Generating continuous models from sparse measurements remains a key challenge in remote sensing due to the limited availability and high cost of dense data collection. Traditional interpolation methods such as ordinary Kriging and natural neighbor interpolation often degrade significantly in accuracy under sparse measurements. To address this, we introduce T-GMSI, a Transformer-based Generative Model for Spatial Interpolation approach, which leverages the Vision Transformer (ViT) architecture for high-quality spatial interpolation from sparse inputs. We then apply T-GMSI to the task of digital elevation model (DEM) generation using sparse spaceborne laser altimetry data. Results show that T-GMSI maintains high accuracy even with over 70% data sparsity and generalizes well across diverse landscapes without requiring fine-tuning. Compared to baseline methods, T-GMSI reduces root mean square error (RMSE) by 40% and 25% over ordinary Kriging and natural neighbor interpolation, respectively, on airborne lidar datasets, and by 23% and 10% on spaceborne laser altimeter data. It also outperforms a state-of-the-art conditional generative adversarial network (CEDGAN), improving RMSE by 35% and 20% for airborne and spaceborne data, respectively. The study highlights the potential of learning-based interpolation methods for improving Earth observation modeling with sparse and incomplete remote sensing data.

Recent developments in spaceborne laser altimetry have revolutionized the way DEMs can be created with unprecedented productivity and accuracy. This paper aims to develop a holistic mathematic framework that is able to combine current multiple space-based terrain data sources for large-scale DEM generation. To achieve this goal, the developed framework can accommodate the heterogeneity of the involved multi-sensor data. Under the context model estimation or regression, the ICESat-2 ATL08 terrain dataset is treated as the observations for the target variable, while the predictor variables are derived from the GEDI Level 2A terrain data and the SRTM DEM. Three different models are then applied to determine their performance and identify the most accurate and robust one. Comparative evaluation for areas of over 11,000 square kilometers demonstrates that the support vector regression approach consistently yields superior and satisfactory results, surpassing the quality of current SRTM 30 m and 90 m DEMs. Using the 3DEP DEM as independent reference, the corrected SRTM DEMs exhibit a substantial reduction in the mean of the DEM error by one order of magnitude (~10 times), and a 27% to 36% significant improvement in RMSE. As for the corrected GEDI L2A terrain data, it achieves an exceptional accuracy of -0.4±1.4 m for plain suburban area and -0.6±9.3 m for mountain area. This study underscores the benefits of integrating multiple spaceborne altimeter data and the necessity of adopting holistic data integration models for such purpose.

The ICESat-2 ATL13 product provides measurements of inland water surface height or water level. However, it has been shown that such water level observations are subject to noticeable uncertainties and need to be carefully handled for reliable water level determination. This paper presents an approach to detect outliers in the ATL13 product so that accurate outcome can be achieved. Lake Huron and Lake Superior of the Great Lakes are selected as the study areas. The extracted water levels from ATL13 over a period of four years are validated by using field observations at the closest NOAA hydrological stations. This work demonstrates the critical need on outlier removal and the capability of the ATL13 data. A bias of 9-10 cm is found in the ATL13 product. Such an uncertainty is positively related to the frozen precipitation, but mostly independent from the laser beam intensity and data acquisition time.

Widely used in geoscience, global or large-scale digital elevation model (DEM) is an essential depiction of the 3-dimenional information of the bare earth [1] . Among others, the most popular DEMs include the Shuttle Radar Topography Mission (SRTM) DEM (1" for USA and 3" for global), ASTER GDEM (30m), and several other similar ones [1] . The most significant problem for these existing global DEMs is the temporal latency (more than 20-year-old for SRTM [2] , more than 10-year-old for ASTER [3] ). Furthermore, some of these foundation data lack consistencies due to the inclusion of multiple data sources collected over a long period of time [1] . The successful launches of the Global Ecosystem Dynamics Investigation (GEDI) mission in December 2018 [4] and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission [5] in September 2018 provide current, complementary, and dense on-orbit global elevation with unprecedented accuracy and coverage in the history of space laser altimetry. However, many researchers have found that GEDI and ICESat-2 data have inconsistent quality, e.g., a root mean square error (RMSE) of 4.48 m for GEDI terrain height [6] and an uncertainty ranging from 0.2 m to 2 m for ICESat-2 ATL08 terrain height [7] . Beyond the abundant research focusing on quality assessment, there are a significant amount of work on wall-to-wall mapping by integrating GEDI and other Earth observations to overcome the spatial heterogeneity of spaceborne lidar data [8] - [11] . However, there are no recent studies to generate terrain height using GEDI since it is originally designed for forestry studies; and the capability and accuracy of its terrain measurements are less promising than canopy measurements [6] , [7] . Subsequently, few research was reported to combine the terrain measurements from GEDI and ICESat-2 to achieve an even denser coverage than using GEDI or ICESat-2 alone.

The Advanced Topographic Laser Altimeter System (ATLAS) laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2) can measure the elevation of the Earth’s surface with unprecedented spatial detail. However, the quality of the derived signal and ground photons depends on the signal-to-noise ratio and canopy coverage. Current algorithms underperform for data collected during daytime over mountain areas with dense canopy. We demonstrate a novel procedure for signal photon detection and subsequent ground photon detection from ICESat-2 ATL03 data. We first introduce a gravity-based density model to characterize the anisotropic properties of photon distribution. Through jointly using the photon densities from the weak–strong beam pair, we are able to find key photons that have high probability being signals. A directional regional growing approach then takes these key photons as seeds to label all remaining signal photons. Finally, we introduce a weighted iterative median filter (WIMF) algorithm to identify ground photons whose height is closest to the estimated ground surface. A total of 36 ATL03 beams of two entire counties in USA are used for test and evaluation. Compared to the ATL03 and ATL08 algorithms, our signal photon finding method is more robust to the variation of topography, canopy coverage, and data collection time. Remarkably, the mislabeling caused by the after-pulsing effect does not present in our detected signal photons. Comparing current ATL03 and ATL08 products, the detected ground photons from our method are more consistent with reference to the 3DEP DEM, especially for strong beam data collected during daytime in dense canopy, high relief areas.

Current spaceborne lidar Ice, Cloud, and Land Elevation Satellite (ICESat)-2 provides ATL08 product for global terrain height, whose quality properties are yet to be fully understood. This article performs a comprehensive evaluation on its quality by using 3-D elevation program (3DEP) digital elevation model (DEM) and hundreds of survey marks of two counties in the USA. The evaluation is carried out in terms of data specification, survey marks, land cover, season and time (day or night) of acquisition, incidence angle, and terrain slope. The ATL08 height errors are further modeled as a function of laser incidence angle and canopy coverage. It is found out the height from ATL08 product lies between the 3DEP DEM and true ground surface. The uncertainty of ATL08 height is 0.2 m for plain terrain, and 2 m for mountainous terrain where the majority of ATL08 segments are not useful for terrain extraction. The terrain height gets more underestimated by ATL08 products in regions with large terrain slope or incidence angle and more overestimated where the terrain is covered by dense canopy. Furthermore, seasonal variation of terrain height error can be as high as 0.8 m, while the impact of acquisition time is less than 0.3 m. We expect these findings to be informative for the utilization of ATL08 terrain product by worldwide users, and for the improvement of future ATL08 product.