Ocean surveying is of great significance to mankind’s development and utilization of the ocean. Island and reef area surveying is an important part of ocean surveying and mapping. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) has been proven to have a certain bathymetric capability. However, the precise extraction of seafloor signal photons in these regions remains a challenge. This study introduces a method for extracting seafloor photons that is water depth adaptive and works at various depths. In addition, we propose a method to evaluate ICESat-2 seafloor signal photons by underwater light-beam propagation and noise modeling, using the decision tree method to classify signal photons into high-, medium-, and low-confidence levels. The results indicate that the method exhibits better signal continuity, better slope adaptability, and better SNR adaptability in seafloor signal photon detection, and remain more surface object signal photons in island signal photon detection thanAVEBMmethod. The high-, medium-, and low-confidence seafloor signals exhibit consistencies (R2) of 0.9954, 0.9926, and 0.9874, respectively. The root-mean-square errors (RMSEs) are 0.49 m, 0.66 m, and 0.93 m, and the mean absolute errors (MAEs) are 0.24 m, 0.44 m, and 0.86 m, correspondingly. Higher-confidence photons perform significantly better than lower-confidence photons. The confidence evaluation of seafloor photons will provide an important reference for users, and will lay the foundation for further research into the use of ICESat-2 for offshore bathymetry.

Spaceborne laser altimetry technology assists global DEMs to improve the accuracy of elevation data due to its highly accurate range and wide coverage. As compared to the previous laser altimeter systems used for Earth observation, ICESat-2 has a sensitivity for photon detection that can provide more accurate and denser surface elevation observations. This paper proposed a DEM correction model using ICESat-2 data. The model used the altimetric data to verify the DEM elevation errors in ICESat-2 coverage areas firstly. Then an attribute set was constructed to evaluate the error sources of the global-scale DEM. The evaluations of the error sources include the location/positioning of the platform, atmospheric conditions, topographic relief, land cover, and heterologous infill data, etc. Finally, a regression model was constructed by the attribute set and the DEM elevation errors within ICESat-2 coverage areas, in order to correct the DEM in areas without ICESat-2. In the validation experiments, this study conducted elevation correction experiments using the ASTER Global Digital Elevation Model (GDEM) and the Shuttle Radar Topography Mission (SRTM) in three regions around the world and applied the airborne LiDAR data in each region to verify the corrected results. The results showed that the proposed model was suitable for the elevation correction of global-scale DEMs and can be applied to more than 90% of global land, i.e., land areas with a slope less than 25°. The accuracy improvement ratios of the corrected GDEM were 17.89%–33.33% across the different types of topography, and the accuracy improvement ratios of the corrected SRTM were 27.77%–44.64% across different types of topography.

A full-waveform laser altimeter measures the round-trip time-of-flight of the laser pulse to estimate the range between the altimeter and the target, while the vertical distribution information of the terrain within the laser footprint is recorded in the full-waveform data. However, the waveform width is broadened by the target surface slope and roughness. In previous studies, the relationship between the laser altimetry waveform width and the target surface slope and roughness has been modeled based on the assumption that the laser footprint on the Earth’s surface is a circle. In this letter, based on the previous model, we propose an improved within-footprint slope estimation model by combining the shape and orientation information of the elliptic laser footprint, which further improves the accuracy of the model. The validation and accuracy assessment were performed using a high-resolution digital elevation model (DEM) of the Antarctic ice sheet. The results show that the slopes within the footprint calculated using the improved model are close to the slopes extracted from the DEM, with the mean value of the slope bias being 0.18°, standard deviation (STD) being 1.36° and root-mean-square error being 1.46°.

As the accuracy of satellite laser altimetry is susceptible to real-time atmospheric conditions, with-in footprint topography fluctuation, and detector noise, etc., we proposed a method by comprehensively analyzing the laser ranging error and evaluation labels to extract high-accuracy elevation control points that is suitable for satellite imagery based topographic mapping applications. Using the ICESat laser altimetry data, a global high accuracy laser altimetry dataset including more than 60 million control points, based on the laser altimetry ranging error model and waveform quality analysis is proposed by the paper. For land areas, except for areas of water, snow/ice, and polar ice sheets, the dataset can provide the elevation control points for worldwide satellite topographic mapping using high spatial resolution imageries or other science researches that depend on accurate earth’s elevation information. We further used airborne lidar data from six study areas around the world to carefully validate the dataset’s accuracy. The results showed that, this dataset can meet the accuracy requirement of global mapping using high spatial resolution satellite imageries in terrains with a slope below 25°. Compared to the raw dataset, the proportion of footprint elevations that conform to the accuracy standard (0.5m@ slope<2°, 1.5m@ 2°≤slope<6° and 3m@ 6°≤slope<25°) is increased from 68.24%, 59.97% and 26.52% to 87.58%, 90.04% and 83.91% respectively. This method can assure that its extracted results’ accuracy is either very close to or better than that obtained by the methods proposed in relevant studies, with a much larger number of laser footprints have been reserved.

Due to its high elevation accuracy and wide coverage, satellite laser altimetry plays an important role in many scientific fields, such as polar ice sheet monitoring, vegetation canopy height measurement, and topography mapping. However, the elevation accuracy of satellite laser altimetry data is affected by many factors, such as the atmosphere, instrument noise, terrain fluctuation, etc., which leads to an uncertain accuracy. In this letter, to solve this problem, we propose a method based on random forest to extract satellite laser altimetry footprints that meet the elevation accuracy requirements of certain applications in complex terrain. Using ICESat, we take the elevation control point accuracy requirement for 1:10 000 mapping as an example to verify the proposed method. Experimental results show that the elevation root mean square errors (RMSEs) of the selected high-quality footprints are 0.41, 0.70, and 0.87 m in flat land, hills land, and mountainous areas, respectively, which meets the requirements of 1:10 000 topography mapping. The percentage of extracted footprints that meet the elevation accuracy requirement from the three terrains are all higher than 90%.

Satellite stereo mapping, together with laser altimetry, can be used to obtain three-dimensional geospatial information. Spaceborne laser altimeter can provide high-accuracy elevation information; however, due to the lack of detailed intensity information, its planimetric accuracy is usually worse than the ranging accuracy. The Chinese Gaofen-7 (GF-7) satellite, which was designed for civilian mapping application, was launched on November 3, 2019. The GF-7 satellite’s main payloads are a laser altimeter system (with footprint camera) and a dual-linear charge-coupled device (CCD) mapping camera. According to the pixel coordinate of the laser footprint in the stereo image, the laser altimeter together with the footprint camera can provide planimetric geodetic coordinates for the control points of a higher accuracy than the other traditional satellite laser altimeters, and represents a new technology for satellite mapping. In this article, a laser footprint planimetric location method for the GF-7 satellite is proposed. The method is designed based on the main payload characteristics of GF-7 and the working modes of the laser altimeter by the combined use of subpixel phase correlation image matching and four types of laser spot center detection methods. The planimetric positioning accuracies of the laser spots in urban, suburban, farmland, forest, mountainous, and ice sheet areas were also analyzed. The experimental results show that the accuracy of planimetric location relative to stereo image for the laser footprint is 0.3–1.0 m (except for ice sheets ~12 m) when the footprint camera works under the synchronous mode, and 0.2–0.4 m when the footprint camera works under asynchronous mode (AM).

With the development of the Chinese BeiDou satellite navigation system, the applications of BeiDou reflected (BeiDou-R) signals would play a key role in Global Navigation Satellite System reflected signals. Different from other navigation systems, the BeiDou satellite navigation system has certain unique characteristics, and the Geostationary Earth Orbit (GEO) satellite is one of them. The aim of this letter is to prove the feasibility of coastal ocean phase altimetry using BeiDou GEO reflected signals. The coastal experiment was performed from October 18, 2014, to October 19, 2014, in Dayang Shan, Zhejiang, China. This is the first coastal ocean phase altimetry experiment using BeiDou GEO reflected signals. The phase altimetry results can invert the slope of ocean surface height variation (in time), and its highest accuracy can reach centimeter level.