Global Data Sets of Vegetation Leaf Area Index (LAI)3g

A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: monthly temporal frequency, 0.5 degree spatial resolution and temporal span of July 1981 to December 2011.

本研究首先针对2000至2009年的重叠观测时段，以改进后的第三代全球库存建模与制图研究（Global Inventory Modeling and Mapping Studies, GIMMS）归一化植被指数（NDVI3g）与高质量Terra卫星中分辨率成像光谱仪（MODIS）叶面积指数（LAI）产品为配对训练数据，开发了神经网络算法。随后，利用训练完成的该神经网络算法，生成了具备以下特征的对应LAI3g与光合有效辐射吸收比率（FPAR3g）数据集：时间分辨率为月度，空间分辨率为0.5度，时间跨度为1981年7月至2011年12月。