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Colored dissolved organic matter estimation using Sentinel-2 imagery in small-scale reservoir: Classification and

Jinuk Kim¹, Jin Hwi Kim¹, Wonjin Jang²

¹Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University, Seoul, 02841, Republic of Korea.

Environmental Research

|December 14, 2025

Summary

Machine learning models combined with synthetic data effectively estimate Colored Dissolved Organic Matter (CDOM) concentrations. Deep Neural Networks with SMOTE-SVM and SmoteR significantly improved accuracy, enhancing water quality monitoring.

Keywords:

CDOM Classification data resampling Machine learning Regression data resampling Sentinel-2

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Area of Science:

Environmental Science
Remote Sensing
Machine Learning

Background:

Imbalanced datasets pose challenges for accurately estimating Colored Dissolved Organic Matter (CDOM) in aquatic environments.
Accurate CDOM estimation is crucial for water quality monitoring and environmental management.

Purpose of the Study:

To integrate machine learning models with synthetic data to overcome data imbalance issues in CDOM concentration estimation.
To evaluate the performance of various machine learning models and synthetic data generation techniques for CDOM estimation.

Main Methods:

Utilized Sentinel-2 imagery to derive five input variables (band ratios) using the HSIC-Lasso model.
Generated synthetic data using classification-based (SMOTE-SVM, SMOTE-Tomek) and regression-based (SmoteR) techniques to address data imbalance.
Trained and evaluated ensemble models including Random Forest (RFR), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVR), and Deep Neural Network (DNN).

Main Results:

Deep Neural Networks (DNN) combined with SMOTE-SVM and SmoteR achieved the highest prediction accuracies (0.88 and 0.89, respectively).
These advanced models significantly reduced errors, especially in high CDOM concentration regions.
Classification-based oversampling techniques proved effective in addressing data imbalances, particularly at high-concentration boundaries.

Conclusions:

Synthetic data generation and machine learning offer a powerful approach for robust water quality monitoring.
The proposed framework provides a scalable solution for estimating CDOM concentrations, supporting environmental management and policy.
DNN models integrated with specific oversampling techniques demonstrate superior performance in handling imbalanced aquatic datasets.