ICESat Research Articles

A decade-plus of Antarctic sea ice thickness and volume estimates from CryoSat-2 using a physical model and waveform fitting

Abstract. We estimate the snow depth and snow freeboard of Antarctic sea ice using a comprehensive retrieval method (referred to as CryoSat-2 Waveform Fitting for Antarctic sea ice, or CS2WFA) consisting of a physical waveform model and a waveform-fitting process that fits modeled waveforms to CryoSat-2 data. These snow depth and snow freeboard estimates are combined with snow, sea ice, and sea water density values to calculate the sea ice thickness and volume over an 11+ year span between 2010 and 2021. We first compare our snow freeboard, snow depth, and sea ice thickness estimates to other altimetry- and ship-based observations and find good agreement overall in both along-track and monthly gridded comparisons. Some discrepancies exist in certain regions and seasons that are theorized to come from both sampling biases and the differing assumptions in the retrieval methods. We then present an 11+ year time series of sea ice thickness and volume both regionally and pan-Antarctic. This time series is used to uncover intra-decadal changes in the ice cover between 2010 and 2021, showing small, competing regional thickness changes of less than 0.5 cm yr−1 in magnitude. Finally, we place these thickness estimates in the context of a longer-term, snow freeboard-derived, laser–radar sea ice thickness time series that began with NASA's Ice, Cloud, and land Elevation Satellite (ICESat) and continues with ICESat-2 and contend that reconciling and validating this longer-term, multi-sensor time series will be important in better understanding changes in the Antarctic sea ice cover.

A methodological framework for specular return removal from photon-counting LiDAR data

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data may contain abundant noise photons induced by specular reflection, which make the signal photon detection and surface elevation extraction challenging, especially on flat surfaces with high reflectivity. However, no efficient algorithm for specular return noise removal has been proposed. Therefore, this study aims to propose a novel methodological framework based on statistical features to remove noise photons induced by specular reflection. First, ICESat-2 data preprocessing was performed to improve the speed and accuracy of subsequent processing. Second, the statistical features of outlier distance were calculated and utilized to filter out noise photons. Third, the specular return noise was removed according to the extreme points in the elevation distribution histogram. Finally, the manually labeled of photons and in-situ data were utilized to evaluate the performance of the proposed framework. Additionally, the proposed framework was compared with three existing signal photon extraction algorithms (ATL03, ATL13, AVEBM) for further assessment. The experimental results show that the F1-score of the proposed framework is 99.9 % and the accuracy of water level estimation is extremely high (bias = 0.041 m, and RMSE = 0.082 m). Further analysis demonstrates that our framework has the high robustness because it is not sensitive to input parameter. Meanwhile, the weak beam achieved higher water elevation accuracy than that of the strong beam. Besides, the proposed framework performs better than the existing methods in extracting water surface photons and water level. All in all, the proposed framework has been shown to effectively remove noise photons of specular returns in the raw ICESat-2 data and reduce the underestimation of the water level.

Subglacial lake activity beneath the ablation zone of the Greenland Ice Sheet

Abstract. Hydrologically active subglacial lakes can drain large volumes of water and sediment along subglacial pathways, affecting the motion and mass balance of ice masses and impacting downstream sediment dynamics. To date, only eight active lakes have been reported beneath the Greenland Ice Sheet (GrIS), and thus the understanding of their spatial distribution and dynamic processes is still lacking. Here, using ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) ATL11 data, we identify 18 active subglacial lakes, 16 of which have not been previously reported. Multi-temporal ArcticDEM (digital elevation model of the Arctic) strip maps were used to extend the time series to verify lakes and determine their drainage history. The identification of active subglacial lakes beneath the GrIS is complicated by the occurrence of supraglacial lakes, which also fill and drain and are hypothesized to be almost co-located. We therefore used the temporal pattern of ice-surface elevation change to discriminate subglacial lakes and utilized the ability of ICESat-2 to penetrate through surface water to correct the elevation provided by the ATL11 data. A significant localized elevation anomaly (−16.03–10.30 m yr−1) was measured in all detected subglacial lakes after correction, revealing that six subglacial lakes are twinned with supraglacial lakes. The active subglacial lakes have large upstream hydrological catchments and are located near or below the equilibrium line. Lakes have a median area of 1.20 km2, and 12 lakes exhibited positive elevation-change rates during the ICESat-2 period. These observations illustrate the potential for combining ICESat-2 and the ArcticDEM to differentiate small subglacial lakes in the ablation zone and beneath supraglacial lakes.

Assessment of ICESat-2’s Horizontal Accuracy Using an Iterative Matching Method Based on High-Accuracy Terrains

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.

A Robust Algorithm for Photon Denoising and Bathymetric Estimation Based on ICESat-2 Data

The Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) is equipped with an Advanced Terrain Laser Altimeter System (ATLAS) with the capability of penetrating water bodies, making it a widely utilized tool for the bathymetry of various aquatic environments. However, the laser sensor often encounters a significant number of noise photons due to various factors such as sunlight, water quality, and after-pulse effect. These noise photons significantly compromise the accuracy of bathymetry measurements. In an effort to address this issue, this study proposes a two-step method for photon denoising by utilizing a method combining the DBSCAN algorithm and a two-dimensional window filter, achieving an F1 score of 0.94. A robust M-estimation method was employed to estimate the water depth of the denoised and refraction-corrected bathymetric photons, achieving an RMSE of 0.30 m. The method proposed in this paper preserves as much information as possible about signal photons, increases the number of bathymetric points, enhances the resistance to gross error, and guarantees the accuracy of bathymetry measurements while outlining the underwater topography. While the method is not fully automated and requires setting parameters, the fixed parameter values allow for efficient batch denoising of underwater photon points in different environments.

Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC

Abstract. Information about sea ice surface topography and related deformation is crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), part of the National Aeronautics and Space Administration (NASA) Earth Observing System, has been on orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of sea ice surface topography are required. During the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS), achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution centimeter-scale measurements of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography are represented in different ICESat-2 products as well as how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here, we compare the ALS measurements with ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small, narrow, and only partly open cracks that appear to be overlooked by ATL07.

Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

Most satellite-derived bathymetry (SDB) methods developed thus far from passive remote sensing data have required in situ water depth, thus limiting their utility in areas with no in situ data. Recently, new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) observations have shown great potential in providing high-precision a priori water depth benefits from range-resolved lidar. In this study, we propose a combined active and passive remote sensing SDB method using only satellite data. An adaptive ellipse DBSCAN (AE-DBSCAN) algorithm is introduced to derive a priori bathymetric data from ICESat-2 data to automatically adapt to the terrain change complexity, and then we use these a priori bathymetric data in Sentinel-2 images to help build a model between remote sensing reflectance (Rrs) and water depth. Three machine learning (ML) methods are then employed, and the performances compared with conventional empirical SDB models are presented. After that, the results using different Sentinel-2 Rrs band combinations and the effects with and without atmospheric correction on ML-based SDB are discussed. The results showed that our AE-DBSCAN method performs better than the standard DBSCAN method, and the ML-based SDB can achieve an overall RMSE of less than 1.5 m in St. Thomas better than the traditional SDB method. They also indicate that ML-based SDB can obtain a relatively high-precision water depth without atmospheric correction, which helps to improve processing efficiency by avoiding a complex atmospheric correction process.

Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter

Abstract. With the continuous development of the China ocean dynamic environment satellite series (Haiyang-2, HY-2), it is urgent to explore the potential application of HY-2B in Arctic sea ice thickness retrievals. In this study, we first derive the Arctic radar freeboard and sea ice thickness during two cycles (from October 2019 to April 2020 and from October 2020 to April 2021) using the HY-2B radar altimeter and compare the results with the Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) products. We evaluate our HY-2B sea ice freeboard and thickness products using Operation IceBridge (OIB) airborne data and Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) products. Finally, we estimate the uncertainties in the HY-2B sea ice freeboard and sea ice thickness. Here, we derive the radar freeboard by calculating the difference between the relative elevation of the floe obtained by subtracting the mean sea surface (MSS) height and sea surface height anomaly (SSHA) determined by an average of the 15 lowest points method. The radar freeboard deviation between HY-2B and CS-2 is within 0.02 m, whereas the sea ice thickness deviation between HY-2B and CS-2 is within 0.2 m. The HY-2B radar freeboards are generally thicker than AWI CS-2, except in spring (March and April). A spring segment likely has more floe points than an early winter segment. We also find that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice (MYI) are larger than those over first-year ice (FYI). The correlation between HY-2B (CS-2) sea ice freeboard retrievals and OIB values is 0.77 (0.84), with a root mean square error (RMSE) of 0.13 (0.10) m and a mean absolute error (MAE) of 0.12 (0.081) m. The correlation between HY-2B (CS-2) sea ice thickness retrievals and OIB values is 0.65 (0.80), with an RMSE of 1.86 (1.00) m and an MAE of 1.72 (0.75) m. The HY-2B sea ice freeboard uncertainty values range from 0.021 to 0.027 m, while the uncertainties in the HY-2B sea ice thickness range from 0.61 to 0.74 m. The future work will include reprocessing the HY-2B L1 data with a dedicated sea ice retracker, and using the radar waveforms to directly identify leads to release products that are more reasonable and suitable for polar sea ice thickness retrieval.

Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program.

A Pre-Pruning Quadtree Isolation Method with Changing Threshold for ICESat-2 Bathymetric Photon Extraction

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data.

A Methodological Framework for Mapping Canopy Cover Using ICESat-2 in the Southern USA

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides exceptional opportunities for characterizing the structure of ecosystems through the acquisition of along-track, three-dimensional observations. Focusing on canopy cover as a fundamental parameter for assessing forest conditions, the overall goal of this study was to establish a framework for generating a gridded 30 m canopy cover product with ICESat-2. Specifically, our objectives were to (1) Determine and compare ICESat-2-derived canopy cover with airborne lidar-derived and the 2016 National Land Cover Database (NLCD) cover product estimates, and (2) Evaluate a methodology for wall-to-wall mapping of canopy cover. Using two Southern US sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, four measures of canopy cover estimated with ICESat-2′s Land-Vegetation Along-Track Product, or ATL08, were evaluated at the 30 m pixel scale. Comparisons were made using spatially coinciding NLCD pixels and airborne lidar-derived reference canopy cover. A suite of Landsat and Landsat-derived parameters were then used as predictors to model and map each measure of canopy cover with Random Forests (RF), and their accuracies were assessed and compared. Correlations (r) between ICESat-2-derived and airborne lidar canopy cover at the pixel scale ranged from 0.57 to 0.78, and R2 up to 0.81 was produced between NLCD and ICESat-2-derived canopy cover. RF models developed for extrapolating ICESat-2-derived canopy cover estimate yielded R2 values between 0.50 and 0.61 (RMSEs between 16% and 20%) when evaluated with airborne lidar-derived canopy cover. With a demonstrated capability of ICESat-2 to estimate vegetation biophysical parameters, the findings serve to support the spatially comprehensive mapping of other vegetation attributes, especially forest aboveground biomass, and contribute to the development of an up-to-date gridded canopy cover product.

DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics

China’s first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements’ accuracies. The results indicated that the accuracy of GF-7 DEMMethod1 was the worst and that of GF-7 DEMMethod3 was superior to that of GF-7 DEMMethod2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets.

Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R2 = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.

ICESat-2 data classification and estimation of terrain height and canopy height

ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) was launched in 2018 with a photon-counting LiDAR (Light Detection and Ranging) system, ATLAS (Advanced Topographic Laser Altimeter System). It is collecting massive earth elevation data all over the world, which has shown the potential of large-scale forest monitoring. However, the energy emitted by the LiDAR system is low, and the received signals are easily affected by noise. Accurate classification of photons is an important step for forest parameter retrieval. Given the limitations of existing photon classification algorithms in areas with complex terrain, we proposed an improved local outlier factor algorithm with a rotating search area (LOFR). First, photons are transformed to the along-track direction, and noise photons are preliminarily filtered out by using the elevation histogram and elevation statistical methods. Next, ground photons are extracted by using the LOF (Local Outlier Factor) with the horizontal ellipse search area algorithm (LOFE) during an initial classification stage to filter photons that are far away from the ground. During the refined classification stage, which is the core of the algorithm, the terrain slope is calculated according to the ground photons extracted during initial classification. The elliptic search area is then rotated to align its long axis with the terrain slope. Finally, the LOFR scores of the photons are calculated to remove noise photons and signal photons are classified into top-of-canopy photons, canopy photons, and ground photons. The results show that the algorithm can effectively classify photons. Both estimated terrain height and canopy height derived using the classified photons are in good agreement with airborne LiDAR data. The mean absolute error (MAE) of the estimated terrain height relative to airborne data was 1.45 m and the root mean square error (RMSE) was 2.82 m. For canopy height validation, the correlation coefficient (R2), MAE, and RMSE at the best study scale (80 m) were 0.86, 1.82 m, and 2.72 m, respectively. These results demonstrated that the proposed LOFR algorithm can improve photon classification over complex terrain without prior knowledge of the terrain. Therefore, it could provide a robust approach for large-scale ATLAS data processing.

Estimation of Arctic Sea Ice Thickness from Chinese HY-2B Radar Altimetry Data

Sea ice thickness (SIT) is an important parameter in the study of climate change. During the past 20 years, satellite altimetry has been widely used to observe sea ice thickness. The Chinese Haiyang-2B (HY-2B) radar altimeter, launched in October 2018, can provide data up to 80.6° latitude and can be used as a supplementary means to observe polar sea ice. Reliable HY-2B SIT products will contribute to the sea ice community. In this study, we aimed to assess the Arctic sea ice thickness retrieval ability of the HY-2B radar altimetry data. We processed the HY-2B radar altimetry data from January 2019 to April 2022 and used the processed data to retrieve the Arctic SIT. The Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) SIT products were used to calibrate the HY-2B SIT estimates with a linear regression method. The Goddard Space Flight Center (GSFC) CS-2, Jet Propulsion Laboratory (JPL), and GSFC ICESat-2 (IS-2) SIT products were used to validate the HY-2B calibrated SIT estimates. The HY-2B calibrated SIT estimates have good, consistent spatial distributions with the CS-2 and IS-2 SIT products. The comparison with the IS-2 and IS-2 SIT products shows the root-mean-square error (RMSE) and bias for the HY-2B SIT estimates are significantly reduced after calibration. The HY-2B SIT estimates were also validated using the ice thickness data from Operation IceBridge (OIB) and the ice draft data from the Beaufort Gyre Exploration Project (BGEP). Finally, the monthly variations of the HY-2B SIT estimates were analyzed. Results show that the HY-2B calibrated SIT estimates are reliable, especially when the SIT values are lower than 3 m. The HY-2B altimetry data is a possible source for sea ice thickness data at lower latitudes and will help us better understand the sea ice response to climate change.

Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 108 m3·yr−1 (R2 = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 108 m3·yr−1, R2 = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management.

Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.

A noise removal algorithm based on adaptive elevation difference thresholding for ICESat-2 photon-counting data

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown great potential for near-shore bathymetry. Unavoidably, the photon data acquired has a lot of noise. In order to remove noise more accurately, a photon denoising algorithm based on adaptive elevation difference thresholding (AEDTA) is proposed under the assumption that the terrain is continuously changing. The algorithm uses a Gaussian function obtained by fitting an elevation histogram to ICESat-2 data for the extraction of underwater photons. Repeatedly count the elevation difference histogram of underwater photons and adaptively determine the threshold. And the final denoised underwater signal photons are obtained. Compared with the conventional clustering method of ordering points to identify the clustering structure (OPTICS), our algorithm achieves better photon detection results for underwater topography for different densities of ICESat-2 data and has the advantage of parameter adaption without human intervention. ICESat-2 data from Ganquan Island, Dongdao Island and Oahu Island were selected for testing, and the ICESat-2 bathymetry results calculated by the algorithm were compared with in situ bathymetry data to assess the reliability and accuracy of the proposed denoising algorithm. The results show that the MAE of the bathymetry results calculated using the ICESat-2 signal photon data extracted by the method ranged from 0.54 to 1.57 m and the RMSE ranged from 0.64 to 1.74 m in different seas. The algorithm can provide high quality denoised data for bathymetric mapping around coastal areas and islands. Meanwhile, the AEDTA algorithm also has a good denoising effect on terrestrial photons, and the effect of cloud shading on the Gaussian fit analysis of ICESat-2 data cannot be ignored.

Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations

Satellite laser altimetry has been widely used for monitoring surface height changes in inland waters. However, constructing time series of water levels is partially limited in temporal resolution only based on the individual orbit of altimeter observations. To densify and optimize the time series of altimetry-based water levels is crucial to the scientific understanding of lake hydrologic dynamics. This paper focuses on synthesizing the multi-orbit on-lake observations from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) to densify and refine the water level time series for large lakes. The approach of synthesizing water level time series has been validated through experiments applied to 18 large lakes worldwide, resulting in an average R of 0.93, RMSE of 0.14 m, MAE of 0.12 m, NSE of 0.67, and CV of 2.86, according to the hydrologic gauge stations. The evaluation results demonstrate that our approach can provide an effective solution for densifying the water level series of large lakes covered by multi-orbit ICESat-2 observations. Further, the approach can be extended to monitor the high-frequency variation of other lakes covered by the multiple ICESat-2 orbits. This approach provides the potential of generating higher-frequency estimates of water levels based on satellite altimetry, which could not only help to reveal the characteristics of the seasonal dynamics of lakes but also be used to investigate the abrupt water level changes due to hydrological extreme events (e.g., floods, droughts, etc.).

Combining ICESat-2 photons and Google Earth Satellite images for building height extraction

Building heights are one of the crucial data for comprehending the functions of urban systems. Employing optical remote sensing imagery, the shadow-based method is one of the most promising methods which have been proposed for estimating building height. However, the existing shadow-based studies for building height estimation are restricted to a small area due to the lack of building height annotations and ignorance of building azimuth variations. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) allows large-scale building height retrieval in the along-track direction and thus can be taken as ground truth building height annotations to support the shadow-based algorithms for large-scale building height extraction. Here, we proposed an approach for extracting building height by combining Google Earth Satellite (GES) images and ICESat-2 photons. Building and shadow instances were first extracted using a U-Net deep learning framework. Based on the building height annotations retrieved from ICESat-2 photons, an improved shadow-based building height estimation model by minimizing the global error across all sample buildings was developed. A typical urban area located in the city center of Shanghai, China with an area of around 90 km2 was selected to validate the proposed method. In total 15,966 buildings were successfully extracted and the results indicated that the estimated building heights have high accuracy with an absolute mean error of 4.08 m. Moreover, the proposed method shows a better performance compared to the existing shadow-based method and existing building height datasets. The method holds great potential for large-scale building-level height retrieval which contributes to further studies of urban morphologies.