AI-powered spatiotemporal imputation and prediction of chlorophyll-a concentration in coastal oceans

This repository contains the code for the STIMP method, an advanced AI framework to impute and predict Chl_a across a broad spatiotemporal scale in coastal oceans. STIMP's results can be utilized to diagnose and analyze the ecosystem health of coastal oceans based on the remote sensing measurement.

Installation

You can install the development version of STIMP:

git clone https://github.com/YangLabHKUST/STIMP.git
cd /path/to/STIMP
conda create -n stimp python=3.9
conda activate stimp
pip install -r requirements.txt

Four representative coastal ocean area analysis

The code for reproducing the results presented in our paper are available on the tutorial website. To reproduce our resluts, it is necessary to first train STIMP and the baselines, which can be found in the tutorials:

• Train STIMP on each coastal ocean area
• Train baselines, including imputation methods and prediction methods, on each coastal ocean area

The resluts presented in our paper are available:

• Pearl River Estuary
• Northern Gulf of Mexico
• Chesapeake Bay
• Yangtze River Estuary

Reference

If you find the STIMP package or any of the source code in this repository useful for your work, please cite:

AI-powered spatiotemporal imputation and prediction of chlorophyll-a concentration in coastal ecosystems.

Fan Zhang, Hiuseut Kung, Fa Zhang, Can Yang# and Jianping Gan#. 2025.

Development

The python repository STIMP is developed and maintained by Fan Zhang.

Contact

Please feel free to contact Fan Zhang ([email protected]), Prof. Can Yang ([email protected]), or Prof. Jianping Gan ([email protected]) if any inquiries.