, Tel: +86-10-82105089, E-mail: chenzhongxin@caas.cn   
About author:  LI He, Mobile: +86-18811153465, E-mail: lihe_caas@163.com

Cite this article: 

LI He, CHEN Zhong-xin, JIANG Zhi-wei, WU Wen-bin, REN Jian-qiang, LIU Bin, Tuya Hasi. 2017. Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat. Journal of Integrative Agriculture, 16(02): 266-285.

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