Introduction

This repository provides code used in the manuscript:

"Ground-area-based chlorophyll content derived from the GCOM-C satellite with a computationally efficient algorithm free from LAI and vegetation classification"

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 (atmospheric-corrected reflectance) data from the G-Portal (https://gportal.jaxa.jp/gpr/?lang=en). This free service provides data from spaceborne sensors developed or involved by the Japan Aerospace Exploration Agency (JAXA).

Version

This repository corresponds to: v1.0.0 (submitted version)

Data

Input data are NOT included due to size limitations.

Users should prepare the necessarry datasets following the instruction.

Requirments

1. Linux
2. Python

Step 1: Preparation

Create the G-Portal account

Go to the G-Portal and create a user account.

Change the user's settings in the codes.

Download SGLI daily RSRF tile data (250-m resolution)

Check your target tile (vvhh). To download the data, run "download_SGLI_tile.sh"

./download_SGLI_tile.sh [start_year] [end_year] [SGLI_tile]

Download SGLI daily RSRF global data (0.05-degree resolution, about 5-km resolution)

./download_SGLI_global_RSRF.sh [start_year] [end_year]

Download the SGLI_Python_Open_Tool via GitHub

https://github.com/K0gata/SGLI_Python_Open_Tool

Step 2: Calculate the ground-area-based chlorophyll content through SGLI_CI (SGLI chlorophyll index)

Daily point data (250-m resolution)

Download "get_data_from_lat_lon.py" and "calc_point_Chl_timeseries.py". Then,

./get_point_Chl_timeseries.sh [start_year] [end_year] [SGLI_tile] [latitude] [longitude]

Global monthly and annual maps (0.05-degree resolution, about 5-km resolution)

python L3_plot_global_Chl_monthly_yearly_maps.py [target_year]

Supplemental information

Deriving point data from the SGLI H5 tile file.

python get_data_from_lat_lon.py [H5file_name] [DATASET_name] [latitude] [longitude]

Checking an SGLI dataset name in the SGLI H5 tile file.

h5dump -n [H5file_name]

Citation

If you use this code for chlorophyll content in your research, please cite:

Akitsu T.K., Kume A., Lai R., Kobayashi H., Nakaji T., Hanba Y.T., Murakami H. (major revision) Ground-area-based chlorophyll content derived from the GCOM-C satellite with a computationally efficient algorithm free from LAI and vegetation classification.

License

MIT License