基于MODIS-NDVI的春玉米叶面积指数和地上生物量估算

为了构建春玉米叶面积指数和地上生物量的估算模型，基于辽宁省大田条件下9个站点春玉米不同生育期叶面积指数LAI和地上鲜生物量数据，利用MODIS提取的10种植被指数NDVI、RVI、DVI、PVI、EVI、GNDVI、RDVI、SAVI、OSAVI和NLI反演春玉米LAI和地上鲜生物量。结果表明：10种植被指数与春玉米LAI和地上鲜生物量相关性显著，采用植被指数反演春玉米LAI和地上鲜生物量是可行的；分别基于回归分析法和人工神经网络法，利用10种植被指数反演春玉米LAI和地上鲜生物量，利用回归分析法结合OSAVI和NDVI反演春玉米LAI和地上鲜生物量效果较好；利用人工神经网络法进行模拟反演采用RVI、DVI和EVI效果最佳，反演精度高于回归分析法。

Estimation of Leaf Area Index and Aboveground Biomass of Spring Maize by MODIS-NDVI

In order to construct the model of the leaf area index and aboveground biomass of spring maize, the leaf area index and aboveground biomass of spring maize were measured in the field experiments at different growth stage in 9 sites. In order to estimate the LAI and aboveground biomass, ten spectral vegetation indices (NDVI, RVI, DVI, PVI, EVI, GNDVI, RDVI, SAVI, OSAVI and NLI) were extracted from MODIS data. The results showed that the correlation between ten vegetation index and LAI, aboveground biomass was significant, using vegetation index to inverse LAI and aboveground biomass was feasible. Based on regression analysis method and artificial neural network method, LAI and aboveground biomass of spring maize were inversed by using ten vegetation indexes. With regression analysis method and OSAVI, NDVI, the inversion results were better. With artificial neural network method and RVI, DVI, EVI, the inversion accuracy was higher than that of regression analysis method.