Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multidate images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this article, using only satellite data, including multitemporal Sentinel-2 images and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, a multitemporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multitemporal Sentinel-2 images can achieve a mean root mean square error (RMSE) of 1.08 m (R ² = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R ² = 0.92, RMSE = 1.46 m) derived from single date image based methods.Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

Accurate and detailed information on lake/reservoir water levels and temporal changes around the globe is urgently required for water resource management and related studies. The traditional satellite radar altimeters normally monitor water level changes of large lakes and reservoirs (i.e., greater than 1 km²) around the world. Fortunately, the recent Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) makes it possible to monitor water level changes for some small lakes and reservoirs (i.e., less than 1 km²). ICESat-2 ATL13 products provide observations of inland water surface heights, which are suitable for water level estimation at a global scale. In this study, ICESat-2 ATL13 products were used to conduct a global estimation and assessment of lake/reservoir water level changes. We produced monthly water levels for 13,843 lakes and reservoirs with areas greater than 0.1 km² and all-season ATL13 products across the globe, in which 2257 targets are smaller than 1 km². In total, the average valid number of months covered by ICESat-2 is 5.41 months and only 204 of 13,843 lakes and reservoirs have water levels in all the months in 2019. In situ water level data from 21 gauge stations across the United States and 12 gauge stations across Australia were collected to assess the monthly lake/reservoir water levels, which exhibited a high accuracy (RMSE = 0.08 m, r = 0.999). According to comparisons between the monthly water levels and changes from ATL08 products in another study and ATL13 products in this study, we found that both products can accurately estimate the monthly water level of lakes and reservoirs, but water levels derived from ATL13 products exhibited a higher accuracy compared with water levels derived from ATL08 products (RMSE = 0.28 m, r = 0.999). In general, the ATL13 product is more convenient because the HydroLAKES mask of inland water bodies, the orthometric height (with respect to the EGM2008 geoid) of water surfaces, and several data quality parameters specific to water surfaces were involved in the ATL13 product.

Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.

Acquiring accurate and timely datasets on tidal flat topography is fundamental for studies, management, and policy‐making in coastal zones. In this study, we report a fusion method of freely accessed and global‐scale Sentinel‐2 (imagery) and Ice,Cloud, and Land Elevation Satellite‐2 (ICESat‐2) (laser altimetry) datasets for deriving tidal flat topography. The basic idea is to construct a model on characterizing the specific relationship between the elevations of the tidal flat (from ICESat‐2 data) and corresponding inundation frequencies (from Sentinel‐2 time‐series). The proposed method was validated for the largest tidal flat on the Chinese continental shelf with a root mean square error of 0.23 m. We highlight that this method solely relies on satellite data and does not require any in‐situ data because ICESat‐2 provides dense and accurate along‐track points as valuable prior elevation information, which enhance the ability to obtain tidal flat topography in many coastal regions even in the absence of local in‐situ data.

Monitoring global lake/reservoir water level changes is needed to understand the global water cycle and investigate its potential drivers. The existing global water level products only cover lakes/reservoirs with large sizes (>100 km²). Here, Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 altimetry data with small footprints are employed to examine global water level changes for 22 008 lakes/reservoirs greater than 1 km². We report that 77.56% of them exhibited rising water levels over 2003–2021. Across the globe, 78.84% of lakes exhibit a rising water level, while the proportion for reservoirs is only 56.01%. Global lake/reservoir is estimated to experience a median water level change rate of +0.02 ± 0.02 m yr⁻¹ over 2003–2021, and lakes have a larger water level rise (+0.02 ± 0.02 m yr⁻¹) than reservoirs (+0.008 ± 0.14 m yr⁻¹). We detect large-scale rising water levels in the Tibetan Plateau, the Mississippi River basin, and high-latitude regions of the Northern Hemisphere. Our calculation also suggests a negative relationship between the percentage of water level rise in lakes/reservoirs and population density for global river basins (r = −0.41, p-value < 0.05) and 11 hotspots (r = −0.48, p-value < 0.05). Our result suggests that inland water level has tended to rise in recent years under natural processes while human activities (i.e. with higher population density) can balance the water level rise via reservoir regulation. We find the existing datasets underestimated global water level rise, which may be caused by the exclusion of numerous small lakes/reservoirs. Our estimated global water level change rates (that include numerous small lakes with areas of 1–10 km²) can improve the understanding of global hydrological cycle and water resource management under the double pressure of climate warming and human activities.

Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.