CN114821361A - Method and device for calculating snow depth, computer equipment and readable storage medium - Google Patents
Method and device for calculating snow depth, computer equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for calculating snow depth, computer equipment and a readable storage medium, which are applied to the field of weather, wherein the method comprises the following steps: the method comprises the steps that computer equipment firstly obtains geostationary satellite remote sensing data to obtain sensitive band data corresponding to accumulated snow, namely green band spectrum data and short wave infrared band spectrum data; then, calculating the normalized snow cover index of each pixel according to a normalized snow cover index calculation formula and the green wave band spectral data and the short wave infrared band spectral data of each pixel, so as to calculate the snow cover depth according to the normalized snow cover index of each pixel in the subsequent process; and finally, calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model. Therefore, the snow depth calculation method and the snow depth calculation device adopt the geostationary satellite to realize the calculation of the snow depth, so that the snow depth acquisition efficiency is improved.
Description
Technical Field
The invention relates to the field of meteorology, in particular to a method and a device for calculating snow depth, computer equipment and a readable storage medium.
Background
In the research on accumulated snow, such as snow water equivalent estimation, drainage basin water balance and snow-melting runoff forecasting and remote sensing monitoring of snow disasters in pastoral areas, the accumulated snow depth is one of indispensable important parameters. Therefore, how to efficiently acquire the snow depth is one of the current research hotspots.
Currently, methods for acquiring snow depth include ground observation methods such as a manual observation method and a snow depth field network automatic observation method. The manual observation method is the simplest ground measurement method, but only can provide single-time observation of the snow accumulation condition, and the measurement precision and the time-space distribution in the alpine region are severely restricted. Although the automatic observation method of the snow depth field network improves the timeliness and the accuracy of measurement, the requirement of rapidly extracting the snow depth in a large range cannot be met due to the fact that the instrument is expensive and is influenced by aspects such as field weather and environment.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a computer device and a readable storage medium for calculating a snow depth, which are used to improve the current situation that it is difficult to efficiently acquire a snow depth by a ground observation method.
In a first aspect, an embodiment of the present invention provides a snow depth calculation method, including:
acquiring remote sensing data of a stationary satellite, wherein the remote sensing data comprises green band spectrum data and short wave infrared band spectrum data of each pixel;
calculating a normalized snow index from the green band spectral data and the short wave infrared band spectral data of the pixel based on a normalized snow index calculation formula for each of the pixels;
calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
Optionally, in a feasible manner provided by the embodiment of the present invention, the geostationary satellite includes a wind cloud star a.
Further, in a feasible manner provided by the embodiment of the present invention, the green band spectrum data includes spectrum data of a second band of the wind cloud star iv a, and the short wave infrared band spectrum data includes spectrum data of a fifth band of the wind cloud star iv a.
Further, in a possible manner provided by the embodiment of the present invention, before the calculating the snow depth of each of the image elements by using the normalized snow index of each of the image elements based on a preset snow depth estimation model, the method further includes:
acquiring a remote sensing data sample of the Fengyun No. four A star and a real accumulated snow depth corresponding to the remote sensing data sample;
obtaining a normalized snow index corresponding to the remote sensing data sample according to a calculation formula based on the normalized snow index;
determining an effective snow cover index interval by utilizing a normalized snow cover index corresponding to the remote sensing data sample and the real snow cover depth, wherein when the normalized snow cover index belongs to the effective snow cover index interval, snow exists in a pixel corresponding to the normalized snow cover index;
and fitting the normalized snow index corresponding to the remote sensing data sample and the real snow depth through linear regression based on the effective snow cover index interval and a preset effective real snow depth interval to obtain the preset snow depth estimation model.
Furthermore, in a possible manner provided by the embodiment of the present invention, the effective snow cover index interval is [0.23, 0.65 ], and the effective real snow depth interval is [2, 10 ").
Optionally, in a feasible manner provided by the embodiment of the present invention, the preset snow depth estimation model includes:
Y=14.043*NDSI-0.9226
where Y represents the snow depth and NDSI represents the normalized snow index.
Optionally, in a feasible manner provided by the embodiment of the present invention, the method further includes:
and determining the snow depth grade of each pixel according to the snow depth of each pixel and a preset snow depth grade mapping table, wherein the preset snow depth grade mapping table represents the snow depth grades corresponding to different snow depths.
In a second aspect, an embodiment of the present invention provides a snow depth calculation apparatus, including:
the acquisition module is used for acquiring remote sensing data of the geostationary satellite, wherein the remote sensing data comprises green band spectrum data and short wave infrared band spectrum data of each pixel;
the index calculation module is used for calculating a normalized snow cover index according to the green wave band spectrum data and the short wave infrared wave band spectrum data of each pixel on the basis of a normalized snow cover index calculation formula;
and the depth calculation module is used for calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the computer program, when running on the processor, executes the snow depth calculation method as disclosed in any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, the computer program, when running on a processor, executing the snow depth calculation method as disclosed in any one of the first aspects.
In the method for calculating the snow depth provided by the embodiment of the invention, computer equipment firstly acquires the remote sensing data of the geostationary satellite so as to obtain the data of the sensitive wave band corresponding to snow, namely green wave band spectrum data and short wave infrared wave band spectrum data; then, calculating the normalized snow cover index of each pixel according to a normalized snow cover index calculation formula and the green wave band spectral data and the short wave infrared band spectral data of each pixel, so as to calculate the snow cover depth according to the normalized snow cover index of each pixel in the subsequent process; and finally, calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
Based on this, the computer device of the embodiment of the invention adopts the geostationary satellite with high time resolution and high spatial resolution to realize the calculation of the snow depth, so that the time resolution of snow monitoring is greatly improved, a plurality of areas can be monitored at the same time, and the snow depth acquisition efficiency is improved.
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In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention. Like components are numbered similarly in the various figures.
Fig. 1 is a schematic flow chart illustrating a first snow depth calculation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second method for calculating snow depth according to an embodiment of the present invention;
FIG. 3 illustrates a data distribution graph provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a snow depth calculating device provided by an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
Example 1
Referring to fig. 1, a flowchart illustrating a first method for calculating a snow depth according to an embodiment of the present invention is shown, where the method for calculating a snow depth according to an embodiment of the present invention includes:
s110, obtaining remote sensing data of the geostationary satellite, wherein the remote sensing data comprise green band spectrum data and short wave infrared band spectrum data of each pixel.
It is understood that snow has extremely high Visible Light (Visible Light) reflectance and extremely low Short Wave Infrared (Short Wave Infrared) reflectance, so in snow related research snow is often distinguished from most cloud types based on green band spectral data and Short Wave Infrared band spectral data in the image.
It is also understood that a geostationary satellite refers to an artificial earth satellite whose orbit is geostationary, and has a wide observation coverage area, and can perform high-repetition observation on the order of minutes with respect to the observation area. Therefore, the embodiment of the invention adopts the geostationary satellite to realize snow observation in a large area and complete corresponding snow depth calculation.
Further, in an optional manner provided by the embodiment of the present invention, the geostationary satellite includes a wind cloud star No. four (FY-4A).
It should be noted that, as a new generation of stationary orbit quantitative remote sensing meteorological satellite, compared with the past meteorological satellite such as wind cloud second number G star (FY-2G), wind cloud fourth number a star has realized the leap-over type development in performance and function, for example, the radiation imaging channel is increased from 5 of wind cloud second number G star to 14, and covers the bands of visible light, short wave infrared, medium wave infrared, long wave infrared and the like, so that wind cloud fourth number a star has a channel capable of identifying snow.
Moreover, compared with the past meteorological satellite, the spatial resolution of the wind cloud A star is greatly improved, and the image resolution for snow monitoring can reach 2 km. And compared with the time resolution of 1 day or 10 days of other meteorological satellites, the high time resolution of wind, cloud, satellite four A for up to 5 minutes ensures the real-time performance of snow monitoring.
It should be further noted that, because the spectrum data corresponding to the wind cloud number four a star does not include the green band spectrum data with the wavelength of 500nm to 600nm commonly used in the snow cover research, in a feasible manner, in the embodiment of the present invention, the green band spectrum data is replaced with the visible light band spectrum data, that is, the green band spectrum data includes the spectrum data of the second band of the wind cloud number four a star, and the short-wave infrared band spectrum data includes the spectrum data of the fifth band of the wind cloud number four a star.
It should be noted that, the aeolian cloud No. four a star includes two visible light band spectrum data with the wavelength of 470nm and the wavelength of 650nm, but the applicant is verified by a large number of experiments that it is determined that compared with the visible light band spectrum data with the wavelength of 470nm, a data result with higher precision can be obtained when the visible light band spectrum data with the wavelength of 650nm is used to replace the green band spectrum data, so the embodiment of the invention adopts the band 2 with the wavelength of 650nm, namely the second band, of the aeolian cloud No. four a star, and cooperates with the band 5 with the wavelength of 1610nm, namely the fifth band, of the aeolian cloud No. four a star to complete corresponding snow depth calculation.
And S120, calculating the normalized snow cover index according to the green wave band spectrum data and the short wave infrared wave band spectrum data of each pixel based on a normalized snow cover index calculation formula.
Specifically, the calculation formula of the normalized snow index is as follows:
in the formula, NDSI represents normalized snow index, Green represents Green band spectrum data with the wavelength of 500nm to 600nm, and SWIR1 represents short wave infrared band spectrum data with the wavelength of 1550nm to 1750 nm.
It can be understood that, when the spectrum data of the second wave band of the aeolian star-four and the spectrum data of the fifth wave band are used for calculating the normalized snow cover index, a formula corresponding to the normalized snow cover index can be expressed as follows:
in the formula, B2 represents spectral data of the second wavelength band of wind cloud No. four a stars, and B5 represents spectral data of the fifth wavelength band of wind cloud No. four a stars.
And S130, calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
It can be understood that after the snow depth exceeds a certain value, the reflectivity of the snow does not change along with the depth change, in other words, before the snow depth does not exceed a certain value, the snow depth and the normalized snow index have a mapping relation. Therefore, in the embodiment of the invention, after the mapping relation between the normalized snow index and the snow depth obtained through multiple tests is utilized, namely the snow depth estimation model is preset, the normalized snow index obtained through the spectrum data of the geostationary satellite is input into the snow depth estimation model so as to calculate the snow depth corresponding to the normalized snow index.
It can be further understood that the preset snow depth estimation model can be obtained according to actual conditions, for example, in a feasible manner provided by the embodiment of the present invention, the preset snow depth estimation model is obtained by training a preset neural network model through a plurality of data points composed of a normalized snow index and a snow depth. It is understood that the preset snow depth estimation model formed based on the neural network model can predict the snow depth corresponding to the normalized snow index with higher accuracy.
In yet another possible way provided by the embodiment of the present invention, the computer device in the embodiment of the present invention constructs a mathematical model of the normalized snow index and the snow depth, that is, a preset snow depth estimation model, by performing function fitting on a plurality of data points composed of the normalized snow index and the snow depth.
Optionally, in a feasible manner provided by the embodiment of the present invention, the preset snow depth estimation model includes:
Y=14.043*NDSI-0.9226
where Y represents the snow depth and NDSI represents the normalized snow index.
In the method for calculating the snow depth provided by the embodiment of the invention, computer equipment firstly acquires the remote sensing data of the geostationary satellite so as to obtain the data of the sensitive wave band corresponding to snow, namely green wave band spectrum data and short wave infrared wave band spectrum data; then, calculating the normalized snow cover index of each pixel according to a normalized snow cover index calculation formula and the green wave band spectral data and the short wave infrared band spectral data of each pixel, so as to calculate the snow cover depth according to the normalized snow cover index of each pixel in the subsequent process; and finally, calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
Based on this, the computer device of the embodiment of the invention adopts the geostationary satellite with high time resolution and high spatial resolution to realize the calculation of the snow depth, so that the time resolution of snow monitoring is greatly improved, a plurality of areas can be monitored at the same time, and the snow depth acquisition efficiency is improved.
Optionally, in a feasible preset snow depth estimation model obtaining manner provided by the embodiment of the present invention, specifically referring to fig. 2, a flow chart of a second snow depth calculation method provided by the embodiment of the present invention is shown, that is, before S130, the method further includes:
s140, acquiring a remote sensing data sample of the wind cloud number-four A star and a real snow depth corresponding to the remote sensing data sample;
s150, obtaining a normalized snow index corresponding to the remote sensing data sample according to a calculation formula based on the normalized snow index;
s160, determining an effective snow cover index interval by utilizing the normalized snow cover index corresponding to the remote sensing data sample and the real snow cover depth, wherein when the normalized snow cover index belongs to the effective snow cover index interval, snow exists in a pixel corresponding to the normalized snow cover index;
and S170, fitting the normalized snow index corresponding to the remote sensing data sample and the real snow depth through linear regression based on the effective snow cover index interval and a preset effective real snow depth interval to obtain the preset snow depth estimation model.
That is, in the embodiment of the invention, a plurality of groups of remote sensing data obtained by a wind cloud star A are used as samples, namely remote sensing data samples; meanwhile, the real snow depth of the area corresponding to the remote sensing data sample is obtained, and then the remote sensing data sample and the corresponding real snow depth are used as a data point.
It can be understood that the manner of obtaining the remote sensing data sample and the corresponding real snow depth can be set according to actual conditions. In a feasible mode, the remote sensing data of the area with the ground snow observation station is used as a remote sensing data sample, and then after the remote sensing data sample is obtained, the real snow depth is obtained from each ground snow observation station.
After obtaining the plurality of data points, the computer device in the embodiment of the present invention correspondingly calculates the normalized snow index according to the remote sensing data samples of the plurality of data points, and each data point further includes the normalized snow index and a real snow depth corresponding to the normalized snow index.
It should be understood that, after obtaining a plurality of data points, the computer device according to the embodiment of the present invention determines an effective snow cover index interval according to the plurality of data points to determine which value the normalized snow cover index in the data points takes, and then has a linear relationship with the corresponding normalized snow cover index, so as to obtain a preset snow cover depth estimation model based on linear regression in the subsequent steps.
It should be further understood that, after the snow depth exceeds a certain value, the reflectivity of the snow will not change with the depth change, and therefore, the computer device in the embodiment of the present invention further determines, according to a preset effective real snow depth interval, what value the snow depth in the data point takes, and then has a linear relationship with the corresponding normalized snow index.
It can be understood that the obtaining mode of the effective snow cover index interval and the effective real snow depth interval can be set according to actual conditions, for example, in a feasible mode, the outliers in all the data points and the data points belonging to the same cluster can be determined through the existing data aggregation method, and then the effective snow cover index interval and the effective real snow depth interval can be obtained.
In a feasible manner provided by the embodiment of the present invention, the effective snow cover index interval and the effective actual snow depth interval are obtained by the following method, that is:
in the snow accumulation research, when the snow accumulation depth is less than or equal to 2cm, the snow accumulation is regarded as no snow on the ground, so that the computer equipment in the embodiment of the invention compares a plurality of data points to obtain the value of the corresponding normalized snow accumulation index when the real snow accumulation depth is greater than 2 cm.
If the actual snow depth corresponding to the normalized snow index larger than the first preset value is larger than 2cm, the embodiment of the invention performs linear regression according to the normalized snow index larger than the first preset value and the actual snow depth corresponding to the normalized snow index larger than the first preset value so as to fit the mapping relation between the normalized snow index and the actual snow depth.
Furthermore, the wind cloud A satellite is insensitive to the data reflectivity of the snow depth of more than or equal to 10cm in the Qinghai-Tibet plateau area, so that the reflectivity of the snow obtained through the FY-4A satellite cannot change along with the change of the depth after the snow depth exceeds 10cm, therefore, the embodiment of the invention only adopts the data point of which the real snow depth is less than 10cm, and the computer equipment also determines the value of the normalized snow index corresponding to the real snow depth of more than or equal to 10cm when comparing a plurality of data points.
Further, in a preferred mode provided by an embodiment of the present invention, the computer device performs linear regression using data points, in which the normalized snow index is smaller than the second preset value and greater than or equal to the first preset value, in which the true snow depth is smaller than or equal to 10 cm.
Optionally, in a feasible manner provided by the embodiment of the present invention, the second preset value is 0.65, and the first preset value is 0.23, that is, the effective snow cover index interval is [0.23, 0.65 ], and the effective real snow depth interval is [2, 10 ].
To better illustrate this preferred mode provided by the embodiments of the present invention, please refer to fig. 3, which shows a data distribution diagram provided by the embodiments of the present invention. In fig. 3, the vertical axis represents the Snow Depth (SD) in centimeters, and the horizontal axis represents the normalized Snow index. As can be seen from fig. 3, when the snow depth is equal to 10cm, the data points can be distributed on the curve corresponding to y =14.043x-0.9226 in a relatively reasonable manner, i.e., on both sides of the curve corresponding to the preset snow depth estimation model, but above the horizontal axis, the snow depth of each data point is small, i.e., there is no linear relationship between the snow depth and the normalized snow index. Therefore, the embodiment of the invention only adopts the data in the effective snow cover index interval to deduce the preset snow depth estimation model.
Based on the preferable mode, the preset accumulated snow depth estimation model derived by the computer device in the embodiment of the invention comprises:
Y=14.043*NDSI-0.9226
where Y represents the snow depth and NDSI represents the normalized snow index.
It should be understood that the dotted line below the formula y =14.043x-0.9226 in fig. 3 represents a straight line corresponding to the preset snow depth estimation model, and x in the formula y =14.043x-0.9226 represents the NDSI. It can also be appreciated that R in fig. 3 represents a correlation coefficient representing the correlation of snow depth to normalized snow index.
Furthermore, in a preferred mode, the computer device in the embodiment of the present invention correspondingly deletes the abnormal value in the data point where the normalized snow cover index is smaller than the second preset value and larger than the first preset value, according to the snow depth grading information received in advance, that is, the information that the ground is snow-free when the snow cover depth is smaller than the preset snow cover depth, in cooperation with the condition that snow cover exists when the normalized snow cover index is larger than the first preset value. That is, the computer device in the embodiment of the present invention deletes, as the abnormal data points, the data points in which the normalized snow index is smaller than the second preset value and larger than the first preset value, the snow depth is smaller than the preset snow depth but the normalized snow index is larger than the first preset value, and the data points in which the snow depth is greater than or equal to the preset snow depth but the normalized snow index is smaller than or equal to the first preset value, from among the data points in which the normalized snow index is smaller than the second preset value and larger than the first preset value, so as to ensure the effectiveness of the preset snow depth estimation model.
It should be noted that, although S140 to S170 are only located before S130 in fig. 2, in practical cases, S140 to S170 are executed once before any step of S110 to S130 to obtain the predetermined snow depth estimation model, and fig. 2 is only used to better illustrate such a possible way provided by the embodiment of the present invention.
Optionally, to improve the prediction accuracy of the computer device, in a feasible manner provided by the embodiment of the present invention, the method further includes:
and determining the snow depth grade of each pixel according to the snow depth of each pixel and a preset snow depth grade mapping table, wherein the preset snow depth grade mapping table represents the snow depth grades corresponding to different snow depths.
It can be understood that different snow depths correspond to different snow depth grades in the embodiment of the invention, and further, different normalized snow indexes correspond to different snow depth grades in the embodiment of the invention.
Illustratively, the snow depth levels include a small snow level, a medium snow level, and a large snow level, as in one possible approach. When the snow depth grade is a small snow grade, the snow depth corresponding to the normalized snow index is smaller than 3 cm; when the snow depth grade is a middle snow grade, the snow depth corresponding to the normalized snow index is 3cm to 5 cm; and when the snow depth grade is a large snow grade, the snow depth corresponding to the normalized snow index is larger than 5 cm.
In a feasible manner provided by the embodiment of the present invention, specifically, referring to table 1, a comparison table of the normalized snow index and the snow depth level obtained by presetting the snow depth level mapping table in the embodiment of the present invention is shown, that is:
TABLE 1
That is, the different values of NDSI in table 1 correspond to 4 different snow grades.
It can also be understood that errors exist in snow observation, so that the accuracy of data used for deducing the preset snow depth estimation model is difficult to guarantee, and further the snow depth obtained by using the preset snow depth estimation model is difficult to keep consistent with the actual snow depth. Therefore, the embodiment of the invention maps different snow depths into corresponding snow depth grades. It should be understood that, because the snow depth is converted into the relatively fuzzy snow depth grade, the embodiment of the present invention can correspondingly reduce the negative impact caused by the observation error when the snow depth result corresponding to the normalized snow index is converted into the relatively fuzzy snow depth grade prediction result.
Further, when the snow depth grade is used as an output result, and the remote sensing data sample and the real snow depth are used for performing corresponding linear regression, the embodiment of the invention converts the real snow depth into the real snow depth grade, converts the snow depth corresponding to the remote sensing data sample into the predicted snow depth grade, verifies the fitting condition of the preset snow depth estimation model by using the matching condition of the real snow depth grade and the predicted snow depth grade, and is used for guiding the construction of the preset snow depth estimation model.
Example 2
Corresponding to the method for calculating the snow depth provided in embodiment 1 of the present invention, embodiment 2 of the present invention provides a snow depth calculating apparatus, and referring to fig. 4, a schematic structural diagram of the snow depth calculating apparatus provided in the embodiment of the present invention is shown, and the snow depth calculating apparatus 200 provided in the embodiment of the present invention includes:
the obtaining module 210 is configured to obtain remote sensing data of a stationary satellite, where the remote sensing data includes green band spectrum data and short wave infrared band spectrum data of each pixel;
an index calculation module 220, configured to calculate, for each of the pixels, a normalized snow index from the green band spectral data and the short wave infrared band spectral data of the pixel based on a normalized snow index calculation formula;
a depth calculating module 230, configured to calculate a snow depth of each of the pixels by using the normalized snow index of each of the pixels based on a preset snow depth estimation model.
Optionally, in a feasible manner provided by the embodiment of the present invention, the geostationary satellite includes a wind cloud star a.
Further, in a feasible manner provided by the embodiment of the present invention, the green band spectrum data includes spectrum data of a second band of the wind cloud star iv a, and the short wave infrared band spectrum data includes spectrum data of a fifth band of the wind cloud star iv a.
Optionally, in a feasible manner provided by the embodiment of the present invention, the apparatus further includes:
the depth acquisition module is used for acquiring a remote sensing data sample of the Fengyun No. four A star and a real snow depth corresponding to the remote sensing data sample;
the calculation module is used for obtaining a normalized snow index corresponding to the remote sensing data sample according to a calculation formula based on the normalized snow index;
the interval determining module is used for determining an effective snow cover index interval by utilizing the normalized snow cover index corresponding to the remote sensing data sample and the real snow cover depth, wherein when the normalized snow cover index belongs to the effective snow cover index interval, snow exists in a pixel corresponding to the normalized snow cover index;
and the fitting module is used for fitting the normalized snow index corresponding to the remote sensing data sample and the real snow depth through linear regression based on the effective snow cover index interval and a preset effective real snow depth interval to obtain the preset snow depth estimation model.
Optionally, in a possible manner provided by the embodiment of the present invention, the effective snow cover index interval is [0.23, 0.65 ], and the effective real snow depth interval is [2, 10 ").
Optionally, in a feasible manner provided by the embodiment of the present invention, the preset snow depth estimation model includes:
Y=14.043*NDSI-0.9226
where Y represents the snow depth and NDSI represents the normalized snow index.
Optionally, in a feasible manner provided by the embodiment of the present invention, the apparatus further includes:
and the mapping module is used for determining the snow depth grade of each pixel according to the snow depth of each pixel and a preset snow depth grade mapping table, wherein the preset snow depth grade mapping table represents the snow depth grades corresponding to different snow depths.
The device 200 for calculating the snow depth provided in embodiment 2 of the present application can implement each process of the method for calculating the snow depth in the method embodiment corresponding to embodiment 1, and can achieve the same technical effect, and for avoiding repetition, details are not repeated here.
The embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the computer program, when running on the processor, executes the snow depth calculation method disclosed in the method embodiment corresponding to embodiment 1.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program runs on a processor, the method for calculating the snow depth disclosed in the method embodiment corresponding to embodiment 1 is performed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A snow depth calculation method is characterized by comprising the following steps:
acquiring remote sensing data of a stationary satellite, wherein the remote sensing data comprises green band spectrum data and short wave infrared band spectrum data of each pixel;
calculating a normalized snow index from the green band spectral data and the short wave infrared band spectral data of the pixel based on a normalized snow index calculation formula for each of the pixels;
calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
2. The method of calculating snow depth of claim 1, wherein the geostationary satellite comprises aeolian-fourth a star.
3. The method of calculating snow depth according to claim 2, wherein the green band spectral data comprises spectral data of a second band of the wind cloud a-star, and the short wave infrared band spectral data comprises spectral data of a fifth band of the wind cloud a-star.
4. The snow depth calculation method according to claim 2, wherein before calculating the snow depth of each of the image elements using the normalized snow index of each of the image elements based on a preset snow depth estimation model, the method further comprises:
acquiring a remote sensing data sample of the Fengyun No. four A star and a real accumulated snow depth corresponding to the remote sensing data sample;
obtaining a normalized snow index corresponding to the remote sensing data sample according to a calculation formula based on the normalized snow index;
determining an effective snow cover index interval by utilizing a normalized snow cover index corresponding to the remote sensing data sample and the real snow cover depth, wherein when the normalized snow cover index belongs to the effective snow cover index interval, snow exists in a pixel corresponding to the normalized snow cover index;
and fitting the normalized snow index corresponding to the remote sensing data sample and the real snow depth through linear regression based on the effective snow cover index interval and a preset effective real snow depth interval to obtain the preset snow depth estimation model.
5. A snow depth calculation method according to claim 4, wherein the effective snow cover index interval is [0.23, 0.65 ] and the effective true snow depth interval is [2, 10 ").
6. The snow depth calculation method according to claim 1 or 4, wherein the preset snow depth estimation model includes:
Y=14.043*NDSI-0.9226
where Y represents the snow depth and NDSI represents the normalized snow index.
7. The method of calculating snow depth of claim 1, further comprising:
and determining the snow depth grade of each pixel according to the snow depth of each pixel and a preset snow depth grade mapping table, wherein the preset snow depth grade mapping table represents the snow depth grades corresponding to different snow depths.
8. A snow depth calculation apparatus, comprising:
the acquisition module is used for acquiring remote sensing data of the geostationary satellite, wherein the remote sensing data comprises green band spectrum data and short wave infrared band spectrum data of each pixel;
the index calculation module is used for calculating a normalized snow cover index according to the green wave band spectrum data and the short wave infrared wave band spectrum data of each pixel on the basis of a normalized snow cover index calculation formula;
and the depth calculation module is used for calculating the snow depth of each pixel by using the normalized snow index of each pixel based on a preset snow depth estimation model.
9. A computer device comprising a memory and a processor, the memory storing a computer program which, when run on the processor, performs a snow depth calculation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when run on a processor, performs the snow depth calculation method according to any one of claims 1-7.
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