High spatial resolution elevation change dataset derived from icesat-2 crossover points on the tibetan plateau

ABSTRACT Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources.

However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to

extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points

indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing

studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding

high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change

data on the Tibetan Plateau. SIMILAR CONTENT BEING VIEWED BY OTHERS ELEVATIONAL DYNAMICS OF VEGETATION CHANGES IN RESPONSE TO CLIMATE CHANGE ON THE TIBETAN PLATEAU Article Open access 21

March 2025 30 M 5-YEARLY LAND COVER MAPS OF QILIAN MOUNTAIN AREA (QMA_LC30) FROM 1990 TO 2020 Article Open access 18 December 2024 BEDMAP3 UPDATED ICE BED, SURFACE AND THICKNESS GRIDDED

DATASETS FOR ANTARCTICA Article Open access 10 March 2025 BACKGROUND & SUMMARY Surface elevation changes on the Tibetan Plateau are important indicators for studying the geography,

environment, and climate of the region. These changes can be attributed to various causes. Lithospheric deformation causes slow surface changes, while natural disasters can lead to more

dramatic changes1,2. Additionally, surface elevation changes can occur due to the impact of climate change on snow mountains, glaciers, permafrost, and snow3. High spatial resolution

elevation change data is essential for several reasons. Firstly, it allows for the capture of smaller-scale terrain changes, such as those triggered by natural disasters like mudslides and

landslides. This data helps us understand surface deformation in inaccessible areas caused by these disasters. Secondly, high spatial resolution surface elevation change data provides a more

detailed quantitative analysis of surface elevation change. For instance, it can offer permafrost elevation changes in small areas, which can serve as a reference for engineering and

construction safety in cold regions. Lastly, high spatial resolution surface elevation change data can serve as basic data support for other studies. It can validate model simulation results

or refine existing large-scale products as supplementary data. However, high spatial resolution data on surface elevation changes is lacking. Existing data on elevation changes in the

Tibetan Plateau are either specific to individual areas rather than the entire plateau or have low spatial resolution. For example, a lake-level dataset generated from multi-source altimetry

satellite data and Landsat optical imagery covers the water level changes of 52 large and medium-sized lakes on the Qinghai-Tibetan Plateau3. Another lake-level dataset, which integrates

eight laser altimeter data such as Envisat, ICESat, and CryoSat-2, covers lakes larger than 10 km2 on the Tibetan Plateau4. These provide lake water level elevations for the entire lake. A

glacier change dataset generated from SRTM 2000 and ASTER stereo pairs before and after 2015 estimates elevation changes in over 40 typical glacier regions on the Tibetan Plateau5. Among

satellite altimetry data, ICESat-2 data have high accuracy (about 0.1 m), a small spatial footprint (17 m), and a very close distance between laser footprints along the orbital direction (20

 m)6. However, ICESat-2 track revisit positions are not fixed, and points of the same track are not exactly repeated in different cycles7. A comprehensive ICESat-2 crossover points dataset,

encompassing all terrains, is necessary. While ICESat-2 can provide precise surface elevation data, we often lack the specific elevation before any surface changes. This issue presents a

challenge in capturing the surface elevation change within certain areas. Many researchers have utilized the complete ICESat-2 data to analyze surface elevation changes8,9,10. However, it’s

not suitable to simply fit the data from an identical time point over a vast area to a single plane in complex terrain environments. This method could overlook critical details of surface

deformation. Furthermore, with the evolution of various models for the Tibetan Plateau (including hydrological, glacier, ecological and vegetation models)11,12,13, there is an increasing

demand for high spatial resolution surface elevation changes to be used as training data. Therefore, several scholars have used the ICESat-2 crossover points method in their studies. For

example, one study on the rate of descent of the Svalbard Archipelago ice sheet utilized the crossover points method. In this study, the authors used ICESat-1 and ICESat-2 to extract

crossover points, interpolated the elevation change at those points, and finally calculated the ice sheet’s decline rate14. However, due to the long distance of 170 m between neighbouring

footprints along the track direction of ICESat-1, the number of crossover points is low. Another study examined the accuracy of ICESat-2 in shallow water and referred to using crossover

points to obtain elevation changes. And, the authors illustrated the higher accuracy of the results obtained at the crossover points of the ICESat-2 strong beams15. Additionally, the

effectiveness of ICESat-2 crossover points for snow depth identification has been evaluated in flat areas16. Although the crossover points method can resolve surface variations associated

with changes in snow depth and seasonal melting of the active permafrost layer, its accuracy is affected by the slope and roughness of the terrain17. Specifically, (1) existing datasets

obtained based on reanalysis of ICESat-2, although using the crossover points method, only cover glaciers and lakes, not the wider surface types of the Tibetan Plateau, such as permafrost,

vegetation, deserts, and others. (2) Existing studies lack a uniform definition standard for ICESat-2 crossover points, a clear description of crossover points methods, and a more detailed

description of extraction algorithms. (3) The remote location of the Tibetan Plateau poses challenges for researchers in collecting accurate data on surface deformation. Consequently, many

studies lack the required validation data to accurately measure elevation changes. To address these issues, we produced the ICESat-2 crossover points dataset for the entire Tibetan Plateau,

covering the period from September 2018 to October 2022. We calculated the accuracy of the elevation differences provided by each group of crossover points using existing ICESat-2 validation

results on the plateau. To assess its reliability, we compared the crossover points dataset with existing studies on glaciers and lakes on the Tibetan Plateau. This experiment introduces

the definition criteria of crossover points and shows the extraction algorithm, distinguishing it from previous studies. Furthermore, the crossover point dataset extracted in this experiment

covers most of the Tibetan Plateau, including all types of terrain and slope, providing a reference for studying surface elevation changes on the plateau. It offers high-resolution surface

elevation change information at crossover point locations. The ICESat-2 crossover points dataset has a wide range of applications. For instance, it can be used as research data on changes in

the permafrost layer of the Tibetan Plateau, as supplementary data for existing glacials and lakes studies, or combined with multi-source remote sensing data to form surface elevation

changes on a continuous time series. It can also serve as validation data for various types of topographic studies. METHODS STUDY AREA The Tibetan Plateau, situated in the southern part of

Asia (67° E-105° E, 25° N-41° N), features a continental climate with strong radiation and substantial temperature differences18. It boasts an average elevation exceeding 4,000 m, with the

lowest point at 900 m and the highest peak reaching 8,844 m19. The plateau encompasses various regions including the Qiangtang Plateau, the Southern Tibetan Valley, the Qaidam Basin, the

Qilian Mountains, the Qinghai Plateau, and the Sichuan-Tibet alpine canyon area (Hengduan Mountains) (Fig. 1). Additionally, it includes diverse landscapes such as permafrost, glaciers,

grasslands, deserts, and lakes, contributing to its complex and undulating terrain.Apart from the North and South Poles, the Tibetan Plateau is the most extensively glaciated region on

earth20. It has the highest and largest area of alpine and plateau permafrost in the mid-latitudes of the Northern Hemisphere21. The plateau is also densely covered with lakes, glaciers,

frozen soils, swamps, and wetlands, which together store a significant amount of the world’s water resources. In recent years, global environmental changes significantly impacted water

resources, such as glaciers, permafrost, and lakes, leading to rapid changes in the surface elevation of the Tibetan Plateau. These changes have a broader impact on the surrounding countries

and even global climate change22. DATA SOURCES ICESAT-223 ICESat-2 was launched on September 15, 2018, equipped with the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS captures

elevation data in six tracks, emitting photons of different intensities, divided into three strong and three weak tracks. The ICESat-2 footprint measures 17 meters in diameter and has a

repetition period of 91 days. NASA has published 21 data products from ATL00 to ATL21 for different research objectives, categorized into four data levels. For this experiment, we utilized

the Level 3 land ice height product ICESat-2 ATL06 and the Level 3 land and vegetation height product ICESat-2 ATL08, which includes parameters such as latitude, longitude, time, elevation

value, and quality control parameters (Table 1). UAV (UNMANNED AERIAL VEHICLE) DATA We conducted an observational experiment using a UAV in the Qilian Mountains region, located in the

northeastern Tibetan Plateau. The UAV was equipped with DJI L1 LIDAR. The observations were made in March 2023. The initial data for drone construction was a laser point cloud. This data was

first denoised, then the ground points were extracted, and finally, it was converted into a 0.5 m DEM. We used this data to validate the accuracy of the ATL08 and ATL06 products from

ICESat-2 (Table 1). TIBETAN PLATEAU GLACIER DATA24,25 The “Glacier coverage data on the Tibetan Plateau in 2017 (TPG2017, Version1.0)” dataset published on the Spatio-Temporal Tertiary

Environmental Big Data Platform is the source of the glacier data on the Tibetan Plateau. The dataset used 210 Landsat8 OLI multispectral remote sensing images from 2013 to 2018. It was

generated using visual interpretation, with Landsat8 OLI data from 2017 accounting for 90 percent of the total data. 85 percent of the Landsat8 OLI data was imaged in winter. The dataset

provides a glacier raster resolution of 30 m. We used the data to filter ICESat-2 crossover points on the glacier and compared the filtered ICESat-2 crossover points with existing studies on

glaciers on the Tibetan Plateau (Table 1). TIBETAN PLATEAU LAKE DATA26,27,28 The “The lakes larger than 1 km² in Tibetan Plateau (v3.1) (1970s-2022)” published on the Spatial and Temporal

Tertiary Environmental Big Data Platform is the source of the lake data on the Tibetan Plateau. The dataset used long-time Landsat remote sensing data to obtain lake observations for 16

phases over nearly 50 years from the 1970s to 2022 on the Tibetan Plateau. The dataset counted the number and area of lakes larger than 1 km² and provided year-by-year lake ranges. The lake

year chosen for this experiment is 2020. We used the data to filter ICESat-2 crossover points (Table 1). DETERMINING ICESAT-2 CROSSOVER POINTS Crossover points of ICESat-2 are determined in

two ways (Fig. 2a): * 1. ICESat-2 has 1387 tracks. In the same track, ICESat-2 of different cycles may overlap. In this experiment, these repetition points are called intersections. * 2.

Crossover points are extracted at locations where different orbits intersect. Additionally, this experiment sets a distance limit between two points in the crossover points. If the distance

between the centers of two ICESat-2 laser footprints is less than 2 m, these two points form a group of crossover points. Figure 2b provides general features of ICESat-2. The overlap between

the two footprints of each group of ICESat-2 crossover points is high (Fig. 2b), allowing for more accurate elevation change measurements. There are several loops and iterations to extract

crossover points from all ICESat-2 points (Fig. 3). First, we identified a set of all ICESat-2 points in a region from September 2018 to October 2022 and assigned a specific ordinal number

to each point (assuming a total of _n_ ICESat-2 points). In the first loop, we calculated the distance between point 1 and the remaining points in this set (point 2 to point _n_) one by one.

If the distance between two points was less than 2 m, we considered them a group of ICESat-2 crossover points. After the first loop, we started the second loop. We excluded point 1 and then

calculated the distance between point 2 and the remaining points in this set (point 3 to point _n_) to determine all crossover points. We applied a similar method to identify crossover

points between point 3 and point n-1 within each loop. We combined all the crossover points within each loop to obtain the ICESat-2 crossover points data for the region. To calculate the

distance between two points, we used the Haversine formula29. In ICESat-2 ATL06 and ATL08, NASA provides the latitude and longitude of each point. The Haversine formula is a method of

calculating the distance between two points using their latitude and longitude. The equation for the Haversine formula is as follows. $${\varphi }_{1}=\frac{{{\rm{lat}}}_{{\rm{A}}}\times \pi

}{180},\,{\varphi }_{2}=\frac{{{\rm{lat}}}_{{\rm{B}}}\times \pi }{180}$$ (1) $${\theta }_{1}=\frac{{{\rm{lon}}}_{{\rm{A}}}\times \pi }{180},\,{{\rm{\theta

}}}_{2}=\frac{{{\rm{lon}}}_{{\rm{B}}}\times \pi }{180}$$ (2) $${\rm{d}}=2{\rm{r}}\times \arcsin \left(\sqrt{{\sin }^{2}\left(\frac{{\varphi }_{1}-{\varphi }_{2}}{2}\right)+\cos \,{\varphi

}_{1}\times \cos \,{\varphi }_{2}\times {\sin }^{2}\left(\frac{{\theta }_{1}-{\theta }_{2}}{2}\right)}\right)$$ (3) In the equation, A and B represent the two ICESat-2 points. latA and latB

represent the latitude of points A and B, respectively, while lonA and lonB represent their respective longitudes. _φ_1 and _φ_2 represent the radian regime measures of latitude of points A

and B, respectively, while θ1 and θ2 represent their respective radian regime measures of longitude. d represents the actual distance between A and B, and r represents the radius of the

Earth. Furthermore, we arranged the two points within each group of crossover points in chronological order and calculated the elevation difference using Eq. 4.

$${{\rm{DH}}}_{{\rm{ICESat}}}-2={\rm{H}}\_{\rm{Time}}2-{\rm{H}}\_{\rm{Time}}1$$ (4) In the equation, DH_ICESat–2 represents the elevation difference at each group of crossover points.H_Time2

and H_Time1 represent the elevation of two points in each group of crossover points, respectively, with Time1 being the earlier time. To obtain all ICESat-2 crossover points over the entire

Tibetan Plateau, we first performed data filtering and partitioning on the original ICESat-2 data. We also removed outliers from all crossover points. FILTERING AND PARTITIONING OF ICESAT-2

DATA OVER THE TIBETAN PLATEAU We followed a two-step process to filter the data before extracting ICESat-2 crossover points. First, we filtered the strong and weak photons. ICESat-2

acquires elevation data with six beams, which are divided into three weak and three strong beams. Previous studies have shown that the strong beam photons lose less energy in propagation,

have a wider range of applications, and are more accurate30,31. Therefore, we chose to use information only from the photons of the three strong beams in this experiment. Second, we filtered

out the faulty photons. ICESat-2 ATL06 provides a laser point elevation quality filter parameter, “atl06_quality_summary.” The data label for faulty photons is 1, while it is 0 for accurate

photons. We removed the faulty photons by filtering out all data labelled with 0. We partitioned the ICESat-2 data based on spatial location. The number of ATL06 and ATL08 points on the

Tibetan Plateau from 2018 to 2022 is very large, making it difficult to run the crossover points algorithm for all ICESat-2 laser points. In this experiment, we divided the Tibetan Plateau

into 5051 small regions at 0.25° intervals for latitude and longitude and extracted the crossover points of ICESat-2 for each region. By combining the extraction results from all regions, we

obtained the ICESat-2 crossover points dataset for the Tibetan Plateau. REMOVING OUTLIERS FROM ICESAT-2 CROSSOVER POINTS Outliers in the elevation difference at ICESat-2 crossover points

can be caused by factors such as cloud cover and complex terrain. These outliers can be identified and removed using the 3σ criterion. If the difference between the elevation difference at

the crossover points and the mean elevation difference is more than three times the standard deviation, it is deemed an outlier. The average elevation difference at all ICESat-2 ATL06

crossover points is −0.052 m with a standard deviation of 3.314 m. After outlier removal, the average becomes −0.040 m with a standard deviation of 1.369 m. For ICESat-2 ATL08 crossover

points, the average elevation difference is −0.301 m with a standard deviation of 5.767 m. After outlier removal, these values change to an average of −0.260 m and a standard deviation of

3.178 m. STATISTICS ON SURROUNDING POINTS Crossover points may cluster at the same location, providing repeated surface elevation data. To identify these points accurately, we first

establish a 4 m buffer around each point. Then, we count the number of points within each buffer. We chose a 4 m buffer to ensure we capture enough neighboring crossover points. Finally, we

record the total number of surrounding points in the “Around_PT” field for each point. If “Around_PT” equals 2, it signifies that only one group of ICESat-2 crossover points is around that

point. However, if the value exceeds 2, it indicates multiple groups of crossover points around the point. DATA RECORDS The “Tibetan Plateau ICESat-2 Crossover Points Dataset” can be found

in CSV and SHP formats at the National Tibetan Plateau Data Center (https://doi.org/10.11888/RemoteSen.tpdc.300749)32. This dataset includes all crossover points on the Tibetan Plateau. The

CSV files are suitable for data extraction and analysis, while the SHP files are useful for data visualization. We’ve arranged the crossover points in the Tibetan Plateau into files labeled

A2, A3, A4, …, D10 (Fig. 4) according to their latitude and longitude for easier access. There are two folders, one containing the ATL06 crossover points and the other the ATL08 crossover

points. Both folders include SHP and CSV files. The ICESat-2 crossover points file, which contains ATL06 and ATL08 crossover points, includes the following data columns: the first column

(number) represents the group number of the ICESat-2 crossover points; the second (Lon) and third (Lat) columns represent the longitude and latitude of each point; the fourth column (H)

displays the elevation information, based on the geodesic height of the WGS84 Ellipsoid (the same as in the ICESat-2 ATL06 and ATL08 source files); the fifth column (Time) shows the time

each point was acquired, with data converted from GPS seconds in the ICESat-2 raw data to a date; the sixth column (Ds) displays the distance in meters between two points in each group of

crossover points; the seventh column (Dh) represents the elevation difference in meters between the two points in each group of crossover points; and the eighth column (Around_PT) lists the

number of points within a 4 m buffer. The CSV files can be processed with programming languages like Python, C, C++, and MATLAB, and can be read directly as text. The SHP files can be opened

with ArcGIS for data visualization and filtering. TECHNICAL VALIDATION ACCURACY ASSESSMENT OF ICESAT-2 CROSSOVER POINTS The accuracy of ICESat-2, which numerous scholars have confirmed to

be around 0.1 m, is widely recognized33,34. To assess its measurement accuracy on the Tibetan Plateau, we referenced studies that validated ICESat-2 accuracy over lakes and reservoirs larger

than 10 km², Qinghai Lake, and Qilian Mountains (Fig. 5)35,36,37. These studies included both land and water bodies. Additionally, we verified the accuracy of ICESat-2 using field-acquired

UAV elevation data (Fig. 5). We compared this data from crossover points with existing glacier and lake studies. For the glaciers, we used the mean annual rate of glacier change (Eqs. 5 and

6) for comparison. $$\Delta {\rm{Hi}}=\frac{{\rm{H}}\_{\rm{Time}}2-{\rm{H}}\_{\rm{Time}}1}{{\rm{Time}}2-{\rm{Time}}1}$$ (5) $$\Delta {\rm{H}}=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{n}}}\Delta

{\rm{Hi}}}{{\rm{n}}}\times 365$$ (6) ΔHi represents the rate of change between two ICESat-2 points in the i-th group of crossover points. H_Time1 and H_Time2 are the elevations of these

points, and Time1 and Time2 are their respective times (Time1 being earlier). ΔH represents the rate of change of the glacier’s mean annual elevation in the validation area. To calculate the

RMSE of the elevation difference at ICESat-2 crossover points, the error propagation formula was utilized based on the ICESat-2 RMSE. Additionally, the RMSE of the rate of change in glacier

elevation and the RMSE of the change in lake elevation were calculated using the error propagation formula. $${\rm{z}}={{\rm{f}}}_{1}\cdot {{\rm{x}}}_{1}+{{\rm{f}}}_{2}\cdot

{{\rm{x}}}_{2}+\cdots \cdots +{{\rm{f}}}_{2}\cdot {{\rm{x}}}_{2}$$ (7) $${{\rm{m}}}_{{\rm{z}}}^{2}={{\rm{f}}}_{1}^{2}\cdot {{\rm{m}}}_{{\rm{x}}1}^{2}+{{\rm{f}}}_{2}^{2}\cdot

{{\rm{m}}}_{{\rm{x}}2}^{2}+\cdots \cdots +{{\rm{f}}}_{n}^{2}\cdot {{\rm{m}}}_{{\rm{xn}}}^{2}$$ (8) In the equation, _z_ is an indirect measurement and _x_1, _x_2…, _xn_ are direct

measurements independent of each other. _mx_1, _mx_2…, _mxn_ represent the RMSE of _x_1, _x_2, …, _xn_ respectively, _mz_ represents the RMSE of _z_, and _f_1, _f_2, …, _fn_ represent

constants respectively. ACCURACY EVALUATION FOR ICESAT-2 CROSSOVER POINTS The validation results of ICESat-2 on the Tibetan Plateau are as follows: 1) Using water level data as the

reference, the root mean square error (RMSE) of ICESat-2 on lakes larger than 10 km² is 0.06 m35. 2) When using water level station data as the reference, the RMSE of ICESat-2 on Qinghai

Lake is 0.07 m36. 3) Using ground control points measured by a continuously operating reference system (CORS) as the reference, the RMSE of ICESat-2 on Qilian Mountain is 0.084 m37. 4) When

using unmanned aerial vehicle (UAV) elevation data as the reference, the RMSE is 0.124 m for ICESat-2 ATL06 and 0.139 m for ICESat-2 ATL08 (Fig. 6). From these four studies, we compute the

average of the accuracy results to determine the ICESat-2 accuracy for a single point in the crossover points dataset. The average precision from the four studies is 0.071 m. The elevation

difference for each group of crossover points is determined by the difference between the elevations of two ICESat-2 points. As a result, the accuracy of the elevation difference for each

group of ICESat-2 crossover points is 0.13 m (Eq. 8). COMPARISON OF ICESAT-2 CROSSOVER POINTS WITH EXISTING STUDIES In addition to validating the accuracy of ICESat-2 data, we utilized

existing studies to demonstrate the usability of ICESat-2 crossover points data. ICESat-2 produces good measuring results in gentle terrain areas38. Most of the glaciers on the Tibetan

Plateau are in mountainous areas with high elevation and complex terrain, or in areas that are difficult for humans to access. Moreover, the glaciers on the Tibetan Plateau are more affected

by climate change39. The lakes on the Tibetan Plateau are evenly distributed, but located in elevation zones that vary greatly, and they experience different situations such as drought and

freeze. Therefore, we compared ICESat-2 crossover points on glaciers and lakes with the results of existing studies to explore the usability of ICESat-2 crossover points data for obtaining

surface elevation changes on the Tibetan Plateau. * 1. COMPARISON OF ICESAT-2 CROSSOVER POINTS ON GLACIERS WITH EXISTING STUDIES We compared the data from Maurer _et al_.‘s “Products of

change in mean glacier thickness in the Himalaya (2000–2017)“40 and Zeng _et al_.‘s “Qilian Mountains glacier mean annual rate of change.“41. Both datasets provide annual change in glacier

elevation, and we calculated these rates based on ICESat-2 crossover points. Table 2 provides descriptions of the comparison data and ICESat-2 crossover points. To generate the “Products of

change in mean glacier thickness in the Himalaya (2000–2017),” the authors first extracted DEM from KH-9 Hexagon images on 650 glaciers, then fitted the Himalayan glacier elevation on the

time series, and finally obtained the changes in glacier elevation, volume, and area. We used this product and ICESat-2 crossover points to calculate the annual mean glacier elevation change

separately in parts of the Himalaya and compared the results of the two datasets (Fig. 7b). The results of the mean annual rate of change of glacier elevation obtained from the “Products of

change in mean glacier thickness in the Himalaya (2000–2017)” and the ICESat-2 crossover points data are very similar. From 2000–2017, the mean annual change in glacier elevation obtained

from the “Products of change in mean glacier thickness in the Himalaya” is −0.4803 m/year. From 2018–2022, the mean annual change in glacier elevation obtained from ICESat-2 crossover points

is 0.4885 ± 0.002 m/year. The difference between the two datasets for the mean annual rate of change of Himalayan glacier elevation is 0.0082 ± 0.002 m/year, which is very small. The

difference between the results obtained from the two datasets is related to the elevation band. We discussed the differences between the two datasets in elevation bands. There are 3007

groups of ICESat-2 crossover points on the Himalayan glaciers, and the elevations of these crossover points are distributed over the 4000–6500 m elevation band. We divided the comparative

experimental area into three elevation bands: 4000–5500 m, 5500–6000 m, and 6000–6500 m. Based on the 30 m digital elevation model of the Himalayas, the number of 4000–5500 m points is less.

The difference between the two datasets for the results was 0.488 ± 0.042 m/year, 0.059 ± 0.026 m/year, and 0.007 ± 0.032 m/year in the three elevation bands, respectively. The difference

between the results of the two datasets is the largest in the 4000–5500 m elevation band, and the higher the elevation, the smaller the difference between the results. If the “Products of

change in mean glacier thickness in the Himalaya” is taken as the true value, it shows that the higher the elevation, the better the results of ICESat-2 crossover points when measuring

glaciers. It should be noted that ICESat-2 was launched in September 2018, so the rate of change of Himalayan glaciers calculated from ICESat-2 crossover points is for 2018–2022. The

“Products of change in mean glacier thickness in the Himalaya” gives the rate of glacier change for the period 2000–2017. There is no overlap in the timing of the two datasets, and the

confidence in the comparison results is reduced. Therefore, we used the Qilian Mountain glacier for further comparative experiments. This experiment also utilized the rate of glacier change

in the Qilian Mountains from existing research and ICESat-2 crossover points for comparison (Fig. 7c). To obtain the rate of glacier change in the Qilian Mountains, the authors corrected the

ICESat-2 data using the pyramid registration method, used ICESat-2 and ALOS DEM to obtain the elevation difference, and calculated the elevation change rate of six glaciers within the

Qilian Mountains. Both our experiment and this study used ICESat-2 data to obtain glacier elevation. Still, we used the elevation difference of ICESat-2 data in each group of crossover

points, while this study used the elevation difference between ICESat-2 and ALOS DEM. The mean annual rate of change in glacier elevation in the Qilian Mountains obtained from ICESat-2

crossover points is very close to the results of existing research. We obtained the overall glacier change in the Qilian Mountains by averaging the rates of change in elevation of the six

glaciers in the Qilian Mountains from existing research, and the result was −0.1117 ± 0.755 m/year. The mean annual rate of change in glacier elevation in the Qilian Mountains, which we

obtained using ICESat-2 crossover points, is −0.5626 ± 0.014 m/year. The results calculated from the ICESat-2 crossover points were within the value range of the existing Qilian Mountains

glacier study results. * 2. COMPARISON OF ICESAT-2 CROSSOVER POINTS ON LAKES WITH EXISTING STUDIES In our selected lakes, we took into consideration their elevation, freezing conditions, and

area. * (1) Lakes on the Tibetan Plateau are predominantly found within the elevation range of 2700 m to 5500 m. We classified all Tibetan Plateau lakes into three categories, namely 2700

m-4000 m (with fewer lakes below 4000 m in elevation), 4000 m-4700 m, and 4700 m-5400 m. Among the 2700 m-4000 m lakes, we selected Ayakkum Lake and Sugan Lake. Among the lakes of 4000

m-4700 m, we chose Selin Co and Zhari Namco. Among the lakes of 4700 m-5400 m, we chose Nam Co and Puma Yumco (Fig. 8). * (2) These six lakes have varying freezing conditions. Ayakkum Lake

has no freezing period, while the edge of Lake Sugan experiences freezing. Selin Co, Zhari Namco, Nam Co, and Puma Yumco have freezing periods. We used this information to determine the

applicability of ICESat-2 crossover points in the presence of lake ice. * (3) Among the six lakes, Sugan Lake has the smallest area of 128.12 km², Nam Co has the largest area of 2423.39 km²,

and the remaining four lakes have areas distributed between them. This information was used to determine the applicability of ICESat-2 crossover points on lakes of various areas. The lake

information and ICESat-2 crossover points information are presented in Table 3. We chose the high-resolution dataset of lake level changes on the Tibetan Plateau from 2002 to 2021 as the

comparison data for the ICESat-2 crossover points42. The dataset uses eight satellites to fit the elevation changes of lakes larger than 1 km² on the Tibetan Plateau. We selected the

crossover points at one- or two-time intervals on each lake (Table 3), and then used the lake elevations provided in the dataset to obtain the elevation change of the lake for each group of

time intervals. Finally, we compared the results with those of the ICESat-2 crossover points. The comparative results are shown in Table 4. There is a strong correlation between the ICESat-2

crossover points and the elevation changes obtained from the high-resolution dataset of lake level changes on the Tibetan Plateau from 2002 to 2021 (Fig. 9a). In the eleven groups of

compared data, the absolute difference between the results obtained from the two datasets was a minimum of 0.009 m and a maximum of 0.2174 m, with an RMSE of 0.1675 m. The results of the two

datasets in calculating the lake elevation change are very similar. Furthermore, the R² value of the two datasets is 0.8460, indicating a strong correlation. Therefore, it is feasible to

calculate lake elevation changes using ICESat-2 crossover points data. In addition, during the freezing period, the absolute differences between the three groups of data were 0.1675 m,

0.0724 m, and 0.0881 m. It was observed that lake ice did not affect the measurements at the ICESat-2 crossover points. Lake elevation and area impact the absolute difference between the two

datasets. (1) The higher the lake elevation, the larger the absolute difference between the two datasets (Fig. 9b). Among the six lakes, Ayakkum Lake has the lowest elevation (around 3800 

m), and the mean value of the absolute difference between the two datasets within the lake is 0.0505 m. Puma Yumco has the highest elevation (around 4980 m), and the mean value of the

absolute difference between the two datasets in the lake is 0.1159 m. The mean values of the absolute differences of the two datasets in the four lakes of Sugan Lake, Selin Co, Zhari Namco,

and Nam Co are 0.1552 m, 0.0551 m, 0.1342 m, and 0.1449 m, respectively. The absolute difference between the two datasets shows an increasing trend with increasing lake elevation. USAGE

NOTES ADVANTAGES OF THE ICESAT-2 CROSSOVER POINTS DATASET ON THE TIBETAN PLATEAU The ICESat-2 crossover point data can be used in two ways. Firstly, it can be used as elevation difference

data. The ICESat-2 crossover point data provides elevation differences before and after a specific time. The RMSE of the elevation difference provided by each group of ICESat-2 crossover

points is 0.322 m. The crossover point datasets can give surface elevation changes on a point scale with high accuracy. The existing applications of ICESat-2 include obtaining changes in

water bodies, land vegetation, glaciers, and terrain, generating DEM, and using it as validation data. However, no dataset provides surface elevation changes over the entire Tibetan Plateau.

The elevation difference on point scales given in the crossover points dataset can provide a reference for surface changes on the Tibetan Plateau. The high spatial resolution and rich

amount of data in the ICESat-2 crossover points can provide more terrain change details. The diameter of the laser footprint of ICESat-2 is 17 m, which is much smaller than the other

satellites, such as Envisat, CryoSat-2, Jason-3, and Sentinel-2A. Smaller laser footprint diameters reduce errors due to slopes or complex terrain. Some scholars use the elevation of all

points along the track direction to get the surface elevation change in that direction43. This provides a method for us to understand the terrain of inaccessible areas. The ICESat-2

crossover points data gives the elevation difference on the point scale, and the density of points on the Tibetan Plateau reaches 2.050 group/km², so the crossover points dataset can provide

more terrain details for the surface changes on the Tibetan Plateau. There are two types of ICESat-2 crossover points. The first type involves continuous crossover points determined by

repeated tracks, while the second type involves discrete crossover points determined by different tracks (Fig. 10). Crossover points determined by repeating orbits have the characteristic of

appearing continuously in space in many groups with the same time interval in the direction of the track (Fig. 10c). This improves the accuracy of surface changes over the same time

interval. On the other hand, crossover points determined by different tracks are more discrete (Fig. 10d), and each group of crossover points has different time intervals. This improves the

time coverage of ICESat-2 crossover points. ICESat-2 data are commonly used to measure elevation changes. However, crossover point datasets offer unique values in some scenarios. Firstly,

they can reveal subtle features of various terrain elevation changes, such as those in glaciers, permafrost, forests, and lakes. These features might not be apparent in the overall change of

the Tibetan Plateau, but they are vital for studying environmental changes in specific areas. They can shed light on phenomena like terrain collapse caused by permafrost melting, glacier

collapse within large glaciers, and lake disappearance due to extreme climate. Secondly, crossover point datasets can enhance many existing models of the Tibetan Plateau. For instance, they

can refine hydrological models by including elevation changes of glaciers or permafrost, and provide additional validation data for terrain models. Moreover, crossover point datasets

supplement, rather than replace, large-scale elevation change datasets. They offer detailed information that the large-scale datasets might miss. By combining the two, a more comprehensive

data system can be created. STATISTICAL MEASURES OF ELEVATION CHANGES AT CROSSOVER POINTS The elevation difference of the crossover points follows a normal distribution (Fig. 11). According

to the ICESat-2 ATL06 product, the elevation changes range from −9.99 m to 9.89 m, averaging −0.04 m with a variance of 1.87 m. On the other hand, the ATL08 product shows a broader range

from −17.59 m to 16.99 m, averaging −0.26 m with a variance of 10.10 m². The ATL08 product has a noticeably larger variance than the ATL06, and its data surpasses ATL06 in both maximum and

minimum values. This further confirms the greater dispersion of the ATL08 data in elevation difference. We chose the ATL08 product to supplement the areas of the Tibetan Plateau, namely the

eastern and western regions, not covered by the ATL06 product. This region encompasses the Tarim Basin, the Hengduan Mountains, and the Pamir Plateau. The Tarim Basin includes a vast desert

area with significant southward dune movement44. The Hengduan Mountains are characterized by hazardous terrain with intersecting mountains and rivers, as well as diverse vegetation45. The

ATL08 product’s measurements of elevation changes in surface and vegetation in these areas can be affected by seasonal vegetation, desert alterations, and terrain relief. This could lead to

more significant fluctuations in elevation at ATL08 crossover points. The number of crossover points determined by ATL08 is less than that determined by ATL06. We selected a 10,000 km² area

in the central Tibetan Plateau for comparison. Within this area, the density of crossover points determined by ATL06 is 3.61 groups/km², whereas the density determined by ATL08 is 0.08

groups/km². Given that ATL06 has a spatial resolution of 20 m and ATL08 has a resolution of 100 m, the ATL06 product has more laser points in the same area, leading to more data on crossover

points. FACTORS AFFECTING THE ACCURACY OF CROSSOVER POINTS The accuracy of elevation differences obtained from ICESat-2 crossover points is influenced by several factors. In this

experiment, we compared the results of crossover points with existing studies on glaciers and lakes. We discovered that the accuracy of ICESat-2 crossover points on glaciers increases with

elevation, while the accuracy of ICESat-2 crossover points on lakes decreases with elevation. The relationship between the accuracy of ICESat-2 crossover points and elevation varies for

different surface types. Additionally, the accuracy of ICESat-2 crossover points on lakes increases with lake area. This is because larger lakes have more crossover points, resulting in a

smaller chance for error. Slope may also affect the accuracy of ICESat-2 crossover points. The overall slope of the Tibetan Plateau ranges from 0 to 54 degrees, with the majority of slopes

concentrated in the 0 to 50-degree range46,47. Some scholars have analyzed slope in relation to ICESat-2 ATL06 accuracy using CORS and UAV data in the Qilian Mountains. The results showed

that while slope affects ICESat-2 ATL06, ICESat-2 improves the impact of terrain on the data by increasing the sampling frequency and crossover measurements37. In this experiment, the

ICESat-2 crossover points dataset was not filtered for slopes to ensure an adequate amount of data. However, in future research, we can filter the crossover points dataset using slope

depending on the specific research objectives. APPLICABLE SCENARIOS FOR USING THE ICESAT-2 CROSSOVER POINTS DATASET The ICESat-2 crossover points dataset offers a new method for obtaining

surface elevation changes on the Tibetan Plateau. This dataset is a high spatial resolution surface elevation change data produced based on discrete ICESat-2 ATL06 and ATL08 points. Each

group of crossover points gives the amount of change in elevation before and after a period of time over an area of about 17 m in diameter. So, this dataset can be used to obtain elevation

changes of finer surface details on the plateau. Here are some ideas for using ICESat-2 crossover points data: * (1) The Tibetan Plateau has the world’s largest area of perennial permafrost

at low and middle latitudes. Climate change has led to the degradation of this permafrost, threatening infrastructure security. One study showed that by 2050, about 38 percent of roads, 39

percent of railways, 39 percent of power lines, and 21 percent of buildings will be exposed to high-risk areas48. To address this issue, we can use ICESat-2 crossover points data to obtain

permafrost surface elevation changes in the corresponding areas (such as along railways and highways) and understand intra- and inter-annual changes of permafrost. Moreover, drastic

small-scale surface deformation often occurs in permafrost regions. This includes surface fractures, subsidence, and landslides caused by melting permafrost49,50. Therefore, the ICESat-2

crossover points data can also be used as validation data for other permafrost active layer change studies. * (2) Glaciers on the Tibetan Plateau are abundant, but their changes have

intensified in recent years. Some studies showed that a quarter of the glaciers have melted in the last forty years, which could harm the climate and people. To address this issue, we can

use ICESat-2 crossover points data to obtain inter- and intra-annual glacier changes. In addition, ICESat-2 crossover points data can also refine existing studies of glacier elevation

change. Although existing glacier studies on the Tibetan Plateau can obtain large-scale glacier changes, finer elevation changes that address surface details are still needed. ICESat-2

crossover points data can be used as supplementary data for large-scale glacier studies, supplementing internal details of large glaciers and elevation changes of small glaciers. * (3)

Combining ICESat-2 crossover points data with multi-source remote sensing data. In the ICESat-2 crossover points data, each group of crossover points is spatially and temporally discretely

distributed without forming a continuous time series, so it cannot get an area’s continuous surface elevation change directly. We can merge Envisat, CryoSat-2, Jason-3, and Sentinel-3A data

with ICESat-2 crossover points to obtain elevation changes in a continuous time series. The spatial resolution and acquisition method of each type of data is different. Therefore, this

method may obtain better results in areas with flatter terrain, such as large lakes, deserts, or permafrost with lower slopes. Additionally, the multiple groups of crossover points extracted

from this experiment in a specific area can provide more data for studying time series changes in elevation. * (4) As validation data. Firstly, ICESat-2 crossover points data can be used to

validate surface elevation changes on the Tibetan Plateau. Secondly, since the terrain of the Tibetan Plateau is complex, the ICESat-2 crossover points data can be used as validation data

for many inaccessible areas when it is used as a single point. Compared with the existing DEM data, ICESat-2 crossover points data have higher accuracy, and each point elevation has

acquisition time. When validating the existing data with the help of ICESat-2 crossover points, averaging the spatially neighboring ICESat-2 crossover points can remove accidental errors if

we do not consider the acquisition time. The crossover points data, which cover the entire Tibetan Plateau, can significantly contribute to the study of surface elevation there. For

instance, they can be used to verify elevation changes in mountains and deserts, areas typically inaccessible to humans. * (5) Combining ICESat-2 crossover points data with various models.

Since ICESat-2 ATL06 is the product for land ice heights, the ATL06 crossover points dataset does not cover the Qaidam Basin and the eastern part of the Tibetan Plateau. We supplemented the

areas with missing ATL06 data. The final crossover points now include a variety of terrain types across the Tibetan Plateau region, such as glaciers, permafrost, lakes, deserts, and forests.

The advancements in machine learning have enabled the use of high-precision discrete surface elevation changes as a rich sample set. This allows for the attainment of higher-resolution

surface elevation changes across the Tibetan Plateau. The development of models for glaciers, snowpack, hydrology, ecology, and vegetation on the Tibetan Plateau also would benefit from

this. Crossover point datasets can provide ample validation data and serve as input data for these models. The ICESat-2 satellite is still operational, and we will continue to update the

crossover points dataset with more ICESat-2 data volume, resulting in more crossover points. As ICESat-2 data grows, the occurrence of multiple crossover point groups within a certain range

will increase and be collected, with updates in future dataset versions. CODE AVAILABILITY The script used to process the ICESat-2 data and extract ICESat-2 crossover points from it is

available at the following link: https://github.com/snowhydro/icesat-cross-point. REFERENCES * Duan, H., Guo, J., Chen, L., Jiao, J. & Jian, H. Vertical Crustal Deformation Velocity and

its Influencing Factors over the Qinghai-Tibet Plateau based on the Satellite Gravity Data. _Earth Planet. Phys_. 6 (2022). * Zhou, X., Zhong, W., Tang, D. & Han, H. Application of

D-InSAR Technology in Monitoring Land-surface Subsidence After an Earthquake. in _2021 7th International Conference on Hydraulic and Civil Engineering & Smart Water Conservancy and

Intelligent Disaster Reduction Forum_ (_ICHCE & SWIDR)_ 990–995 (2021). * Li, X. _et al_. Climate change threatens terrestrial water storage over the Tibetan Plateau. _Nat. Clim. Change_

12, 801–807 (2022). Article  ADS  Google Scholar  * Chen, J. & Duan, Z. Monitoring Spatial-Temporal Variations of Lake Level in Western China Using ICESat-1 and CryoSat-2 Satellite

Altimetry. _Remote Sens._ 14, 5709 (2022). Article  ADS  Google Scholar  * Chen, A. The third polar area glacier surface elevation change data product v1.0. _National Tibetan Plateau Data

Center_ https://doi.org/10.11888/Glacio.tpdc.270329 (2019). * Smith, B. _et al_. Land ice height-retrieval algorithm for NASA’s ICESat-2 photon-counting laser altimeter. _Remote Sens.

Environ._ 233, 111352 (2019). Article  Google Scholar  * Li, G. _et al_. Extraction and Analysis of the Three-Dimensional Features of Crevasses in the Amery Ice Shelf Based on ICESat-2 ATL06

Data. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._ 14, 5796–5806 (2021). Article  ADS  Google Scholar  * Carabajal, C. C. & Boy, J.-P. ICESAT-2 ALTIMETRY AS GEODETIC CONTROL. _Int.

Arch. Photogramm. Remote Sens. Spat. Inf. Sci._ XLIII-B3-2020, 1299–1306 (2020). Article  Google Scholar  * Wang, Q. & Sun, W. Seasonal Cycles of High Mountain Asia Glacier Surface

Elevation Detected by ICESat-2. _J. Geophys. Res. Atmospheres_ 127, e2022JD037501 (2022). Article  ADS  Google Scholar  * Dandabathula, G., Bera, A. K., Sitiraju, S. R. & Jha, C. S.

Inferring Lake Ice Status Using ICESat-2 Photon Data. _Remote Sens. Earth Syst. Sci._ 4, 264–279 (2021). Article  ADS  Google Scholar  * Gao, H. _et al_. Frozen soil hydrological modeling

for a mountainous catchment northeast of the Qinghai–Tibet Plateau. _Hydrol. Earth Syst. Sci._ 26, 4187–4208 (2022). Article  ADS  Google Scholar  * Xu, X. _et al_. Last Glacial Maximum

glacier modelling in the Quemuqu Valley, southern Tibetan Plateau, and its climatic implications. _Boreas_ 49, 286–295 (2020). Article  Google Scholar  * Deng, M. _et al_. The Response of

Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model. _Remote Sens._ 14, 3337 (2022). Article  ADS  Google

Scholar  * Sochor, L., Seehaus, T. & Braun, M. H. Increased Ice Thinning over Svalbard Measured by ICESat/ICESat-2 Laser Altimetry. _Remote Sens._ 13, 2089 (2021). Article  ADS  Google

Scholar  * Ranndal, H., Sigaard Christiansen, P., Kliving, P., Baltazar Andersen, O. & Nielsen, K. Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals

from ICESat-2 ATL03 Photon Data. _Remote Sens._ 13, 3548 (2021). Article  ADS  Google Scholar  * Hu, X. _et al_. Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case

Investigation in Altay, Northwest China. _IEEE Geosci. Remote Sens. Lett._ 19, 1–5 (2022). Google Scholar  * Michaelides, R. J., Bryant, M. B., Siegfried, M. R. & Borsa, A. A.

Quantifying Surface-Height Change Over a Periglacial Environment With ICESat-2 Laser Altimetry. _Earth Space Sci._ 8, e2020EA001538 (2021). Article  ADS  CAS  PubMed  PubMed Central  Google

Scholar  * Hou, Z. _et al_. Assessment of suitable cultivation region for Pepino (Solanum muricatum) under different climatic conditions using the MaxEnt model and adaptability in the

Qinghai–Tibet plateau. _Heliyon_ 9 (2023). * Yang, K., Wu, H., Qin, J. & Lin, C. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review.

_Glob. Planet. Change_ 112, 79–91 (2014). Article  ADS  Google Scholar  * Liu, X. _et al_. Distribution and sources of REEs in suspended particulate matter of cryospheric water in northeast

Tibetan plateau. _Appl. Geochem._ 148, 105536 (2023). Article  CAS  Google Scholar  * Lu, Z. _et al_. Change trend of natural gas hydrates in permafrost on the Qinghai-Tibet Plateau

(1960–2050) under the background of global warming and their impacts on carbon emissions. _China Geol._ 5, 475–509 (2022). ADS  CAS  Google Scholar  * Zhao, L. _et al_. Changing climate and

the permafrost environment on the Qinghai–Tibet (Xizang) plateau. _Permafr. Periglac. Process._ 31, 396–405 (2020). Article  Google Scholar  * Smith, B. _et al_. ATLAS/ICESat-2 L3A Land Ice

Height, Version 6. _NASA National Snow and Ice Data Center Distributed Active Archive Cente_ https://doi.org/10.5067/ATLAS/ATL06.006 (2023). * Ye, Q. Glacier coverage data on the Tibetan

Plateau in 2017 (TPG2017, Version1.0). _National Tibetan Plateau Data Center_ https://doi.org/10.11888/Glacio.tpdc.270924 (2019). * Ye, Q. _et al_. Glacier changes on the Tibetan Plateau

derived from Landsat imagery: mid-1970s – 2000–13. _J. Glaciol._ 63, 273–287 (2017). Article  ADS  Google Scholar  * Zhang, G. The lakes larger than 1km2 in Tibetan Plateau (v3.1)

(1970s-2022). _National Tibetan Plateau Data Center_ https://doi.org/10.1016/j.scib.2019.07.018 (2022). * Zhang, G., Luo, W., Chen, W. & Zheng, G. A robust but variable lake expansion on

the Tibetan Plateau. _Sci. Bull._ 64, 1306–1309 (2019). Article  Google Scholar  * Zhang, G., Yao, T., Xie, H., Zhang, K. & Zhu, F. Lakes’ state and abundance across the Tibetan

Plateau. _Chin. Sci. Bull._ 59, 3010–3021 (2014). Article  Google Scholar  * Robusto, C. C. The Cosine-Haversine Formula. _Am. Math. Mon._ 64, 38–40 (1957). Article  MathSciNet  Google

Scholar  * Neumann, T. A. _et al_. The Ice, Cloud, and Land Elevation Satellite – 2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System.

_Remote Sens. Environ._ 233, 111325 (2019). Article  Google Scholar  * Queinnec, M., White, J. C. & Coops, N. C. Comparing airborne and spaceborne photon-counting LiDAR canopy structural

estimates across different boreal forest types. _Remote Sens. Environ._ 262, 112510 (2021). Article  Google Scholar  * Chen, T., Li, H. & Wang, J. High spatial resolution elevation

change dataset derived from ICESat-2 crossover points for the Tibetan Plateau (2018-2022)(V2.0). _National Tibetan Plateau Data Center_ https://doi.org/10.11888/RemoteSen.tpdc.300749 (2023).

* Zhu, J., Yang, P., Li, Y., Xie, Y. & Fu, H. Accuracy assessment of ICESat-2 ATL08 terrain estimates: A case study in Spain. _J. Cent. South Univ_. 29, 226–238 (2022). Article  Google

Scholar  * Brunt, K. M., Neumann, T. A. & Smith, B. E. Assessment of ICESat-2 Ice Sheet Surface Heights, Based on Comparisons Over the Interior of the Antarctic Ice Sheet. _Geophys. Res.

Lett_. 46, 13072–13078 (2019). Article  ADS  Google Scholar  * Yuan, C., Gong, P. & Bai, Y. Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over

Lakes and Reservoirs in China. _Remote Sens._ 12, 770 (2020). Article  ADS  CAS  Google Scholar  * Liu, C. _et al_. Monitoring water level and volume changes of lakes and reservoirs in the

Yellow River Basin using ICESat-2 laser altimetry and Google Earth Engine. _J. Hydro-Environ. Res._ 44, 53–64 (2022). Article  Google Scholar  * Zhang, Y., Pang, Y., Cui, D., Ma, Y. &

Chen, L. Accuracy Assessment of the ICESat-2/ATL06 Product in the Qilian Mountains Based on CORS and UAV Data. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._ 14, 1558–1571 (2021). Article

  ADS  Google Scholar  * Liu, A., Cheng, X. & Chen, Z. Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals. _Remote Sens. Environ._

264, 112571 (2021). Article  Google Scholar  * Latif, A. _et al_. Review on global change status and its impacts on the Tibetan Plateau environment. _J. Plant Ecol._ 12, 917–930 (2019).

Article  Google Scholar  * Maurer, J., Rupper, S. & Schaefer, J. High Mountain Asia Gridded Glacier Thickness Change from Multi-Sensor DEMs, Version 1. _NASA National Snow and Ice Data

Center Distributed Active Archive Center_ https://doi.org/10.5067/GGGSQ06ZR0R8 (2018). * Zeng, J., Xie, J., Liu, R., Mo, F. & Yang, X. Research on Glacier Elevation Variability in the

Qilian Mountains of the Qinghai-Tibet Plateau Based on Topographic Correction by Pyramid Registration. _Remote Sens._ 15, 62 (2023). Article  ADS  Google Scholar  * Chen, J. _et al_.

High-resolution datasets for lake level changes in the Qinghai-Tibetan Plateau from 2002 to 2021 using multi-altimeter data. _Earth Syst. Sci. Data Discuss._ 1, 18 (2022). Google Scholar  *

Neuenschwander, A. L. & Magruder, L. A. Canopy and Terrain Height Retrievals with ICESat-2: A First Look. _Remote Sens._ 11, 1721 (2019). Article  ADS  Google Scholar  * Heermance, R. V.

_et al_. Erg deposition and development of the ancestral Taklimakan Desert (western China) between 12.2 and 7.0 Ma. _Geology_ 46, 919–922 (2018). Article  ADS  Google Scholar  * Xing, Y.

& Ree, R. H. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. _Proc. Natl. Acad. Sci._ 114, E3444–E3451 (2017). Article  ADS  CAS  PubMed 

PubMed Central  Google Scholar  * Farr, T. G. _et al_. The Shuttle Radar Topography Mission. _Rev. Geophys._ 45, RG2004 (2007). Article  ADS  Google Scholar  * Food and Agriculture

Organization of the United Nations. The slope map of the Tibetan Plateau (2000). _National Tibetan Plateau Data Center_ https://doi.org/10.11888/Geogra.tpdc.270545 (2014). * Ran, Y. _et al_.

Permafrost degradation increases risk and large future costs of infrastructure on the Third Pole. _Commun. Earth Environ._ 3, 1–10 (2022). Article  ADS  Google Scholar  * Lin, C.

SBAS-InSAR-based surface deformation monitoring of permafrost on Banks Island. in _International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023)_ vol. 12710, 146–150 (SPIE,

2023). * Li, X. _et al_. Time-series InSAR monitoring of surface deformation in Yakutsk, a city located on continuous permafrost. _Earth Surf. Process. Landf._ 49, 918–932 (2024). Article 

ADS  Google Scholar  * Zhang, Y. Integration dataset of Tibet Plateau boundary. _National Tibetan Plateau Data Center_ https://doi.org/10.11888/Geogra.tpdc.270099 (2019). * Qiu, H. DEM of

the Himalaya (30m). _National Tibetan Plateau Data Center._ https://data.tpdc.ac.cn/zh-hans/data/4c0bc7b6-911f-4380-a9d7-90cd0738fefc (2023). Download references ACKNOWLEDGEMENTS This work

was supported by the National Natural Science Foundation of China (Program No. U22A20564), the National Science Fund for Distinguished Young Scholars (Program No. 42125604) and the LZJTU EP

201806 (Program No. 201806). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and

Resources, Chinese Academy of Sciences, Lanzhou, 730000, China Tengfei Chen, Jian Wang, Tao Che, Xiaohua Hao & Hongyi Li * Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou,

730000, China Tengfei Chen * National-Local Joint Engineering Research Center of Technologies and Applications for National Geo-graphic State Monitoring, Lanzhou, 730000, China Tengfei Chen

* Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou, 730000, China Tengfei Chen & Hongyi Li * Heihe Remote Sensing Experimental Research Station,

Key Laboratory of Remote Sensing of Gansu Province, Chinese Academy of Sciences, Lanzhou, 730000, China Jian Wang, Tao Che, Xiaohua Hao & Hongyi Li Authors * Tengfei Chen View author

publications You can also search for this author inPubMed Google Scholar * Jian Wang View author publications You can also search for this author inPubMed Google Scholar * Tao Che View

author publications You can also search for this author inPubMed Google Scholar * Xiaohua Hao View author publications You can also search for this author inPubMed Google Scholar * Hongyi Li

View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Project conceptualization was conducted by Li, H. Funding for the project is provided by

Li, H. and Che, T. Writing (original draft preparation), formal analysis, investigation, and methodology are Chen, T.’s responsibilities. Wang, J., Che, T., and Hao, X. reviewed and edited

the article. CORRESPONDING AUTHOR Correspondence to Hongyi Li. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a

Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit

to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are

included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and

your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this

licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Chen, T., Wang, J., Che, T. _et al._ High spatial resolution

elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau. _Sci Data_ 11, 394 (2024). https://doi.org/10.1038/s41597-024-03214-2 Download citation * Received: 01

November 2023 * Accepted: 02 April 2024 * Published: 17 April 2024 * DOI: https://doi.org/10.1038/s41597-024-03214-2 SHARE THIS ARTICLE Anyone you share the following link with will be able

to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing

initiative