Tropical Geography ›› 2020, Vol. 40 ›› Issue (2): 314-322.

### Estimating Inland Water Depth Based on Remote Sensing and Machine Learning Technique

Wen Kaixiang1,2,5, Li Yong1,2,5(), Wang Hua1, Yang Ji2,5, Jing Wenlong2,5, Yang Chuanxun2,3,4,5

1. 1.School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510070, China
2.Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System//Guangdong Open Laboratory of Geospatial Information Technology and Applicaton//Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)//Guangzhou Institute of Geography, Guangzhou 510070, China
3.Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
• Received:2019-12-02 Revised:2020-03-31 Online:2020-03-10 Published:2020-05-15
• Contact: Li Yong E-mail:59525546@qq.com

Abstract:

This study investigates the application of three machine learning algorithms, e.g. the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) algorithms, in water depth estimation using Sentinel-2 multispectral satellite images. A case study is conducted in the Meizhou Reservoir in Guangdong, China, and a comparative analysis of the inversion results is performed. The results are validated against in-situ measured data using an unmanned ship, in which a global positioning system and a single wave velocity sounding system are integrated. The experimental results based on our water depth inversion models demonstrate good inversion accuracy and efficiency of the machine learning water depth inversion model constructed using 7 925 water depth data samples and satellite multispectral images. By adjusting the key parameters of each model such that the model reaches the optimal state, the determination coefficient (R 2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias were used to evaluate the accuracy of the models. For water depth inversion, the RF model indicated that R 2 = 0.80, RMSE = 2.21, MAE = 1.51, and Bias = 0.00; the XGBoost model indicated that R 2 = 0.79, RMSE = 2.22, MAE = 1.50, and Bias = -0.01; and the SVM model indicated that R 2 = 0.78, RMSE = 2.23, MAE = 1.60, and Bias = 0.01. To determine the efficiency of each model, the models’ running times were obtained: 3.92 s for RF, 4.26 s for XGBoost, and 6.66 s for SVM. Notably, the RF model is superior to the XGBoost and SVM models in terms of inversion accuracy and efficiency. Moreover, the advantages and disadvantages of each model can be inferred from the prediction result graph. The prediction results of the RF model are more detailed, and the terrain is clearer compared with those of the XGBoost model. The SVM model does not achieve ideal prediction using the same dataset, and the error at a shallow water depth is much worse than those of the other two models. The scatter plots indicate that the inversion deviation is not obvious between the RF and XGBoost models, and the prediction results are more consistent with the actual results. However, the scatter plot of the SVM has a horizontal bar near 11 m; therefore, it predicts the water depth value in this interval incorrectly. Furthermore, the errors of the RF and XGBoost models evaluated by the standard evaluation method are lower than that of the SVM model, indicating that those models have a higher prediction accuracy for water depth inversion. In summary, the machine learning models for water depth estimation yielded good performances in this study, and they are suitable for estimating the water depth using remote sensing images with lower economy and time cost, especially in unreachable waters.

CLC Number:

• P715.7