CN116975784A - High-space-time resolution MPDI data set construction method, system and storage medium - Google Patents
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Abstract
The invention discloses a method, a system and a storage medium for constructing a high space-time resolution MPDI data set, wherein the method comprises the steps of carrying out space-time data fusion on high time resolution low spatial resolution remote sensing data and a plurality of low time resolution high spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set; calculating a high-time-resolution low-spatial-resolution MPDI data set; calculating a high spatial-temporal resolution MPDI data set; aggregating the MPDI data set with high space-time resolution to the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set; calculating a correction coefficient of the MPDI data set with high space-time resolution; and carrying out MPDI correction on the MPDI data set with high space-time resolution by using the correction coefficient to obtain the MPDI data set with high space-time resolution after correction. According to the invention, by correcting the obtained MPDI data set with high space-time resolution, a more accurate MPDI data set result with high space-time resolution can be obtained, so that the monitoring effect is improved.
Description
Technical Field
The invention relates to the technical field of remote sensing image space-time data fusion and drought monitoring, in particular to a method, a system and a storage medium for constructing a high space-time resolution MPDI data set.
Background
The drought monitoring has important significance for guaranteeing agricultural production. The traditional drought monitoring method is mainly based on ground site data, has the advantages of higher authenticity, but has the disadvantages of higher monitoring cost, easily influenced by site distribution, and limited space representativeness in the regional scale range. The remote sensing technology can effectively make up for the defects of the traditional monitoring means. Remote sensing data has become an important data source for drought monitoring because of the advantages of objectivity, low cost, large range, strong data continuity and the like. MPDI (modified perpendicular drought index), namely improving the vertical drought index, is a common drought monitoring index, and various researches prove that the soil moisture content can be effectively monitored. On a large regional scale, the time-space dynamic change of drought can be monitored by constructing a time sequence MPDI data set. However, on a small and medium scale, the spatial resolution of the existing time-series MPDI data set is often insufficient.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method, a system and a storage medium for constructing a high-space-time resolution MPDI data set, remote sensing data of different space-time characteristics are fused through a space-time data fusion algorithm to obtain high-space-time resolution reflectivity data, and then the high-space-time resolution MPDI data set is constructed to effectively monitor medium-small scale drought.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for constructing an MPDI data set with high spatial-temporal resolution, comprising the following steps:
carrying out space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high-space-time resolution remote sensing data set;
calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set;
calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain a high space-time resolution MPDI data set;
aggregating the MPDI data set with high space-time resolution to the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
calculating a correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
and carrying out MPDI correction on the MPDI data set with high space-time resolution by using the correction coefficient to obtain the MPDI data set with high space-time resolution after correction.
Further, the performing space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high-space-time resolution remote sensing data set specifically includes:
and carrying out space-time data fusion on the first time sequence low-spatial resolution remote sensing data, the first time high-spatial resolution remote sensing data and the second time high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set.
Further, the calculation method of the MPDI value is as follows:
，
wherein ,FVCfor the coverage of vegetation,Mis the slope of the soil line and is defined as,for the reflectance of the red band corrected for the atmosphere, +.>For the reflectance of the near-red band corrected by the atmosphere,>is a constant of red wave band>Is a near red band constant.
Further, the calculating method of the vegetation coverage comprises the following steps:
，
wherein ,NDVIin order to normalize the vegetation index,is a pixel composed of bare soilNDVIThe value of the sum of the values,for picture elements covered entirely by vegetationNDVIValues.
Further, the method for aggregating the MPDI data set with high space-time resolution to the scale of the remote sensing data with high time resolution and low space resolution comprises the following steps:
，
wherein ,for the MPDI value of the polymerized picture element, +.>For MPDI values in a high spatial-temporal resolution MPDI dataset, n is high temporal resolution lowThe number of the pixels with high spatial-temporal resolution MPDI value is contained in the pixels in the spatial resolution remote sensing data.
Further, the method for calculating the correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set comprises the following steps:
，
wherein ,Vfor the correction coefficients of the high spatio-temporal resolution MPDI data set,for MPDI values in the high temporal resolution low spatial resolution MPDI dataset, +.>Is the MPDI value of the polymerized pixel.
Further, the method for obtaining the modified high spatial-temporal resolution MPDI data set by performing MPDI modification on the high spatial-temporal resolution MPDI data set by using the correction coefficient comprises the following steps:
，
wherein ,for the modified high spatial-temporal resolution MPDI values,Vcorrection factors for high spatial-temporal resolution MPDI data set, +.>MPDI values in the MPDI dataset are high spatial-temporal resolution.
In a second aspect, the present invention provides a high spatial-temporal resolution MPDI data set construction system, including:
the space-time data fusion module is used for carrying out space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set;
the MPDI calculation module is used for calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set; and calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain a high space-time resolution MPDI data set;
the space aggregation module is used for aggregating the MPDI data set with high space-time resolution into the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
the correction coefficient calculation module is used for calculating the correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
and the MPDI correction module is used for carrying out MPDI correction on the MPDI data set with high space-time resolution by utilizing the correction coefficient to obtain the MPDI data set with high space-time resolution after correction.
In a third aspect, the present invention proposes a computer readable storage medium, on which a computer program is stored, which when being executed by a processor implements the steps of a high spatio-temporal resolution MPDI dataset construction method as described above.
The invention has the following beneficial effects:
(1) The method can solve the defects that the spatial resolution of the MPDI data set of the common time sequence is low and is not suitable for monitoring the drought in medium and small scale;
(2) According to the invention, by correcting the obtained MPDI data set with high space-time resolution, a more accurate MPDI data set result with high space-time resolution can be obtained, so that the monitoring effect is improved.
Drawings
Fig. 1 is a flow chart of a method for constructing MPDI data sets with high spatial-temporal resolution in embodiment 1 of the invention;
FIG. 2a is a schematic diagram of a 5 month 2 day high spatial resolution image according to embodiment 1 of the present invention;
FIG. 2b is a schematic diagram of a 6 month 3 day high spatial resolution image according to embodiment 1 of the present invention;
FIG. 2c is a diagram of a 5 month 2 day low spatial resolution image according to embodiment 1 of the present invention;
FIG. 2d is a diagram of a 5 month 23 day low spatial resolution image according to embodiment 1 of the present invention;
FIG. 2e is a diagram of a 6 month 3 day low spatial resolution image according to embodiment 1 of the present invention;
FIG. 3a is a schematic diagram of a 5 month 2 day high spatial resolution image according to embodiment 1 of the present invention;
FIG. 3b is a schematic diagram of a 5 month 23 day high spatial resolution image synthesized in example 1 of the present invention;
FIG. 3c is a schematic view of a 6 month 3 day high spatial resolution image according to embodiment 1 of the present invention;
FIG. 4a is a diagram of high spatial resolution MPDI data for 5 month 2 day in example 1 of the present invention;
FIG. 4b is a schematic diagram of high spatial resolution MPDI data for 5 months and 23 days in example 1 according to the present invention;
FIG. 4c is a schematic diagram of the 6 month 3 day high spatial resolution MPDI data in example 1 of the present invention;
FIG. 4d is a graph showing the modified 5 month 2 day high spatial resolution MPDI data according to example 1 of the present invention;
FIG. 4e is a graph showing the modified 5 month 23 day high spatial resolution MPDI data according to example 1 of the present invention;
FIG. 4f is a graph showing the modified 6-month 3-day high spatial resolution MPDI data in example 1 of the present invention;
fig. 5 is a schematic structural diagram of an MPDI data set construction system with high spatial-temporal resolution according to embodiment 2 of the invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Example 1:
as shown in fig. 1, an embodiment of the present invention provides a method for constructing an MPDI data set with high spatial-temporal resolution, which includes steps S1 to S6 as follows:
s1, carrying out space-time data fusion on high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high-space-time resolution remote sensing data set;
in an optional embodiment of the present invention, the present embodiment performs space-time data fusion of high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high-space-time resolution remote sensing data set, and specifically includes:
and carrying out space-time data fusion on the first time sequence low-spatial resolution remote sensing data, the first time high-spatial resolution remote sensing data and the second time high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set.
Specifically, in this embodiment, the remote sensing data with different temporal and spatial characteristics are fused to obtain the characteristic data with two advantages of the two data, that is, the remote sensing data with high spatial resolution but low temporal resolution, such as GF6 WFV, and the remote sensing data with low spatial resolution but high temporal resolution, such as MOD09GA, are fused to obtain the remote sensing data with high temporal and spatial resolution.
As shown in fig. 2a to 2e, wherein fig. 2a and 2b are two-phase high spatial resolution images (GF 6 WFV 16m images) with a time of 5 months and 2 days and 6 months and 3 days, respectively; fig. 2c, 2d and 2e show three-phase corresponding low spatial resolution images (MOD 09GA 500m images) for 5 months and 2 days, 5 months and 23 days, and 6 months and 3 days, respectively. In this embodiment, for two-stage high spatial resolution images (GF 6 WFV 16m images), the time is 5 months 2 days, 6 months 3 days, respectively; corresponding to the three-period low spatial resolution image (MOD 09GA 500m image), the time is 5 months 2 days, 5 months 23 days and 6 months 3 days respectively, and a three-period high spatial-temporal resolution surface reflectivity dataset is obtained based on a spatial-temporal data fusion algorithm (such as an ESTARFM algorithm), as shown in fig. 3a to 3c, wherein fig. 3a, 3b and 3c are three-period high spatial resolution images with the time of 5 months 2 days, 5 months 23 days and 6 months 3 days respectively; fig. 3b is a high spatial-temporal resolution image constructed based on a spatial-temporal data fusion algorithm.
S2, calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set;
in an optional embodiment of the present invention, for improving a vertical drought index, i.e. an MPDI value, the present embodiment calculates, according to high-time-resolution low-spatial-resolution remote sensing data, the MPDI value corresponding to each pixel, where the calculating method includes:
，
wherein ,FVCfor the coverage of vegetation,Mis the slope of the soil line and is defined as,to go through the atmosphere to correctReflectance of positive red band, +.>For the reflectance of the near-red band corrected by the atmosphere,>is a constant of red wave band>Is a near red band constant. Wherein->，/>Can be calculated from known remote sensing data of vegetation areas. Vegetation coverageFVCThe pixel binary model based on the normalized vegetation index NDVI is adopted for estimation, and the calculation method comprises the following steps:
，
wherein ,NDVIin order to normalize the vegetation index,is a pixel composed of bare soilNDVIThe value of the sum of the values,for picture elements covered entirely by vegetationNDVIValues.
The method for calculating the vertical drought index comprises the following steps:
，
wherein ,PDIand (5) calculating a result value for the vertical drought index remote sensing.
Specifically, the present embodiment calculates MPDI data of three-phase low spatial resolution images (MOD 09GA 500m images) by the above method, respectivelyThereby obtaining the MPDI data set with high time resolution and low space resolution.
S3, calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain the high space-time resolution MPDI data set;
in an alternative embodiment of the present invention, the present embodiment calculates MPDI data of the three-phase high spatial-temporal resolution surface reflectivity dataset obtained in step S1, respectively, using a method similar to that of step S2, that isThereby obtaining the MPDI data set with high space-time resolution.
S4, aggregating the MPDI data set with high space-time resolution to the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
in an optional embodiment of the present invention, the spatial aggregation is performed on the high spatial-temporal resolution MPDI data set obtained in step S3, and the high spatial-temporal resolution MPDI data set is aggregated to a scale of high temporal resolution low spatial resolution remote sensing data, which specifically includes:
，
wherein ,for the MPDI value of the polymerized picture element, +.>And n is the number of pixels containing the MPDI value with high spatial-temporal resolution in the pixels in the remote sensing data with high temporal resolution and low spatial resolution.
Specifically, in this embodiment, the three-phase high spatial-temporal resolution MPDI data set obtained in step S3 is spatially aggregated to a resolution of 500m to obtain aggregated MPDI data, i.e。
S5, calculating a correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
in an alternative embodiment of the present invention, the method for calculating the correction coefficient of the MPDI data set with high spatial-temporal resolution according to the MPDI data set with high temporal resolution and low spatial resolution and the aggregated MPDI data set according to the present embodiment is as follows:
，
wherein ,Vfor the correction coefficients of the high spatio-temporal resolution MPDI data set,for MPDI values in the high temporal resolution low spatial resolution MPDI dataset, +.>Is the MPDI value of the polymerized pixel.
Specifically, in this embodiment, high-temporal-resolution low-spatial-resolution MPDI data is obtained according to step S2And the polymerized MPDI data obtained in step S4 +.>Respectively calculating correction coefficients of three-period high-space-time resolution MPDI dataV。
And S6, performing MPDI correction on the MPDI data set with the high spatial-temporal resolution by using the correction coefficient to obtain the MPDI data set with the corrected high spatial-temporal resolution.
In an alternative embodiment of the present invention, the present embodiment uses a correction coefficient to perform MPDI correction on a MPDI data set with high spatial-temporal resolution, which specifically includes:
，
wherein ,for the modified high spatial-temporal resolution MPDI values,Vcorrection factors for high spatial-temporal resolution MPDI data set, +.>MPDI values in the MPDI dataset are high spatial-temporal resolution.
Specifically, the present embodiment uses the correction coefficients of the three-phase high spatial-temporal resolution MPDI data obtained in step S5VMPDI data of the three-phase high-space-time resolution surface reflectivity data set obtained in the step S3 are respectively processedCorrecting to obtain corrected high space-time resolution MPDI data>. As shown in fig. 4a to 4f, wherein fig. 4a, 4b and 4c are three-period high spatial-temporal resolution MPDI data, respectively, with the time being 5 months 2 days, 5 months 23 days and 6 months 3 days, respectively; fig. 4d, 4e and 4f are respectively three periods of modified high spatial-temporal resolution MPDI data.
Example 2:
the embodiment provides a high spatial-temporal resolution MPDI data set construction system based on the high spatial-temporal resolution MPDI data set construction method described in embodiment 1, as shown in fig. 5, including:
the space-time data fusion module is used for carrying out space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set;
the MPDI calculation module is used for calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set; and calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain a high space-time resolution MPDI data set;
the space aggregation module is used for aggregating the MPDI data set with high space-time resolution into the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
the correction coefficient calculation module is used for calculating the correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
and the MPDI correction module is used for carrying out MPDI correction on the MPDI data set with high space-time resolution by utilizing the correction coefficient to obtain the MPDI data set with high space-time resolution after correction.
The high-space-time resolution MPDI data set construction system provided by the embodiment of the invention has the beneficial effects of the high-space-time resolution MPDI data set construction method.
Example 3:
the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of a high spatio-temporal resolution MPDI data set construction method as described in embodiment 1, based on the high spatio-temporal resolution MPDI data set construction method described in embodiment 1.
The computer readable storage medium provided by the embodiment of the invention has the beneficial effects of the high space-time resolution MPDI data set construction method.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (9)
1. The method for constructing the MPDI data set with high space-time resolution is characterized by comprising the following steps of:
carrying out space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high-space-time resolution remote sensing data set;
calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set;
calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain a high space-time resolution MPDI data set;
aggregating the MPDI data set with high space-time resolution to the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
calculating a correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
and carrying out MPDI correction on the MPDI data set with high space-time resolution by using the correction coefficient to obtain the MPDI data set with high space-time resolution after correction.
2. The method for constructing the MPDI data set with high spatial-temporal resolution according to claim 1, wherein the performing the spatial-temporal data fusion on the high-temporal resolution low-spatial resolution remote sensing data and the plurality of low-temporal resolution high-spatial resolution remote sensing data to obtain the high-spatial-temporal resolution remote sensing data set specifically comprises:
and carrying out space-time data fusion on the first time sequence low-spatial resolution remote sensing data, the first time high-spatial resolution remote sensing data and the second time high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set.
3. The method for constructing the MPDI data set with high spatial-temporal resolution of claim 1, wherein the method for calculating the MPDI value comprises the following steps:
，
wherein ,FVCfor the coverage of vegetation,Mis the slope of the soil line and is defined as,to pass through the atmosphereThe reflectance of the corrected red band of wavelengths,for the reflectance of the near-red band corrected by the atmosphere,>is a constant of red wave band>Is a near red band constant.
4. The method for constructing a high spatial-temporal resolution MPDI data set according to claim 3, wherein the vegetation coverage calculating method comprises:
，
wherein ,NDVIin order to normalize the vegetation index,is a pixel composed of bare soilNDVIThe value of the sum of the values,for picture elements covered entirely by vegetationNDVIValues.
5. The method for constructing the MPDI data set with high spatial-temporal resolution according to claim 1, wherein the method for aggregating the MPDI data set with high spatial-temporal resolution to the scale of the remote sensing data with high temporal resolution and low spatial resolution is as follows:
，
wherein ,is polymerized pixelMPDI value of->And n is the number of pixels containing the MPDI value with high spatial-temporal resolution in the pixels in the remote sensing data with high temporal resolution and low spatial resolution.
6. The method for constructing the MPDI data set with high spatial and temporal resolution according to claim 1, wherein the method for calculating the correction coefficient of the MPDI data set with high spatial and temporal resolution according to the MPDI data set with high temporal resolution and low spatial resolution and the aggregated MPDI data set comprises the following steps:
，
wherein ,Vfor the correction coefficients of the high spatio-temporal resolution MPDI data set,for MPDI values in the high temporal resolution low spatial resolution MPDI dataset, +.>Is the MPDI value of the polymerized pixel.
7. The method for constructing the MPDI data set with high spatial-temporal resolution according to claim 1, wherein the method for performing MPDI correction on the MPDI data set with high spatial-temporal resolution by using correction coefficients to obtain the MPDI data set with high spatial-temporal resolution after correction is as follows:
，
wherein ,for the modified high spatial-temporal resolution MPDI values,Vfor high spatial-temporal resolution MPDCorrection factors for the I dataset,>MPDI values in the MPDI dataset are high spatial-temporal resolution.
8. A high spatial-temporal resolution MPDI dataset construction system, comprising:
the space-time data fusion module is used for carrying out space-time data fusion on the high-time resolution low-spatial resolution remote sensing data and a plurality of low-time resolution high-spatial resolution remote sensing data to obtain a high space-time resolution remote sensing data set;
the MPDI calculation module is used for calculating a corresponding MPDI value according to the high-time-resolution low-spatial-resolution remote sensing data to obtain a high-time-resolution low-spatial-resolution MPDI data set; and calculating a corresponding MPDI value according to the high space-time resolution remote sensing data set to obtain a high space-time resolution MPDI data set;
the space aggregation module is used for aggregating the MPDI data set with high space-time resolution into the scale of the remote sensing data with high time resolution and low space resolution to obtain an aggregated MPDI data set;
the correction coefficient calculation module is used for calculating the correction coefficient of the MPDI data set with high space-time resolution according to the MPDI data set with high time resolution and low spatial resolution and the aggregated MPDI data set;
and the MPDI correction module is used for carrying out MPDI correction on the MPDI data set with high space-time resolution by utilizing the correction coefficient to obtain the MPDI data set with high space-time resolution after correction.
9. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the steps of a high spatiotemporal resolution MPDI data set construction method according to any of claims 1 to 7 when executed by a processor.
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