论文部分内容阅读
以中国东北小兴安岭五营林区为研究区,基于MODIS BRDF遥感模型参数产品数据,首先利用4-Scale模型建立查找表计算像元尺度上各组分比例,估算研究区森林乔木冠层反射率,然后利用冠层反射率数据,获取研究区3种常用森林冠层植被指数,最后基于植被指数与实测叶面积指数构建研究区冠层叶面积指数反演模型,并选取最优模型实现研究区森林冠层叶面积指数反演。结果表明:研究区冠层LAI遥感反演模型中,基于比值植被指数SR(simple ratio,SR)构建的二次多项式反演模型精度最高,且反演精度比未考虑背景反射影响的SR反演模型精度有较大幅度提高,模型决定系数由0.38提高至0.54;反演获取的研究区冠层LAI在2.38~12.67,平均值6.52,LAI值在阔叶林区域相对较高。
Based on the MODIS BRDF remote sensing model parameters product data, a lookup table was built to calculate the proportion of each component on the cell scale to estimate the canopy reflectance of the forest arbor in the study area. Then using the canopy reflectance data to obtain three kinds of commonly used canopy vegetation index in the study area. Finally, based on the vegetation index and the measured leaf area index, the canopy leaf area index inversion model of the study area was selected, and the optimal model was selected to realize the forest Canopy leaf area index inversion. The results show that the accuracy of the quadratic polynomial inversion model constructed based on SR (simple ratio) is the highest and the accuracy of inversion is higher than that of SR inversion without consideration of background reflection in the canopy LAI remote sensing inversion model The accuracy of the model was greatly improved, and the coefficient of model determination was increased from 0.38 to 0.54. The canopy LAI of the retrieved area was 2.38 ~ 12.67, with an average of 6.52. The LAI value in the study area was relatively high in the broad-leaved forest.