Snow coverage rate remote sensing estimation method based on image information adaptive regression

Abstract

The invention relates to the technical field of satellite remote sensing, in particular to an accumulated snow coverage rate remote sensing estimation method based on image information adaptive regression, and the method comprises the steps: calculating a general proportion accumulated snow index of a snow image and a snow-free image based on snow image information and snow-free image information, and calculating a normalized difference accumulated snow index of the snow image; according to the general proportion accumulated snow index and the normalized difference accumulated snow index of the snowy image, constructing an accumulated snow index change scatter density map before and after snowing, so as to obtain a high-value region density change curve and a low-value region density change curve based on the accumulated snow index change scatter density map before and after snowing; based on the low-value region density change curve and the high-value region density change curve, low-value region density feature points and high-value region density feature points are extracted, the difference value and the average value of the low-value region density feature points and the high-value region density feature points are calculated, and regression model parameters are calculated according to the difference value and the average value; and estimating the accumulated snow coverage rate of the target area based on the regression model parameters.

Description

Technical Field

[0001] The present application relates to the field of satellite remote sensing technology, and in particular to a snow cover remote sensing estimation method based on adaptive regression of image information. Background Art

[0002] Changes in seasonal snow and glaciers will affect the regional climate, water cycle and surface radiation balance. Accurate and efficient acquisition of snow information is an important prerequisite for studying regional climate and hydrological changes. Satellite remote sensing technology has the advantages of fast data acquisition, wide coverage, and less restrictions on ground conditions. It has been widely used in snow change research. There are two main ways to characterize snow distribution in satellite remote sensing observations: the first is binary snow, that is, directly judging whether a pixel in the image is snow or non-snow; the second is snow cover rate, that is, characterizing the proportion of snow covered in a single image pixel. Binarized snow products can directly estimate the snow-covered area of ​​the entire image, but a 30-meter resolution image pixel is usually a mixed pixel formed by a combination of snow and other non-snow objects. Obviously, binary snow has a large error in describing the precise distribution of snow. Snow cover rate describes the snow cover inside the pixel, which has obvious advantages in analyzing the fine changes in snow cover.

[0003] There are currently two main methods for extracting snow cover over a large area, namely the empirical regression algorithm based on the snow index and the spectral unmixing method. The empirical regression algorithm uses a fixed coefficient, and the model accuracy is not completely consistent in different study areas; the spectral unmixing method requires the collection of spectral information of surface endmembers. When applied in different study areas, the main endmember spectral information of the area needs to be re-collected or adjusted and optimized, and solving the best pixel combination for each pixel in the image also consumes high computing resources. At present, machine learning methods have also been used to estimate snow cover, but the construction and training process of the network is complicated, and retraining and learning are required in different study areas.

[0004] In summary, although the snow cover extraction method based on optical remote sensing has made certain progress, it still has certain shortcomings, including: 1) the spatial coverage range and spatial coverage rate of existing optical satellite snow cover products cannot meet the requirements of high-precision, large-scale regional snow monitoring, and it is necessary to propose a simple and efficient algorithm suitable for medium and high resolution images; 2) Spectral unmixing consumes high computing resources, and the selection and construction of machine learning networks are complex; 3) It is impossible to dynamically adjust the model and method according to different surface types and image information. The current main estimation methods all use fixed coefficients, end members or networks, and their universality and automation are not high. Summary of the invention

[0005] The present application provides a snow cover remote sensing estimation method based on adaptive regression of image information, so as to solve the problem of unstable estimation accuracy of fixed parameter regression model under different snowing scenes.

[0006] The first aspect of the present application provides a remote sensing estimation method for snow coverage based on adaptive regression of image information, comprising the following steps: obtaining snow image information and snow-free image information of a target area, and calculating a universal proportional snow index for snow images and snow-free images based on the snow image information and the snow-free image information, and calculating a normalized difference snow index for the snow image; constructing a scatter density map of snow index changes before and after snowing based on the universal proportional snow index and the normalized difference snow index of the snow image, so as to obtain a high-value area density change curve and a low-value area density change curve based on the scatter density map of snow index changes before and after snowing; extracting low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve, calculating the difference and average value between the low-value area density feature points and the high-value area density feature points, and calculating regression model parameters based on the difference and the average value, so as to estimate the snow coverage of the target area based on the regression model parameters.

[0007] Optionally, the extracting of low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve includes: judging whether the low-value area density curve satisfies a preset smooth condition; if the low-value area density curve does not satisfy the preset smooth condition, then when there is only a single peak in the low-value area density curve, if the single peak is located in the left half of the low-value area density curve, then extracting from the single peak the point where the low-value area density curve changes from convex to concave and has the largest curvature as the low-value area density feature point; if the single peak is located in the right half of the low-value area density curve, then extracting from the single peak to the left the point where the low-value area density curve changes from convex to concave and has the largest curvature as the low-value area density feature point. Area density feature points; if the low-value area density curve has double peaks, the points corresponding to the troughs between the double peaks are extracted as the low-value area density feature points; if the low-value area density curve has more than two peaks, when the highest peak is the first peak or the last peak, the trough closest to the highest peak is determined as the low-value area density feature point, if the highest peak and the second highest peak do not meet the proximity condition, the average value of the low-value area density feature point corresponding to the highest peak and the low-value area density feature point corresponding to the second highest peak is taken as the low-value area density feature point, if the highest peak and the second highest peak meet the proximity condition, the point corresponding to the trough between the highest peak and the second highest peak is taken as the low-value area density feature point.

[0008] Optionally, after determining whether the low-value area density curve satisfies the preset smooth condition, the method includes: if the low-value area density curve satisfies the preset smooth condition, calculating the average value of the universal proportional snow index of the snow-free image, and adding the average value to the first preset threshold value to obtain the low-value area density feature point.

[0009] Optionally, the extracting of low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve also includes: when there is only a single peak in the high-value area density change curve, judging whether the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to a second preset threshold; if the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to the second preset threshold, then when the highest peak of the high-value area density curve is located in the left half of the curve, taking the first preset proportion of the current highest density value as the high-value area density feature point; if the highest peak is located in For the right half of the curve, the second preset proportion of the current highest density value is taken as the density characteristic point of the high-value area; if the universal proportional snow index of the highest peak of the high-value area density curve is greater than the second preset threshold and less than or equal to the third preset threshold, the universal proportional snow index corresponding to the density point at the first preset position to the right of the highest peak is determined as the density characteristic point of the high-value area; if the universal proportional snow index of the highest peak of the high-value area density curve is greater than the third preset threshold, the universal proportional snow index corresponding to the density point at the second preset position to the left of the highest peak is determined as the density characteristic point of the high-value area.

[0010] Optionally, the extracting of low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve also includes: when there are multiple peaks in the high-value area density change curve, if the difference between the universal proportional snow index of the second highest peak of the high-value area density change curve and the universal proportional snow index between the highest peak of the high-value area density change curve is greater than a fourth preset threshold, then taking the average value of the universal proportional snow index of the highest peak and the universal proportional snow index between the second highest peak as the high-value area density feature point.

[0011] Optionally, the method of calculating a universal proportional snow index of the snow image and the snow-free image based on the snow image information and the snow-free image information, and after calculating the normalized difference snow index of the snow image, includes: if the growth value of the universal proportional snow index of the snow image relative to the snow-free image is less than a fifth preset threshold, or the universal proportional snow index of the snow image is less than a sixth preset threshold, then determining that the current snow coverage rate is 0.

[0012] Optionally, estimating the snow coverage of the snow image based on the regression model parameters includes: calculating the snow coverage of the snow image using a preset snow coverage calculation formula, wherein the preset snow coverage calculation formula is:

[0013]

[0014] Among them, FSC is the snow cover fraction, tanh() is the hyperbolic tangent function, URSI is the value of the universal proportional snow index URSI for snow images, and a and b are regression model parameters.

[0015] Optionally, the calculating of the regression model parameters according to the difference and the average value of the low-value area density feature point and the high-value area density feature point comprises: calculating the regression model parameters using a preset regression model parameter calculation formula, wherein the preset regression model parameter calculation formula is:

[0016] a=-12.294×URSI diff +11.323

[0017] b=-a×(0.9895×URSI mean )

[0018] Among them, a and b are regression model parameters, URSI diff is the difference between the density feature points in the low-value area and the density feature points in the high-value area, URSI mean It is the average value of the density feature points in the low-value area and the density feature points in the high-value area.

[0019] Optionally, the calculating of the universal proportional snow index of the snow image and the snow-free image based on the snow image information and the snow-free image information, and calculating the normalized difference snow index of the snow image, includes: calculating the universal proportional snow index using a first calculation formula, and calculating the normalized difference snow index using a second calculation formula, wherein the first calculation formula is:

[0020]

[0021] The second calculation formula is:

[0022]

[0023] Among them, R Green is the green band reflectivity, R NIR is the reflectivity in the near infrared band, R SWIR is the shortwave infrared reflectivity.

[0024] In the above implementation, based on the information of the snow image and the snow-free image, the universal proportional snow accumulation index of the snow image and the snow-free image is calculated, and the normalized difference snow accumulation index of the snow image is calculated. A scatter density map of the change of the snow accumulation index before and after snowing is constructed based on the universal proportional snow accumulation index and the normalized difference snow accumulation index of the snow image, so as to obtain the high-value area density change curve and the low-value area density change curve based on the scatter density map of the snow accumulation index change before and after snowing, extract the low-value area density feature points and the high-value area density feature points based on the low-value area density change curve and the high-value area density change curve, calculate the difference and average value of the low-value area density feature points and the high-value area density feature points, and calculate the regression model parameters based on the difference and average value, so as to estimate the snow coverage rate of the target area based on the regression model parameters. Thus, the problem of unstable estimation accuracy of the fixed parameter regression model under different snowing scenes is solved, the dependence of the regression model on training data is reduced, the scope of application and accuracy of snow coverage rate estimation can be improved, and dynamic monitoring of snow coverage rate can be achieved in different research areas.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A flowchart of a snow cover remote sensing estimation method based on image information adaptive regression according to an embodiment of the present application;

[0028] Figure 2 This is a flowchart of the implementation of snow cover extraction according to an embodiment of the present application;

[0029] Figure 3 A schematic diagram of a low-value area scatter plot and a high-value area scatter plot and a corresponding low-value area density curve and a high-value area density curve according to an embodiment of the present application;

[0030] Figure 4 A schematic diagram of an abnormal point and a flat curve according to an embodiment of the present application;

[0031] Figure 5 is a low-value area feature point URSI according to an embodiment of the present application L Flowchart of extraction;

[0032] Figure 6 is a schematic diagram of a low-value region density curve according to an embodiment of the present application;

[0033] Figure 7URSI is a high-value area feature point according to an embodiment of the present application. H Flowchart of extraction;

[0034] Figure 8 It is a schematic diagram of a high-value area density curve according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0036] The following describes the snow cover remote sensing estimation method based on adaptive regression of image information of the embodiment of the present application with reference to the accompanying drawings. In view of the problem of unstable estimation accuracy of the fixed parameter regression model mentioned in the above background technology under different snowing scenes, the present application provides a snow cover remote sensing estimation method based on adaptive regression of image information, in which a universal proportional snow index of snow images and snowless images is calculated based on snow image information and snowless image information, and a normalized difference snow index of snow images is calculated, and a scatter density map of the change of snow index before and after snowing is constructed based on the universal proportional snow index and the normalized difference snow index of snow images, so as to obtain a high-value area density change curve and a low-value area density change curve based on the scatter density map of the change of snow index before and after snowing, extract the low-value area density feature points and the high-value area density feature points based on the low-value area density change curve and the high-value area density change curve, calculate the difference and average value of the low-value area density feature points and the high-value area density feature points, and calculate the regression model parameters based on the difference and average value, so as to estimate the snow cover of the target area based on the regression model parameters. As a result, the problem of unstable estimation accuracy of the fixed parameter regression model under different snowing scenarios is solved, the dependence of the regression model on training data is reduced, the applicability and accuracy of snow cover estimation can be improved, and dynamic monitoring of snow cover can be achieved in different research areas.

[0037] Since the empirical regression method of the snow index is simple, efficient, and can be produced on a large scale, the empirical regression method with fixed parameters has inconsistent accuracy in different scenarios and is not very universal. Therefore, this application designs a snow cover estimation method based on a nonlinear regression method, which adaptively adjusts the regression model parameters in different scenarios according to the image change characteristics before and after snowing, and can realize dynamic monitoring of snow cover estimation in different study areas.

[0038] Specifically, Figure 1 A schematic flow chart of a method for remote sensing estimation of snow cover fraction based on adaptive regression of image information provided in an embodiment of the present application.

[0039] like Figure 1 As shown, the snow cover remote sensing estimation method based on image information adaptive regression includes the following steps:

[0040] In step S101, the snow image information and the snow-free image information of the target area are obtained, and based on the snow image information and the snow-free image information, the universal proportional snow index of the snow image and the snow-free image is calculated, and the normalized difference snow index of the snow image is calculated.

[0041] Optionally, in some embodiments, based on the snow image information and the snow-free image information, a universal proportional snow index of the snow image and the snow-free image is calculated, and a normalized difference snow index of the snow image is calculated, including: calculating the universal proportional snow index using a first calculation formula, and calculating the normalized difference snow index using a second calculation formula, wherein the first calculation formula is:

[0042]

[0043] The second calculation formula is:

[0044]

[0045] Among them, R Green is the green band reflectivity, R NIR is the reflectivity in the near infrared band, R SWIR is the shortwave infrared reflectivity.

[0046] Optionally, in some embodiments, based on the snow image information and the snow-free image information, a universal proportional snow accumulation index of the snow image and the snow-free image is calculated, and after calculating the normalized difference snow accumulation index of the snow image, if the growth value of the universal proportional snow accumulation index of the snow image relative to the snow-free image is less than the fifth preset threshold, or the universal proportional snow accumulation index of the snow image is less than the sixth preset threshold, then it is determined that the current snow coverage rate is 0.

[0047] This application takes the Landsat-8OLI surface reflectance image with strip number 193027 on April 21, 2019 as an example.

[0048] Obtain Landsat-8OLI surface reflectance data (including snow image information and snow-free clear sky image information), NASADEM data, and 2020 Globeland30 land cover type data in the target area, and perform preprocessing such as stitching, reprojection, resampling, and cropping on the data. Among them, the Landsat-8OLI surface reflectance data includes the snow image with strip number 193027 on April 21, 2019, and the snow-free clear sky image with strip number 193027 on November 17, 2020 at the same location. Use the cloud mask method Fmask4.0 to mask the cloud and cloud shadow range in the snow surface reflectance data. Mask the water body according to the 2020 Globeland30 land cover type data, calculate the mountain shadow according to the solar azimuth and solar altitude provided by NASADEM and Landsat images, and obtain the completely invisible mountain shadow range. The masked data is considered to be valid data and can be used for subsequent calculations and processing, such as Figure 2 shown.

[0049] The first calculation formula and the second calculation formula are further used to calculate the universal proportional snow index URSI of the snow image and the corresponding snow-free clear sky image, as well as the normalized difference snow index NDSI of the snow image. For each valid pixel of the snow image, if the increase of its URSI value relative to the corresponding pixel of the snow-free image is greater than or equal to 0.03 or the URSI value of the pixel itself is greater than or equal to 0.5, it is considered to be a potential snow pixel, and the pixel judged to be potential snow will participate in the subsequent snow coverage estimation; if the increase value of the universal proportional snow index of the snow image relative to the snow-free image is less than 0.03 (i.e., the fifth preset threshold), or the universal proportional snow index of the snow image is less than 0.5 (i.e., the sixth preset threshold), then it is determined that the current snow coverage is 0.

[0050] In step S102, a scatter density map of the change of the snow index before and after snowing is constructed according to the universal proportional snow index and the normalized difference snow index of the snow image, so as to obtain a high-value area density change curve and a low-value area density change curve based on the scatter density map of the change of the snow index before and after snowing.

[0051] Using the URSI and NDSI of the snow image and the snow-free image, a scatter plot of the low-value area is drawn based on the URSI of the snow image (called SnowedURSI) and the URSI of the snow-free image (called Snow-free URSI). Figure 3 Based on the URSI and NDSI (called Snowed NDSI), a scatter plot of high-value areas is drawn (as shown in Figure 3 (3)). Calculate the Gaussian kernel density corresponding to the two scatter plots and draw the density curve of the low-value area and the density curve of the high-value area (as shown in Figure 3(2) and 3(4)).

[0052] Specifically, pixels with URSI values ​​ranging from 0 to 0.6 in the snowy image are extracted as low-value areas. The URSI values ​​of the low-value area pixels in the snowy image (Snowed URSI) are used as the horizontal axis, and the URSI values ​​of the low-value area pixels in the snowy image (Snow-free URSI) are used as the vertical axis to draw a scatter plot of the low-value area. Then, the number of pixels in the scatter plot is uniformly sampled to 20,000, and the Gaussian kernel density corresponding to the scatter plot of the low-value area is calculated. Starting from the minimum value of the Snowed URSI, the maximum value of the Gaussian kernel density is extracted at intervals of 0.005, and a density change curve is drawn. This curve is called the low-value area density curve.

[0053] Pixels with URSI values ​​ranging from 0.6 to 1.2 in the snow image are extracted as high-value areas. The URSI value of the high-value area pixels in the snow image is Snowed URSI as the horizontal axis, and the NDSI value in the snow image is Snowed NDSI as the vertical axis. A scatter plot of the high-value area is drawn, and then the number of pixels in the scatter plot is uniformly sampled to 20,000, and the Gaussian kernel density corresponding to the scatter plot of the high-value area is calculated. Starting from the minimum value of Snowed URSI, the maximum value of the Gaussian kernel density is extracted at intervals of 0.005, and a density change curve is drawn. This curve is called the high-value area density curve.

[0054] In step S103, based on the low-value area density change curve and the high-value area density change curve, the low-value area density feature points and the high-value area density feature points are extracted, the difference and the average value of the low-value area density feature points and the high-value area density feature points are calculated, and the regression model parameters are calculated according to the difference and the average value to estimate the snow cover rate of the target area based on the regression model parameters.

[0055] Optionally, in some embodiments, based on the low-value area density change curve and the high-value area density change curve, the low-value area density feature points and the high-value area density feature points are extracted, including: judging whether the low-value area density curve meets the preset flat condition; if the low-value area density curve does not meet the preset flat condition, then when there is only a single peak in the low-value area density curve, if the single peak is located in the left half of the low-value area density curve, then the low-value area density curve is extracted from the single peak from convex to convex, and the point with the largest curvature is used as the low-value area density feature point; if the single peak is located in the right half of the low-value area density curve, then the low-value area density curve is extracted from the single peak to the left from convex to convex, and the point with the largest curvature is used as the low-value area density feature point. density feature points; if there are double peaks in the low-value area density curve, the points corresponding to the troughs between the double peaks are extracted as the low-value area density feature points; if there are more than two peaks in the low-value area density curve, when the highest peak is the first peak or the last peak, the trough closest to the highest peak is determined as the low-value area density feature point; if the highest peak and the second highest peak do not meet the proximity condition, the average value of the low-value area density feature point corresponding to the highest peak and the low-value area density feature point corresponding to the second highest peak is taken as the low-value area density feature point; if the highest peak and the second highest peak meet the proximity condition, the point corresponding to the trough between the highest peak and the second highest peak is taken as the low-value area density feature point.

[0056] Optionally, in some embodiments, after determining whether the low-value area density curve meets the preset smooth condition, the method includes: if the low-value area density curve meets the preset smooth condition, calculating the average value of the universal proportional snow index of the snow-free image, and adding the average value to the first preset threshold value to obtain the low-value area density feature point.

[0057] After filtering the abnormal points in the low-value area density curve and the high-value area density curve, as well as the peaks and troughs of the low-value area density flat curve, the low-value area density feature point URSI is extracted. L .

[0058] The outlier point filtering in the density curve is to delete the outliers in the two density curves (such as the sudden drop point in the rising curve). Figure 4 (1). For the curves with Snowed URSI values ​​between 20% and 80% in the low-value area scatter plot, the difference between the maximum density Dmax and the minimum density Dmin is calculated, and the ratio of the difference to the minimum density Dmin is calculated. If the ratio is less than 0.2, it is considered that the entire curve meets the preset flat condition, that is, the curve is a flat curve (such as Figure 4 (2)), and filter out its peaks and troughs.

[0059] Low-value area density feature point URSI L Extraction process Figure 5 The specific implementation process is as follows:

[0060] (1) The low-value area density curve has only one steep and obvious single peak: If the single peak is located in the left half of the curve (such as Figure 6 (1)), then search from the peak to the right and extract the first inflection point with obvious change, that is, the point where the curve changes from convex to concave, and the point with the largest curvature is the low-value area density feature point URSI L ; If the single peak is located in the right half of the curve (such as Figure 6 (2)), then search from the single peak to the left and extract the first inflection point with obvious change as the low-value area density feature point URSI L .

[0061] (2) The density curve in the low-value area has two steep peaks (e.g. Figure 6 (3)), the trough between the two peaks is extracted as the low-value area density feature point URSI L .

[0062] (3) When the density curve in the low-value area has more than two steep peaks (e.g. Figure 6 (4)): If the highest peak is located at the first peak or the last peak, then find the trough closest to it as the low-value area density feature point URSI L ; If the highest peak is in the middle, the average value of the URSI at the highest peak and the second highest peak is taken as the URSI of the low-value area density feature point L .

[0063] (4) For all other cases except the above three, the URSI average value of the entire snow-free image is calculated, and the average value plus 0.15 (i.e., the first preset threshold) is used as the URSI of the low-value area density feature point. L .

[0064] Optionally, in some embodiments, the low-value area density feature points and the high-value area density feature points are extracted based on the low-value area density change curve and the high-value area density change curve, and also include: when there is only a single peak in the high-value area density change curve, judging whether the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to a second preset threshold value; if the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to the second preset threshold value, then when the highest peak of the high-value area density curve is located in the left half of the curve, taking the first preset proportion of the current highest density value as the high-value area density feature point; if the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to the second preset threshold value, then taking the first preset proportion of the current highest density value as the high-value area density feature point; if If the peak is located in the right half of the curve, the second preset proportion of the current highest density value is taken as the density characteristic point of the high value area; if the universal proportional snow index of the highest peak of the high value area density curve is greater than the second preset threshold and less than or equal to the third preset threshold, the universal proportional snow index corresponding to the density point at the first preset position to the right of the highest peak is determined as the density characteristic point of the high value area; if the universal proportional snow index of the highest peak of the high value area density curve is greater than the third preset threshold, the universal proportional snow index corresponding to the density point at the second preset position to the left of the highest peak is determined as the density characteristic point of the high value area.

[0065] Optionally, in some embodiments, the low-value area density feature points and the high-value area density feature points are extracted based on the low-value area density change curve and the high-value area density change curve, and also include: when there are multiple peaks in the high-value area density change curve, if the difference between the universal proportional snow index of the second highest peak of the high-value area density change curve and the universal proportional snow index between the highest peak of the high-value area density change curve is greater than a fourth preset threshold, then the average value of the universal proportional snow index of the highest peak and the universal proportional snow index between the second highest peak is taken as the high-value area density feature point.

[0066] High-value area density feature point URSI H Extraction process Figure 7 The specific implementation process is as follows:

[0067] (1) When the URSI value of the highest peak of the high-value density curve is less than or equal to 0.82 (i.e., the second preset threshold), if the highest peak is located in the left half of the curve (e.g. Figure 8 (1)), the maximum URSI value at the first preset ratio (i.e. 60%) of the highest density is taken as the high-value area density feature point URSI H ; If the highest peak is in the right half of the curve (such as Figure 8

[0068] (2)), the maximum URSI value at the second preset ratio (i.e. 90%) of the highest density is taken as the high-value area density feature point URSI H .

[0069] (2) The URSI value of the highest peak of the high-value area density curve is > 0.82 and <= 1.02 (i.e., the third preset threshold) (e.g. Figure 8 (3)), then find the URSI value corresponding to the density point at the first preset position to the right of the highest peak as the high-value area density feature point URSI H In the embodiment of the present application, the density point at the first preset position is the fifth density point to the right of the highest peak.

[0070] (3) When the URSI value of the highest peak of the high-value area density curve is > 1.02 (e.g. Figure 8 (4)), then find the URSI value corresponding to the density point at the second preset position to the left of the highest peak as the high-value area density feature point URSI H In the embodiment of the present application, the density point at the second preset position is the tenth density point to the left of the highest peak.

[0071] (4) When the high-value area density curve has more than one steep and obvious peak, if the URSI distance between the highest peak and the second highest peak is greater than the fourth preset threshold (the fourth preset threshold is 0.2 in the embodiment of the present application), and the universal proportional snow index of the second highest peak is higher than half of the universal proportional snow index of the highest peak (that is, the difference between the universal proportional snow index of the second highest peak and the universal proportional snow index between the highest peak is greater than the fourth preset threshold), then the average value of the universal proportional snow index of the highest peak and the universal proportional snow index between the second highest peak is taken as the URSI of the high-value area density feature point. H .

[0072] Further, the difference URSI between the low-value area density feature point and the high-value area density feature point is calculated diff and the mean URSI mean , the calculation formula of this process is as follows:

[0073] URSI diff =URSI H -URSI L ;

[0074] URSI mean =(URSI L +URSI H ) / 2;

[0075] Among them, URSI L It is the low-value area density feature point, URSI H It is the density feature point of the high value area.

[0076] Further, in some embodiments, the difference URSI between the low-value area density feature point and the high-value area density feature point is calculated. diff and the mean URSImean Calculating the regression model parameters includes: calculating the regression model parameters using a preset regression model parameter calculation formula, wherein the preset regression model parameter calculation formula is:

[0077] a=-12.294×URSI diff +11.323

[0078] b=-a×(0.9895×URSI mean )

[0079] Among them, a and b are regression model parameters, URSI diff is the difference between the density feature points in the low-value area and the density feature points in the high-value area, URSI mean It is the average value of the density feature points in the low-value area and the density feature points in the high-value area.

[0080] Further, in some embodiments, estimating the snow coverage of the snow image based on the regression model parameters includes: calculating the snow coverage of the snow image using a preset snow coverage calculation formula, wherein the preset snow coverage calculation formula is:

[0081]

[0082] Among them, FSC is the snow cover fraction, tanh() is the hyperbolic tangent function, URSI is the value of the universal proportional snow index URSI for snow images, and a and b are regression model parameters.

[0083] Compared with the prior art, the embodiments of the present application have the following advantages:

[0084] (1) It makes full use of the image spectral information and time series information to adaptively adjust the regression model parameters. It is no longer limited by the large accuracy variation of the fixed parameter regression model on different surfaces and can be applied on a global large scale.

[0085] (2) The algorithm is simple, efficient, easy to implement, and suitable for massive data production.

[0086] According to the snow coverage remote sensing estimation method based on image information adaptive regression proposed in the embodiment of the present application, based on the snow image information and the snow-free image information, the universal proportional snow index of the snow image and the snow-free image is calculated, and the normalized difference snow index of the snow image is calculated, and the scatter density map of the snow index change before and after snowing is constructed based on the universal proportional snow index and the normalized difference snow index of the snow image, so as to obtain the high-value area density change curve and the low-value area density change curve based on the scatter density map of the snow index change before and after snowing, extract the low-value area density feature points and the high-value area density feature points based on the low-value area density change curve and the high-value area density change curve, calculate the difference and average value of the low-value area density feature points and the high-value area density feature points, and calculate the regression model parameters based on the difference and average value, so as to estimate the snow coverage of the target area based on the regression model parameters. Thus, the problem of unstable estimation accuracy of the fixed parameter regression model in different snowing scenes is solved, the dependence of the regression model on training data is reduced, the application scope and accuracy of the snow coverage estimation can be improved, and dynamic monitoring of the snow coverage can be realized in different research areas.

[0087] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0088] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0089] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

Claims

1. A snow cover remote sensing estimation method based on image information adaptive regression, characterized in that: The following steps are involved: Acquire snow image information and snow-free image information of the target area, and calculate the universal proportional snow accumulation index of the snow image and the snow-free image based on the snow image information and the snow-free image information, and calculate the normalized difference snow accumulation index of the snow image; Constructing a scatter density map of the change of the snow index before and after snowing according to the universal proportional snow index and the normalized difference snow index of the snow image, so as to obtain a high-value area density change curve and a low-value area density change curve based on the scatter density map of the change of the snow index before and after snowing; Based on the low-value area density change curve and the high-value area density change curve, low-value area density feature points and high-value area density feature points are extracted, the difference and average value of the low-value area density feature points and the high-value area density feature points are calculated, and the regression model parameters are calculated according to the difference and the average value to estimate the snow cover rate of the target area based on the regression model parameters.

2. The method according to claim 1, characterized in that The extracting low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve comprises: Determine whether the low-value area density curve meets a preset flat condition; If the low-value area density curve does not meet the preset flat condition, then when there is only a single peak in the low-value area density curve, if the single peak is located in the left half of the low-value area density curve, the point where the low-value area density curve is convex to concave and has the largest curvature is extracted from the single peak as the low-value area density feature point; if the single peak is located in the right half of the low-value area density curve, the point where the low-value area density curve is convex to concave and has the largest curvature is extracted from the single peak to the left as the low-value area density feature point; If the low-value area density curve has double peaks, extracting the points corresponding to the troughs between the double peaks as the low-value area density feature points; If there are more than two peaks in the low-value area density curve, when the highest peak is the first peak or the last peak, the trough closest to the highest peak is determined as the low-value area density feature point; if the highest peak and the second highest peak do not meet the proximity condition, the average value of the low-value area density feature point corresponding to the highest peak and the low-value area density feature point corresponding to the second highest peak is taken as the low-value area density feature point; if the highest peak and the second highest peak meet the proximity condition, the point corresponding to the trough between the highest peak and the second highest peak is taken as the low-value area density feature point.

3. The method according to claim 2, characterized in that After determining whether the low-value area density curve meets the preset flat condition, the method includes: If the low-value area density curve meets the preset flat condition, the average value of the universal proportional snow index of the snow-free image is calculated, and the average value is added to the first preset threshold to obtain the low-value area density feature point.

4. The method according to claim 1, characterized in that: The step of extracting low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve also includes: When there is only a single peak in the high-value area density change curve, determining whether the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to a second preset threshold; If the universal proportional snow index of the highest peak of the high-value area density curve is less than or equal to the second preset threshold, then when the highest peak of the high-value area density curve is located in the left half of the curve, the first preset proportion of the current highest density value is taken as the high-value area density feature point; if the highest peak is located in the right half of the curve, the second preset proportion of the current highest density value is taken as the high-value area density feature point; If the universal proportional snow index of the highest peak of the high-value area density curve is greater than the second preset threshold and less than or equal to the third preset threshold, determine the universal proportional snow index corresponding to the density point at the first preset position to the right of the highest peak as the high-value area density feature point; If the universal proportional snow index of the highest peak of the high-value area density curve is greater than the third preset threshold, the universal proportional snow index corresponding to the density point at the second preset position to the left of the highest peak is determined as the high-value area density feature point.

5. The method according to claim 4, characterized in that The step of extracting low-value area density feature points and high-value area density feature points based on the low-value area density change curve and the high-value area density change curve also includes: When there are multiple peaks in the high-value area density change curve, if the difference between the universal proportional snow index of the second highest peak of the high-value area density change curve and the universal proportional snow index between the highest peak of the high-value area density change curve is greater than the fourth preset threshold, the average value of the universal proportional snow index of the highest peak and the universal proportional snow index between the second highest peak is taken as the high-value area density characteristic point.

6. The method according to claim 1, characterized in that After calculating the universal proportional snow accumulation index of the snow image and the snow-free image based on the snow image information and the snow-free image information, and calculating the normalized difference snow accumulation index of the snow image, the method comprises: If the growth value of the general proportional snow index of the snowy image relative to the snowless image is less than the fifth preset threshold, or the general proportional snow index of the snowy image is less than the sixth preset threshold, it is determined that the current snow coverage rate is 0.

7. The method according to claim 1, characterized in that The estimating the snow coverage rate of the snow image based on the regression model parameters includes: The snow coverage rate of the snow image is calculated using a preset snow coverage rate calculation formula, wherein the preset snow coverage rate calculation formula is: Among them, FSC is the snow cover fraction, tanh() is the hyperbolic tangent function, URSI is the value of the universal proportional snow index URSI for snow images, and a and b are regression model parameters.

8. The method according to claim 1, characterized in that The step of calculating the regression model parameters according to the difference and average value of the low-value area density feature points and the high-value area density feature points comprises: The regression model parameters are calculated using a preset regression model parameter calculation formula, wherein the preset regression model parameter calculation formula is: a=-12.294×URSI diff +11.323 b=-a×(0.9895×URSI mean ) Among them, a and b are regression model parameters, URSI diff is the difference between the density feature points in the low-value area and the density feature points in the high-value area, URSI mean It is the average value of the density feature points in the low-value area and the density feature points in the high-value area.

9. The method according to claim 1, characterized in that: The calculating, based on the snow image information and the snow-free image information, the universal proportional snow accumulation index of the snow image and the snow-free image, and calculating the normalized difference snow accumulation index of the snow image, comprises: The general proportional snow index is calculated using a first calculation formula, and the normalized difference snow index is calculated using a second calculation formula, wherein the first calculation formula is: The second calculation formula is: Among them, R Green is the green band reflectivity, R NIR is the reflectivity in the near infrared band, R SWIR is the shortwave infrared reflectivity.

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