Retrieval of Chlorophyll-a Concentrations Using Sentinel-2 MSI Imagery in Lake Chagan Based on Assessments with Machine Learning Models

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 19; p. 4924
• Main Authors: Shi, Xuming, Gu, Lingjia, Jiang, Tao, Zheng, Xingming, Dong, Wen, Tao, Zui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Artificial intelligence; Assessments; Carbon; Carbon cycle; Chlorophyll; Chlorophyll-a; Eutrophication; Lake Chagan; Lakes; Learning algorithms; Machine learning; Neural networks; Optical properties; Remote sensing; Satellites; seasonal pattern; Seasonal variations; Sensors; Sentinel-2; Spatial distribution; Water quality
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14194924

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R2 = 0.79, RMSE = 2.51 μg L−1, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes.