ALBERT

Description: The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to 2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy nonphotosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals that the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC ( R2=0.77 and RMSE = 6.9μg /cm2). The global eight-day LCC data at 500-m resolution in 2000–2020 were generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.