Proximal remote sensing of dissolved organic matter in aqua-culture ponds via multi-temporal spectral correction - Lv, Wenxu; Wang, Yancang; Cao, Huiqiong; Cheng, Peng; Gu, Xiaohe; Ma, Zhuoran; Li, Mengjie; Tang, Ruiyin; Zhao, Qichao; Li, Xuqing; Zhang, Lan; Liu, Shuaifei (Frontiers in Water, 2025)
Dissolved organic matter (DOM) is a critical indicator of aquatic environmental quality, and its concentration affects the quality of aquaculture products. Integrating unmanned aerial vehicle (UAV)-based multispectral data with machine learning algorithms enables accurate estimation of DOM. However, the stability of models in different periods—such as those affected by seasonal variations and environmental condition changes—is the key factor affecting their application. This study employed a spectral correction method to unify multi-temporal datasets. Estimation models were constructed using the 2023 dataset with Light Gradient Boosting Machine, Extreme Gradient Boosting, and Random Forest algorithms, and their cross-year performance was validated on the 2024 dataset through transfer learning. Results showed that models trained on corrected data outperformed those using raw spectra, with an average R2 increase of 15.67%, and reductions of 10.27% in RMSE and 6.44% in MAE on the test set. After transfer learning optimization, the model using the corrected spectrum still exhibited superior performance in 2024. Compared with the original spectrum, an average R2 improvement of 30.67%, along with reductions of 17% in RMSE and 11.67% in MAE. Among the three algorithms, the Random Forest model yielded the best performance, with an R2 of 0.82, RMSE of 3.1 mg/L, and MAE of 2.37 mg/L on the test set. The proposed approach in this study effectively mitigates the temporal impact on model performance and enhances the temporal generalization capability of DOM estimation models.