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.

Soil moisture is one of the most critical elements of the Earth system and is essential for the study of the terrestrial water cycle, ecological processes, climate change, and disaster warnings. In this study, the training sample was selected to divide the dataset according to months from 2000 to 2018 after the advantages of three training samples were compared using a backpropagation (BP) neural network model. Furthermore, the monthly surface soil moisture in China in 2019 and 2020 was simulated based on various meteorological elements. The results demonstrate that evapotranspiration has the greatest influence on soil moisture among the various meteorological factors, followed by precipitation on a national scale throughout the year. Additionally, the accuracy of the training and simulation results with BP neural networks in the national winter months is slightly worse. In the future, the training samples of the BP neural network can be optimized following the differences in the dominant influence of various meteorological factors on soil moisture in different areas at different times to improve the simulation prediction accuracy.