Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model

Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has been widely used, but its current version (V5) from Moderate Resolution Imaging Spectroradiometer (MODIS) data has several limitations, such as frequent temporal fluctuation, large data gaps, high dependence on the quality of surface reflectance, and low computational efficiency. To address these issues, this paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward. Unlike most existing algorithms that estimate one LAI value at one time for each…

• NA
  National Aeronautics and Space Administration
• BN
  Beijing Normal University
• CP
  China Postdoctoral Science Foundation

  Award: 2019M652707
• NK
  National Key Research and Development Program of China
• NK
  National Key Research and Development Program of China Stem Cell and Translational Research

  Award: 2016YFA0600103