Global Leaf Chlorophyll Content Dataset (GLCC) from 2003-2012 to 2018-2020 Derived from MERIS and OLCI Satellite Data: Algorithm and Validation

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R-2 = 0.41 and RMSE = 8.94 mu g cm(-2). Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003-2012 to 2018-2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics.

Automated summary

In this study, a global 500 m leaf chlorophyll content (LCC) weekly dataset (GLCC) was generated using satellite data and a physical radiative transfer modeling approach. The GLCC dataset facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. The dataset was validated using field measurements and showed good consistency with the existing MERIS LCC dataset.