A method and device for estimating snow cover fraction by combining SAR and optical imagery

Abstract

The application provides a kind of combined SAR and optical image snow cover rate remote sensing estimation method and device, it is related to satellite remote sensing technical field, wherein, the method comprises: obtaining the SAR snow image and SAR snow-free image of target area, and estimating the SAR image snow cover rate of the target area;Obtain the optical snow image and optical snow-free image of the target area, and estimate the optical image snow cover rate of the target area;Based on the SAR image snow cover rate and the optical image snow cover rate, determine the fusion snow cover rate of the target area.By the application, it solves the problem of spatial missing and low spatial resolution of estimation result under complex environment by single sensor, improves the spatial integrity of snow cover rate estimation, and can meet the requirements of fine-scale snow cover research under complex ground conditions.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing technology, and in particular to a remote sensing estimation method and apparatus for snow cover based on combined SAR and optical imagery. Background Technology

[0002] Snow cover is a crucial component of the cryosphere, exhibiting significant spatial and temporal variations. Characterized by high albedo, snow cover is a vital factor influencing Earth's energy balance, and its seasonal variations have a significant impact on global and regional climate change. Accurate and efficient acquisition of snow cover information is essential for predicting snowmelt runoff, monitoring the hydrological cycle, and conducting climate change analysis. Satellite remote sensing technology offers significant advantages in snow cover observation, providing continuous and comprehensive snow cover data at high frequency. In satellite remote sensing observations, snow cover distribution is characterized primarily in two ways: the first is binary snow cover discrimination, which determines whether a pixel in a remote sensing image is covered by snow; the second is snow cover percentage, representing the proportion of snow in a single pixel, reflecting snow cover information at the sub-pixel scale. Comparatively, snow cover percentage reflects the spatial distribution characteristics of snow cover at the sub-pixel scale and is more sensitive to dynamic changes in snow cover area, thus possessing greater applicability and research value in snow cover studies.

[0003] Optical sensors are the primary means of detecting and monitoring snow cover. Leveraging their high snow cover identification accuracy and high resolution, hybrid spectral decomposition, machine learning, and empirical regression methods have become the main approaches for estimating snow cover over large areas. Existing snow cover products based on optical sensors offer high accuracy, but are often limited by cloud and cloud shadow effects, resulting in significant spatial gaps and cloud-snow confusion. On the other hand, microwave sensors have the ability to penetrate clouds and operate in all weather conditions, but passive microwave remote sensing data typically has low spatial resolution, primarily focusing on binary snow cover studies, making it difficult to meet the requirements of fine-scale snow cover research under complex surface conditions. While active microwave data offers higher spatial resolution, large-scale snow cover products are currently unavailable.

[0004] In summary, while current remote sensing methods for estimating snow cover have made some progress, they still have certain shortcomings, including: 1) Existing optical satellite snow cover products are often affected by clouds and cloud shadows, resulting in spatial gaps and confusion between clouds and snow; 2) Snow cover products estimated using passive microwave data have low spatial resolution, making it difficult to meet the needs of dynamic snow cover detection under complex surface conditions; 3) Snow classification products using active microwave data are limited to binary snow cover and dry / wet snow classification products, and large-scale snow cover products are not yet available.

[0005] There is currently no effective solution to the problem of spatial missing data and low spatial resolution in the estimation results of single sensors in complex environments in existing related technologies. Summary of the Invention

[0006] This invention provides a remote sensing estimation method and apparatus for snow cover based on combined SAR and optical imagery, which addresses the shortcomings of existing related technologies where single-sensor estimation results in complex environments suffer from spatial gaps and low spatial resolution.

[0007] In a first aspect, the present invention provides a remote sensing estimation method for snow cover based on combined SAR and optical imagery, comprising: Acquire SAR images of the target area with snow and SAR images of the target area without snow, and estimate the SAR image snow cover rate of the target area; Acquire optical images of the target area with snow and optical images of the target area without snow, and estimate the optical image snow coverage of the target area; Based on the snow coverage of the SAR image and the snow coverage of the optical image, the fused snow coverage of the target area is determined.

[0008] According to the present invention, a remote sensing method for estimating snow cover using combined SAR and optical imagery acquires SAR images of a target area with snow and SAR images of a target area without snow, and estimates the SAR image snow cover of the target area, comprising: The difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations is determined, and the difference in backscattering coefficients is calculated and combined based on the local incident angle. The snow cover estimation function is calculated based on the preset segmentation threshold; The snow cover rate of the target area is estimated based on the snow cover rate estimation function.

[0009] According to the present invention, a remote sensing estimation method for snow cover based on combined SAR and optical imagery determines the difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations, and calculates and combines the difference in backscattering coefficients based on the local incident angle, including: The difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations is determined using a first calculation formula; the first calculation formula is:

[0010] in, This represents the difference in backscattering coefficients in VV polarization. This represents the difference in backscattering coefficients in VH polarization. This indicates the backscattering coefficient of a snow-covered image. Indicates the backscattering coefficient of a snowless image, subscript vv and vh Both indicate the polarization direction; The weights are calculated based on the local incident angle using the second calculation formula; the second calculation formula is:

[0011] in, w Indicates weight, Indicates the local angle of incidence. , , k For coefficients, ; The difference in backscattering coefficients is calculated using a third calculation formula; the third calculation formula is:

[0012] in, This represents the difference in the backscattering coefficients after merging.

[0013] According to a remote sensing method for estimating snow cover using combined SAR and optical imagery provided by the present invention, the method acquires optical snow-covered and optical snow-free images of the target area and estimates the optical image snow cover of the target area, including: Based on the optical snow-covered image and the optical snowless image, calculate the general proportional snow cover index for the snow-covered image and the snowless image. Key data points are extracted based on the general proportional snow cover index, and model parameters are calculated based on the key data points. The optical image snow coverage of the target area is estimated by combining the model parameters.

[0014] According to a remote sensing estimation method for snow cover based on combined SAR and optical imagery provided by the present invention, a general proportional snow cover index is calculated for both the optical snow-covered and optical snow-free images, including: use URSI The general proportional snow cover index is calculated using the formula; URSI The calculation formula is:

[0015] in, This indicates the general percentage of snow cover. Indicates green band reflectivity. Indicates near-infrared reflectivity. This indicates shortwave infrared reflectivity.

[0016] According to the remote sensing estimation method for snow cover based on combined SAR and optical imagery provided by the present invention, the calculation formula for the key data points is as follows:

[0017] in, Indicates key points in the low-value area data. Indicates key points in the high-value area data. General proportional snow cover index indicating snow-covered images URSI value; The formula for calculating the model parameters is as follows:

[0018]

[0019] in, and These are the model parameters.

[0020] According to the present invention, a remote sensing estimation method for snow cover based on combined SAR and optical imagery estimates the optical image snow cover of the target area by combining the model parameters, including: Define a snow cover estimation function and combine it with the model parameters to estimate the optical image snow cover of the target area; The preset snow cover estimation function is:

[0021] in, represents the snow coverage rate of the optical image, and tanh() represents the hyperbolic tangent function.

[0022] According to a remote sensing estimation method for snow cover based on combined SAR and optical imagery provided by the present invention, the method determines the fused snow cover of the target area based on the snow cover of the SAR imagery and the snow cover of the optical imagery, including: The correlation coefficient is calculated using the correlation coefficient calculation formula; the correlation coefficient calculation formula is:

[0023] in, Represents the spatial correlation coefficient. E Expressing expectations, This indicates the snow cover rate in SAR imagery. This indicates the snow coverage rate in optical images. Var () represents variance. i Indicates the pixel number; The blended snow cover rate is calculated using the blended snow cover rate calculation formula; the blended snow cover rate calculation formula is as follows:

[0024] in, Indicates the combined snow cover rate. k Represents the threshold constant of the function. nan This indicates a null value.

[0025] Secondly, the present invention also provides a remote sensing estimation device for snow cover based on combined SAR and optical imagery, comprising: The radar processing module is used to acquire SAR images of the target area with snow and SAR images without snow, and to estimate the SAR image snow coverage of the target area. An optical processing module is used to acquire optical images of the target area with snow and optical images of the target area without snow, and to estimate the optical image snow coverage of the target area. The data fusion module is used to determine the fused snow coverage of the target area based on the snow coverage of the SAR image and the snow coverage of the optical image.

[0026] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the remote sensing estimation method for snow cover based on joint SAR and optical imagery as described in the first aspect above.

[0027] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the remote sensing estimation method for snow cover based on joint SAR and optical imagery as described in the first aspect above.

[0028] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the remote sensing estimation method for snow cover based on joint SAR and optical imagery as described in the first aspect above.

[0029] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a remote sensing estimation method for snow cover based on combined SAR and optical imagery. Based on SAR snow-covered and snow-free imagery, it calculates the difference in backscattering coefficients between snow-covered and snow-free images of the target area in VV and VH polarizations. It then calculates and merges these backscattering coefficient differences based on local incident angles. A snow cover estimation function is calculated based on a preset segmentation threshold, and the snow cover coverage of the target area's SAR imagery is estimated using this function. Furthermore, based on optical snow-covered and snow-free imagery, a general proportional snow cover index is calculated for both images. Key data points are extracted based on this index, and model parameters are calculated. The snow cover coverage of the target area's optical imagery is estimated based on these parameters. Finally, the fused snow cover coverage of the target area is estimated based on both SAR and optical imagery. This method solves the problems of spatial gaps and low spatial resolution in estimation results from a single sensor in complex environments, improves the spatial integrity of snow cover estimation, and meets the requirements for fine-scale snow cover research under complex surface conditions. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the remote sensing estimation method for snow cover using combined SAR and optical imagery provided by the present invention. Figure 2 This is a schematic diagram of the snow coverage extraction process in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the estimation of snow coverage in the target area in an embodiment of the present invention; Figure 4 This is a structural block diagram of the remote sensing estimation device for snow cover based on combined SAR and optical imagery provided by the present invention. Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0033] This invention provides a remote sensing method for estimating snow cover using a combination of Synthetic Aperture Radar (SAR) and optical imagery. Figure 1 This is a flowchart of the remote sensing estimation method for snow cover using combined SAR and optical imagery provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step S101: Acquire SAR images with snow and SAR images without snow in the target area, and estimate the snow coverage of the SAR images in the target area. Step S102: Obtain optical images of the target area with snow and optical images of the target area without snow, and estimate the snow coverage of the target area in the optical images. Step S103: Determine the fused snow coverage of the target area based on the snow coverage of SAR image and the snow coverage of optical image.

[0034] In this method, firstly, based on SAR snow-covered and snow-free imagery information and combined with the polarization parameters of the radar system, the SAR-affected snow cover rate of the target area is estimated. Then, based on optical snow-covered and snow-free imagery information, the optical image snow cover rate is estimated. Finally, the SAR and optical image snow cover rates are fused, addressing the issues of spatial incompleteness and low spatial resolution in estimation results from a single sensor under complex environments. This improves the spatial integrity of snow cover estimation and meets the requirements for fine-scale snow cover research under complex surface conditions.

[0035] Next, the above method will be described in detail with specific examples. The Sentinel-1 GRD image with stripe T233649 dated January 19, 2020, and the Sentinel-2 surface reflectance image with stripe T46SGC dated January 19, 2020, are used as examples. Surface reflectance data (including snow-covered and snow-free image information), NASADEM data, and 2020 Globeland30 land cover type data of the target area are acquired from the Sentinel-1 GRD and Sentinel-2 surface reflectance images. Preprocessing of the data includes stitching, reprojection, resampling, and cropping. The Sentinel-1 GRD imagery includes a snow-covered image (band T233649) dated January 19, 2020, and a snow-free image (band T233656) dated August 22, 2020, at the same location. The Sentinel-2 surface reflectance imagery includes a snow-covered image (band T46SGC) dated January 19, 2020, and a snow-free, clear-sky image (band T46SGC) dated August 30, 2020. Figure 2 As shown, Figure 2 This is a schematic diagram of the snow coverage extraction process in an embodiment of the present invention.

[0036] The cloud masking method Fmask4.0 was used to mask clouds and cloud shadow extents in Sentinel-2 snow surface reflectance data. Water bodies were masked based on 2020 Globeland30 land cover type data. Mountain shadows were calculated using solar azimuth and solar altitude angles provided by NASADEM and Sentinel-2 imagery to obtain completely invisible mountain shadow extents. The masked data was considered valid and could be used for subsequent calculations and processing.

[0037] In some embodiments, step S101, acquiring SAR snow-covered and SAR snow-free images of the target area and estimating the SAR image snow coverage of the target area, includes: determining the difference in backscattering coefficients between the SAR snow-covered and SAR snow-free images in VV and VH polarizations, and calculating the merged backscattering coefficient difference based on the local incident angle; calculating a snow coverage estimation function based on a preset segmentation threshold; and estimating the SAR image snow coverage of the target area based on the snow coverage estimation function.

[0038] In this embodiment, determining the difference in backscattering coefficients between SAR snow-covered and SAR snow-free images in VV and VH polarizations, and calculating the combined backscattering coefficient difference based on the local incident angle, includes: determining the difference in backscattering coefficients between SAR snow-covered and SAR snow-free images in VV and VH polarizations using a first calculation formula; the first calculation formula is:

[0039] in, This represents the difference in backscattering coefficients in VV polarization. This represents the difference in backscattering coefficients in VH polarization. This indicates the backscattering coefficient of a snow-covered image. Indicates the backscattering coefficient of a snowless image, subscript vv and vh Both indicate the polarization direction; The weights are calculated based on the local incident angle using the second calculation formula; the second calculation formula is:

[0040] in, w Indicates weight, Indicates the local angle of incidence. , , k For coefficients, ; The difference in the combined backscattering coefficients is calculated using the third calculation formula; the third calculation formula is:

[0041] in, This represents the difference in the backscattering coefficients after merging.

[0042] For example, the segmentation center threshold is set to -2dB, and the snow cover rate of the SAR snow-covered image is calculated using a preset snow cover rate estimation function. The preset snow cover rate estimation function is calculated using the following formula:

[0043] in, This represents the snow cover rate of the SAR image, and tanh() represents the hyperbolic tangent function. This represents the difference in the combined backscattering coefficients.

[0044] In some embodiments, step S102, acquiring optical snow-covered and optical snow-free images of the target area and estimating the optical image snow coverage of the target area, includes: calculating a general proportional snow cover index for the snow-covered and snow-free images based on the optical snow-covered and optical snow-free images; extracting key data points based on the general proportional snow cover index and calculating model parameters based on the key data points; and estimating the optical image snow coverage of the target area by combining the model parameters.

[0045] In this embodiment, based on optical snow-covered images and optical snow-free images, a general proportional snow cover index is calculated for both snow-covered and snow-free images, including: using... URSI The general proportional snow cover index is calculated using the following formula; URSI The calculation formula is:

[0046] in, This indicates the general percentage of snow cover. This indicates the reflectivity in the green band (wavelength range 0.53~0.59m). This indicates the reflectivity in the near-infrared band (wavelength range 0.85~0.88m). This indicates short-wave infrared reflectivity (wavelength range 1.57~1.65m).

[0047] The formula for calculating key data points is:

[0048] in, Indicates key points in the low-value area data. Indicates key points in the high-value area data. General proportional snow cover index indicating snow-covered images URSI value; The formula for calculating the model parameters is:

[0049]

[0050] in, and These are the model parameters.

[0051] Estimating the snow cover rate of the target area using optical imagery by combining model parameters includes: setting a snow cover rate estimation function and estimating the snow cover rate of the target area using optical imagery by combining model parameters; the preset snow cover rate estimation function is:

[0052] in, represents the snow coverage rate of the optical image, and tanh() represents the hyperbolic tangent function.

[0053] In some embodiments, step S103, determining the fused snow coverage of the target area based on the snow coverage of SAR imagery and optical imagery, includes: calculating the correlation coefficient using the correlation coefficient calculation formula; the correlation coefficient calculation formula is:

[0054] in, Represents the spatial correlation coefficient. E Expressing expectations, This indicates the snow cover rate in SAR imagery. This indicates the snow coverage rate in optical images. Var () represents variance. i Indicates the pixel number; The blended snow cover rate is calculated using the blended snow cover rate calculation formula; the blended snow cover rate calculation formula is:

[0055] in, Indicates the combined snow cover rate. k This represents the function threshold constant, set to 0.6. nan This indicates a null value.

[0056] like Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the estimation of snow cover rate in the target area in an embodiment of the present invention. Through the process described, the snow cover rate of the target area in SAR image and the snow cover rate in optical image are estimated, and finally the snow cover rate of the target area after fusion is obtained.

[0057] Compared with existing technologies, this method has the following advantages: 1. Utilize the complementarity of SAR and optical remote sensing data to overcome the limitations of single satellite data in snow cover monitoring.

[0058] 2. By using SAR and optical remote sensing data to estimate fine-resolution snow cover, the data loss and cloud-snow confusion caused by clouds and their shadows are effectively reduced, improving the accuracy and completeness of snow cover estimation and meeting the needs of dynamic snow cover detection under complex surface conditions.

[0059] In summary, this method calculates the backscattering coefficient difference between snow-covered and snow-free SAR images of the target area in VV and VH polarizations based on SAR snow-covered and snow-free images. The difference is then combined based on the local incident angle. A snow cover estimation function is calculated using a preset segmentation threshold, and the snow cover coverage of the target area's SAR image is estimated using this function. Furthermore, a general proportional snow cover index is calculated for both snow-covered and snow-free optical images. Key data points are extracted based on this index, and model parameters are calculated using these parameters. Finally, the fused snow cover coverage of the target area is estimated based on both SAR and optical images. This method addresses the spatial incompleteness and low spatial resolution issues inherent in single-sensor estimations in complex environments, improving the spatial integrity of snow cover estimation and meeting the requirements for fine-scale snow cover research under complex surface conditions.

[0060] The present invention also provides a remote sensing estimation device for snow cover using combined SAR and optical imagery. The remote sensing estimation device for snow cover using combined SAR and optical imagery provided by the present invention will be described below. The remote sensing estimation device for snow cover using combined SAR and optical imagery described below can be referred to in correspondence with the remote sensing estimation method for snow cover using combined SAR and optical imagery described above. Figure 4 This is a structural block diagram of the remote sensing estimation device for snow cover using combined SAR and optical imagery provided by the present invention, as shown below. Figure 4 As shown, the device includes: The radar processing module 401 is used to acquire SAR snow-covered and SAR snow-free images of the target area and estimate the SAR image snow coverage of the target area. The optical processing module 402 is used to acquire optical snow-covered and optical snow-free images of the target area and estimate the optical image snow coverage of the target area. The data fusion module 403 is used to determine the fused snow coverage of the target area based on the snow coverage of SAR images and the snow coverage of optical images.

[0061] In operation, this device first uses radar processing module 401 to estimate the SAR-affected snow cover rate of the target area based on SAR snow-covered and snow-free image information, combined with the polarization parameters of the radar system. Then, optical processing module 402 estimates the optical image snow cover rate based on optical snow-covered and snow-free image information. Finally, data fusion module 403 fuses the SAR image snow cover rate and the optical image snow cover rate, solving the problems of spatial missing values ​​and low spatial resolution in estimation results from a single sensor under complex environments. This improves the spatial integrity of snow cover rate estimation and meets the requirements for fine-scale snow cover research under complex surface conditions.

[0062] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 501, a communication interface 502, a memory 503, and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other via the communication bus 504. The processor 501 can call logical instructions in the memory 503 to execute a remote sensing estimation method for snow cover based on joint SAR and optical imagery, the method including: Acquire SAR images of the target area with snow and SAR images without snow, and estimate the SAR snow cover of the target area; Acquire optical images of the target area with and without snow, and estimate the snow cover of the target area based on the optical images. Based on the snow cover rate of SAR imagery and the snow cover rate of optical imagery, the fused snow cover rate of the target area is determined.

[0063] Furthermore, the logical instructions in the aforementioned memory 503 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the remote sensing estimation method for snow cover based on joint SAR and optical imagery provided by the above methods. The method includes: Acquire SAR images of the target area with snow and SAR images without snow, and estimate the SAR snow cover of the target area; Acquire optical images of the target area with and without snow, and estimate the snow cover of the target area based on the optical images. Based on the snow cover rate of SAR imagery and the snow cover rate of optical imagery, the fused snow cover rate of the target area is determined.

[0065] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a remote sensing estimation method for snow cover based on joint SAR and optical imagery provided by the methods described above, the method comprising: Acquire SAR images of the target area with snow and SAR images without snow, and estimate the SAR snow cover of the target area; Acquire optical images of the target area with and without snow, and estimate the snow cover of the target area based on the optical images. Based on the snow cover rate of SAR imagery and the snow cover rate of optical imagery, the fused snow cover rate of the target area is determined.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing method for estimating snow cover using combined SAR and optical imagery, characterized in that, include: Acquire SAR images of the target area with snow and SAR images of the target area without snow, and estimate the SAR image snow cover rate of the target area; Acquire optical images of the target area with snow and optical images of the target area without snow, and estimate the optical image snow coverage of the target area; Based on the snow coverage of the SAR image and the snow coverage of the optical image, the fused snow coverage of the target area is determined.

2. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 1, characterized in that, Acquire SAR images of the target area with and without snow, and estimate the SAR snow cover of the target area, including: The difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations is determined, and the difference in backscattering coefficients is calculated and combined based on the local incident angle. The snow cover estimation function is calculated based on the preset segmentation threshold; The snow cover rate of the target area is estimated based on the snow cover rate estimation function.

3. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 2, characterized in that, Determine the difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations, and calculate and combine the difference in backscattering coefficients based on the local incident angle, including: The difference in backscattering coefficients between the SAR snow-covered image and the SAR snow-free image in VV and VH polarizations is determined using a first calculation formula; the first calculation formula is: in, This represents the difference in backscattering coefficients in VV polarization. This represents the difference in backscattering coefficients in VH polarization. This indicates the backscattering coefficient of a snow-covered image. Indicates the backscattering coefficient of a snowless image, subscript vv and vh Both indicate the polarization direction; The weights are calculated based on the local incident angle using the second calculation formula; the second calculation formula is: in, w Indicates weight, Indicates the local angle of incidence. , , k For coefficients, ; The difference in backscattering coefficients is calculated using a third calculation formula; the third calculation formula is: in, This represents the difference in the backscattering coefficients after merging.

4. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 1, characterized in that, Acquire optical images of the target area with and without snow, and estimate the optical image snow cover of the target area, including: Based on the optical snow-covered image and the optical snowless image, calculate the general proportional snow cover index for the snow-covered image and the snowless image. Key data points are extracted based on the general proportional snow cover index, and model parameters are calculated based on the key data points. The optical image snow coverage of the target area is estimated by combining the model parameters.

5. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 4, characterized in that, Based on the optical snow-covered image and the optical snowless image, a general proportional snow cover index is calculated for both the snow-covered and snowless images, including: use URSI The general proportional snow cover index is calculated using the formula; URSI The calculation formula is: in, This indicates the general percentage of snow cover. Indicates green band reflectivity. Indicates near-infrared reflectivity. This indicates shortwave infrared reflectivity.

6. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 4, characterized in that, The formula for calculating the key data points is as follows: in, Indicates key points in the low-value area data. Indicates key points in the high-value area data. General proportional snow cover index indicating snow-covered images URSI value; The formula for calculating the model parameters is as follows: in, and These are the model parameters.

7. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 4, characterized in that, Estimating the optical image snow cover of the target area using the model parameters includes: Define a snow cover estimation function and combine it with the model parameters to estimate the optical image snow cover of the target area; The preset snow cover estimation function is: in, represents the snow coverage rate of the optical image, and tanh() represents the hyperbolic tangent function.

8. The remote sensing estimation method for snow cover based on combined SAR and optical imagery according to claim 1, characterized in that, Based on the snow cover rate of the SAR image and the snow cover rate of the optical image, the fused snow cover rate of the target area is determined, including: The correlation coefficient is calculated using the correlation coefficient calculation formula; the correlation coefficient calculation formula is: in, Represents the spatial correlation coefficient. E Expressing expectations, This indicates the snow cover rate in SAR imagery. This indicates the snow coverage rate in optical images. Var () represents variance. i Indicates the pixel number; The blended snow cover rate is calculated using the blended snow cover rate calculation formula; the blended snow cover rate calculation formula is as follows: in, Indicates the combined snow cover rate. k Represents the threshold constant of the function. nan This indicates a null value.

9. A remote sensing estimation device for snow cover based on combined SAR and optical imagery, characterized in that, include: The radar processing module is used to acquire SAR images of the target area with snow and SAR images without snow, and to estimate the SAR image snow coverage of the target area. An optical processing module is used to acquire optical images of the target area with snow and optical images of the target area without snow, and to estimate the optical image snow coverage of the target area. The data fusion module is used to determine the fused snow coverage of the target area based on the snow coverage of the SAR image and the snow coverage of the optical image.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the remote sensing estimation method for snow cover based on combined SAR and optical imagery as described in any one of claims 1 to 8.

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