冬小麦叶面积指数真实性检验尺度扩展方法研究

为在空间尺度上实现冬小麦LAI地面观测与遥感观测直接匹配,从1 m×1 m范围的实测LAI出发,通过优化采样方法扩展得到16 m×16 m范围的冬小麦LAI,然后利用空间分辨率为16 m的高分1号卫星的多光谱数据计算样本点的植被指数,建立其与冬小麦LAI的拟合模型,从四种植被指数的拟合模型中挑选表现最好的LAI估测模型,获得16 m×16 m尺度的LAI分布图,并经过重采样聚合为250 m×250 m尺度的LAI格点图,从而实现从地面点测量数据到卫星尺度数据的扩展。检验结果表明,16 m×16 m和250 m×250 m两个研究区域模拟点值和实测点值的相对误差分别为4.18%和3.64%,说明这种尺度扩展方法是科学可行的。

Study on the Scale Expansion Method of Leaf Area Index of Winter Wheat

The high precision simulation of the leaf area index(LAI) of winter wheat and realization of the scale expansion from single point to region is of great significance to the validation of remote sensing LAI products and the assimilation of remote sensing and crop models. However,at present,ground-based observation LAI can not be directly matched with remote sensing observations at the spatial scale. In this study,based on the measured LAI of 1 m×1 m scale,the winter wheat LAI of 16 m*16 m scale was obtained by optimizing the sampling method. Then the vegetation index of the sample point was calculated using the multi spectral data of the GF-1 satellite with spatial resolution of 16 m. The fitting model of winter wheat LAI was established,and the best LAI estimation model was selected from the four vegetation index fitting model. The LAI distribution map of the research area of the 16 m*16 m scale was obtained,and the LAI map of the 250 m×250 m scale was aggregated by resampling,which achieved the expansion of the data from the ground point measurements to the satellite scale. The results show that the relative error of simulated point values and measured values in the two areas are 4.18% and 3.64%,and this scaling method is scientific and feasible.