Evaluated the performance of 3 billion+ band combinations of 9 vegetation index types by conducting regression analysis to determine the optimal chlorophyll index for chlorophyll content estimation.
An optimized tool in Python for simulating eutrophication, nutrient cycle and transport and phytoplankton dynamics, which is compatible with parameter optimization tools.
The codes provide methods to create monthly and annual global maps by calculating ground-area-based chlorophyll content through SGLI_CI (SGLI chlorophyll index), and to generate time-series data of ground-area-based chlorophyll content at a point scale. Before running the code, please download the SGLI RSRF data from the G-Portal.
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