Sea ice surface snow estimation method and system combining laser and radar satellite altimetry

Abstract

The application provides a sea ice surface snow depth estimation method combined with laser satellite altimetry and radar satellite altimetry, which comprises satellite altimetry data preprocessing, satellite altimetry sampling point matching, obtaining a joint observation track of two satellites according to the time and spatial position of the orbit of the laser altimetry satellite and the orbit of the radar altimetry satellite, taking the sampling point of the radar altimetry satellite along the orbit as a reference point, searching for the sampling point of the laser altimetry satellite in a preset space-time range to obtain a joint observation sampling point set, obtaining the total freeboard value of the laser altimetry satellite corresponding to the radar freeboard of the radar altimetry satellite in each search range by using the inverse distance weighting method to obtain a sea ice freeboard matching data set, and obtaining the sea ice surface snow depth estimation result and outputting the same based on the obtained sea ice freeboard matching data set and considering the penetration characteristics of the radar signal on the snow. The application is free from the dependence on field observation by combining the laser satellite altimetry data and the radar satellite altimetry data, and the reliability of the sea ice surface snow depth estimation is improved.

Description

Technical Field

[0001] This invention relates to the field of remote sensing measurement technology, specifically to a technical solution for estimating the depth of snow accumulation on the surface of sea ice based on satellite laser and radar altimetry data. Background Technology

[0002] Sea ice is an important indicator of global climate change. Snow accumulation on sea ice surfaces is a crucial component of the sea ice system and a significant factor influencing the atmospheric-sea ice-ocean thermal energy balance. Snow accumulation not only possesses high albedo and low thermal conductivity, but also modulates thermal radiation and energy transport between the atmosphere and sea ice by altering the roughness of the ice surface, affecting heat exchange processes and further influencing energy exchange between the ocean and atmosphere. Snow accumulation on sea ice surfaces also significantly impacts the freezing and melting processes of sea ice. On the one hand, the insulating effect of snow prevents air from entering, thus slowing down the temperature-regulated freezing and melting of sea ice. On the other hand, surface snow accumulation increases the albedo of the sea ice surface, reducing the input of solar radiation energy to the sea ice, thereby mitigating sea ice melting.

[0003] The depth of snow accumulation on sea ice is a necessary parameter for retrieving sea ice thickness using satellite altimetry and is one of the main sources of uncertainty in sea ice thickness estimation. Whether using a laser altimeter to obtain the total freeboard of sea ice or a radar altimeter to obtain the radar freeboard, the influence of the depth and density of snow accumulation on the sea ice surface must be considered when retrieving sea ice thickness through hydrostatic equilibrium equations. For radar altimeters with penetrating capabilities, the velocity delay of radar waves in the snow accumulation also needs to be considered, and prior information on snow accumulation thickness is crucial for correcting this delay. Furthermore, surface snow accumulation also affects the accuracy of the freeboard obtained by the altimeter by altering the surface roughness of the sea ice.

[0004] Therefore, accurate estimation of snow depth on sea ice surface is crucial for research on environmental change and sea ice mechanisms. Current studies commonly use the climatological snow depth model (W99) or a modified W99 model (mW99) to obtain estimates of surface snow depth. However, because the W99 model uses outdated and spatially unevenly distributed data, with most data originating from multi-year ice-covered areas, this model is no longer suitable for estimating contemporary Arctic sea ice surface snow depth. Some studies indirectly obtain snow depth estimates using snowfall data from atmospheric reanalysis data, but this is also affected by the data source and cannot provide high-resolution, high-precision snow depth estimates.

[0005] Satellite observation data can provide large-scale, long-term estimates of sea ice surface snow depth. The method of estimating sea ice snow depth using the spectral gradient ratio of the dual vertical polarization channels (18.7 GHz and 37 GHz) of the SSM / I radiometer has been widely used. Subsequently, this method was applied to the AMSR-E and AMSR-2 radiometers, and operational products were released. The surface snow thickness obtained using this method has proven to be accurate in one-year ice regions, but its snow thickness retrieval capability is poor in multi-year ice regions. Although establishing a linear regression formula using field-measured data can effectively improve the snow thickness retrieval capability, it is highly dependent on the measured data, making it difficult to establish a spatiotemporally consistent snow thickness model. Similarly, snow thickness retrieval using SMOS passive microwave data is also highly dependent on field-measured data. Therefore, the purpose of this invention is to propose a new sea ice thickness estimation method based on satellite radar and laser altimetry data to eliminate the dependence on field observations for snow thickness estimation and reduce the uncertainty of sea ice thickness estimation. Summary of the Invention

[0006] The purpose of this invention is to address the significant uncertainties and reliance on in-situ observation data in existing methods for estimating sea ice surface snow depth, and to provide a sea ice thickness estimation scheme based on satellite radar and laser altimetry data. Sea ice surface snow is a crucial component of the sea ice system, and snow depth is a necessary parameter for retrieving sea ice thickness using satellite altimetry. However, current snow models and passive microwave snow depth products exhibit considerable uncertainty and are heavily dependent on in-situ measurement data, failing to meet the needs of large-scale, long-term polar sea ice research.

[0007] To achieve the above objectives, the technical solution proposed in this invention is a method for estimating sea ice surface snow accumulation by combining laser and radar satellite altimetry, comprising the following steps.

[0008] Step 1, satellite altimetry data preprocessing, includes extracting longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification marker data of laser altimetry data sampling points within the observation area based on the total freeboard height obtained by laser altimetry satellites and the radar freeboard height obtained by radar altimetry satellites; extracting longitude, latitude, radar freeboard height, and radar freeboard height uncertainty data of radar altimetry data sampling points within the observation area; and removing observation point data located in inter-ice channels based on sea ice surface classification markers.

[0009] Step 2, satellite altimetry sampling point matching, is implemented as follows:

[0010] Step 2.1: Obtain the joint observation trajectory of the two satellites according to their time and spatial positions in the orbits of the laser altimeter and radar altimeter.

[0011] Step 2.2: Using the radar altimeter satellite sampling points along the orbit as reference points, retrieve the laser altimeter satellite sampling points within the preset spatiotemporal range to obtain a joint observation sampling point set;

[0012] Step 2.3: Use the inverse distance weighting method to obtain the total freeboard value of the laser altimeter satellite corresponding to the radar freeboard of the radar altimeter satellite within each search range, and obtain the sea ice freeboard matching dataset;

[0013] Step 3: Based on the sea ice freeboard matching dataset obtained in Step 2, considering the penetration characteristics of radar signals through snow, the estimated snow depth on the sea ice surface is obtained and output using the following formula.

[0014] h sn =(h f -h fr ) / η sn

[0015] Among them, h sn h represents the depth of snow accumulation on the sea ice surface. f For the total freeboard altitude of the laser altimeter satellite, h fr For the radar freeboard altitude of radar altimetry satellites, η sn The refractive index of radar waves in snow.

[0016] Furthermore, the laser altimeter uses ICESat-2, and the radar altimeter uses CryoSat-2.

[0017] Furthermore, in step 2.2, the preset spatiotemporal range is such that the time interval for trajectory matching is less than 24 hours, and the search range size is 1.5 × 0.3 km.

[0018] Moreover, the η sn It is calculated by the following formula,

[0019] η sn =c / c sn (ρ sn )=(1+1.51ρ sn ) 1.5

[0020] Where c is the speed of light in a vacuum, c sn Let ρ be the speed at which light travels through snow. sn This represents the density of the snow cover.

[0021] On the other hand, the present invention provides a sea ice surface snow accumulation estimation system that combines laser and radar satellite altimetry, for implementing the sea ice surface snow accumulation estimation method described above.

[0022] Moreover, it includes the following modules,

[0023] The first module is used for satellite altimetry data preprocessing. It includes extracting longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification marker data of laser altimetry data sampling points in the observation area based on the total freeboard height obtained by laser altimetry satellites and the radar freeboard height obtained by radar altimetry satellites; extracting longitude, latitude, radar freeboard height, and radar freeboard height uncertainty data of radar altimetry data sampling points in the observation area; and removing observation point data located in inter-ice channels based on sea ice surface classification markers.

[0024] The second module is used for satellite altimetry sampling point matching, and is implemented as follows:

[0025] Based on the time and spatial location of the laser altimeter satellite and the radar altimeter satellite orbits, obtain the joint observation trajectory of the two satellites; using the radar altimeter satellite sampling points along the orbit as reference points, retrieve the laser altimeter satellite sampling points within a preset time and space range to obtain the joint observation sampling point set;

[0026] The inverse distance weighting method is used to obtain the total freeboard value of the laser altimetry satellite corresponding to the radar freeboard of the radar altimetry satellite within each search range, and the sea ice freeboard matching dataset is obtained.

[0027] The third module, based on the acquired sea ice freeboard matching dataset and considering the penetration characteristics of radar signals through snow, calculates and outputs the estimated snow depth on the sea ice surface using the following formula.

[0028] h sn =(h f -h fr ) / η sn

[0029] Among them, h sn h represents the depth of snow accumulation on the sea ice surface. f For the total freeboard altitude of the laser altimeter satellite, h fr η is the radar freeboard height of the radar altimeter satellite. sn The refractive index of radar waves in snow.

[0030] Alternatively, it may include a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute a sea ice surface snow estimation method based on combined laser and radar satellite altimetry as described above.

[0031] Alternatively, it may include a readable storage medium storing a computer program that, when executed, implements a method for estimating sea ice surface snow accumulation using combined laser and radar satellite altimetry as described in any of the preceding claims.

[0032] This invention combines satellite altimetry data from laser and radar, employing a method of matching sampling points along the orbit to obtain a spatiotemporally consistent freeboard matching dataset. It then considers the penetration characteristics of radar signals through snow to estimate snow depth. This invention provides a scheme for estimating sea ice surface snow depth based on satellite laser and radar altimetry data, eliminating reliance on on-site observation and improving the reliability of sea ice surface snow depth estimation. Attached Figure Description

[0033] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0034] Figure 2 This is a map showing the distribution of snow depth on the surface of Arctic sea ice in an embodiment of the present invention, including the results of the distribution of snow depth on the surface of sea ice during the Arctic sea ice growth season (October to April) from 2018 to 2021. Detailed Implementation

[0035] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0036] First, we provide an explanation of the technical terms:

[0037] Sea ice freeboard: refers to the height of the sea ice surface above the local sea level. The basic principle of sea ice freeboard inversion is to measure the elevation of the sea ice surface, subtract it from the elevation in the inter-ice channel, and then correct for elevation.

[0038] Overall freeboard: Including the snow depth on the sea ice surface and the ice freeboard, it is the height from the snow-air interface to the local sea level.

[0039] Ice freeboard: the height of the ice-snow interface above the local sea level.

[0040] Radar freeboard: Since radar signals can penetrate the snow on the surface of sea ice, the radar scattering interface is located on the surface of sea ice or in the snow layer. When using a radar altimeter to estimate ice freeboard, the propagation delay of the radar signal in the snow layer needs to be considered. The freeboard without radar delay correction is called radar freeboard. Ice freeboard = radar freeboard + delay correction.

[0041] The sea ice surface snow accumulation estimation method based on satellite laser and radar altimetry proposed in this embodiment of the invention includes the following steps:

[0042] Step 1: Preprocess satellite altimetry data. Extract laser altimetry data and radar altimetry data within the observation area, including information such as longitude, latitude, sea ice radar freeboard height, sea ice radar freeboard height uncertainty, total sea ice freeboard height, total sea ice freeboard height uncertainty, and sea ice surface classification markers. Based on the sea ice surface classification markers, remove observation point data located in inter-ice channels.

[0043] The example uses a combination of ICESat-2 laser altimetry and CryoSat-2 radar altimetry as an illustration. In practice, other similar satellite altimetry data can also be used instead.

[0044] Based on the total freeboard obtained from ICESat-2 laser altimetry data and the radar freeboard obtained from CryoSat-2 radar altimetry data, taking the Arctic Central Sea, Beaufort Sea, Chukchi Sea, East Siberian Sea, and Laptev Sea during the Arctic sea ice growth season (October to April) from 2018 to 2022 as examples, data such as longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification markers of ICESat-2 sampling points within the range of 65°N to 88°N were extracted, as well as data such as longitude, latitude, radar freeboard height, and radar freeboard height uncertainty of CryoSat-2 sampling points.

[0045] Step 2, satellite altimetry sampling point matching, can be implemented in the following steps:

[0046] Step 2.1: Obtain the joint observation orbit, including obtaining the joint observation trajectory of the two satellites according to their time and spatial positions.

[0047] The time interval for trajectory matching is set to be less than 24 hours and the distance to be less than 5 km. By traversing the transit time and spatial location of the ICESat-2 and CryoSat-2 ground trajectories, the trajectories of the two satellites that meet the trajectory matching conditions are obtained, which is the joint observation orbit.

[0048] Step 2.2: Obtain the joint observation sampling point set. Prioritize the CtyoSat-2 sampling points along the track as reference points, and search for ICESat-2 sampling points within a range of 1.5×0.3km to obtain the joint observation sampling point set.

[0049] Using the CryoSat-2 ground trajectory as a reference trajectory, ICESat-2 along-track sampling points within a certain spatiotemporal range are matched to each CryoSat-2 sampling point to obtain a joint observation sampling point set. To minimize the influence of sea ice drift, the trajectory matching time interval is set to less than 24 hours. To maximize the matching with the CryoSat-2 radar searchlight strip, the search range is set to 1.5 × 0.3 km. This ensures that the ground positions of the ICESat-2 sampling points are within the range of the CryoSat-2 radar echo, thereby minimizing the error introduced by deviations from the nadir position.

[0050] Step 2.3: Obtain the sea ice freeboard matching dataset. Use the inverse distance weighting method to obtain the ICESat-2 total freeboard value corresponding to the CryoSat-2 radar freeboard within each search range, and obtain the sea ice freeboard matching dataset.

[0051] The inverse range-weighted method is used to obtain the total ICESat-2 freeboard value corresponding to the CryoSat-2 radar freeboard within each search range. The calculation formula is as follows:

[0052]

[0053]

[0054] In the formula, h f_bin For each search range, h represents the total freeboard value corresponding to the CryoSat-2 radar freeboard. fi Let N be the total freeboard value of the i-th ICESat-2 sampling point within the search range, and N be the total number of ICESat-2 sampling points within the search range. w is the distance from the ICESat-2 sampling point to the CryoSat-2 sampling point. i For weights.

[0055] Step 3, sea ice surface snow depth estimation, including estimating the sea ice surface snow depth based on the sea ice freeboard matching dataset obtained in Step 2, taking into account the penetration characteristics of radar signals through snow, using the following formula.

[0056] The combined laser and radar altimeter method for retrieving sea ice surface snow depth is based on the penetration characteristics of radar waves through snow. Snow thickness is estimated by calculating the sea ice freeboard difference obtained from radar and laser altimeter data. It is assumed that the main scattering interface of Ku-band radar waves is located at the sea ice-snow interface. The freeboard value obtained by the CryoSat-2 Ku-band radar altimeter is the distance from the sea ice-snow interface to the sea surface, i.e., the ice freeboard. ICESat-2, however, can obtain the total sea ice freeboard, including the surface snow depth. Because Ku-band radar waves can penetrate sea ice surface snow, the signal speed decreases and the propagation path changes when propagating through snow, leading to an increase in signal transmission time. The radar freeboard obtained from radar altimeter data needs to be further corrected for snow propagation delay to convert it into ice freeboard.

[0057] Based on the joint observations obtained in step 2, and considering the penetration characteristics of radar signals through snow, the depth of snow accumulation on the sea ice surface is estimated using the following formula:

[0058] h sn =(h f -h fr ) / η sn

[0059] Among them, h sn h represents the depth of snow accumulation on the sea ice surface. f The total freeboard height of the ICESat-2 is h. fr For the freeboard height of the CryoSat-2 radar, ηsn Let be the refractive index of Ku-band radar waves in snow, which is a function of snow density and can be calculated using the following formula:

[0060] η sn =c / c sn (ρ sn )=(1+1.51ρ sn ) 1.5

[0061] Where c is the speed of light in a vacuum, c sn Let ρ be the speed at which light travels through snow. sn This represents the density of the snow cover.

[0062] Satellite altimetry data from the 2018-2022 Arctic sea ice growth season (October to April of the following year) were processed to obtain the final Arctic sea ice surface snow depth distribution as shown in the figure. Figure 2 As shown.

[0063] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0064] In another possible embodiment, a sea ice surface snow accumulation estimation system combining laser and radar satellite altimetry is provided, comprising the following modules:

[0065] The first module is used for satellite altimetry data preprocessing. It includes extracting longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification marker data of laser altimetry data sampling points in the observation area based on the total freeboard height obtained by laser altimetry satellites and the radar freeboard height obtained by radar altimetry satellites; extracting longitude, latitude, radar freeboard height, and radar freeboard height uncertainty data of radar altimetry data sampling points in the observation area; and removing observation point data located in inter-ice channels based on sea ice surface classification markers.

[0066] The second module is used for satellite altimetry sampling point matching, and is implemented as follows:

[0067] Based on the time and spatial location of the laser altimeter satellite and the radar altimeter satellite orbits, obtain the joint observation trajectory of the two satellites; using the radar altimeter satellite sampling points along the orbit as reference points, retrieve the laser altimeter satellite sampling points within a preset time and space range to obtain the joint observation sampling point set;

[0068] The inverse distance weighting method is used to obtain the total freeboard value of the laser altimetry satellite corresponding to the radar freeboard of the radar altimetry satellite within each search range, and the sea ice freeboard matching dataset is obtained.

[0069] The third module, based on the acquired sea ice freeboard matching dataset and considering the penetration characteristics of radar signals through snow, calculates and outputs the estimated snow depth on the sea ice surface using the following formula.

[0070] h sn =(h f -h fr ) / η sn

[0071] Among them, h sn h represents the depth of snow accumulation on the sea ice surface. f For the total freeboard altitude of the laser altimeter satellite, h fr For the radar freeboard altitude of radar altimetry satellites, η sn The refractive index of radar waves in snow.

[0072] In another possible embodiment, a sea ice surface snow estimation system combining laser and radar satellite altimetry is provided, including a processor and a memory. The memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the sea ice surface snow estimation method combined with laser and radar satellite altimetry as described above.

[0073] In another possible embodiment, a sea ice surface snow accumulation estimation system combining laser and radar satellite altimetry is provided, comprising a readable storage medium storing a computer program that, when executed, implements a sea ice surface snow accumulation estimation method combining laser and radar satellite altimetry as described in any of the preceding embodiments.

[0074] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for estimating snow accumulation on sea ice surface using a combination of laser and radar satellite altimetry, characterized in that: A spatiotemporally consistent freeboard matching dataset is obtained by matching sampling points along the track. Then, considering the penetration characteristics of radar signals through snow, the snow depth is estimated. This includes the following steps: Step 1, satellite altimetry data preprocessing, includes extracting longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification marker data of laser altimetry data sampling points within the observation area based on the total freeboard height obtained by laser altimetry satellites and the radar freeboard height obtained by radar altimetry satellites; extracting longitude, latitude, radar freeboard height, and radar freeboard height uncertainty data of radar altimetry data sampling points within the observation area; and removing observation point data located in inter-ice channels based on sea ice surface classification markers. Step 2, satellite altimeter sampling point matching adopts the method of along-orbit sampling point matching, and the implementation method is as follows: Step 2.1: Obtain the joint observation trajectory of the two satellites according to their time and spatial positions in the orbits of the laser altimeter and radar altimeter. Step 2.2: Using the radar altimeter satellite sampling points along the orbit as reference points, retrieve the laser altimeter satellite sampling points within a preset spatiotemporal range to obtain a joint observation sampling point set; within the preset spatiotemporal range, the retrieval range is set based on radar searchlight strip matching; Step 2.3: Use the inverse distance weighting method to obtain the total freeboard value of the laser altimeter satellite corresponding to the radar freeboard of the radar altimeter satellite within each search range, and obtain the sea ice freeboard matching dataset; Step 3: Without relying on on-site observations, based on the sea ice freeboard matching dataset obtained in Step 2, and considering the penetration characteristics of radar signals through snow, the estimated snow depth on the sea ice surface is obtained and output using the following formula. in, This refers to the depth of snow accumulation on the surface of sea ice. The total freeboard altitude of the laser altimeter satellite, The radar freeboard height of radar altimetry satellites. The refractive index of radar waves in snow.

2. The sea ice surface snow accumulation estimation method based on combined laser and radar satellite altimetry according to claim 1, characterized in that: The laser altimeter uses ICESat-2, and the radar altimeter uses CryoSat-2.

3. The method for estimating sea ice surface snow cover using combined laser and radar satellite altimetry as described in claim 2, characterized in that: In step 2.2, the preset spatiotemporal range is that the time interval for trajectory matching is less than 24 hours, and the search range size is 1.5 × 0.3 km.

4. The sea ice surface snow accumulation estimation method based on combined laser and radar satellite altimetry according to claim 2, characterized in that: The It is calculated by the following formula, in The speed of light in a vacuum. The speed at which light travels through snow. This represents the density of the snow cover.

5. A sea ice surface snow accumulation estimation system combining laser and radar satellite altimetry, characterized in that: This method is used to implement the sea ice surface snow accumulation estimation method based on combined laser and radar satellite altimetry as described in any one of claims 1-4.

6. The sea ice surface snow accumulation estimation system based on combined laser and radar satellite altimetry as described in claim 5, characterized in that: Includes the following modules, The first module is used for satellite altimetry data preprocessing. It includes extracting longitude, latitude, total freeboard height, total freeboard height uncertainty, and sea ice surface classification marker data of laser altimetry data sampling points in the observation area based on the total freeboard height obtained by laser altimetry satellites and the radar freeboard height obtained by radar altimetry satellites; extracting longitude, latitude, radar freeboard height, and radar freeboard height uncertainty data of radar altimetry data sampling points in the observation area; and removing observation point data located in inter-ice channels based on sea ice surface classification markers. The second module is used for satellite altimetry sampling point matching, and is implemented as follows: Based on the time and spatial location of the laser altimeter satellite and the radar altimeter satellite orbits, obtain the joint observation trajectory of the two satellites; Using radar altimeter satellite sampling points along the orbit as reference points, laser altimeter satellite sampling points within a preset spatiotemporal range are retrieved to obtain a joint observation sampling point set; The inverse distance weighting method is used to obtain the total freeboard value of the laser altimetry satellite corresponding to the radar freeboard of the radar altimetry satellite within each search range, and the sea ice freeboard matching dataset is obtained. The third module, based on the acquired sea ice freeboard matching dataset and considering the penetration characteristics of radar signals through snow, calculates and outputs the estimated snow depth on the sea ice surface using the following formula. in, This refers to the depth of snow accumulation on the surface of sea ice. The total freeboard altitude of the laser altimeter satellite, The radar freeboard height of radar altimetry satellites. The refractive index of radar waves in snow.

7. The sea ice surface snow accumulation estimation system based on combined laser and radar satellite altimetry as described in claim 5, characterized in that: It includes a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute the sea ice surface snow accumulation estimation method according to any one of claims 1-4.

8. The sea ice surface snow accumulation estimation system based on combined laser and radar satellite altimetry as described in claim 5, characterized in that: It includes a readable storage medium on which a computer program is stored, and when the computer program is executed, it implements the sea ice surface snow accumulation estimation method according to any one of claims 1-4.

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