Machine-learning-estimation of high-spatiotemporal-resolution chlorophyll-a concentration using multi-satellite imagery

Abstract

Abstract Chlorophyll-a concentration for quantifying phytoplankton biomass is commonly used as an indicator for evaluating the trophic level of lakes and water quality. This research aimed to develop a high spatiotemporal-resolution model for the retrieval of chlorophyll-a in inland water. Firstly, the machine learning based models considering Sentinel-2 Multispectral Instrument and Sentinel-3 Ocean and Land Color Instrument (OLCI) images were applied to estimate chlorophyll-a concentrations (R 2 = 0.873 and 0.822, respectively). The spatiotemporal fusion was performed to fuse the OLCI and MSI chlorophyll-a images with low temporal resolution but fine spatial-resolution, and with high temporal resolution but coarse spatial-resolution. The random forest was applied to fuse images from two distinct sensors, and to refine the spatial resolution of OLCI estimations to be the same as those of Sentinel-2 MSI. Results showed that the spatiotemporal fusion can estimate dense-temporal 10 m spatial resolution chlorophyll-a concentration in the Tsengwen Reservoir (Root-Mean-Square Error, RMSE = 1.25–1.47 μg L−1). The spatiotemporal fusion model was effectively applied to determine high spatiotemporal-resolution chlorophyll-a measurements in the aquatic system.

Keywords

• Chlorophyll-a estimation
• Spatiotemporal fusion
• Machine learning
• Band ratio