肖志强等提出了一种利用广义回归神经网络（GRNN）集成时间序列卫星观测数据反演LAI的方法，利用MODIS和AVHRR地表反射率数据，生产了250m、500m、1km以及5km分辨率的MUSES LAI全球产品(Xiao et al., 2014; Xiao et al., 2016; Xiao et al., 2022)。

基于该方法，对Landsat地表反射率数据进行处理，生成了北京地区时间分辨率16天、空间分辨率30米的空间完整、时间连续的MUSES LAI产品。

图1 北京地区2021年第1、97、193和273天30米分辨率的MUSES LAI空间分布图

图2 连续平滑的30米分辨率的MUSES LAI时间序列曲线

数据下载网址：

https://zenodo.org/record/7159053#.Y0E7nz1Bypo

参考文献

Zhiqiang Xiao, Jinling Song, Hua Yang, Rui Sun and Juan Li. A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225, 2022. (下载)

Xiao Zhiqiang, et al. Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing,52, 209-223, 2014. (下载)

Xiao Zhiqiang, et al. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surfacere flectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318, 2016. (下载)

Xiao Zhiqiang, et al. Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230, 2017. (下载)