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.

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.

Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.