Remote sensing estimation method for ice shelf bottom ablation rate

Abstract

The invention provides a remote sensing estimation method for the ablation rate of the bottom of an ice shelf, and the method comprises the steps: calculating the time difference between the data timestamp of ICESat-2 ATL15 and the data reference time of ATL14, multiplying the time difference by ATL15 to obtain the elevation change in the corresponding time, obtaining the elevation of the ice shelf of the corresponding timestamp based on ATL14, and taking the obtained elevation of the ice shelf as the reference to estimate the ablation rate of the bottom of the ice shelf. Repeating the steps to obtain ice shelf elevations of different timestamps; carrying out average dynamic terrain correction and ellipsoid elevation conversion on the elevation of the ice shelf; correcting the influence of the air content of the snow on the elevation change of the ice shelf; obtaining the thickness of the ice shelf through a static balance method; based on the principle of mass conservation, the thickness change caused by surface mass balance and rapid movement of the ice shelf is subtracted from the total thickness change of the ice shelf, then the thickness change caused by ice shelf bottom ablation is obtained, and finally the bottom ablation rate is estimated. According to the method, the ice shelf bottom ablation rate can be quickly and efficiently obtained from the ICESat-2 data.

Description

Technical Field

[0001] The invention relates to a remote sensing estimation method for the ablation rate of an ice shelf bottom, and belongs to the technical field of remote sensing applications. Background Art

[0002] Ice shelves are the parts of glaciers on the ice sheet that extend into the sea and float on the sea surface. They exert a reaction force on the upstream ice sheet through lateral friction and bottom support points, thereby stabilizing the ice sheet. This mechanism is called the "support effect". In recent years, due to climate warming, more and more warm seawater has invaded the continental shelf and entered the cavity under the ice shelf, causing the ice shelf to thin rapidly. The thinning of the ice shelf weakens its support effect, accelerates the flow of upstream glaciers, promotes dynamic thinning, and thus increases the mass loss of the ice sheet, and its contribution to global sea level rise is also increasingly significant. Studies have shown that the increasing bottom ablation has become one of the main drivers of ice sheet mass loss in recent years.

[0003] Over the past few decades, ice shelf bottom ablation has been monitored primarily through three methods: in situ field observations, ocean numerical simulations, and estimates based on satellite remote sensing. While field observations are highly accurate, their measurements are spatially sparse, making long-term continuous observations difficult and requiring high logistical costs. Ocean simulations can provide high-resolution reconstructions of ice shelf bottom ablation rates, but due to the lack of direct observations of bottom ablation and oceanographic conditions within the ice shelf cavity, simplified parameters are often used to explain sea-ice interactions, potentially leading to deviations between simulation results and actual oceanographic or glaciological estimates. In contrast, satellite remote sensing methods enable large-scale, long-term continuous observations of ice shelves, providing estimates of bottom ablation with higher spatial and temporal resolution.

[0004] Estimating ice shelf bottom ablation rates using satellite data primarily relies on monitoring surface elevation changes. Current research methods, depending on the data acquisition method, include stereoscopic optical imagery, synthetic aperture radar (SAR), and satellite altimetry. Stereoscopic imagery and SAR methods can provide high-resolution estimates of ice shelf bottom ablation rates, but are generally limited to specific ice shelf regions and often rely on data sources that require payment. In contrast, satellite altimetry data can be used not only to estimate bottom ablation for individual ice shelves but also to monitor large-scale, long-term changes. Current studies primarily rely on radar altimetry data from ERS-1, ERS-2, Envisat, and CryoSat-2 to estimate ice shelf bottom ablation rates. However, these studies are also limited by the density of altimetry data points, spatial coverage, and orbital repetition period. They often suffer from low spatial resolution and discontinuous time series, making it difficult to fully characterize the spatial distribution of ice shelf bottom ablation.

[0005] Therefore, this method is based on the more accurate and densely covered ICESat-2 laser altimetry data, combined with multi-source remote sensing information, to estimate the bottom ablation rate of the ice shelf. Summary of the Invention

[0006] The technical problem to be solved by this invention is that, in order to solve the problems of low spatial resolution and discontinuous time series in remote sensing estimation of ice shelf bottom ablation rate, a method for estimating bottom ablation rate is proposed using ICESat-2 land ice digital elevation model data ATL14 and land ice digital elevation change rate data ATL15 from the U.S. National Snow and Ice Data Center. Figure 1 The flowchart of the method for estimating the ablation rate of the bottom of an ice shelf provided by the present invention includes the following steps: Step 1: Data acquisition: Obtain the 4th version of ATL14 data (reference time is January 1, 2020) and ATL15 data (covering the period from February 2019 to February 2024) from the National Snow and Ice Data Center (NSIDC); the height difference data between the WGS84 ellipsoid and the EIGEN-6C4 geoid provided by the MEaSUREs ice bed topography dataset version 3 (BedMachine V3); and the ice shelf mask provided by the boundary dataset; download monthly ice flow velocity data from 2019 to 2021 from the ENVEO CryoPortal platform; obtain monthly snow particle air content data (GSFC-FDM v1.2.1) from the Zenodo open science data platform from 2019 to 2024; obtain the mean dynamic topography dataset (MDT-CNES-CLS2022) from the AVISO satellite ocean altitude observation platform; and obtain monthly continental surface mass balance data (RACMO2.3p2) from the Brice Noël research team. The second step is data preprocessing: first, ice flow velocity, snow particle air content, and surface mass balance data are averaged monthly to fill in missing data. Then, the acquired data are uniformly resampled to a spatial resolution of 1 km using the nearest neighbor interpolation method. The third step is to construct the time series DEM: sort the ATL15 data by timestamp, first select the data with the ATL14 reference time (January 1, 2020) ( )The closest ATL15 data ( ), calculate the time difference between it and the reference time, and compare the time difference with the elevation change rate provided by ATL15 ( ) to obtain the corresponding elevation change value. Add this change value to the ATL14 reference elevation to obtain the surface elevation at the corresponding time point ( ). Using this result as the new reference elevation, we continue to iteratively calculate the elevations corresponding to other time stamps, and finally construct a time series DEM dataset covering 2019 to 2024. The calculation formula is as follows: The fourth step is data masking: Based on the ice shelf mask provided in the boundary dataset, the above ice flow velocity, snow particle air content, surface mass balance and time series DEM data are masked to extract ice shelf area information.

[0007] Step 5: DEM correction and conversion: Use MDT-CNES-CLS2022 data to perform average dynamic terrain correction on the generated DEM ( ), and the DEM is converted to an ellipsoidal elevation using the WGS84 ellipsoid and EIGEN-6C4 geoid height difference data provided by the BedMachine V3 dataset ( ), the calculation formula is as follows, Step 6: Granular snow air content correction: Subtract the granular snow air content from the corrected and converted DEM ( ), eliminating its influence on the change of ice shelf elevation, the calculation formula is as follows; Step 7: Estimation of ice shelf thickness: Based on the principle of static equilibrium, take seawater ( ) and sea ice ( ) have densities of 1028 kg / m 3 and 917 kg / m 3 , the ice shelf DEM elevation Convert to ice shelf thickness , the calculation formula is as follows, Step 8: Estimation of bottom ablation rate: All methods for calculating the bottom ablation rate of ice shelves based on remote sensing data are based on the assumption of static equilibrium. The remote sensing surface elevation is converted into ice shelf thickness. Through the mass conservation method, bottom ablation is evaluated as the residual of ice shelf rapid motion, surface mass balance, and air content of granular snow that contribute to the net surface height change. ) minus the surface mass balance ( ) and ice flow divergence and advection ( ) to obtain the ice shelf bottom melting rate ( ), the calculation formula is as follows Step 9: Mosaic: Mosaic the estimated bottom ablation results of a single ice shelf to finally obtain the bottom ablation rate of the ice shelf in the entire study area.

[0008] This paper utilizes ICESat-2 ATL14 and ATL15 data, which are relatively easy to obtain. ICESat-2 has higher altimetry accuracy and more dense footprints, and can provide large-scale, continuous time series estimates of ice shelf bottom ablation rates. This is of great significance for better understanding the dynamic process of ice shelf bottom ablation. This not only helps to more accurately estimate ice sheet mass loss, optimize future sea level rise predictions, support the verification of ice-sea numerical models, but also serves as an important reference for evaluating the long-term stability of ice shelves. The present invention will be further described below in conjunction with the accompanying drawings.

[0009] Figure 1 This is a flow chart of a remote sensing estimation method for ice shelf bottom ablation rate according to the present invention.

[0010] Figure 2 It is the distribution of ice shelves on the Antarctic continent.

[0011] Figure 3 This is the DEM processing of the Amery Ice Shelf on February 15, 2019. (a) DEM generated by ATL14 and ATL15; (b) DEM after average dynamic terrain correction; (c) DEM after ellipsoid elevation conversion.

[0012] Figure 4 This is the snow granule air content on the Amery Ice Shelf on February 15, 2019.

[0013] Figure 5 This is the DEM of the Amery Ice Shelf corrected for air content of grain snow on February 15, 2019.

[0014] Figure 6 This is the thickness of the Amery Ice Shelf on February 15, 2019.

[0015] Figure 7 This is the thickness change of the Amery Ice Shelf in autumn 2019.

[0016] Figure 8 is the surface mass balance change on the Amery Ice Shelf in autumn 2019.

[0017] Figure 9 Ice flow divergence and advection on the Amery Ice Shelf in autumn 2019.

[0018] Figure 10 This is the estimated result of the bottom melting rate of the Amery Ice Shelf in the autumn of 2019.

[0019] Figure 11 This is the estimated result of the bottom melting rate of the Antarctic ice shelf in the autumn of 2019.

[0020] Figure 12 is the melting rate at the base of the Antarctic ice shelves from 2019 to 2023. DETAILED DESCRIPTION

[0021] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] Figure 1 The flowchart of the method for estimating the ablation rate of the bottom of an ice shelf provided by the present invention includes the following steps: The first step is data acquisition: ATL14 data (reference time is January 1, 2020) and ATL15 data (covering the period from February 2019 to February 2024) covering the study area are obtained from the National Snow and Ice Data Center (NSIDC) of the United States. The height difference data between the WGS84 ellipsoid and the EIGEN-6C4 geoid provided by the MEaSUREs ice bed topography dataset version 3 (BedMachine V3) and the ice shelf masks provided in the boundary dataset (all Antarctic ice shelf masks are as follows) are obtained. Figure 2 As shown, the red range is the Amery Ice Shelf, and the subsequent estimation of the bottom melting rate of a single ice shelf will take the Amery Ice Shelf as an example); monthly ice flow velocity data from 2019 to 2021 were downloaded from the ENVEOCryoPortal platform; monthly particle snow air content data from 2019 to 2024 were obtained from the Zenodo open science data platform (GSFC-FDM v1.2.1); the average dynamic topography dataset (MDT-CNES-CLS2022) was obtained from the AVISO satellite ocean altitude observation platform; and monthly continental surface mass balance data (RACMO2.3p2) were obtained from the Brice Noël research team.

[0023] The second step is data preprocessing: first, ice flow velocity data, particle snow air content data, and surface mass balance data are averaged on a monthly basis to fill in missing data; then, the acquired data are uniformly resampled to a 1 km spatial resolution consistent with the ATL15 data using the nearest neighbor interpolation method.

[0024] The third step is to build a time series DEM: sort the ATL15 data by timestamp, first select the reference time of the ATL14 data (January 1, 2020) The closest ATL15 data, the reference time of this data is , calculate the time difference between it and the reference time and compare this time difference with the elevation change rate provided by the ATL15 data Multiply them to get the corresponding elevation change value. Add this change value to the reference elevation of the ATL14 data to get the surface elevation at the corresponding time point. Using this result as the new reference elevation, we continue to iteratively calculate the elevations corresponding to other time stamps, and finally construct a time series DEM dataset covering 2019 to 2024.

[0025] The surface elevation calculation formula in this step is as follows: Where, represents the surface elevation at the corresponding time point, Indicates the elevation of ATL14 data. Indicates the reference time of ATL14 data, Indicates the timestamp of the ATL15 data closest to the ATL14 reference time. Indicates the time difference between the ATL15 data closest to the reference time and the ATL14 data. Indicates the rate of elevation change provided by the ATL15 data.

[0026] The fourth step is data masking: Based on the ice shelf mask provided in the boundary dataset, the above ice flow velocity, snow particle air content, surface mass balance and time series DEM data are masked to extract ice shelf area information.

[0027] Step 5: DEM correction and conversion: The generated time series DEM is corrected using the average dynamic terrain dataset MDT-CNES-CLS2022 data obtained from the AVISO satellite ocean height observation platform ( ), and the ellipsoid elevation conversion of the time series DEM is performed using the WGS84 ellipsoid and EIGEN-6C4 geoid height difference data provided by the BedMachine V3 dataset ( ), calculated as follows: Where, represents the corrected and transformed elevation, represents the surface elevation before correction and transformation, represents the average dynamic terrain correction value, The ellipsoid elevation conversion value is shown in the following table: Figure 3 shown.

[0028] Step 6: Granular snow air content correction: Subtract the granular snow air content from the corrected and converted DEM ( ), eliminating its influence on the change of ice shelf elevation, the calculation formula is as follows; Where, represents the corrected elevation, represents the elevation after correction and conversion in step 5, The granular snow air content and the corrected DEM of the Amery Ice Shelf on February 15, 2019 are as follows: Figure 4 and Figure 5 shown.

[0029] Step 7: Estimation of ice shelf thickness: Based on the principle of static equilibrium, take seawater ( ) and sea ice ( ) have densities of 1028 kg / m 3 and 917 kg / m 3 , the ice shelf DEM elevation Convert to ice shelf thickness , the calculation formula is as follows: Where, represents the thickness of the ice shelf, represents the density of seawater, represents the sea ice density, represents the corrected elevation in step 6. The thickness of the Amery Ice Shelf on February 15, 2019 is Figure 6 shown.

[0030] Step 8: Estimation of bottom ablation rate: The calculation methods of ice shelf bottom ablation rate based on remote sensing data are based on the assumption of static balance, converting the remote sensing surface elevation into ice shelf thickness, and using the mass conservation method to evaluate bottom ablation as the residual of ice shelf rapid motion, surface mass balance, and air content of granular snow that contribute to the net surface height change. Subtract surface mass balance and ice flow divergence and advection , to obtain the melting rate at the bottom of the ice shelf , the calculation formula is as follows: Where, represents the melting rate at the base of the ice shelf, Indicates the thickness of the ice shelf The rate of change, represents ice flow divergence and advection; represents the surface mass balance. The estimated results of ice shelf thickness change, surface mass balance, ice flow divergence and advection, and bottom ablation rate of the Amery Ice Shelf in autumn 2019 are Figure 7-10 shown.

[0031] The ninth step is mosaicking: mosaicking the estimated bottom ablation results of individual ice shelves to obtain the bottom ablation rate of the ice shelves in the entire study area. The bottom ablation rate of the Antarctic ice shelf in autumn 2019 is as follows: Figure 11 As shown, Figure 12 is the seasonal bottom melting rate of the Antarctic ice shelf from 2019 to 2023.

[0032] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A remote sensing method for estimating the ice shelf bottom ablation rate, comprising the following steps: The first step is data acquisition: obtain land ice digital elevation model data covering the study area, land ice digital elevation change rate data, MEaSUREs ice bed topography dataset, and boundary dataset from the National Snow and Ice Data Center; download ice flow velocity data from the ENVEOCryoPortal platform; download particle snow air content data from the Zenodo open science data platform; obtain the average dynamic topography dataset from the AVISO satellite ocean altitude observation platform; and obtain surface mass balance data from the Brice Noël research team. The second step is data preprocessing: missing ice velocity data, snow particle air content data, and surface mass balance data are supplemented by calculating the average value, and resampled to a spatial resolution consistent with the land ice digital elevation change rate data using the nearest neighbor interpolation method; The third step is to construct a time series DEM: Based on the timestamps and reference time of the land ice digital elevation change rate data and the land ice digital elevation model data, the land ice digital elevation model corresponding to different timestamps of the land ice digital elevation change rate data is calculated, thereby constructing a time series land ice digital elevation model; Step 4: Data masking: Based on the ice shelf mask provided in the boundary dataset, the ice flow velocity, snow particle air content, surface mass balance, and time-series land ice digital elevation model data are masked to extract ice shelf area information. Step 5: DEM correction and conversion: The generated time series land ice digital elevation model is corrected for average dynamic terrain and converted to ellipsoidal elevation using the average dynamic terrain dataset and the MEaSUREs ice bed terrain dataset. Step 6: Granular snow air content correction: Subtract the granular snow air content from the corrected and converted time series land ice digital elevation model to eliminate its influence on ice shelf elevation changes; Step 7: Estimation of ice shelf thickness: Based on the principle of static equilibrium, convert the ice shelf DEM into ice shelf thickness; Step 8: Estimation of bottom ablation rate: Subtract surface mass balance, ice flow divergence, and advection from ice shelf thickness changes to obtain the bottom ablation rate. Step 9, mosaicking: mosaic the estimated results of a single ice shelf to obtain the bottom melting rate of the ice shelf in the study area.

2. The remote sensing estimation method for ice shelf bottom ablation rate as claimed in claim 1, characterized in that: In the third step, the land ice digital elevation change rate data are sorted by timestamp, and the land ice digital elevation change rate data closest to the reference time of the land ice digital elevation model data are selected. The time difference between the two is calculated, and the time difference is multiplied by the elevation change rate of the land ice digital elevation change rate data to obtain the corresponding elevation change value. The elevation change value is added to the reference elevation of the corresponding land ice digital elevation model data to obtain the surface elevation at the corresponding time point. This result is used as the new reference elevation, and the elevations corresponding to other timestamps are iteratively calculated to finally construct a time series land ice digital elevation model.

3. The remote sensing estimation method for ice shelf bottom ablation rate as claimed in claim 1, characterized in that: In the third step, the surface elevation is calculated as follows: Where, represents the surface elevation at the corresponding time point, represents the elevation of the land ice digital elevation model data, Indicates the reference time of the land ice digital elevation model data, Indicates the timestamp of the land ice digital elevation change rate data that is closest to the reference time of the land ice digital elevation model data. Indicates the time difference between the land ice digital elevation change rate data closest to the reference time and the land ice digital elevation model data, Indicates the rate of elevation change provided by the land ice digital elevation change rate data.

4. The remote sensing estimation method for ice shelf bottom ablation rate according to claim 1, characterized in that: The calculation formula for the fifth step is as follows: Where, represents the corrected and transformed elevation, represents the surface elevation before correction and transformation, represents the average dynamic terrain correction value, Represents the ellipsoidal elevation conversion value.

5. The remote sensing estimation method for ice shelf bottom ablation rate according to claim 4, characterized in that: The calculation formula for the sixth step is as follows: Where, represents the corrected elevation, represents the elevation after correction and conversion in step 5, Indicates the granular snow air content.

6. The remote sensing estimation method for ice shelf bottom ablation rate according to claim 5, characterized in that: The calculation formula for the seventh step is as follows: Where, represents the thickness of the ice shelf, represents the density of seawater, represents the sea ice density, Indicates the corrected elevation in step 6.

7. The remote sensing estimation method for ice shelf bottom ablation rate according to claim 6, characterized in that: In step 8, the ice shelf bottom ablation rate is calculated as follows: Where, represents the melting rate at the base of the ice shelf, Indicates the thickness of the ice shelf The rate of change, , For advection, is the divergence, is the ice flow velocity; Represents surface mass balance.

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