无人机与卫星影像的叶面积指数遥感反演研究

针对卫星遥感影像获取的叶面积指数精度较低的问题,该文结合无人机低空航拍影像和卫星影像,基于最小二乘法建立了一种叶面积指数遥感反演方法,并与卫星影像像元二分模型进行了比较。结果表明:从单一植被类型到整体植被叶面积指数的反演,新方法均优于卫星影像的像元二分法,两者整体相对误差分别为27%和35%。4种植被类型中,草本植物对模型的反演精度影响较大,两者相对误差分别为32%和56%。使用该方法准确计算了长汀县相关区域叶面积指数分布,与他人结果一致。该方法提高了卫星遥感影像获取叶面积指数的精度,为大面积高精度估算区域植被提供了一种方法。

Study on remote sensing retrieval of leaf area index based on unmanned aerial vehicle and satellite image

For the low accuracy on leaf area index(LAI),a new model was established based on the least square method by combining low-altitude aerial photos of unmanned aerial vehicle(UAV)with the satellite images.Compared with the pixel dichotomy model based on satellite imagery,the results showed that the new model was all better than the pixel dichotomy model for total or single vegetation types and their total relative errors were 27%and 35%respectively for all vegetation.The herbage greatly affected the retrieval results in 4 kinds of vegetation types and their relative errors were 32%and 56%respectively.The LAI distribution on related Changting county was precisely estimated with the new model and agreed well with the other results.This model improved the accuracy of LAI retrieved from satellite remote sensing images and provided a method for large areas LAI remote sensing retrieval.