# An Optical Water Type-Based Deep Learning Framework for Enhanced Turbidity Estimation in Inland Waters from Sentinel-2 Imagery

## TL;DR

A new deep learning framework improves turbidity estimation in inland waters using Sentinel-2 satellite data and optical water type classification.

## Contribution

A novel deep learning model combining fuzzy c-means clustering with a CNN-RF architecture for accurate turbidity estimation based on optical water types.

## Key findings

- The OWT-based CNN-RF model achieved high turbidity prediction accuracy (R2 = 0.900, RMSE = 11.698 NTU).
- The model effectively captures spatial continuity of turbidity distribution in inland waters.
- The framework enhances remote sensing capabilities for water quality monitoring.

## Abstract

What are the main findings?
A novel framework that integrates the fuzzy c-means method with a weighted blending CNN-RF deep learning model for accurate turbidity estimation based on optical water types was effectively implemented and validated using Sentinel-2 data.The OWT-based deep learning model achieves robust and generalizable turbidity predictions with high accuracy and effectively retrieves turbidity to capture the continuous spatial distribution characteristics of inland waters.

A novel framework that integrates the fuzzy c-means method with a weighted blending CNN-RF deep learning model for accurate turbidity estimation based on optical water types was effectively implemented and validated using Sentinel-2 data.

The OWT-based deep learning model achieves robust and generalizable turbidity predictions with high accuracy and effectively retrieves turbidity to capture the continuous spatial distribution characteristics of inland waters.

What is the implication of the main finding?
This study provides a practical and accurate method for facilitating the application of deep learning models based on the optical classification of inland waters in turbidity estimation.The validated framework and methods enhance the operational capabilities of remote sensing for water quality monitoring and provide algorithmic support for a more comprehensive understanding of aquatic environmental conditions and ecosystem dynamics.

This study provides a practical and accurate method for facilitating the application of deep learning models based on the optical classification of inland waters in turbidity estimation.

The validated framework and methods enhance the operational capabilities of remote sensing for water quality monitoring and provide algorithmic support for a more comprehensive understanding of aquatic environmental conditions and ecosystem dynamics.

Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression or traditional machine learning techniques, the application of deep learning models for turbidity estimation remains limited. This study proposed deep learning models for turbidity estimation based on optical classification of inland waters using Sentinel-2 data. Specifically, the fuzzy c-means (FCM) clustering method was employed to classify optical water types (OWTs) based on their spectral reflectance characteristics. A weighted sum of the turbidity prediction results was generated by the OWT-based convolutional neural network-random forest (CNN-RF) model, with weights derived from the FCM membership degrees. Turbidity for four typical waters was mapped by the proposed method using Sentinel-2 images. The FCM method efficiently classified waters into three OWTs. The OWT-based weighted CNN-RF model demonstrated strong robustness and generalization performance, achieving a high prediction accuracy (R2 = 0.900, RMSE = 11.698 NTU). The turbidity maps preserved the spatial continuity of the turbidity distribution and accurately reflected water quality conditions. These findings facilitate the application of deep learning models based on optical classification in turbidity estimation and enhance the capabilities of remote sensing for water quality monitoring.