【摘要】 叶面积指数(leaf area index,LAI)是重要的生物物理参数,亦是各种生态模型、生产力模型以及碳循环研究等的重要生物物理参量,因此具有重要的研究意义。通过分析大量实测数据,选用归一化植被指数(normalized difference vegetation index,NDVI)和比值植被指数(ratio vegetation index,RVI)、主成分分析(principcal component analysis,PCA)、神经网络(neural network NN)三种方法对大豆使LAI进行了估算,比较分析了三种方法的估算结果。研究结果表明,植被指数法(NDVI,RVI),主成分分析,神经网络方法LAI都取得了较为理想的结果,验证模型的确定性系数分别达0.758和0.753,0.954,0.899,其中主成分分析方法和神经网络方法精度较高,主成分分析方法验证模型的稳定性更好,其验证模型的RMSE为0.267,明显低于两个植被指数(NDVI和RVI的RMSE分别为0.594和0.616)和神经网络(RMSE=0.413)。当叶面积指数较小时,植被指数能够较好地去除土壤、大气等条件影响,并精确估算LAI;当叶面积指数较大时,主成分分析能够弥补植被指数饱和的缺陷,得到很好的LAI估算效果。神经网络受LAI大小的影响效果居中,其对叶面积指数较小和较大时具有一致的估算效果,具有较好的应用潜力。更多还原

【Abstract】 Leaf area index(LAI) is an important biophysical parameter,and is the critical variable in many ecology models,productivity models and carbon circulation study.Based on the field experiment data,an evaluation of soybean LAI retrieval methods was conducted using NDVI(normalized difference vegetation index) and RVI(ratio vegetation index),principle component analysis(PCA) and neural network(NN) methods,and the estimate effects of three methods were compared.The results showed that the three methods have an ideal effect on the LAI estimation.R2 of validated model of vegetation indices,PCA,NN were 0.753(NDVI),0.758(RVI),0.883,0.899.PCA and NN methods were better with higher precision,and PCA method was the best,as its RMSE(0.202) was slower than the two vegetation indices(RMSEs of NDVI and RVI were 0.594 and 0.616) and NN(RMSE was 0.413) method.While the LAI was small, vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained the good evaluation result.PCA showed better effect for all LAI.LAI affected the estimating result of NN method moderately.As for the NN method,modeled LAI value and measured LAI regression formula slope was the nearest to 1 with R2 of 0.949,which showed a great potential for LAI estimating.As a whole,PCA and NN methods were the prior selection for LAI estimation,which should be attributed to the application of hyperspectral information of many bands.更多还原