Details

Autor(en) / Beteiligte

• Guo, Hengliang
• Yang, Hong
• Qiao, Baojin
• Wang, Mengfei
• Zhu, Liping

Titel

Multi-resolution satellite images bathymetry inversion of Bangda Co in the western Tibetan Plateau

Ist Teil von

• International journal of remote sensing, 2021-11, Vol.42 (21), p.8077-8098

Ort / Verlag

London: Taylor & Francis

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Taylor & Francis Journals Auto-Holdings Collection

Beschreibungen/Notizen

• Water depth is important information for lake research. However, it is difficult to obtain the depth distribution of a whole lake. The method of remote-sensing inversion by combining bathymetric data has been proved to be feasible. In order to improve the inversion accuracy, it is very crucial to research the optimal inversion model and the most suitable depth for modelling, and the most appropriate satellite images for the study area. In this study, we used the three kinds of multispectral image data (Sentinel-2, GF-1, and Landsat-8) to establish models by combining bathymetric data in Bangda Co, which is located in the northwestern Tibetan Plateau. We verified the two empirical models (Stumpf model and Lyzenga model) with the three bands of blue, green, and red in 0-30 m water depth. We created a multi-factor combination model (S-L model) to compare with a BP neural network model using three images in five different water depth ranges (0-10, 0-15, 0-20, 0-25, and 0-30 m). The results indicated that the S-L model with mean absolute error (MAE) of 3.09-4.70 m, which the accuracy was slightly better than that of the two empirical models (3.35-5.03 and 3.23-4.94 m). The MAE of the BP model over the five depth ranges was 0.71-2.41 m for Landsat-8, 1.02-3.53 m for GF-1, and 1.15-3.68 m for Sentinel-2, which was higher than the S-L model. For the five ranges of three images by using the BP model, both the accuracy of Sentinel-2 and GF-1 in the range of 0-15 m was higher, but Landsat-8 was the highest at 0-20 m among five ranges; the mean relative errors (MRE) were 19.5%, 18.7%, 13.1%, and the root-mean-square error (RMSE) were 1.82, 1.72, and 1.86 m, respectively. The best applicability of the three images was Landsat-8, followed by GF-1 and Sentinel-2 in this study. The results suggested that the freely available Sentinel-2, GF-1 and Landsat-8 images could be used to estimate water storage for a shallow lake by combining bathymetric data on the Tibetan Plateau.