Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.
The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.
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