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Comparative analysis of GF-1, H J-1, and Landsat-8 data for estimating the leaf area index of winter wheat 预览
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作者 LI He CHEN Zhong-xin +4 位作者 JIANG Zhi-wei WU Wen-bin REN Jian-qiang LIU Bin Tuya Hasi 《农业科学学报:英文版》 SCIE CAS CSCD 2017年第2期266-285,共20页
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... 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 th 展开更多
关键词 叶面积指数 冬小麦 环境与灾害监测预报 均方根误差 光谱响应函数 空间分辨率 误差估计 估算
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