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

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 mac...

Full description

Saved in:
Bibliographic Details
Published in:Sustainable environment research 2023-03, Vol.33 (1), p.1-14, Article 11
Main Authors: Chusnah, Wachidatin Nisaul, Chu, Hone-Jay, Tatas, Jaelani, Lalu Muhamad
Format: Article
Language:English
Subjects:
Algorithms
Aquatic ecosystems
Aquatic environment
Atmosphere
Band ratio
Chlorophyll
Chlorophyll-a estimation
Datasets
Eutrophication
Image processing
Inland waters
Lakes
Learning algorithms
Machine learning
Phytoplankton
Plankton
Remote sensing
Root-mean-square errors
Satellite imagery
Spatial discrimination
Spatial resolution
Spatiotemporal fusion
Temporal resolution
Trophic levels
Water quality
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2468-2039
2468-2039
DOI:10.1186/s42834-023-00170-1