## 4 Discussion
Most current studies only use spectral reflectance data as the sole input feature, without considering the seasonal effects on water quality inversion. This study incorporates time information based on spectral data, using the month of water quality monitoring as one of the model input features. Water temperature and regional rainfall vary by month and are closely related to water quality. Incorporating time information during model training allows the model to better capture seasonal information, which can more easily establish the relationship between input features and water quality, improve water quality inversion accuracy, and provide valuable references for monitoring TP concentration in water bodies using multispectral image bands. To evaluate the effectiveness of the model in inversing the total phosphorus concentration in Taihu Lake, this study selected three time points to invert the total phosphorus concentration in Taihu Lake. The inversion results indicate that there is some spatial variability in the distribution of total phosphorus in Taihu Lake. Overall, the average total phosphorus concentration has been decreasing annually, decreasing from 0.0428 mg/L to 0.0388 mg/L. The overall total phosphorus concentration remains below 0.1 mg/L, classifying the lake as mesotrophic. Only in the peripheral areas of the northern and western regions does the total phosphorus concentration exceed 0.1 mg/L, indicating a mildly eutrophic state. And the total phosphorus concentration around the lake's periphery is relatively higher. This is primarily due to the significant impact of domestic sewage and industrial and agricultural wastewater pollution in the area, which contains a large amount of phosphorus pollutants. Therefore, the relevant environmental management authorities need to improve the treatment efficiency of wastewater collection and treatment facilities in the region to reduce the discharge of phosphorus-containing pollutants. Overall, the multilayer denoising autoencoder model established in this study is able to reduce the noise in the input data and extract effective information from them, which lays the foundation for the subsequent water quality inversion. Meanwhile, the bagging model captures the complex nonlinear relationship between water quality variables and surface reflectance, demonstrating high inversion accuracy and showing good performance in solving complex water quality regression problems. It provides a reasonable and comprehensive inversion framework for the subsequent inversion of non-optically active water quality indicators using remote sensing data, allowing managers to formulate corresponding watershed environmental management policies based on the inverted water quality data. Due to factors such as weather conditions, low temporal resolution, and missing monitoring data, the dataset collected in this study is not large. In the future, it would be beneficial to collect more training data by using higher spatial and temporal resolution remote sensing data or by integrating remote sensing data from different sources [54]. Alternatively, the method of transfer learning [55; 56] could be employed, where the model is initially trained using a source dataset and then fine-tuned to efficiently predict the target dataset, thereby enhancing the model's generalization capability. With a large amount of training data available, complex deep learning models can be utilized to capture the nonlinear relationship between spectral reflectance and water quality indicators more accurately, leading to more precise water quality inversion results.