Retrieval of Water Quality Parameters in Dianshan Lake Based on Sentinel-2 MSI Imagery and Machine Learning: Algorithm Evaluation and Spatiotemporal Change Research

Abstract

According to current research, machine learning algorithms have been proven to be effective in detecting both optical and non-optical parameters of water quality. The use of satellite remote sensing is a valuable method for monitoring long-term changes in the quality of lake water. In this study, Sentinel-2 MSI images and in situ data from the Dianshan Lake area from 2017 to 2023 were used. Four machine learning methods were tested, and optimal detection models were determined for each water quality parameter. It was ultimately determined that these models could be applied to long-term images to analyze the spatiotemporal variations and distribution patterns of water quality in Dianshan Lake. Based on the research findings, integrated learning algorithms, especially CatBoost, have achieved good results in the retrieval of all water quality parameters. Spatiotemporal analysis reveals that the overall distribution of water quality parameters is uneven, with significant spatial variations. Permanganate index (CODMn), Total Nitrogen (TN), and Total Phosphorus (TP) show relatively small interannual differences, generally exhibiting a decreasing trend in concentrations. In contrast, chlorophyll-a (Chl-a), dissolved oxygen (DO), and Secchi Disk Depth (SDD) exhibit significant interannual and inter-year differences. Chl-a reached its peak in 2020, followed by a decrease, while DO and SDD showed the opposite trend. Further analysis indicated that the distribution of water quality parameters is significantly influenced by climatic factors and human activities such as agricultural expansion. Overall, there has been an improvement in the water quality of Dianshan Lake. The study demonstrates the feasibility of accurately monitoring water quality even without measured spectral data, using machine learning methods and satellite reflectance data. The research results presented in this paper can provide new insights into water quality monitoring and water resource management in Dianshan Lake.

Seasonal Monitoring Method for TN and TP Based on Airborne Hyperspectral Remote Sensing Images

“…It enhances the performance of the model by constructing multiple decision trees and averaging or voting their prediction results. Random Forest is typically characterised by high robustness and interpretability, making it an appropriate choice for problems that require high interpretability and stability [22,23,[38][39][40].…”

“…At present, the most commonly employed machine learning algorithms for the retrieval of water quality parameters include regularised linear regression (LRR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR), and support vector machine regression (SVR) [6]. Previous studies have demonstrated that XGBoost and CatBoost are effective tools for water quality monitoring [23]. The objective of this study is to further explore the performance of ensemble learning algorithms in monitoring TN and TP in order to improve the accuracy and reliability of TN and TP monitoring.…”

“…Consequently, some studies have employed data from the same season to establish inversion models, achieving more accurate results [27,28]. Nevertheless, previous research has also demonstrated that TN and TP undergo certain alterations in accordance with the seasonal changes [23]. In light of this observation, we decided to establish seasonal inversion models for TN and TP with the objective of improving our understanding of and ability to predict their seasonal trends.…”

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

“…The Previous studies also showed a consistent tendency that the DO was lower in summer than in winter. For example, a shallow lake in southern America called University Lake was investigated by Xu et al [50], as well as Xianvu Lake [51], Dianshan Lake [52], and Tianmu Lake [53], which are all located in the southeast of China and have similar climates to the study area. The study of Dianshan Lake also showed that the COD Mn and TN were higher in summer and autumn, while the TP showed an opposite trend.…”

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

“…Based on the water quality data of Dongjiang Lake obtained from the water quality monitoring stations in the watershed, this study identified the dissolved oxygen index (DO), permanganate index (COD Mn ), chemical oxygen demand index (COD), ammonia nitrogen index (NH 3 -N) and total phosphorus index (TP) as the main water quality parameters in the study area [26]. The correlation relationship between each parameter and Landsat multispectral images was established, and the inversion models of five parameters were constructed.…”