CN111191673B - Ground surface temperature downscaling method and system - Google Patents

Ground surface temperature downscaling method and system Download PDF

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CN111191673B
CN111191673B CN201911198692.4A CN201911198692A CN111191673B CN 111191673 B CN111191673 B CN 111191673B CN 201911198692 A CN201911198692 A CN 201911198692A CN 111191673 B CN111191673 B CN 111191673B
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许剑辉
周成虎
邓应彬
杨骥
张菲菲
钟凯文
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Guangzhou Institute of Geography of GDAS
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou
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Abstract

The invention relates to a land surface temperature downscaling method and a land surface temperature downscaling system, wherein land utilization classification data is used for classifying MODIS time sequence data, thermal infrared land surface temperature data and passive microwave remote sensing land surface temperature with high spatial resolution, a triple combination method is used for carrying out error analysis on the MODIS time sequence data, the thermal infrared land surface temperature data and the passive microwave remote sensing land surface temperature with high spatial resolution to obtain space-time weights of the thermal infrared and passive microwave remote sensing land surface temperatures under different land covering types, and weighted calculation is carried out on the time and spatial sequence thermal infrared and downscaling passive microwave remote sensing land surface temperatures of different land covering types to obtain the high spatial resolution land surface temperature of each land covering type. Compared with the prior art, the method realizes effective fusion of thermal infrared and passive microwave remote sensing earth surface temperature products of different land covering types, and obtains the earth surface temperature with high precision and high resolution under the cloud-free condition.

Description

Ground surface temperature downscaling method and system
Technical Field
The invention relates to the technical field of geographic information, in particular to a method and a system for reducing the ground surface temperature.
Background
The earth surface temperature is used as an important state variable in satellite remote sensing monitoring earth surface moisture and energy circulation, provides space-time change information of earth surface energy balance state, and is widely applied to the research fields of weather, geology, hydrology, ecology, numerical prediction, regional climate modes and the like. The space-time continuous high-precision earth surface temperature data is not only beneficial to evaluating earth surface energy and hydrological balance, thermal inertia and soil humidity, but also beneficial to obtaining global surface temperature and mastering long-term change of the global surface temperature, and has important significance for the research of regional and global earth-gas system energy balance and ecological systems.
Space-time continuous high-resolution passive microwave remote sensing earth surface temperature data are obtained, all-weather remote sensing monitoring of earth surface temperature is achieved, and multi-source remote sensing data fusion is a feasible method. The existing remote sensing data fusion method has the defects in the fusion of thermal infrared and passive microwave remote sensing surface data, and the passive microwave remote sensing surface temperature with high resolution and high precision cannot be obtained.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a high-resolution and high-precision ground surface temperature downscaling method and system based on thermal infrared ground surface temperature data and passive microwave remote sensing ground surface temperature.
A method for reducing the scale of the surface temperature comprises the following steps:
obtaining MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
classifying the MODIS time series data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data to obtain earth surface temperature data sets of the MODIS time series data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types;
aiming at pixels without cloud cover and snow cover in a research area, calculating error variances and corresponding truth value variances of thermal infrared earth surface temperatures and passive microwave remote sensing earth surface temperatures under different land cover types by using a triple combination method based on the earth surface temperature data set, and calculating signal-to-noise ratios of the thermal infrared earth surface temperatures and the passive microwave remote sensing earth surface temperatures under different land cover types according to the error variances and the corresponding truth value variances;
acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
performing product operation on the time weight and the space weight to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type;
the step of obtaining the time and space weight of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land covering types specifically comprises the following steps:
acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure GDA0002737714890000021
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
Compared with the prior art, the method has the advantages that the land utilization classification data is used for classifying the MODIS time sequence data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature with high spatial resolution, the triple combination method is used for carrying out error analysis on the MODIS time sequence data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature with high spatial resolution, the space-time weight of the thermal infrared remote sensing earth surface temperature and the passive microwave remote sensing earth surface temperature under different land covering types is obtained, the thermal infrared remote sensing earth surface temperature products and the passive microwave remote sensing earth surface temperature products of different land covering types are effectively fused, and the earth surface temperature with high precision and high resolution under the non-cloud condition is.
In one embodiment, the step of obtaining the passive microwave remote sensing surface temperature with high spatial resolution comprises the following steps:
acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
and carrying out downscaling on the passive microwave remote sensing earth surface temperature with low spatial resolution by using a surface-to-point Krigin interpolation method to obtain the passive microwave remote sensing earth surface temperature with high spatial resolution.
In one embodiment, the passive microwave remote sensing surface temperature data is FY-3C passive microwave remote sensing surface temperature data, the low spatial resolution is 25 kilometers, the high spatial resolution is 1 kilometer, and the down-scaling of the passive microwave remote sensing surface temperature with the low spatial resolution by using a surface-to-point kriging interpolation method includes:
and reducing the scale of the passive microwave remote sensing earth surface temperature to obtain the passive microwave remote sensing earth surface temperature with the resolution of 1 kilometer:
Figure GDA0002737714890000022
wherein x is a grid point to be interpolated with a resolution of 1 km, viGrid at 25 km resolution, z (v)i) Passive microwave remote sensing earth surface temperature lambda of 25 km resolution gridxIn order to be the weight, the weight is,
Figure GDA0002737714890000031
passive microwave remote sensing earth surface temperature with 1 kilometer resolution; the weight λxCalculated according to the following way:
Figure GDA0002737714890000032
where K is the number of grids at 25 km resolution, μxIn order to be a lagrange multiplier,
Figure GDA0002737714890000033
representing a 25 km resolution grid viAnd a 25 km resolution grid vjThe covariance function of (a) of (b),
Figure GDA0002737714890000034
representing a 25 km resolution grid viCovariance function with 1 km resolution grid x.
In one embodiment, the surface temperature downscaling method further comprises the steps of:
aiming at cloud coverage pixels in a research area, passive microwave remote sensing earth surface temperature with high spatial resolution is adopted as earth surface temperature;
and deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels aiming at the snow cover pixels in the research area.
The invention also provides a ground surface temperature downscaling system, which comprises:
the data acquisition module is used for acquiring MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
the classification module is used for classifying the MODIS time series data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data to obtain earth surface temperature data sets of the MODIS time series data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types;
the signal-to-noise ratio calculation module is used for calculating error variances and corresponding truth variances of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types by utilizing a triple combination method aiming at the pixels without cloud cover and accumulated snow cover in a research area based on the earth surface temperature data set, and calculating the signal-to-noise ratio of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types according to the error variances and the corresponding truth variances;
the weight calculation module is used for acquiring time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
the space-time weight calculation module is used for performing product operation on the time and space weights to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
the earth surface temperature acquisition module is used for taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type;
the weight calculation module includes:
the weight calculation unit is used for acquiring the time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure GDA0002737714890000041
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
In one embodiment, the data acquisition module comprises:
the data acquisition unit is used for acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
and the downscaling unit is used for downscaling the passive microwave remote sensing earth surface temperature with the low spatial resolution by using a surface-to-point Krigin interpolation method to obtain the passive microwave remote sensing earth surface temperature with the high spatial resolution.
In one embodiment, the passive microwave remote sensing surface temperature data is FY-3C passive microwave remote sensing surface temperature data, the low spatial resolution is 25 kilometers, the high spatial resolution is 1 kilometer, and the downscaling unit downscales the passive microwave remote sensing surface temperature according to the following method to obtain the passive microwave remote sensing surface temperature with high spatial resolution:
Figure GDA0002737714890000042
wherein x is a grid point to be interpolated with a resolution of 1 km, viGrid at 25 km resolution, z (v)i) Passive microwave remote sensing earth surface temperature lambda of 25 km resolution gridxIn order to be the weight, the weight is,
Figure GDA0002737714890000043
remote sensing surface temperature with 1 km resolution; the weight λxCalculated according to the following way:
Figure GDA0002737714890000044
where K is the number of grids at 25 km resolution, μxIn order to be a lagrange multiplier,
Figure GDA0002737714890000045
representing a 25 km resolution grid viAnd a 25 km resolution grid vjThe covariance function of (a) of (b),
Figure GDA0002737714890000046
representing a 25 km resolution grid viCovariance function with 1 km resolution grid x.
In one embodiment, the surface temperature downscaling system further comprises:
the cloud pixel processing module is used for adopting the passive microwave remote sensing earth surface temperature with high spatial resolution as the earth surface temperature aiming at the cloud coverage pixel in the research area;
and the snow cover pixel processing module is used for deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels aiming at the snow cover pixels in the research area.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a method for downscaling a surface temperature in an exemplary embodiment of the invention;
FIG. 2 is a flowchart of step S1 in an exemplary embodiment of the invention;
FIG. 3 is a schematic diagram of a city expansion prediction system according to an exemplary embodiment of the present invention;
fig. 4 is a schematic structural diagram of the data acquisition module 1 in an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
It should be understood that the embodiments described are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the embodiments in the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or", the association relation describing the associated object means that there may be three relations, for example, a and/or B may mean that a exists alone, a and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Referring to fig. 1, the present invention provides a method for scaling a surface temperature, comprising the following steps:
step S1: obtaining MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
the MODIS time sequence data refer to a series of images of land, sea and atmosphere of the earth observed by a medium-resolution imaging spectrometer. The thermal infrared earth surface temperature data is thermal infrared radiation information which is collected and recorded by a thermal infrared detector and cannot be seen by human eyes and is radiated by an earth object, and the thermal infrared information can be utilized to obtain the earth surface temperature parameter. The land utilization classification data is a land resource data product which is based on remote sensing images, adopts a full digital man-machine interaction rapid extraction method, and divides land types according to a land coverage classification system at home and abroad, a remote sensing information source and the actual situation of land surface coverage. The passive microwave remote sensing is to receive microwaves reflected and emitted by the ground in a natural state by utilizing a microwave radiometer or a microwave scatterometer and other sensors, and the passive microwave remote sensing ground surface temperature data is obtained from the passive microwave remote sensing data.
Step S2: and classifying the MODIS time sequence data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data, and acquiring earth surface temperature data sets of the MODIS time sequence data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types.
The land utilization classification data starts from the current land utilization situation, and divides the land utilization situation of one country or region into a plurality of different land utilization categories according to a certain hierarchical level system according to the region differentiation rule, the land use, the land utilization mode and the like of land utilization. For example, the MODIS time series data, the thermal infrared surface temperature data and the passive microwave remote sensing surface temperature may be divided into surface temperature data sets of several different land cover types according to the spectral information of the land use classification data.
Step S3: and calculating error variances and corresponding truth variances of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land cover types by using a triple combination method based on the earth surface temperature data set aiming at the pixels without cloud cover and snow cover in the research area, and calculating the signal-to-noise ratios of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land cover types according to the error variances and the corresponding truth variances.
The Triple combination method (Triple Collocation) can be used for cross evaluation of three groups of mutually independent products under the condition of unknown true values, and comprehensively evaluating the time-space distribution characteristics of the thermal infrared and reduced scale passive microwave remote sensing surface temperature product errors of different land cover types based on the Triple combination method.
Step S4: acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
step S5: performing product operation on the time weight and the space weight to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
and S6, taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type.
The method classifies the MODIS time sequence data, the thermal infrared surface temperature data and the passive microwave remote sensing surface temperature with high spatial resolution by utilizing land utilization classification data, performs error analysis on the MODIS time sequence data, the thermal infrared surface temperature data and the passive microwave remote sensing surface temperature with high spatial resolution by utilizing a triple combination method, obtains the space-time weight of the thermal infrared and passive microwave remote sensing surface temperature under different land covering types, realizes effective fusion of thermal infrared and passive microwave remote sensing surface temperature products of different land covering types, and accordingly obtains the surface temperature with high precision and high resolution under the cloud-free condition.
Referring to fig. 2, in an exemplary embodiment, the step of obtaining the high-spatial-resolution passive microwave remote sensing surface temperature includes:
step S101: acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
step S102: and carrying out downscaling on the passive microwave remote sensing earth surface temperature with low spatial resolution by using a surface-to-point Krigin interpolation method to obtain the passive microwave remote sensing earth surface temperature with high spatial resolution.
The surface-to-point Kriging downscaling method considers the scale difference and spatial correlation of surface and point data, and ensures that the 1-kilometer microwave remote sensing earth surface temperature can keep the original spatial distribution pattern of the 25-kilometer microwave remote sensing earth surface temperature.
In an exemplary embodiment, the passive microwave remote sensing earth surface temperature data is FY-3C passive microwave remote sensing earth surface temperature data, the FY-3C passive microwave remote sensing earth surface temperature data refers to passive microwave remote sensing earth surface temperature data acquired from an FY-3C satellite, and the spatial resolution of the data is 25 kilometers; the method comprises the following steps of obtaining a passive microwave remote sensing earth surface temperature with high spatial resolution by carrying out downscaling on the passive microwave remote sensing earth surface temperature with low spatial resolution by using a surface-to-point kriging interpolation method, wherein the low spatial resolution is 25 kilometers, the high spatial resolution is 1 kilometer, and the step of obtaining the passive microwave remote sensing earth surface temperature with high spatial resolution comprises the following steps:
and reducing the scale of the passive microwave remote sensing earth surface temperature to obtain the passive microwave remote sensing earth surface temperature with the resolution of 1 kilometer:
Figure GDA0002737714890000071
wherein x is a grid point to be interpolated with a resolution of 1 km, viGrid at 25 km resolution, z (v)i) Passive microwave remote sensing earth surface temperature lambda of 25 km resolution gridxIn order to be the weight, the weight is,
Figure GDA0002737714890000072
passive microwave earth surface remote sensing temperature of 1 kilometer; the weight λxCalculated according to the following way:
Figure GDA0002737714890000081
where K is the number of grids at 25 km resolution, μxIs a lagrange multiplier.
In an exemplary embodiment, the step of calculating, according to the error variance and the corresponding true variance, signal-to-noise ratios of the thermal infrared surface temperatures of different land cover types and the passive microwave remote sensing surface temperature with high spatial resolution specifically includes:
by using threeThe recombination method calculates the error variance of the thermal infrared earth surface temperature of different land cover types
Figure GDA0002737714890000082
And corresponding true variance
Figure GDA0002737714890000083
The error variance of the passive microwave remote sensing earth surface temperature with high spatial resolution
Figure GDA0002737714890000084
And corresponding true variance
Figure GDA0002737714890000085
The truth value variance refers to the variance between the thermal infrared surface temperature and the passive microwave remote sensing surface temperature with high spatial resolution of different land cover types in time or space sequence and the corresponding surface temperature truth value, and reflects the sensitivity of the thermal infrared surface temperature and the passive microwave remote sensing surface temperature with high spatial resolution to the surface temperature truth value change.
The error variance of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature with high spatial resolution is calculated according to the following mode:
Figure GDA0002737714890000086
wherein the content of the first and second substances,
Figure GDA0002737714890000087
respectively representing the variance of the thermal infrared of a temporal or spatial sequence,
Figure GDA0002737714890000088
representing the variance of the high-spatial-resolution passive microwave remote sensing earth surface temperature; sigmaVIRR,MODISRepresenting the covariance, σ, between thermal infrared and remote sensing dataVIRR,MWRICo-equation for representing temperature between thermal infrared and high spatial resolution passive microwave remote sensing earth surfaceDifference, σMODIS,MWRIRepresenting the covariance, σ, between the remote sensing data and the high spatial resolution passive microwave remote sensing surface temperatureMWRI,VIRRRepresenting the covariance, σ, between the high spatial resolution passive microwave remote sensing surface temperature and the thermal infraredMWRI,MODISThe covariance between the high spatial resolution passive microwave remote sensing earth surface temperature and the remote sensing data;
according to the error variance and the corresponding true value variance, calculating to obtain the signal-to-noise ratios of the thermal infrared earth surface temperatures of different land cover types and the passive microwave remote sensing earth surface temperature with high spatial resolution according to the following modes:
Figure GDA0002737714890000091
Figure GDA0002737714890000092
wherein the SNRVIRR,TSSignal-to-noise ratio, SNR, for thermal infrared surface temperatureMWRI,TSThe signal-to-noise ratio of the surface temperature is remotely sensed by passive microwaves with high spatial resolution.
In an exemplary embodiment, the step of obtaining the time and space weights of the time and space series thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types specifically includes:
acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure GDA0002737714890000093
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
In an exemplary embodiment, the step of calculating, according to the error variance and the corresponding true variance, signal-to-noise ratios of the thermal infrared surface temperatures of different land cover types and the passive microwave remote sensing surface temperature with high spatial resolution specifically includes:
according to the error variance and the corresponding true value variance, calculating to obtain the signal-to-noise ratios of the thermal infrared earth surface temperatures of different land cover types and the passive microwave remote sensing earth surface temperature with high spatial resolution according to the following modes:
Figure GDA0002737714890000094
Figure GDA0002737714890000095
wherein the SNRVIRR,TSSignal-to-noise ratio, SNR, for thermal infrared surface temperatureMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature with high spatial resolution is obtained; the variance of the thermal infrared earth surface temperature errors of different land cover types
Figure GDA0002737714890000096
And corresponding true variance
Figure GDA0002737714890000097
The error variance of the passive microwave remote sensing earth surface temperature with high spatial resolution
Figure GDA0002737714890000098
And corresponding true variance
Figure GDA0002737714890000099
In an exemplary embodiment, the performing a weighted calculation on the time and space series thermal infrared and downscale passive microwave remote sensing surface temperatures of different land cover types to obtain the high spatial resolution surface temperature of the land cover type specifically includes:
according to the following mode, the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types is weighted and calculated to obtain the high spatial resolution earth surface temperature of the land cover type:
LSTfusion＝ωVIRRLSTVIRR+ωMWRILSTMWRI。
wherein, LSTfusionThe fusion result is the surface temperature of the passive microwave remote sensing surface temperature with high spatial resolution and the thermal infrared surface temperature; LSTVIRRAnd LSTMWRIThe thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature with high spatial resolution are respectively; omegaVIRRAnd ωMWRIThe space-time weight of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature with high spatial resolution are respectively. The space-time weight is obtained by performing product operation on time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land covering types.
In one exemplary embodiment, the surface temperature downscaling method further comprises the steps of:
for the cloud coverage pixels in the research area, passive microwave remote sensing earth surface temperature with high spatial resolution after size reduction is adopted as earth surface temperature;
and deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels in the research area.
Because the MODIS and FY-3C thermal infrared remote sensing earth surface temperature data are lost due to cloud coverage, a downscale passive microwave remote sensing earth surface temperature product is adopted for the earth surface temperature of the cloud coverage pixel, the uncertainty of the earth surface temperature product covered by the accumulated snow is large, and the MODIS time sequence data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature data of the accumulated snow coverage pixel are deleted;
referring to fig. 3, the present invention further provides a ground temperature downscaling system, including:
the data acquisition module 1 is used for acquiring MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
the classification module 2 is used for classifying the MODIS time series data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data to obtain earth surface temperature data sets of the MODIS time series data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types;
the signal-to-noise ratio calculation module 3 is used for calculating error variances and corresponding truth variances of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types by utilizing a triple combination method aiming at the pixels without cloud cover and accumulated snow cover in the research area based on the earth surface temperature data set, and calculating the signal-to-noise ratio of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types according to the error variances and the corresponding truth variances;
the weight calculation module 4 is used for acquiring time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
the space-time weight calculation module 5 is used for performing product operation on the time and space weights to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
and the earth surface temperature acquisition module 6 is used for taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type.
Referring to fig. 4, in an exemplary embodiment, the data obtaining module 1 includes:
the data acquisition unit 101 is used for acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
and the downscaling unit 102 is configured to downscale the passive microwave remote sensing earth surface temperature with the low spatial resolution by using a surface-to-point kriging interpolation method to obtain a passive microwave remote sensing earth surface temperature with a high spatial resolution.
In an exemplary embodiment, the passive microwave remote sensing earth surface temperature data is FY-3C passive microwave remote sensing earth surface temperature data, the low spatial resolution is 25 kilometers, the high spatial resolution is 1 kilometer, and the downscaling unit downscales the passive microwave remote sensing earth surface temperature to obtain the passive microwave remote sensing earth surface temperature with the high spatial resolution according to the following manner:
Figure GDA0002737714890000111
wherein x is a grid point to be interpolated with high spatial resolution, viGrid at the original resolution, z (v)i) Passive microwave remote sensing of surface temperature, lambda, for an original resolution gridxIn order to be the weight, the weight is,
Figure GDA0002737714890000112
remote sensing the temperature for the earth surface with high spatial resolution; the weight λxCalculated according to the following way:
Figure GDA0002737714890000113
wherein K is the number of grids of the original resolution, muxIs a lagrange multiplier.
In an exemplary embodiment, the surface temperature downscaling system further comprises:
the cloud pixel processing module is used for adopting the passive microwave remote sensing earth surface temperature with high spatial resolution as the earth surface temperature aiming at the cloud coverage pixel in the research area;
and the snow cover pixel processing module is used for deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels aiming at the snow cover pixels in the research area.
In an exemplary embodiment, the weight calculation module includes:
the weight calculation unit is used for acquiring the time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure GDA0002737714890000121
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
In an exemplary embodiment, the signal-to-noise ratio calculation module 3 calculates the signal-to-noise ratios of the thermal infrared surface temperatures of different land cover types and the passive microwave remote sensing surface temperature with high spatial resolution according to the error variance and the corresponding true variance in the following manner:
Figure GDA0002737714890000122
Figure GDA0002737714890000123
wherein the SNRVIRR,TSSignal-to-noise ratio, SNR, for thermal infrared surface temperatureMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature with high spatial resolution is obtained; the variance of the thermal infrared earth surface temperature errors of different land cover types
Figure GDA0002737714890000124
And corresponding true variance
Figure GDA0002737714890000125
The high spatial resolution passive micromicroError variance of wave remote sensing earth surface temperature
Figure GDA0002737714890000126
And corresponding true variance
Figure GDA0002737714890000127
In an exemplary embodiment, the surface temperature obtaining module 6 performs weighted calculation on the time and space series thermal infrared and downscale passive microwave remote sensing surface temperatures of different land cover types to obtain the high spatial resolution surface temperature of the land cover type according to the following manner:
LSTfusion＝ωVIRRLSTVIRR+ωMWRILSTMWRI。
wherein, LSTfusionThe fusion result is the surface temperature of the passive microwave remote sensing surface temperature with high spatial resolution and the thermal infrared surface temperature; LSTVIRRAnd LSTMWRIThe thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature with high spatial resolution are respectively; omegaVIRRAnd ωMWRIThe space-time weight of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature with high spatial resolution are respectively. The space-time weight is obtained by performing product operation on time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land covering types.
Compared with the prior art, the method has the advantages that the multi-source remote sensing data are fused, a spatial downscaling method and the multi-source remote sensing data fusion method are combined by means of a geostatistics theory and a Triple Collocation method, the spatial autocorrelation and the quality guarantee of the surface temperature are considered in a passive microwave remote sensing surface temperature downscaling model, a fusion model of a thermal infrared and downscaling passive microwave remote sensing surface temperature product is constructed, the error distribution characteristics of the thermal infrared and downscaling passive microwave remote sensing surface temperature products of different land coverage types are fully considered, and the time-series high-resolution FY-3C remote sensing surface temperature data set under the non-cloud and cloud conditions is generated.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included within the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.

Claims (8)

1. A method for reducing the scale of the surface temperature is characterized by comprising the following steps:
obtaining MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
classifying the MODIS time series data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data to obtain earth surface temperature data sets of the MODIS time series data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types;
aiming at pixels without cloud cover and snow cover in a research area, calculating error variances and corresponding truth value variances of thermal infrared earth surface temperatures and passive microwave remote sensing earth surface temperatures under different land cover types by using a triple combination method based on the earth surface temperature data set, and calculating signal-to-noise ratios of the thermal infrared earth surface temperatures and the passive microwave remote sensing earth surface temperatures under different land cover types according to the error variances and the corresponding truth value variances;
acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
performing product operation on the time weight and the space weight to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type;
the step of obtaining the time and space weight of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land covering types specifically comprises the following steps:
acquiring time and space weights of time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure FDA0002737714880000011
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
2. The method of claim 1, wherein the step of obtaining the high spatial resolution passive microwave remote sensing earth surface temperature comprises:
acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
and carrying out downscaling on the passive microwave remote sensing earth surface temperature with low spatial resolution by using a surface-to-point Krigin interpolation method to obtain the passive microwave remote sensing earth surface temperature with high spatial resolution.
3. The method according to claim 2, wherein the passive microwave remote sensing earth surface temperature data is FY-3C passive microwave remote sensing earth surface temperature data, the low spatial resolution is 25 kilometers, the high spatial resolution is 1 kilometer, and the downscaling of the passive microwave remote sensing earth surface temperature with the low spatial resolution by using a surface-to-point Krigin interpolation method comprises the following steps:
and reducing the scale of the passive microwave remote sensing earth surface temperature to obtain the passive microwave remote sensing earth surface temperature with the resolution of 1 kilometer:
Figure FDA0002737714880000021
wherein x is a grid point to be interpolated with a resolution of 1 km, viGrid at 25 km resolution, z (v)i) Passive microwave remote sensing earth surface temperature lambda of 25 km resolution gridxIn order to be the weight, the weight is,
Figure FDA0002737714880000022
passive microwave remote sensing earth surface temperature with 1 kilometer resolution; the weight λxCalculated according to the following way:
Figure FDA0002737714880000023
where K is the number of grids at 25 km resolution, μxIn order to be a lagrange multiplier,
Figure FDA0002737714880000024
representing a 25 km resolution grid viAnd a 25 km resolution grid vjThe covariance function of (a) of (b),
Figure FDA0002737714880000025
representing a 25 km resolution grid viCovariance function with 1 km resolution grid x.
4. The surface temperature downscaling method of claim 1, further comprising the steps of:
aiming at cloud coverage pixels in a research area, passive microwave remote sensing earth surface temperature with high spatial resolution is adopted as earth surface temperature;
and deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels aiming at the snow cover pixels in the research area.
5. A surface temperature downscaling system, comprising:
the data acquisition module is used for acquiring MODIS time series data, thermal infrared earth surface temperature data, passive microwave remote sensing earth surface temperature with high spatial resolution and land utilization classification data in a research area;
the classification module is used for classifying the MODIS time series data, the thermal infrared earth surface temperature data and the passive microwave remote sensing earth surface temperature according to the land utilization classification data to obtain earth surface temperature data sets of the MODIS time series data, the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different land coverage types;
the signal-to-noise ratio calculation module is used for calculating error variances and corresponding truth variances of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types by utilizing a triple combination method aiming at the pixels without cloud cover and accumulated snow cover in a research area based on the earth surface temperature data set, and calculating the signal-to-noise ratio of the thermal infrared earth surface temperature and the passive microwave remote sensing earth surface temperature under different earth coverage types according to the error variances and the corresponding truth variances;
the weight calculation module is used for acquiring time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land coverage types according to the signal-to-noise ratio;
the space-time weight calculation module is used for performing product operation on the time and space weights to obtain space-time weights of the thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land coverage types;
the earth surface temperature acquisition module is used for taking the space-time weight as weight data, and carrying out weighted calculation on the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperatures of different land cover types to obtain the high spatial resolution earth surface temperature of each land cover type;
the weight calculation module includes:
the weight calculation unit is used for acquiring the time and space weights of the time and space sequence thermal infrared and downscale passive microwave remote sensing earth surface temperature of different land cover types according to the following modes:
Figure FDA0002737714880000031
wherein the SNRVIRR,TSAnd SNRMWRI,TSThe signal-to-noise ratio of the passive microwave remote sensing earth surface temperature data of the thermal infrared and high spatial resolution of the time or space sequence of different land covering types is respectively, and TS represents the earth surface temperature data of the time or space sequence.
6. The surface temperature downscaling system of claim 5, wherein the data acquisition module comprises:
the data acquisition unit is used for acquiring passive microwave remote sensing earth surface temperature data with low spatial resolution;
and the downscaling unit is used for downscaling the passive microwave remote sensing earth surface temperature with the low spatial resolution by using a surface-to-point Krigin interpolation method to obtain the passive microwave remote sensing earth surface temperature with the high spatial resolution.
7. The ground surface temperature downscaling system according to claim 6, wherein the passive microwave remote sensing ground surface temperature data is FY-3C passive microwave remote sensing ground surface temperature data, the low spatial resolution is 25 kilometers, and the high spatial resolution is 1 kilometer, and the downscaling unit downscales the passive microwave remote sensing ground surface temperature to obtain the passive microwave remote sensing ground surface temperature with the high spatial resolution according to the following manner:
Figure FDA0002737714880000032
wherein x is a grid point to be interpolated with a resolution of 1 km, viGrid at 25 km resolution, z (v)i) Passive microwave for 25 km resolution gridRemote sensing of surface temperature, λxIn order to be the weight, the weight is,
Figure FDA0002737714880000041
passive microwave remote sensing earth surface temperature with 1 kilometer resolution; the weight λxCalculated according to the following way:
Figure FDA0002737714880000042
where K is the number of grids at 25 km resolution, μxIn order to be a lagrange multiplier,
Figure FDA0002737714880000043
representing a 25 km resolution grid viAnd a 25 km resolution grid vjThe covariance function of (a) of (b),
Figure FDA0002737714880000044
representing a 25 km resolution grid viCovariance function with 1 km resolution grid x.
8. The surface temperature downscaling system of claim 5, wherein: the surface temperature downscaling system further comprises:
the cloud pixel processing module is used for adopting the passive microwave remote sensing earth surface temperature with high spatial resolution as the earth surface temperature aiming at the cloud coverage pixel in the research area;
and the snow cover pixel processing module is used for deleting MODIS time sequence data, thermal infrared surface temperature data and passive microwave remote sensing surface temperature data of the snow cover pixels aiming at the snow cover pixels in the research area.
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