CN112395989A - Snow coverage mixed pixel decomposition method for multi-satellite sensor - Google Patents
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Abstract
The invention relates to a snow coverage degree mixed pixel decomposition method for a multi-satellite sensor, wherein corresponding image data are obtained based on a plurality of preset remote sensing satellite sensors; extracting end members of a plurality of types of land on the basis of the multispectral reflectivity data and a preset image end member extraction rule to obtain an end member set of the land; when the extraction fails, calling a ground measurement spectrum curve of the ground object from the pre-acquired auxiliary data, calculating an end member matched with the spectrum response of the sensor wave band according to a pre-acquired spectrum response function of the remote sensing satellite sensor, and acquiring a ground measurement end member library; acquiring a typical ground class end member library based on the end member set of the ground class; performing multi-end member spectrum mixed analysis to obtain the coverage of the accumulated snow based on the typical end member library and the ground measurement end member library; and calculating the coverage of the satellite pixel accumulated snow.
Description
Technical Field
The invention relates to the technical field of satellite sensor data processing and application, in particular to a snow coverage degree mixed pixel decomposition method for a multi-satellite sensor.
Background
Snow deposits contribute to the earth's radiant energy balance and, as a wide aquifer, affect various climatic and hydrological processes. The conventional ground station monitoring cannot accurately obtain a large-range monitoring result, and whether the monitoring range of the ground station is representative or not directly influences the final monitoring precision. With the development of satellite technology, the abundant wave band information of the sensor in optical remote sensing can provide accurate snow coverage information and realize large-scale observation.
Disclosure of Invention
Technical problem to be solved
In view of the above-mentioned shortcomings and drawbacks of the prior art, the present invention provides a snow coverage mixed pixel decomposition method for a multi-satellite sensor.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
the embodiment of the invention provides a snow coverage degree mixed pixel decomposition method for a multi-satellite sensor, which comprises the following steps:
s1, acquiring corresponding image data based on a plurality of preset remote sensing satellite sensors;
the image data includes: multispectral earth surface reflectivity data, imaging geometry and mask data;
s2, extracting end members of various land categories based on the multispectral reflectivity data and preset image end member extraction rules to obtain an end member set of the land categories;
the end member set of the land classes comprises end members of a plurality of land classes;
when the extraction fails, calling a ground measurement spectrum curve of the ground object from the pre-acquired auxiliary data, calculating an end member matched with the spectrum response of the sensor wave band according to a pre-acquired spectrum response function of the remote sensing satellite sensor, and acquiring a ground measurement end member library;
the pre-acquired auxiliary data comprises spectral curves of snow cover, vegetation and soil and spectral response data;
s3, acquiring a typical class end member library based on the end member set of the ground class;
s4, performing multi-end-member spectrum mixed analysis to obtain the coverage of the accumulated snow based on the typical end-member library and the ground measurement end-member library;
and S5, calculating a satellite pixel snow coverage product.
Preferably, the first and second liquid crystal materials are,
the image data includes: MODIS video data, Landsat TM/ETM +/OLI video data, Sentinel-2 MSI video data, FY-4A AGRI video data, and Himapari-8 AHI video data.
Preferably, the first and second liquid crystal materials are,
the preset video end member extraction rule in step S2 is:
the snow end member extraction rules in the MODIS image data, the Sentinel-2/MSI image data and the Landsat TM/ETM +/OLI image data are as follows: NDVI<-0.035 and NDSI>0.75 and R0.55>0.7; the vegetation end member extraction rule is as follows: NDVI>0.7 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4; the water body end member extraction rule is as follows: NDWI>0.2 and R0.86<0.2；
NDVI refers to normalized vegetation index; NDSI refers to normalized snow index; r0.55Refers to the surface reflectivity of 0.55 micron wave band;
the snow end member extraction rule in the Hiwari-8/AHI image data and the FY-4A/AGRI image data is as follows: NDVI<-0.03 and NDSI>0.74 and R0.55>0.5; the vegetation end member extraction rule is as follows: NDVI>0.65 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4。
Preferably, the step S3 includes:
and based on the end member set of the land category, screening the image end members in the end member set of the land category to obtain a typical land category end member library.
Preferably, the step S3 specifically includes:
s31, calculating the vector length of each end member spectrum by adopting a formula (1);
wherein,the | r | is the vector length of the end-member spectrum; r iskThe reflectivity of the kth wave band; n is the number of wave bands;
s32, performing ascending arrangement on all vector lengths in each type of end member of the accumulated snow, the vegetation and the soil ground object to obtain a corresponding sequence of each type of end member of the accumulated snow, the vegetation and the soil ground object;
s33, dividing each type of inner end member into n subsets according to equal intervals aiming at the corresponding sequence of each type of end member of the accumulated snow, the vegetation and the soil feature;
wherein the interval is | | Ri||；
n is a preset value; | R | non-conducting phosphormaxThe maximum reflectivity of the pixel; | R | non-conducting phosphorminA pixel minimum reflectance;
s34, taking a median or a mean value of all spectra in each subset as a typical end member of the subset;
s35, acquiring a typical end member library based on the typical end members of each subset;
the typical end member library comprises: n typical end members of snow-type ground features, n typical end members of vegetation-type ground features, and n typical end members of soil-type ground features.
Preferably, the step S4 includes:
s41, regarding to the typical ground-like end member library and the ground measurement end member library, marking the pixel with the reflectivity of NDSI (New data standard) of 0 or 1.6 μm wave band larger than 0.3 as no snow, and marking the pixel with NDSI of 0.7 as pure snow;
s42, marking the pixels successfully extracting the snow end members as pure snow, and marking the pixels extracting non-snow as no snow;
s43, establishing a mixed pixel spectrum database according to the spectrum of the accumulated snow, the soil and the vegetation in the end member database and the preset area proportion constraint of the end members;
s44, processing the mixed pixels one by one according to the mixed pixel spectrum database, and determining an accumulated snow-soil binary model and an accumulated snow-vegetation binary model;
the mixed pixels are pixels marked as pure snow and non-snow pixels;
only using the snow-vegetation model when the NDVI of the pixel exceeds 0.3, otherwise using the snow-soil model and the snow-vegetation model in sequence;
s45, calculating the difference between the mixed pixel spectrum and the mixed pixel spectrum data corresponding to different snow coverage degrees, and recording the result of RMSE minimum passing through residual error test and the corresponding snow coverage degree;
wherein RMSE is root mean square error (root mean square error);
if the difference does not accord with the residual error detection and the current pixel NDVI does not exceed 0.3, recording the RMSE passing the residual error detection, repeating the step S45 by using an accumulated snow-vegetation model to obtain a new RMSE for calculating the accumulated snow coverage, and comparing the new RMSE with the accumulated snow coverage to select the accumulated snow coverage corresponding to the smaller one as a final result.
Preferably, the preset end member area ratio constraint in step S43 is:
the constraint conditions are as follows:
Fi≥0；
wherein R isλIs the reflectance of the mixed pixel at wavelength λ; riλAnd FiRespectively the reflectivity and abundance of the ith end member; epsilonλIs the fitting residual error; m is the number of end members;
when calculating, the method meets preset first conditions, second conditions, third conditions, fourth conditions and fifth conditions;
the first condition is: the area ratio of the end members meets the constraint of 'non-negative' and 'sum being one', but an error of 1% is allowed;
the second condition is: when the NDVI of the pixel exceeds 0.3, using a snow-vegetation mixed model, otherwise, sequentially using a snow-soil model and a snow-vegetation model;
the third condition is: in residual error test, the RMSE of residual errors of all wave bands is not more than 0.025;
the fourth condition is: during residual error detection, the residual error of continuous wave bands of half wave band number is not more than 0.025;
the fifth condition is: the result of the minimum RMSE is the area ratio of the end members.
Preferably, the step S5 includes:
s51, masking the snow coverage inversion result image layer on the basis of the mask image layer;
s52, selecting a color table corresponding to the range of the snow coverage value, and adopting a Geotiff file in an unsigned integer and LZW compression mode, wherein the data file simultaneously stores original projection information of an image, and the data file is a calculated snow coverage product.
(III) advantageous effects
The invention has the beneficial effects that: the snow coverage degree mixed pixel decomposition method for the multi-satellite sensor is suitable for common remote sensing optical images including MODIS, Sentinel-2/MSI, Landsat TM/ETM +/OLI images, and static meteorological satellite Himapari-8/AHI and FY-4A/AGRI images, has good expandability, and provides a practical tool for researches on snow coverage remote sensing mapping, multi-source snow coverage result fusion, contrastive analysis and the like.
Based on the multi-end-element spectrum mixed analysis theory and the automatic construction technology of the image end-element library, the algorithm has a physical basis and high precision.
By utilizing the image-based automatic end member extraction and selection technology and the lookup table technology for calculating the coverage of the accumulated snow, the calculation efficiency is high, and the remote sensing real-time monitoring of the coverage of the accumulated snow can be realized according to the method.
Drawings
FIG. 1 is a flow chart of a snow coverage mixed pixel decomposition method for a multi-satellite sensor according to the present invention;
FIG. 2 is a schematic diagram of a snow coverage mixed pixel decomposition method for a multi-satellite sensor according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of the results of MODIS image snowfall type automatic end member selection (a) and end member extraction (b) in the embodiment of the present invention;
fig. 4 is an exemplary diagram of results of automatic end member selection (c) and end member extraction (d) for MODIS image soil types according to the embodiment of the present invention;
fig. 5 is an exemplary diagram of results of automatic end member selection (e) and end member extraction (f) for the vegetation type of the MODIS image in the embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1, the present embodiment provides a snow coverage mixed pixel decomposition method for a multi-satellite sensor, including:
s1, acquiring corresponding image data based on a plurality of preset remote sensing satellite sensors;
the image data includes: multispectral earth surface reflectivity data, imaging geometry and mask data;
s2, extracting end members of various land categories based on the multispectral reflectivity data and preset image end member extraction rules to obtain an end member set of the land categories;
the end member set of the land classes comprises end members of a plurality of land classes;
when the extraction fails, calling a ground measurement spectrum curve of the ground object from the pre-acquired auxiliary data, calculating an end member matched with the spectrum response of the sensor wave band according to a pre-acquired spectrum response function of the remote sensing satellite sensor, and acquiring a ground measurement end member library;
the pre-acquired auxiliary data comprises spectral curves of snow cover, vegetation and soil and spectral response data;
the auxiliary data in this embodiment includes snow, vegetation, soil spectra extracted from the university of johns hopkins spectral library, and spectral response data obtained officially from various sensor image products.
S3, acquiring a typical class end member library based on the end member set of the ground class;
s4, performing multi-end-member spectrum mixed analysis to obtain the coverage of the accumulated snow based on the typical end-member library and the ground measurement end-member library;
and S5, calculating the satellite pixel snow coverage product and storing the satellite pixel snow coverage product in a file.
Preferably, the first and second liquid crystal materials are,
the image data includes: MODIS video data, Landsat TM/ETM +/OLI video data, Sentinel-2 MSI video data, FY-4A AGRI video data, and Himapari-8 AHI video data.
In this embodiment, the MODIS image data (satellite pixel earth surface reflectivity data provided by NASA (MOD09GA and MYD09GA) includes imaging geometry and mask data), Landsat TM/ETM +/OLI image data (earth surface reflectivity data provided by USGS), Sentinel-2 MSI image data (L2A earth surface reflectivity product provided by the european space agency), FY-4A AGRI image data (AGRI chinese district multichannel image, cloud mask and imaging geometry product provided by the chinese weather agency), and himarwari-8 AHI image data (AHI multichannel image including imaging geometry, cloud mask and imaging geometry product produced by the japanese weather hall).
Preferably, the first and second liquid crystal materials are,
the preset video end member extraction rule in step S2 is:
the accumulated snow end in the MODIS image data, Sentinel-2/MSI image data, Landsat TM/ETM +/OLI image dataThe meta extraction rule is: NDVI<-0.035 and NDSI>0.75 and R0.55>0.7; the vegetation end member extraction rule is as follows: NDVI>0.7 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4; the water body end member extraction rule is as follows: NDWI>0.2 and R0.86<0.2。
The snow end member extraction rule in the Hiwari-8/AHI image data and the FY-4A/AGRI image data is as follows: NDVI<-0.03 and NDSI>0.74 and R0.55>0.5; the vegetation end member extraction rule is as follows: NDVI>0.65 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4。
Preferably, in this embodiment, the step S3 includes: based on the end member set of the land category, the image end members in the end member set of the land category are screened to obtain a typical land category end member library (i.e., the image automatic extraction and selection end member library in fig. 2).
Preferably in this embodiment, the step S3 specifically includes:
s31, calculating the vector length of each end member spectrum by adopting a formula (1);
wherein, r is the vector length of the end-member spectrum; r iskThe reflectivity of the kth wave band; and N is the number of wave bands.
And S32, performing ascending arrangement on all vector lengths in each type of end member of the snow cover, the vegetation and the soil feature to obtain a corresponding sequence of each type of end member of the snow cover, the vegetation and the soil feature.
S33, dividing each type of inner end member into n subsets according to equal intervals aiming at the corresponding sequence of each type of end member of the accumulated snow, the vegetation and the soil feature;
wherein the interval is | | Ri||；
n is a preset value; | R | non-conducting phosphormaxThe maximum reflectivity of the pixel; | R | non-conducting phosphorminThe minimum reflectivity of the picture element.
Fig. 3, 4, and 5 show the automatic end member selection and end member extraction results of snow, soil, and vegetation types in a MODIS image, the number of vegetation end members directly extracted from the image is large, and a few end members with better representativeness, which basically represent the spectrum difference in the class, can be extracted through the end member selection.
S34, taking the median or mean of all spectra within each subset as a representative end member of the subset.
And S35, acquiring a typical end member library based on the typical end members of each subset.
The typical end member library comprises: n typical end members of snow-type ground features, n typical end members of vegetation-type ground features, and n typical end members of soil-type ground features.
Preferably, in this embodiment, the step S4 includes:
and S41, regarding the typical ground-like end member library and the ground measurement end member library, marking the pixels with the reflectivity of NDSI <0 or 1.6 μm wave band larger than 0.3 as snow-free pixels, and marking the pixels with the NDSI >0.7 as pure snow.
And S42, marking the image elements for successfully extracting the snow end members as pure snow, and marking the image elements for extracting non-snow as non-snow.
And S43, establishing a mixed pixel spectrum database according to the spectrum of the accumulated snow, the soil and the vegetation in the end member database and the preset area proportion constraint of the end members.
And S44, processing the mixed pixels one by one according to the mixed pixel spectrum database, and determining an accumulated snow-soil binary model and an accumulated snow-vegetation binary model.
The mixed pixels are pixels outside the pixels marked as pure snow and non-snow.
Only using the snow-vegetation model when the NDVI of the pixel exceeds 0.3, otherwise using the snow-soil model and the snow-vegetation model in sequence.
S45, calculating the difference between the mixed pixel spectrum and the mixed pixel spectrum data corresponding to different snow coverage degrees, and recording the result of RMSE minimum passing through residual error test and the corresponding snow coverage degree;
if the difference does not accord with the residual error detection and the current pixel NDVI does not exceed 0.3, recording the RMSE passing the residual error detection, repeating the step S45 by using an accumulated snow-vegetation model to obtain a new RMSE for calculating the accumulated snow coverage, and comparing the new RMSE with the accumulated snow coverage to select the accumulated snow coverage corresponding to the smaller one as a final result.
In the practical application of the implementation, the specific steps of the multi-end spectrum mixing analysis are as follows:
And 2, marking the pixels successfully extracted with the snow end members as pure snow, and marking the pixels extracting non-snow such as forest lands, grasslands and the like as non-snow.
And 3, constructing a lookup table (one lookup table is constructed for one image, a mixed spectrum with the minimum difference is found in the lookup table according to the pixel mixed spectrum, and the snow coverage calculated according to the condition is the estimated target result). And taking 1% as the step length of the snow coverage, considering 1% of estimation errors, and establishing a mixed pixel spectral database corresponding to different snow coverage under the snow-soil binary model and the snow-vegetation binary model according to the snow, soil and vegetation spectra of the end member library and the area ratio constraint of the end members.
And 4, starting calculation, processing the pixels of the mixed pixels (namely, pixels marked as pure snow and pixels without snow) one by one, and firstly determining a mixed pixel model. Only using the snow-vegetation model when the NDVI of the pixel exceeds 0.3, otherwise using the snow-soil model and the snow-vegetation model in sequence.
And 5, calculating the difference between the mixed pixel spectrum and the mixed pixel spectrum data corresponding to different snow coverage degrees, and recording the result of RMSE minimum passing through residual error detection and the corresponding snow coverage degree.
And 6, if the difference does not accord with the residual error detection and the current pixel NDVI does not exceed 0.3, recording the RMSE in the step 5, repeating the step 5 by using an accumulated snow-vegetation model to obtain a new RMSE for calculating the accumulated snow coverage, and comparing the new RMSE with the accumulated snow coverage to select the accumulated snow coverage corresponding to the smaller one as a final result.
Preferably, the preset end member area ratio constraint in step S43 is:
the constraint conditions are as follows:
Fi≥0；
wherein R isλIs the reflectance of the mixed pixel at wavelength λ; riλAnd FiRespectively the reflectivity and abundance of the ith end member; epsilonλIs the fitting residual error; m is the number of end members.
And when in resolving, the preset first condition, second condition, third condition, fourth condition and fifth condition are met.
The first condition is: the area ratios of the end members meet the "non-negative" and "sum to one" constraint, but allow for a 1% error.
The second condition is: and when the NDVI of the pixel exceeds 0.3, using a snow-vegetation mixed model, and otherwise, sequentially using a snow-soil model and a snow-vegetation model.
The third condition is: the residual RMS of all bands should not exceed 0.025 when the residual is examined.
The fourth condition is: and when the residual error is tested, the residual error of a half wave band number of continuous wave bands does not exceed 0.025.
The fifth condition is: the result of the minimum RMSE is the area ratio of the end members.
Preferably, the step S5 includes:
and S51, masking the snow coverage inversion result image layer on the basis of the mask image layer.
S52, selecting a color table corresponding to the range of the snow coverage (the snow coverage is blue-cyan-yellow gradient), and storing original projection information of the image in a data file which is a product of the calculated snow coverage by adopting a Geotiff file in an unsigned integer and LZW compression mode.
In the embodiment, the accumulated snow coverage estimation error and the imaging geometric layer are reserved in the output file, and reference is provided for quality evaluation of the accumulated snow coverage.
In the method for decomposing the snow cover degree mixed pixel for the multi-satellite sensor, the end member extraction rule is determined according to the spectral band setting of different satellite optical sensors; automatically extracting end members such as snow, soil, vegetation and the like according to the multispectral image, establishing a typical end member library, and selecting the typical end members according to end member spectral vectors; and resolving the mixed pixels according to the typical end members and a least square method to obtain an optimal solution and realize the inversion of the coverage of the accumulated snow. The snow coverage mixed pixel decomposition method of the multi-satellite sensor is suitable for various satellite images including MODIS, Sentinel-2/MSI, Landsat TM/ETM +/OLI images and static meteorological satellite Himawari-8/AHI and FY-4A/AGRI images, realizes automatic calculation of mixed pixel snow coverage under complex surface conditions through a multi-end-member spectral mixed analysis theory and an end-member automatic extraction and selection technology based on images, and can be used for research on mountain area hydrological models, water resource management, numerical weather forecast, land modes and climate change.
The snow coverage degree mixed pixel decomposition method for the multi-satellite sensor is suitable for common remote sensing optical images including MODIS, Sentinel-2/MSI, Landsat TM/ETM +/OLI images, and static meteorological satellite Himapari-8/AHI and FY-4A/AGRI images, is good in expandability, and provides a practical tool for researches such as snow coverage remote sensing mapping, multi-source snow coverage result fusion and contrastive analysis. Based on the multi-end-element spectrum mixed analysis theory and the automatic construction technology of the image end-element library, the algorithm has a physical basis and high precision. By utilizing the image-based automatic end member extraction and selection technology and the lookup table technology for calculating the coverage of the accumulated snow, the calculation efficiency is high, and the remote sensing real-time monitoring of the coverage of the accumulated snow can be realized according to the method.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; either as communication within the two elements or as an interactive relationship of the two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, a first feature may be "on" or "under" a second feature, and the first and second features may be in direct contact, or the first and second features may be in indirect contact via an intermediate. Also, a first feature "on," "above," and "over" a second feature may be directly or obliquely above the second feature, or simply mean that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the second feature, or may simply mean that the first feature is at a lower level than the second feature.
In the description herein, the description of the terms "one embodiment," "some embodiments," "an embodiment," "an example," "a specific example" or "some examples" or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not restrictive, and that those skilled in the art may make changes, modifications, substitutions and alterations to the above embodiments without departing from the scope of the present invention.
Claims (8)
1. A snow coverage degree mixed pixel decomposition method for a multi-satellite sensor is characterized by comprising the following steps:
s1, acquiring corresponding image data based on a plurality of preset remote sensing satellite sensors;
the image data includes: multispectral earth surface reflectivity data, imaging geometry and mask data;
s2, extracting end members of various land categories based on the multispectral reflectivity data and preset image end member extraction rules to obtain an end member set of the land categories;
the end member set of the land classes comprises end members of a plurality of land classes;
when the extraction fails, calling a ground measurement spectrum curve of the ground object from the pre-acquired auxiliary data, calculating an end member matched with the spectrum response of the sensor wave band according to a pre-acquired spectrum response function of the remote sensing satellite sensor, and acquiring a ground measurement end member library;
the pre-acquired auxiliary data comprises spectral curves of snow cover, vegetation and soil and spectral response data;
s3, acquiring a typical class end member library based on the end member set of the ground class;
s4, performing multi-end-member spectrum mixed analysis to obtain the coverage of the accumulated snow based on the typical end-member library and the ground measurement end-member library;
and S5, calculating a satellite pixel snow coverage product.
2. The method of claim 1,
the image data includes: MODIS video data, Landsat TM/ETM +/OLI video data, Sentinel-2 MSI video data, FY-4A AGRI video data, and Himapari-8 AHI video data.
3. The method of claim 2,
the preset video end member extraction rule in step S2 is:
the snow end member extraction rules in the MODIS image data, the Sentinel-2/MSI image data and the Landsat TM/ETM +/OLI image data are as follows: NDVI<-0.035 and NDSI>0.75 and R0.55>0.7; the vegetation end member extraction rule is as follows: NDVI>0.7 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4; the water body end member extraction rule is as follows: NDWI>0.2 and R0.86<0.2；
The snow end member extraction rule in the Hiwari-8/AHI image data and the FY-4A/AGRI image data is as follows: NDVI<-0.03 and NDSI>0.74 and R0.55>0.5; the vegetation end member extraction rule is as follows: NDVI>0.65 and NDSI<-0.4; the extraction rule of the soil and rock end members is as follows: 0<NDVI<0.15 and NDSI<-0.4。
4. The method according to claim 3, wherein the step S3 includes:
and based on the end member set of the land category, screening the image end members in the end member set of the land category to obtain a typical land category end member library.
5. The method according to claim 4, wherein the step S3 specifically includes:
s31, calculating the vector length of each end member spectrum by adopting a formula (1);
wherein, r is the vector length of the end-member spectrum; r iskThe reflectivity of the kth wave band; n is the number of wave bands;
s32, performing ascending arrangement on all vector lengths in each type of end member of the accumulated snow, the vegetation and the soil ground object to obtain a corresponding sequence of each type of end member of the accumulated snow, the vegetation and the soil ground object;
s33, dividing each type of inner end member into n subsets according to equal intervals aiming at the corresponding sequence of each type of end member of the accumulated snow, the vegetation and the soil feature;
wherein the interval is | | Ri||；
n is a preset value; | R | non-conducting phosphormaxThe maximum reflectivity of the pixel; | R | non-conducting phosphorminA pixel minimum reflectance;
s34, taking a median or a mean value of all spectra in each subset as a typical end member of the subset;
s35, acquiring a typical end member library based on the typical end members of each subset;
the typical end member library comprises: n typical end members of snow-type ground features, n typical end members of vegetation-type ground features, and n typical end members of soil-type ground features.
6. The method according to claim 5, wherein the step S4 includes:
s41, regarding to the typical ground-like end member library and the ground measurement end member library, marking the pixel with the reflectivity of NDSI (New data standard) of 0 or 1.6 μm wave band larger than 0.3 as no snow, and marking the pixel with NDSI of 0.7 as pure snow;
s42, marking the pixels successfully extracting the snow end members as pure snow, and marking the pixels extracting non-snow as no snow;
s43, establishing a mixed pixel spectrum database according to the spectrum of the accumulated snow, the soil and the vegetation in the end member database and the preset area proportion constraint of the end members;
s44, processing the mixed pixels one by one according to the mixed pixel spectrum database, and determining an accumulated snow-soil binary model and an accumulated snow-vegetation binary model;
the mixed pixels are pixels marked as pure snow and non-snow pixels;
when the NDVI of the pixel exceeds 0.3, only using a binary model, namely using an accumulated snow-vegetation model, or else using an accumulated snow-soil model and an accumulated snow-vegetation model in sequence;
s45, calculating the difference between the mixed pixel spectrum and the mixed pixel spectrum data corresponding to different snow coverage degrees, and recording the result of RMSE minimum passing through residual error test and the corresponding snow coverage degree;
if the difference does not accord with the residual error detection and the current pixel NDVI does not exceed 0.3, recording the RMSE passing the residual error detection, repeating the step S45 by using an accumulated snow-vegetation model to obtain a new RMSE for calculating the accumulated snow coverage, and comparing the new RMSE with the accumulated snow coverage to select the accumulated snow coverage corresponding to the smaller one as a final result.
7. The method according to claim 6, wherein the predetermined end-member area ratio constraint in step S43 is:
the constraint conditions are as follows:
Fi≥0；
wherein R isλIs the reflectance of the mixed pixel at wavelength λ; riλAnd FiRespectively the reflectivity and abundance of the ith end member; epsilonλIs the fitting residual error; m is the number of end members;
when calculating, the method meets preset first conditions, second conditions, third conditions, fourth conditions and fifth conditions;
the first condition is: the area ratio of the end members meets the constraint of 'non-negative' and 'sum being one', but an error of 1% is allowed;
the second condition is: when the NDVI of the pixel exceeds 0.3, using a snow-vegetation mixed model, otherwise, sequentially using a snow-soil model and a snow-vegetation model;
the third condition is: in residual error test, the RMS of residual errors of all wave bands is not more than 0.025;
the fourth condition is: during residual error detection, the residual error of continuous wave bands of half wave band number is not more than 0.025;
the fifth condition is: the result of the minimum RMSE is the area ratio of the end members.
8. The method according to claim 7, wherein the step S5 includes:
s51, masking the snow coverage inversion result image layer on the basis of the mask image layer;
s52, selecting a color table corresponding to the range of the snow coverage value, and adopting a Geotiff file in an unsigned integer and LZW compression mode, wherein the data file simultaneously stores original projection information of an image, and the data file is a calculated snow coverage product.
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