CN112070055A - Remote sensing monitoring method and device for accumulated snow coverage days and storage medium - Google Patents
Remote sensing monitoring method and device for accumulated snow coverage days and storage medium Download PDFInfo
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Abstract
The invention discloses a remote sensing monitoring method, a remote sensing monitoring device and a storage medium for accumulated snow coverage days. The remote sensing monitoring method comprises the following steps: acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites; carrying out snow information extraction processing on the remote sensing satellite image data to generate a remote sensing snow cover day data set, wherein the snow cover day data set comprises remote sensing snow cover day data of a plurality of grid units; constructing a correction model according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number data of the grid units corresponding to the plurality of sites; and correcting the remote sensing snow cover daily data of each grid unit by using the correction model to form a corrected remote sensing snow cover daily data set. And the remote sensing accumulated snow coverage days of each grid unit are corrected by using the correction model, so that the monitoring accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a remote sensing monitoring method of accumulated snow coverage days, a remote sensing monitoring device thereof and a computer readable storage medium.
Background
The Qinghai-Tibet plateau is used as a typical high-altitude snow accumulation distribution area in the world and the origin of China and many rivers around the Qinghai-Tibet plateau, and the change of snow accumulation coverage and ablation conditions in the area can greatly influence the water circulation and climate change process of the area and even the world. At present, plateau regions are large in area and severe in natural environment, the first-hand accumulated snow observation data are difficult to obtain by means of traditional manual observation, favorable data support is provided for accumulated snow monitoring along with the development of satellite technology in China, and the satellite remote sensing accumulated snow monitoring meteorological service is particularly used in plateau regions to monitor meteorological service work, so that the method is extremely important and has very important significance.
The number of snow cover days is the number of days in which snow cover is present without melting snow layers covering the land and sea ice surfaces, and the unit is a day and is also called the number of snow cover days. The accumulated snow cover day number is used as one of important indexes for monitoring and evaluating the accumulated snow in the plateau area, at present, the accumulated snow cover day number is mostly represented by the accumulated snow number in the process of a meteorological station at home and abroad, but the accumulated snow cover day number is influenced by factors such as the complex shape of the Qinghai-Tibet plateau, rare conventional meteorological stations and the like, and the research for monitoring the spatial distribution and the annual variation of the accumulated snow in the Qinghai-Tibet plateau by only using the accumulated snow observation information of the conventional meteorological station has certain uncertainty.
Disclosure of Invention
(I) technical problems to be solved by the invention
The technical problem solved by the invention is as follows: how to improve the monitoring precision of the accumulated snow coverage days by using remote sensing monitoring data and site monitoring data.
(II) the technical scheme adopted by the invention
A remote sensing monitoring method for accumulated snow coverage days comprises the following steps:
acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites;
performing snow information extraction processing on the remote sensing satellite image data to generate a remote sensing snow cover day data set, wherein the snow cover day data set comprises remote sensing snow cover day data of a plurality of grid units;
constructing a correction model according to the actually measured snow cover day data of the plurality of sites and the remote sensing snow cover day data of the grid units corresponding to the plurality of sites;
and correcting the remote sensing snow cover daily data of each grid unit by using the correction model to form a corrected remote sensing snow cover daily data set.
Preferably, the remote sensing satellite image data includes multiple data sources every day in a preset time period, and the specific method for performing snow information extraction processing on the remote sensing satellite image data to generate the remote sensing snow coverage day data set includes:
generating daily maximum snow coverage data sequentially according to various daily data sources, wherein the daily maximum snow coverage data at least comprises binary data of a plurality of grid units, the binary data is first data when the grid units are covered by snow, the binary data is second data when the grid units are not covered by snow, and the first data is larger than the second data;
and performing spatial superposition on the daily maximum snow coverage range data in the preset time period, and accumulating the binary data of the grid units at the same position in the preset time period respectively to generate a remote sensing snow coverage daily data set in the preset time period.
Preferably, the specific method for generating the daily maximum snow coverage data sequentially according to the plurality of daily data sources comprises the following steps:
sequentially extracting snow cover information of each data source and performing snow judgment to generate binary raster image data, wherein the binary raster image data comprises binary data of a plurality of raster units;
and carrying out spatial superposition on the multiple binary raster image data to generate daily maximum snow coverage data, wherein the binary data of each raster unit in the daily maximum snow coverage data is the maximum binary data of each raster unit in the multiple binary raster image data.
Preferably, after the daily maximum snow coverage data is generated sequentially according to the plurality of daily data sources, the remote sensing monitoring method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether the values of the grid units shielded by the cloud in the preset adjacent days are all first data;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
Preferably, after the daily maximum snow coverage data is generated sequentially according to the plurality of daily data sources, the remote sensing monitoring method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether a preset number of grid units covered by the accumulated snow exist in a preset adjacent space range of the grid units shielded by the cloud;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
Preferably, after the daily maximum snow coverage data is generated sequentially according to the plurality of daily data sources, the remote sensing monitoring method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether the grid unit shielded by the cloud is in the permanent snow accumulation area;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
Preferably, the method for constructing the correction model according to the actually measured snow coverage day data of the plurality of sites and the remotely sensed snow coverage day data of the grid units corresponding to the plurality of sites includes:
according to the formula (1), the actually measured snow cover day number data x of the plurality of sites and the remote sensing snow cover day number data y of the grid units corresponding to the plurality of sites are subjected to linear fitting to obtain a correction coefficient a and a correction parameter b,
y＝a*x+b (1)；
constructing a correction model according to the correction coefficient a and the correction parameter b:
where y1 denotes the remote sensing snow-covered day number data of the grid cell before correction, and y2 denotes the remote sensing snow-covered day number data of the grid cell after correction.
Preferably, the remote sensing monitoring method further comprises:
and calculating to obtain an error value according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number after the correction of the grid units corresponding to the plurality of sites.
The invention also discloses a remote sensing monitoring device for the number of accumulated snow covered days, which comprises:
the data acquisition module is used for acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites;
the snow information processing module is used for extracting snow information from the remote sensing satellite image data to generate a remote sensing snow coverage day data set, wherein the snow coverage day data set comprises remote sensing snow coverage day data of a plurality of grid units;
the model building module is used for building a correction model according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number data of the grid units corresponding to the plurality of sites;
and the data correction module is used for correcting the remote sensing snow cover daily number data of each grid unit by using the correction model so as to form a corrected remote sensing snow cover daily number data set.
The invention also discloses a computer readable storage medium, the computer readable storage medium stores the remote sensing monitoring program of the accumulated snow coverage days, and the remote sensing monitoring program of the accumulated snow coverage days is executed by a processor to realize the remote sensing monitoring method of the accumulated snow coverage days.
(III) advantageous effects
The invention discloses a remote sensing monitoring method for accumulated snow coverage days, which has the following technical effects compared with the traditional calculation method:
(1) the correction model is constructed by utilizing the actually measured snow cover day data of the plurality of sites and the remote sensing snow cover day data of the grid units corresponding to the plurality of sites, so that the remote sensing snow cover day of each grid unit is corrected by utilizing the correction model, the monitoring accuracy is improved, and the monitoring precision can be further improved by combining methods such as near time snow cover correction, adjacent space snow cover correction, permanent snow cover correction and the like.
(2) The accumulated snow coverage remote sensing monitoring service product manufactured according to the method can provide accumulated snow situation information of accumulated snow in accumulated snow seasons for governments at all levels, provides scientific basis for animal husbandry production and disaster prevention and reduction decisions, and has remarkable economic benefit.
Drawings
Fig. 1 is a flowchart of a remote sensing monitoring method for snow cover day according to a first embodiment of the present invention;
FIG. 2 is a detailed flowchart of a remote sensing monitoring method according to a first embodiment of the present invention;
FIG. 3 is a diagram illustrating a method for correcting a proximity time according to a first embodiment of the present invention;
FIG. 4 is a diagram illustrating a method for correcting a proximity space according to a first embodiment of the present invention;
FIG. 5 is a schematic diagram of a remote monitoring device according to a second embodiment of the present invention;
FIG. 6 is a schematic diagram of a computer apparatus according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Before describing in detail the various embodiments of the present application, the inventive concepts of the present application are first briefly described: in the prior art, the snow distribution, accumulation and change conditions are difficult to accurately express through rare site data, and the problem of cloud shielding exists only by using remote sensing satellite monitoring, so that a large error is caused.
Example one
As shown in fig. 1 and fig. 2, the remote sensing monitoring method for the number of covered days of snow according to the first embodiment includes the following steps:
step S10: acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites;
step S20: performing snow information extraction processing on the remote sensing satellite image data to generate a remote sensing snow cover day data set, wherein the snow cover day data set comprises remote sensing snow cover day data of a plurality of grid units;
step S30: constructing a correction model according to the actually measured snow cover day data of the plurality of sites and the remote sensing snow cover day data of the grid units corresponding to the plurality of sites;
step S40: and correcting the remote sensing snow cover daily data of each grid unit by using the correction model to form a corrected remote sensing snow cover daily data set.
Specifically, in step S10, the remote sensing satellite image data directly acquired on the satellite platform is primary remote sensing product data, and the primary remote sensing product data is preprocessed to obtain secondary remote sensing product data. The satellite platform includes, but is not limited to, polar orbit meteorological satellite platforms, for example, a FY3 satellite (wind cloud three), a NOAA satellite (national oceanic and atmospheric administration meteorological satellite), an NPP (national space administration preplanned meteorological satellite), an EOS (earth observation system), a MODIS (medium resolution imaging spectrometer), and other satellite platforms obtain primary remote sensing product data. The remote sensing satellite image data further comprises vector boundaries, auxiliary data and the like, the preprocessing step comprises image processing technologies such as radiometric calibration, atmospheric correction, geometric correction, orthometric correction, splicing, cutting, enhancement, statistical analysis, information extraction, classification and identification, and the specific preprocessing process is the prior art and is well known by the technical personnel in the field and is not repeated herein. The data of the actually measured snow coverage days of the station are obtained by measuring the snow depth of the meteorological station, when the snow depth of the station is greater than or equal to 2 cm, the snow coverage days of the station are 1, and otherwise, the snow coverage days of the station are 0.
In step S20, snow information is extracted using the remote sensing satellite image data, and a remote sensing snow cover day data set of any time interval can be obtained, the remote sensing snow cover day data set including remote sensing snow cover day data of a plurality of grid cells, wherein different grid cells represent different spatial regions. In the actual processing process, a certain area is divided into a plurality of grid units, and the snow coverage conditions of different space areas in the area can be counted by counting the snow coverage days of each grid unit. In order to improve the detection accuracy, a plurality of data sources can be adopted to detect the daily snow coverage condition, namely, the remote sensing satellite image data comprises a plurality of data sources daily in a preset time period.
As a preferred embodiment, step S20 includes the steps of:
step S21: the method comprises the steps of sequentially generating daily maximum snow coverage data according to various data sources of each day, wherein the daily maximum snow coverage data at least comprises binary data of a plurality of grid units, the binary data is first data when the grid units are covered by snow, the binary data is second data when the grid units are not covered by the snow, and the first data is larger than the second data.
Specifically, step S21 includes the steps of:
step S211: the snow coverage information of each data source is sequentially extracted and snow judgment is carried out to generate binary raster image data, wherein the binary raster image data comprises binary data of a plurality of raster units.
Step S212: and carrying out spatial superposition on the multiple binary raster image data to generate daily maximum snow coverage data, wherein the binary data of each raster unit in the daily maximum snow coverage data is the maximum binary data of each raster unit in the multiple binary raster image data.
For example, assuming that there are three data sources, three binary raster image data may be obtained by extracting the snow coverage information of each data source and performing identification, and the identification results obtained from different data sources may be different for the raster unit at the same position. At this time, three sets of binary raster image data are spatially superposed, and the binary data of each raster unit is taken as the largest two-value data of the raster unit in the three sets of binary raster image data. Illustratively, the first data is 1, the second data is 0, and if the values of the grid cell in the first and second binary raster image data are both 0 and the value in the third binary raster image data is 1, the value of the final grid cell is 1. And performing the operation on each grid unit to obtain daily maximum snow coverage data. It should be noted that the first data and the second data may be other values as long as it is convenient to distinguish between the snow and the non-snow.
When the snow cover information is extracted and snow is identified in step S211, the snow can be identified by methods such as a multispectral threshold method, a normalized difference snow index method, and an NDSI threshold method, and specific principles of the three methods can be found in regional standard DB 63/T1565-2017 (a remote sensing monitoring and evaluation method for alpine snow), which is not described herein again.
Step S22: and performing spatial superposition on the daily maximum snow coverage range data in the preset time period, and accumulating the binary data of the grid units at the same position in the preset time period respectively to generate a remote sensing snow coverage daily data set in the preset time period.
For example, within 30 consecutive days, 20 binary data of a certain grid cell have 1, 100, and after the accumulation, the final value of the grid cell is 20, which means that the number of snow covered days of the grid cell within 30 consecutive days is 20 days. And performing the operation on each grid unit to finally obtain a remote sensing accumulated snow coverage day data set in a preset time period. The grid unit is also provided with longitude and latitude information, and the spatial superposition is to place the multi-layer grid data together according to the longitude and latitude information.
Further, in the snow cover identification process, there may be a situation that a part of the grid units is covered by the cloud, and at this time, it is impossible to identify whether snow covers exist in the grid units, so that the monitoring progress is reduced. In a preferred embodiment, the remote sensing snow cover day number data is corrected before the correction model is constructed in step S30 to eliminate the influence of cloud occlusion as much as possible, but in other embodiments, the elimination of the influence of cloud occlusion is not an essential step, and this step may be omitted when the requirement for monitoring accuracy is not high. Three different correction methods are provided below.
The first correction method is a proximity time correction method, which comprises the following steps: step S101: judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud; step S102: if yes, judging whether the values of the grid units shielded by the cloud in the preset adjacent days are all first data; step S103: if so, correcting the values of the grid cells blocked by the cloud to the first data.
Specifically, when the grid cell is obscured by the cloud, it is not possible to identify whether snow is accumulated, and at this time, the value of the grid cell may be set to third data, which is different from the first data and the second data. As shown in fig. 3, the preset adjacent days are exemplarily selected to be 1 day and the day before the current day. That is, if the value of the grid cell of the current day is the third data, it is determined whether the value of the grid cell of the previous day is the first data, and if so, it is determined that the grid cell of the previous day has snow, and in this case, it is considered that the grid cell of the current day is also snow, and the value of the grid cell is corrected to the first data. If the value of the grid unit on the previous day is not the first data, it indicates that the grid unit may be snow-free or covered by clouds on the previous day, and the value of the grid unit is not adjusted. The operation is carried out on each grid unit, the snow cover day number can be further corrected, and therefore the influence caused by cloud shielding is reduced. Generally, as the ablation speed of the strong radiation snow in winter in the Qinghai-Tibet plateau is high, the snow is generally considered to be continuous in a short time (two days) under the cloud coverage condition, for example, when a grid pixel is influenced by cloud in the snow monitoring result on the day and whether the snow exists cannot be judged, if the snow exists on the pixel on the day, the snow coverage is still considered to exist on the day.
Of course in other embodiments, the adjacent days may be selected as two days, the day before and the day after the day, respectively. If the grid unit on the current day is covered by the cloud, whether the values of the grid unit on the previous day and the next day are the first data or not is further judged, namely whether snow exists or not, if yes, the grid unit on the current day can be considered to have the snow, and the value of the grid unit is corrected to be the first data.
The second correction method is a proximity space correction method, which comprises the following steps: step S201: judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud; step S202: if yes, judging whether a preset number of grid units covered by the accumulated snow exist in a preset adjacent space range of the grid units shielded by the cloud; step S203: if so, the values of the grid cells to be masked by the cloud are corrected to the first data.
The snow coverage is spatially continuous, that is, when a certain grid unit is affected by cloud and cannot judge whether snow exists, the snow coverage can be re-estimated according to the snow coverage information of the adjacent grid units. As shown in fig. 4, for example, for a 3 × 3 grid image, assuming that the middle grid cell is covered by cloud, and 5 grid cells around the middle grid cell are covered by snow, it can be considered that the middle grid cell is also covered by snow, and the value of the grid cell covered by cloud is corrected to be the first data. Of course, in other embodiments, the specific range of the preset adjacent space range and the specific value of the predetermined number may be set according to actual requirements, and are not limited herein.
The third correction method is a permanent accumulated snow area correction method, and comprises the following steps: step S301: judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud; step S302: if yes, judging whether the grid unit shielded by the cloud is in the permanent snow accumulation area; step S302: if so, correcting the values of the grid cells blocked by the cloud to the first data.
Since glaciers and permanent snow areas are widely distributed in the Qinghai-Tibet plateau, the area is high in altitude and the number of snow covered days influenced by cloud coverage all the year round is generally low, and when the area is influenced by clouds and whether snow exists cannot be judged, the area is still covered by snow. Therefore, when a grid cell blocked by cloud is in a permanent snow area, the grid cell is considered to be covered by snow, and the value of the grid cell is corrected to the first data.
Through the three correction modes, the influence caused by cloud shielding can be reduced, and the monitoring accuracy is improved. It should be noted that the three correction methods can be used alone, or two or three of them can be used simultaneously.
Further, in step S30, since the number of sites in a region is much smaller than the number of grid cells, the correction model is constructed by finding the linear relationship between the measured snow coverage day data of a small number of sites and the remote sensing snow coverage day data, so that the snow coverage day of other grid cells can be adjusted by using the correction model. As a preferred embodiment, step S30 includes the steps of:
step S31: according to the formula (1), the actually measured snow cover day number data x of the plurality of sites and the remote sensing snow cover day number data y of the grid units corresponding to the plurality of sites are subjected to linear fitting to obtain a correction coefficient a and a correction parameter b,
y＝a*x+b (1)；
step S32: constructing a correction model according to the correction coefficient a and the correction parameter b:
where y1 denotes the remote sensing snow-covered day number data of the grid cell before correction, and y2 denotes the remote sensing snow-covered day number data of the grid cell after correction.
In step S40, the remote sensing snow-covered day number data for each grid cell is corrected using formula (2). The remote sensing snow cover day number data y1 of the grid unit before correction is substituted into the formula (2), and the remote sensing snow cover day number data y2 of the grid unit after correction can be obtained, so that correction of the remote sensing snow cover day number data set is completed, and monitoring accuracy is improved.
Further, the remote sensing monitoring method of the first embodiment further includes:
step S50: and calculating to obtain an error value according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number after the correction of the grid units corresponding to the plurality of sites.
Specifically, after the correction is completed, precision verification is required to determine the accuracy of the remote sensing snow cover days after the correction. Calculating the determination coefficient R according to the formula (3) and the formula (4)2And root mean error RMSE when R2The closer to 1, the smaller the RMSE is, the higher the accuracy of the accumulated snow coverage days acquired by remote sensing is. Wherein R is2When R is in the range of 0 to 12The closer to 1 the higher the correlation between the two series, the better the correction correlation, generally R2Greater than 0.6 is considered to be a better agreement,
in the formula, XiCover the station with snow on days, YiFor the corrected snow cover days extracted by remote sensing,
the corrected average value of the accumulated snow coverage days extracted by remote sensing,
the average value of the snow cover days of the stations is shown, and n is the number of the stations.
The remote sensing method for monitoring the snow cover day number is characterized in that a correction model is constructed by utilizing actually measured snow cover day number data of a plurality of sites and remote sensing snow cover day number data of grid units corresponding to the sites, so that the remote sensing snow cover day number of each grid unit is corrected by utilizing the correction model, the monitoring accuracy is improved, and the monitoring precision is further improved by combining methods such as near time snow cover correction, adjacent space snow cover correction and permanent snow cover correction. Meanwhile, the method and the technical process for calculating the accumulated snow day can be further standardized, and data support is provided for water resource assessment and climate prediction. The accumulated snow coverage remote sensing monitoring service product manufactured according to the method can provide accumulated snow situation information of accumulated snow in accumulated snow seasons for governments at all levels, provides scientific basis for animal husbandry production and disaster prevention and reduction decisions, and has remarkable economic benefit.
Example two
As shown in fig. 5, the second embodiment discloses a remote sensing monitoring device for snow cover day, which includes a data acquisition module 100, a snow information processing module 200, a model building module 300, and a data correction module 400. The data acquisition module 100 is configured to acquire preprocessed remote sensing satellite image data and actually measured snow cover day number data of a plurality of stations; the snow information processing module 200 is configured to extract snow information from the remote sensing satellite image data to generate a remote sensing snow coverage day data set, where the snow coverage day data set includes remote sensing snow coverage day data of a plurality of grid units; the model building module 300 is configured to build a correction model according to the actually measured snow coverage day data of the plurality of sites and the remote sensing snow coverage day data of the grid units corresponding to the plurality of sites; the data correction module 400 is configured to correct the remote sensing snow cover daily data of each grid cell by using the correction model to form a corrected remote sensing snow cover daily data set.
Further, the remote sensing satellite image data includes multiple data sources of each day in a preset time period, and the snow information processing module 200 is further configured to generate maximum snow coverage data of each day sequentially according to the multiple data sources of each day, where the maximum snow coverage data of each day at least includes binary data of multiple grid units, where the binary data is first data when snow covers the grid units, and the binary data is second data when the grid units do not have snow covers, and the first data is larger than the second data; and performing spatial superposition on the daily maximum snow coverage range data in the preset time period, and accumulating the binary data of the grid units at the same positions in the preset time period respectively to generate a remote sensing snow coverage daily data set in the preset time period.
Further, the snow information processing module 200 is further configured to sequentially extract snow coverage information of each data source and perform snow identification to generate binary raster image data, where the binary raster image data includes binary data of a plurality of raster units; and carrying out spatial superposition on the multiple binary raster image data to generate daily maximum snow coverage data, wherein the binary data of each raster unit in the daily maximum snow coverage data is the maximum binary data of each raster unit in the multiple binary raster image data.
The model building module 300 is further configured to:
according to the formula (1), the actually measured snow cover day number data x of the plurality of sites and the remote sensing snow cover day number data y of the grid units corresponding to the plurality of sites are subjected to linear fitting to obtain a correction coefficient a and a correction parameter b,
y＝a*x+b (1)；
constructing a correction model according to the correction coefficient a and the correction parameter b:
where y1 denotes the remote sensing snow-covered day number data of the grid cell before correction, and y2 denotes the remote sensing snow-covered day number data of the grid cell after correction.
Further, the remote sensing monitoring device for the number of snow cover days further comprises an error calculation module 500, and the error calculation module 500 is used for calculating an error value according to the actually measured data of the number of snow cover days of the plurality of sites and the remote sensing snow cover days after the correction of the grid units corresponding to the plurality of sites.
The application also discloses a computer readable storage medium, the computer readable storage medium stores a remote sensing monitoring program of the accumulated snow coverage days, and the remote sensing monitoring program of the accumulated snow coverage days is executed by a processor to realize the remote sensing monitoring method of the accumulated snow coverage days.
The present application also discloses a computer device, and on the hardware level, as shown in fig. 6, the terminal includes a processor 12, an internal bus 13, a network interface 14, and a computer-readable storage medium 11. The processor 12 reads a corresponding computer program from the computer-readable storage medium and then runs, forming a request processing apparatus on a logical level. Of course, besides software implementation, the one or more embodiments in this specification do not exclude other implementations, such as logic devices or combinations of software and hardware, and so on, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices. The computer-readable storage medium 11 stores a remote sensing monitoring program for the number of days covered by snow, and when the remote sensing monitoring program for the number of days covered by snow is executed by a processor, the remote sensing monitoring method for the number of days covered by snow is realized.
Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Although a few embodiments of the present invention have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents, and that such changes and modifications are intended to be within the scope of the invention.
Claims (10)
1. A remote sensing monitoring method for snow cover days is characterized by comprising the following steps:
acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites;
performing snow information extraction processing on the remote sensing satellite image data to generate a remote sensing snow cover day data set, wherein the snow cover day data set comprises remote sensing snow cover day data of a plurality of grid units;
constructing a correction model according to the actually measured snow cover day data of the plurality of sites and the remote sensing snow cover day data of the grid units corresponding to the plurality of sites;
and correcting the remote sensing snow cover daily data of each grid unit by using the correction model to form a corrected remote sensing snow cover daily data set.
2. The method for remotely sensing the number of days covered by snow according to claim 1, wherein the remote sensing satellite image data comprises a plurality of data sources every day in a preset time period, and the specific method for extracting snow information from the remote sensing satellite image data to generate the remote sensing snow-covered day data set comprises the following steps:
generating daily maximum snow coverage data sequentially according to various daily data sources, wherein the daily maximum snow coverage data at least comprises binary data of a plurality of grid units, the binary data is first data when the grid units are covered by snow, the binary data is second data when the grid units are not covered by snow, and the first data is larger than the second data;
and performing spatial superposition on the daily maximum snow coverage range data in the preset time period, and accumulating the binary data of the grid units at the same position in the preset time period respectively to generate a remote sensing snow coverage daily data set in the preset time period.
3. The method for remotely monitoring the number of covered days according to claim 2, wherein the specific method for generating the daily maximum snow coverage data sequentially according to the plurality of daily data sources comprises the following steps:
sequentially extracting snow cover information of each data source and performing snow judgment to generate binary raster image data, wherein the binary raster image data comprises binary data of a plurality of raster units;
and carrying out spatial superposition on the multiple binary raster image data to generate daily maximum snow coverage data, wherein the binary data of each raster unit in the daily maximum snow coverage data is the maximum binary data of each raster unit in the multiple binary raster image data.
4. The method for remotely monitoring the number of snow covered days according to claim 2, wherein after generating the maximum daily snow coverage data from the plurality of daily data sources in sequence, the method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether the values of the grid units shielded by the cloud in the preset adjacent days are all first data;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
5. The method for remotely monitoring the number of snow covered days according to claim 2, wherein after generating the maximum daily snow coverage data from the plurality of daily data sources in sequence, the method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether a preset number of grid units covered by the accumulated snow exist in a preset adjacent space range of the grid units shielded by the cloud;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
6. The method for remotely monitoring the number of snow covered days according to claim 2, wherein after generating the maximum daily snow coverage data from the plurality of daily data sources in sequence, the method further comprises:
judging whether third data exist in the daily maximum snow coverage data, wherein the third data represent that the corresponding grid unit is shielded by the cloud;
if yes, judging whether the grid unit shielded by the cloud is in the permanent snow accumulation area;
if so, correcting the values of the grid cells blocked by the cloud to the first data.
7. The method for remotely sensing the number of covered days according to claim 2, wherein the method for constructing the correction model according to the measured number of covered days of snow of the plurality of sites and the remote sensing number of covered days of snow of the grid cells corresponding to the plurality of sites comprises:
according to the formula (1), the actually measured snow cover day number data x of the plurality of sites and the remote sensing snow cover day number data y of the grid units corresponding to the plurality of sites are subjected to linear fitting to obtain a correction coefficient a and a correction parameter b,
y＝a*x+b (1)；
constructing a correction model according to the correction coefficient a and the correction parameter b:
where y1 denotes the remote sensing snow-covered day number data of the grid cell before correction, and y2 denotes the remote sensing snow-covered day number data of the grid cell after correction.
8. The method for remotely monitoring the number of covered days according to claim 7, further comprising:
and calculating to obtain an error value according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number after the correction of the grid units corresponding to the plurality of sites.
9. A remote sensing monitoring device of snow cover day number, its characterized in that, remote sensing monitoring device includes:
the data acquisition module is used for acquiring preprocessed remote sensing satellite image data and actually measured accumulated snow coverage day data of a plurality of sites;
the snow information processing module is used for extracting snow information from the remote sensing satellite image data to generate a remote sensing snow coverage day data set, wherein the snow coverage day data set comprises remote sensing snow coverage day data of a plurality of grid units;
the model building module is used for building a correction model according to the actually measured snow cover day number data of the plurality of sites and the remote sensing snow cover day number data of the grid units corresponding to the plurality of sites;
and the data correction module is used for correcting the remote sensing snow cover daily number data of each grid unit by using the correction model so as to form a corrected remote sensing snow cover daily number data set.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a remote monitoring program of the number of snow-covered days, which when executed by a processor implements the remote monitoring method of the number of snow-covered days according to any one of claims 1 to 8.
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