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针对单源数据经验模型估算精度较低等问题,提出采用最小二乘法联合光学和雷达遥感数据构建联合估算模型,以中国科学院河北怀来遥感综合实验站为研究区,以夏季玉米为研究对象,利用Landsat8和Radarsat2影像实现研究区叶面积指数估算:首先分别建立了多光谱数据和雷达数据与实测叶面积指数之间的回归模型,然后利用最小二乘算法联合不同数据间的回归模型构建估算模型,最后利用迭代法估算叶面积指数并通过验证数据对估算结果进行评价分析,同时与单源数据经验模型、多源数据加权平均模型和基于物理模型查找表估算结果进行对比。通过对研究区59个样本点数据分析表明:基于最小二乘算法联合光学与雷达遥感数据能够提高叶面积指数的估算精度(R2=0.5442,RMSE=0.81),优于单源遥感数据拟合经验模型(DVI经验模型:(R2=0.485,RMSE=1.27))、基于权重的光学微波联合模型(R2=0.447,RMSE=1.36)和物理模型查找表法(R2=0.333,RMSE=1.36),并当叶面积指数大于3时,对其由于信息饱和或误差引起的低估或高估现象具有一定的抑制作用。
In view of the low estimation accuracy of the empirical model of single-sourced data, a joint estimation model based on least square method and optical and radar remote sensing data is proposed. Taking Hebei Huailai Remote Sensing Comprehensive Experimental Station of Chinese Academy of Sciences as the research area and the summer maize as the research object, Landsat8 and Radarsat2 images were used to estimate the leaf area index in the study area. Firstly, the regression model between multispectral data and radar data and the measured leaf area index was established respectively. Then, the estimation model was constructed by using the least square method combined with the regression model between different data Finally, the leaf area index was estimated by iterative method, and the evaluation results were evaluated by verification data. At the same time, it was compared with single source data empirical model, multi-source data weighted average model and physical model lookup table. By analyzing the data from 59 sample sites in the study area, it is shown that the estimation accuracy of leaf area index (R2 = 0.5442, RMSE = 0.81) can be improved by combining optical and radar remote sensing data, which is better than the single-source remote sensing data fitting experience (R2 = 0.485, RMSE = 1.27), weight-based optical microwave joint model (R2 = 0.447, RMSE = 1.36) and physical model lookup table (R2 = 0.333, RMSE = 1.36) When the leaf area index is more than 3, it has a certain inhibitory effect on the phenomenon of under-estimation or over-estimation caused by information saturation or error.