## 5 Conclusions  
The purpose of this study was to obtain remote-sensing transparency information about lakes more effectively. The combination of the GEE and ACOLITE algorithm was used to achieve the capability to obtain accurate remote-sensing reflectance of measurement points online and to do so in a quick and efficient manner. The effectiveness of the empirical model, SVM model, and MLP model for estimating the transparency of selected lakes in Yunnan was evaluated under the condition of using different time windows. It was found that using a combination of bands and band ratios provided a stronger transparency interpretation capability compared with using only a single band or a combination of bands. The increase in time windows could reduce the prediction accuracy of the model; however, using a combination of band combinations and band ratios would alleviate this. The MLP model had much higher accuracy than the regression and SVM models when predicting trained data but not when the prediction target was outside the training data. The empirical method was more suitable for training with a 1-day time window when the model had the highest accuracy and was still effective in predicting data outside the training data. The SVM method trained with a 7-day time window provided a model with higher accuracy and more reliable and stable performance than the empirical model when dealing with data outside the learning region, similar to the results of the empirical model when trained with a 1-day time window. In general, we found that the MLP model can better explain the relationship between remote-sensing reflectance and lake transparency when sufficient data are available, and the prediction target is the lake to which the training data belong. The performance of the SVM model is expected if the target is the lake outside the study area. Our work makes a significant contribution to the use of remote sensing to monitor lakes in the Yunnan region. Although this work only completed remote-sensing re-reflection extraction and transparency estimation of sample points, only some lakes in the Yunnan region were studied, which cannot fully represent the applicability of the study results in the whole Yunnan region; meanwhile, due to the lack of field data, only field data in some time periods were used for the study. However, the combination of the ACOLITE algorithm and GEE provides a technical basis for the next step to realize the online monitoring of regional lake transparency, and the model evaluation results provide an experimental basis for the next step. In the future, we will expand the temporal and spatial scales of the study based on the findings of this study, and extend the inversion work of transparency from point-based to the whole lake, the whole Yunnan Province, or an even larger scale. The supported time series from Landsat 8 will be expanded to the farthest time that can be traced by Landsat series satellites. This work provides a framework and experimental basis for realizing remote-sensing lake monitoring in Yunnan Province and will be the cornerstone for realizing remote-sensing monitoring of water resources in Yunnan Province. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13064007/s1](https://www.mdpi.com/article/10.3390/app13064007/s1)]([https://www.mdpi.com/article/10.3390/app13064007/s1](https://www.mdpi.com/article/10.3390/app13064007/s1)), Table S1: Statistics of the prediction effect of stepwise regression model on its own training dataset; Table S2: Statistics of the prediction effect of multi-layer perceptron model and support vector machines model on its own training dataset. Conceptualization, K.Y. and L.Z. ; methodology, W.Z. ; software, W.Z. ; validation, W.Z., K.X. and S.C.; formal analysis, W.Z. ; investigation, W.Z. and L.Z. ; resources, L.Z. ; data curation, W.Z. ; writing--original draft preparation, W.Z. ; writing--review and editing, W.Z. ; visualization, W.Z. ; supervision, L.Z. ; project administration, W.Z.