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基于HJ-1ACCD1环境卫星数据,以福建沿海地区普遍分布的台湾相思树为研究对象,利用回归分析法(NDVI、OSAVI、EVI、HJVI)和PROSAIL辐射传输模型,构建台湾相思树LAI反演模型。同时,利用同步野外地面实测数据,将模型估算LAI值与实测LAI值进行对比。结果表明:(1)相比归一化植被指数NDVI、优化土壤调节指数OSAVI和增强型植被指数EVI 3种常用植被指数,引入修正大气、土壤背景影响的蓝、绿波段的环境植被指数HJVI来反演相思树LAI具有更高的精度(R2=0.7344,RMSE=0.1421);(2)本研究所选4种植被指数构建的最优反演模型均为非线性模型,其中,环境植被指数HJVI反演LAI最优模型为幂函数模型,表明相思树LAI与植被指数之间呈非线性变化;(3)PROSAIL辐射传输模型法比回归分析法反演相思树LAI的精度有较大提高(R2=0.7903,RMSE=0.1303),可见PROSAIL模型法构建反演模型能更好地反演相思树LAI。
Based on the HJ-1ACCD1 environmental satellite data, the Acacia crassicarpa habitat widely distributed in the coastal area of Fujian was used as the research object. The LAI inversion model of Acacia mangium was established by regression analysis (NDVI, OSAVI, EVI, HJVI) and PROSAIL radiation transmission model. At the same time, the LAI value of the model was compared with the measured LAI value by using the measured data of the synchronous field. The results showed as follows: (1) Compared with normalized NDVI, OSVI and EVI, the index of common vegetation index (HVIVI) in the blue and green bands amending atmosphere and soil background (2) The optimal inversion models for the four vegetation indices selected in this study are non-linear models, in which the environmental vegetation index HJVI The LAI inversion model was a power function model, which showed that there was a nonlinear change between LAI and vegetation index of Acacia trees. (3) PROSAIL radiation transfer model method had a higher accuracy than LAI = 0.7903, RMSE = 0.1303). Therefore, it can be seen that the PROSAIL model can be used to build LAI inversion model better.