If you are interested in data in this system or have any question, please contact to Prof. Fang Shen's team (email: fshen@sklec.ecnu.edu.cn or 51253904023@stu.ecnu.edu.cn).

This system utilizes numerous open-source software and development packages. We would like to express our gratitude to the developers for their contributions！
Including：Tianditu, Bootstrap, jQuery, OpenLayers, Django, SNAP, Anaconda, GDAL, Numpy, GeoServer, C2RCC, Python, h5py, netCDF4, sentinelsat, WinRAR

The remote sensing data used in this system is Sentinel-3/OLCI. We would like to express our gratitude to the European Space Agency (ESA) for providing this data.

References for data processing in this system:

1. The algal bloom detection method and RDI index refers to the literature:
Shen, Fang, Tang, Rugang, Sun, Xuerong, & Liu, Dongyan (2019). Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea. Remote Sensing of Environment, 235, 111484.

2. The atmospheric correction method for Sentinel-3 A/B satellite images refers to the literature：
Brockmann, C., Doerffer, R., Peters, M., Kerstin, S., Embacher, S., & Ruescas, A. (2016). Evolution of the c2rcc neural network for sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. In, Proceedings of the Living Planet Symposium. Prague, Czech Republic.