CN110210004A - A kind of Remote sensing hair NO emissions reduction method and device - Google Patents
A kind of Remote sensing hair NO emissions reduction method and device Download PDFInfo
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Abstract
The application provides a kind of Remote sensing hair NO emissions reduction method and device, method includes: the meteorological data for obtaining target area and initial remote sensing image data, according to the remotely-sensed data of each pixel in the remotely-sensed data of each pixel in the low spatial resolution image of initial remote sensing image data acquisition target area and high spatial resolution image；The Different Soil Water Deficits degree characteristic index of each pixel in low spatial resolution image is calculated according to the remotely-sensed data of each pixel in meteorological data and low spatial resolution image, which has space scale invariance；Data are sent out using the Remote sensing that the remotely-sensed data and Different Soil Water Deficits degree characteristic index of each pixel in meteorological data, high spatial resolution image calculate each pixel in high spatial resolution image.The application converts high spatial resolution for the Remote sensing hair data of low spatial resolution using the space scale invariance of Different Soil Water Deficits degree characteristic index, realizes the NO emissions reduction of Remote sensing hair.
Description
Technical field
This application involves earth's surface remote sensing technology fields, send out NO emissions reduction method and dress in particular to a kind of Remote sensing
It sets.
Background technique
Evapotranspiration, including soil evaporation and transpiration are the main process and water circulation of epigeosphere energy exchange
In one of most important component, IRMSS thermal band combined mathematical module is the effective means for obtaining Remote sensing hair.It obtains
The sensor of Thermal Remote Sensing Image can be divided into two classes according to the difference of resolution ratio, and one kind is high spatial, low temporal resolution, separately
One kind is low spatial, high time resolution, and the existing mathematical model for obtaining Remote sensing hair needs to rely on surface temperature
Input, and it is limited to the restriction of thermal infrared sensor, the spatial resolution of the surface temperature of acquisition is often lower, therefore model exists
It is only capable of obtaining the Remote sensing hair data of low spatial resolution and can not obtaining under the input of the surface temperature of low spatial resolution
The Remote sensing of high spatial resolution is sent out.
Summary of the invention
The embodiment of the present application is designed to provide a kind of Remote sensing hair NO emissions reduction method and device, is based on soil moisture
The degree that the wanes characteristic index feature identical with high spatial resolution image in low spatial resolution image calculates and obtains high-altitude
Between resolution ratio Remote sensing hair.
In a first aspect, the embodiment of the present application provides a kind of Remote sensing hair NO emissions reduction method, which comprises obtain
The meteorological data of target area and initial remote sensing image data obtain the target area according to the initial remote sensing image data
Low spatial resolution image in the remotely-sensed data of each pixel and the remotely-sensed data of each pixel in high spatial resolution image；Root
The low spatial resolution shadow is calculated according to the remotely-sensed data of each pixel in the meteorological data and the low spatial resolution image
The Different Soil Water Deficits degree characteristic index of each pixel as in, wherein the soil of any pixel in the low spatial resolution image
The Different Soil Water Deficits degree characteristic index of pixel is corresponded in earth Water deficit levels characteristic index and high spatial resolution image
It is identical；It is lost using the remotely-sensed data and the soil moisture of each pixel in the meteorological data, the high spatial resolution image
Lack the Remote sensing hair data that degree characteristic index calculates each pixel in the high spatial resolution image.
In the above scheme, due to the Different Soil Water Deficits degree of each pixel in low spatial resolution image and high spatial point
Corresponded in resolution image pixel Different Soil Water Deficits degree can be considered as it is identical, then can will not based on this characteristic index
The remotely-sensed data of isospace resolution ratio connects, and is converted, so as to steam the earth's surface of large scale, low spatial resolution
Disseminating data is converted into the data of small scale, high spatial resolution, and then the Remote sensing for obtaining high spatial resolution sends out data.
In a kind of possible embodiment of first aspect, the meteorological data includes Air Close To The Earth Surface pressure and near-earth
Face atmospheric temperature, the remote sensing image data include the normalized differential vegetation index of each pixel, earth's surface in low spatial resolution image
The normalization vegetation of each pixel refers in net radiation, soil heat flux and Remote sensing hair data and high spatial resolution image
Number, surface net radiation and soil heat flux.
It is described to be differentiated according to the meteorological data and the low spatial in a kind of possible embodiment of first aspect
The remotely-sensed data of each pixel calculates the Different Soil Water Deficits degree table of each pixel in the low spatial resolution image in rate image
Levy index, comprising:
The evaporite ratio EF of each pixel in low spatial resolution image is calculated by following formulac:
Wherein, LEc、Rn,cAnd GcRemote sensing hair data, the earth's surface of each pixel are net respectively in low spatial resolution image
Radiation and soil heat flux；
According to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characteristic index of each pixel in low spatial resolution image
SMc:
Wherein, Δ is slope of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant, φmax,cAnd φmin,cIt is respectively low
The maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient, Δ in spatial resolution image
It is obtained with γ by the meteorological data.
It is described to calculate each pixel in the high spatial resolution image in a kind of possible embodiment of first aspect
Remote sensing send out data, comprising:
The evaporite ratio EF of each pixel in high spatial resolution image is calculated by following formulaf:
Wherein, SMfFor the Different Soil Water Deficits degree characteristic index of high spatial resolution image, φmax,fAnd φmin,fRespectively
For each pixel of high spatial resolution image maximum Priestley-Taylor coefficient and minimum Priestley-Taylor coefficient,
SMf=SMc；
According to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
Since the present embodiment is thought, the soil moisture that each corresponding pixel symbolizes in different spatial resolutions image is lost
Scarce degree can be considered as identical namely SMf=SMc, and since Different Soil Water Deficits degree is related to evaporation, dissemination process,
Its characteristic index can mutually be calculated between evaporite ratio, it is thus possible to which realization is dealt into from the Remote sensing of low spatial resolution
Evaporite ratio, then Different Soil Water Deficits degree characteristic index is arrived, finally predict the drop ruler of the Remote sensing hair of high spatial resolution
Journey is spent, meanwhile, as can be seen that NO emissions reduction method model provided by the present application letter from two above-mentioned possible embodiments
Single, required input data are also less, have certain practicability and operability.
In a kind of possible embodiment of first aspect, according to the meteorological data and the low spatial resolution
The remotely-sensed data of each pixel calculates the Different Soil Water Deficits degree characterization of each pixel in the low spatial resolution image in image
Before index, the method also includes:
Calculate separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminWith
NDVImaxThe respectively corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage；
The maximum Priestley-Taylor coefficient φ of each pixel is calculated according to the vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
Second aspect, the embodiment of the present application provide a kind of Remote sensing hair NO emissions reduction device, and described device includes: to obtain
Module, for obtain target area meteorological data and initial remote sensing image data, obtained according to the initial remote sensing image data
The target area low spatial resolution image in each pixel remotely-sensed data and high spatial resolution image in each picture
The remotely-sensed data of member；Index computing module, for according to each pixel in the meteorological data and the low spatial resolution image
Remotely-sensed data calculate the Different Soil Water Deficits degree characteristic index of each pixel in the low spatial resolution image, wherein institute
State the Different Soil Water Deficits degree characteristic index of any pixel in low spatial resolution image with it is right in high spatial resolution image
Answer the Different Soil Water Deficits degree characteristic index of pixel identical；NO emissions reduction module, for utilizing the meteorological data, the high-altitude
Between in resolution image the remotely-sensed data of each pixel and the Different Soil Water Deficits degree characteristic index calculate the high spatial point
The Remote sensing of each pixel sends out data in resolution image.
Above-mentioned apparatus can be realized Remote sensing hair from low spatial resolution using Different Soil Water Deficits degree characteristic index
To the conversion of high spatial resolution, data are sent out so as to obtain the Remote sensing of high spatial resolution, compensate for existing number
The blank of evapotranspiration data of high spatial resolution can not be obtained by learning model.
In a kind of possible embodiment of second aspect, the index computing module is specifically used for:
The evaporite ratio EF of each pixel in low spatial resolution image is calculated by following formulac:
Wherein, LEc、Rn,cAnd GcRemote sensing hair data, the earth's surface of each pixel are net respectively in low spatial resolution image
Radiation and soil heat flux；
According to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characteristic index of each pixel in low spatial resolution image
SMc:
Wherein, Δ is slope of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant, φmax,cAnd φmin,cIt is respectively low
The maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient, Δ in spatial resolution image
It is obtained with γ by the meteorological data.
In a kind of possible embodiment of second aspect, the NO emissions reduction module is specifically used for:
The evaporite ratio EF of each pixel in high spatial resolution image is calculated by following formulaf:
Wherein, SMfFor the Different Soil Water Deficits degree characteristic index of high spatial resolution image, φmax,fAnd φmin,fRespectively
For each pixel of high spatial resolution image maximum Priestley-Taylor coefficient and minimum Priestley-Taylor coefficient,
SMf=SMc；
According to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
In a kind of possible embodiment of second aspect, described device further includes coefficients calculation block, the coefficient
Computing module is used for:
Calculate separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminWith
NDVImaxThe respectively corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage；
The maximum Priestley-Taylor coefficient φ of each pixel is calculated according to the vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
The third aspect, the embodiment of the present application provide a kind of electronic equipment, including memory and processor, in which: described
Memory, for storing program instruction；The processor, for executing the program instruction stored in the memory, to realize
Remote sensing described in any possible embodiment sends out NO emissions reduction method in first aspect or first aspect.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application will make below to required in the embodiment of the present application
Attached drawing is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore should not be seen
Work is the restriction to range, for those of ordinary skill in the art, without creative efforts, can be with
Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart that Remote sensing provided by the embodiments of the present application sends out NO emissions reduction method；
Fig. 2 is the schematic diagram that Remote sensing provided by the embodiments of the present application sends out NO emissions reduction device；
Fig. 3 is another schematic diagram that Remote sensing provided by the embodiments of the present application sends out NO emissions reduction device；
Fig. 4 is the schematic diagram of electronic equipment provided by the embodiments of the present application.
Icon: 201- obtains module；202- index computing module；203- NO emissions reduction module；204- coefficients calculation block；
300- electronic equipment；301- processor；302- memory.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application is described.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.It should be noted that
Herein, relational terms such as first and second and the like be used merely to by an entity or operation with another entity or
Operation distinguishes, without necessarily requiring or implying between these entities or operation there are any this actual relationship or
Sequentially.Moreover, the terms "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that
Process, method, article or equipment including a series of elements not only includes those elements, but also including being not explicitly listed
Other element, or further include for elements inherent to such a process, method, article, or device.Do not limiting more
In the case where system, the element that is limited by sentence "including a ...", it is not excluded that include the element process, method,
There is also other identical elements in article or equipment.
First embodiment
The Remote sensing hair of high spatial resolution can fill for region environment monitoring, water management and agricultural
It irrigates with offers guidances such as water decisions.Remote sensing techniques are to obtain the effective way of Remote sensing hair, however existing heat is red
Outer remotely-sensed data combined mathematical module is only capable of obtaining the Remote sensing hair of low spatial resolution, and the present embodiment provides one kind as a result,
Remote sensing sends out NO emissions reduction method, utilizes the Remote sensing of the remotely-sensed data prediction high spatial resolution compared with low spatial resolution
Hair, to improve the spatial resolution of evapotranspiration data, referring to Fig. 1, this method comprises the following steps:
Step 101, obtain target area meteorological data and initial remote sensing image data, and according to initial remote sensing image number
According to the remotely-sensed data for each pixel of different spatial resolutions image for obtaining target area.
Wherein, target area is the geographic area for needing to carry out Remote sensing hair NO emissions reduction, and initial remote sensing image data is
By thermal infrared sensor directly obtain include target area the initial remote sensing image of high spatial resolution and its each pixel
The remotely-sensed data of remotely-sensed data and low spatial resolution initial remote sensing image and its each pixel.Wherein, remotely-sensed data includes height
The normalization vegetation of normalized differential vegetation index, surface net radiation and the soil heat flux of spatial resolution, low spatial resolution refers to
Number, surface net radiation, soil heat flux and Remote sensing send out data, and the remotely-sensed data of high spatial resolution can be by Landsat
The sensor carried on 8 satellites obtains, and the remotely-sensed data of low spatial resolution can be obtained by MODIS sensor, meteorological data
Including Air Close To The Earth Surface pressure and Air Close To The Earth Surface temperature, data can derive from China Meteorological Administration, and selection target area
The meteorological data of meteorological site near domain, to reduce error, the average data that can be measured according to neighbouring multiple meteorological sites
Meteorological data as input.
Before carrying out subsequent calculating, the initial remote sensing image data from different sensors need to be pre-processed,
To eliminate the projection and space coordinate difference of the remote sensing image of different spatial resolutions, make the pixel of different spatial resolutions image
Size reaches unanimity with quantity, and process includes: projection transform, by the high and low spatial resolution under two kinds of different optical projection systems
Remotely-sensed data be converted into same projection system；Image space registration carries out high and low space point based on space coordinate and characteristic point
The registration process of resolution remote sensing image；Resampling is resampled to high spatial resolution image to low spatial resolution image picture element
Pixel size, such as can be using closest interpolation method, bilinear interpolation method or cubic convolution interpolation method etc.；Spatial reference, base
Cutting processing is carried out in remotely-sensed data of the target area boundaries data to high and low spatial resolution.Specific preprocessing process can
With referring to the prior art, it is not described here in detail.After the pre-treatment, low spatial resolution image, the height of entire target area are obtained
The remotely-sensed data of spatial resolution image and its each pixel, it is subsequent for the distant of pretreated high and low spatial resolution image
Sense data are calculated.
Step 102, low spatial point is calculated according to the remotely-sensed data of each pixel in meteorological data and low spatial resolution image
The Different Soil Water Deficits degree characteristic index of each pixel in resolution image.
Land surface energy budget calculates the main foundation of evapotranspiration as remote sensing, is embodied in surface net radiation, Soil Thermal
Equilibrium relation between flux, sensible flux and Surface Latent Heat Over: Rn=H+G+LE, wherein RnFor surface net radiation, H leads to for sensible heat
Amount, G is soil heat flux, and LE is latent heat flux, and LE is soil water evaporation and the energy that plant is evapotranspired, i.e., as evapotranspiration
The Remote sensing that energy representation namely the present embodiment to be obtained sends out data.
Optionally, Different Soil Water Deficits degree characteristic index calculates as follows:
Firstly, calculating the evaporite ratio EF of each pixel in low spatial resolution imagec, evaporite ratio be defined as latent heat flux with can
Using the ratio of energy, is calculated and is obtained according to above-mentioned Land surface energy budget, it may be assumed that
The remotely-sensed data of low spatial resolution, i.e. LE are indicated with footmark cc、Rn,cAnd GcRespectively low spatial resolution image
In each pixel latent heat flux (Remote sensing hair), surface net radiation and soil heat flux.
Then, according to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characterization of each pixel in low spatial resolution image
Index S Mc:
Wherein, Δ is slope (kPa/ DEG C) of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant (kPa/ DEG C), φmax,c
And φmin,cThe maximum Priestley-Taylor coefficient and minimum of each pixel respectively in low spatial resolution image
Priestley-Taylor coefficient, Δ and γ pass through Air Close To The Earth Surface temperature and Air Close To The Earth Surface pressure in meteorological data respectively
It calculates and obtains, calculation formula is as follows:
Wherein, Ta is Air Close To The Earth Surface temperature (DEG C), and P is Air Close To The Earth Surface pressure (kPa).
Step 103, the remotely-sensed data and Different Soil Water Deficits of each pixel in meteorological data, high spatial resolution image are utilized
The Remote sensing that degree characteristic index calculates each pixel in high spatial resolution image sends out data.
The present embodiment combines required meteorological data using the remotely-sensed data of low spatial resolution, is with Land surface energy budget
Point is sent out, by calculating the ratio (i.e. evaporite ratio) of latent heat flux and available energy, to estimate that the earth's surface of high spatial resolution is steamed
It distributes, also, through inventor the study found that the soil moisture of each pixel of low spatial resolution image is lost in remote sensing image
The Different Soil Water Deficits degree for lacking degree pixel corresponding with high spatial resolution image can be considered as identical, i.e. Different Soil Water Deficits
Degree characteristic index is uncorrelated to the spatial resolution of remote sensing image, has space scale invariance, thus there are equation SMf=
SMc, the remotely-sensed data of high spatial resolution image is indicated with footmark f.Therefore, the present embodiment utilizes Different Soil Water Deficits degree table
Sign index the remotely-sensed data of different spatial resolutions is connected, and due to Different Soil Water Deficits degree and evaporation, distributed
Cheng Xiangguan, therefore its characteristic index can mutually be calculated between above-mentioned evaporite ratio, it is thus possible to it realizes and is differentiated from low spatial
The Remote sensing of rate is dealt into evaporite ratio, then arrives Different Soil Water Deficits degree characteristic index, finally predicts high spatial resolution
The NO emissions reduction process of Remote sensing hair.
The Different Soil Water Deficits degree characteristic index SM of each pixel in obtaining high spatial resolution imagefAfterwards, it is predicted
The step of Remote sensing is sent out is as follows:
Firstly, according to Different Soil Water Deficits degree characteristic index SMfCalculate the steaming of each pixel in high spatial resolution image
Hair compares EFf:
Wherein, φmax,fAnd φmin,fThe maximum Priestley- of each pixel respectively in high spatial resolution remote sense image
Taylor coefficient and minimum Priestley-Taylor coefficient.
Then, according to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
It is to be appreciated that the maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient exist
It calculates and obtains before executing step 102, steps are as follows for calculating:
Firstly, calculating separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminWith
NDVImaxThe respectively corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage.NDVIminAnd NDVImaxWith mesh
The vegetation pattern for marking region is related, and when this method is applied to different target region, numerical value may change, for letter
Change model, it can be by NDVIminAnd NDVImaxBe set as common numerical value both at home and abroad at present, i.e., respectively 0.2 and 0.86, but answer
Work as understanding, the size of specific value is not as the restriction to the embodiment of the present application.
Then, the maximum Priestley-Taylor coefficient φ of each pixel is calculated according to vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
In above scheme, based on the space scale invariance of Different Soil Water Deficits degree characteristic index SM, low spatial is utilized
The vegetation coverage of the evaporite ratio of resolution ratio and high and low spatial resolution derives the evaporite ratio of high spatial resolution, Jin Erjie
High spatial resolution Remote sensing hair is calculated in the surface net radiation and soil heat flux data for closing high spatial resolution, realizes
NO emissions reduction process.
It passes by the instantaneous value at moment, thus the high-altitude that estimation obtains since above-mentioned acquired remotely-sensed data only represents satellite
Between resolution ratio Remote sensing hair be also instantaneous latent heat flux to get arrive instantaneous evapotranspiration, then can evapotranspire from instantaneous
The extension of further progress time scale is measured, is calculated from instantaneous value to 24 hours accumulated values, even a longer period of time, for example, sharp
It is extrapolated to daily evapotranspiration with evaporite ratio constant method, specific embodiment is not detailed in the present embodiment.
In the more existing NO emissions reduction algorithm for Remote sensing hair (such as data fusion method), come with some shortcomings,
Mainly there is a following: first, it needs to input largely with the closely related remote sensing of evapotranspiration, meteorological data, however these remote sensing
Data and meteorological data need a large amount of Data Preparation when obtaining, such as the inverting of remotely-sensed data, the interpolation of meteorological data
Deng, a large amount of input data will make Remote sensing hair consume a large amount of time in NO emissions reduction, and in input data deficiency,
Data fusion model will be difficult to apply；Second, the data fusion methods such as common STARFM, SADFAT are all by complicated mathematics object
Model support is managed, it is higher using threshold due to model structure complexity, so that the operability of its NO emissions reduction algorithm is lower.
And the specific embodiment of the Remote sensing hair NO emissions reduction method of the above-mentioned offer of the present embodiment, model is simple, calculates
Efficiently, required remotely-sensed data and meteorological data are less, meanwhile, that is, the Air Close To The Earth Surface that obtains not strong to meteorological data sensibility
There are influences when certain deviation for the Remote sensing hair being finally calculated for temperature and Air Close To The Earth Surface pressure and actual value
It can be ignored, for high spatial resolution image, it is only necessary to it is logical to provide normalized differential vegetation index, surface net radiation and Soil Thermal
The space NO emissions reduction of Remote sensing hair can be realized in amount, and practicability and operability are all relatively strong.
Referring to Fig. 2, the embodiment of the present application also provides a kind of Remote sensings to send out NO emissions reduction device, which includes:
Obtain module 201, for obtain target area meteorological data and initial remote sensing image data, according to initial remote sensing
Image data obtains the remotely-sensed data and high spatial resolution image of each pixel in the low spatial resolution image of target area
In each pixel remotely-sensed data；
Index computing module 202, for the remotely-sensed data according to each pixel in meteorological data and low spatial resolution image
Calculate the Different Soil Water Deficits degree characteristic index of each pixel in low spatial resolution image, wherein low spatial resolution image
In any pixel Different Soil Water Deficits degree characteristic index and high spatial resolution image in correspond to the soil moisture of pixel and lose
It is identical to lack degree characteristic index；
NO emissions reduction module 203, for the remotely-sensed data and soil using each pixel in meteorological data, high spatial resolution image
The Remote sensing that earth Water deficit levels characteristic index calculates each pixel in high spatial resolution image sends out data.
Optionally, index computing module 202 is specifically used for:
The evaporite ratio EF of each pixel in low spatial resolution image is calculated by following formulac:
Wherein, LEc、Rn,cAnd GcRemote sensing hair data, the earth's surface of each pixel are net respectively in low spatial resolution image
Radiation and soil heat flux；
According to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characteristic index of each pixel in low spatial resolution image
SMc:
Wherein, Δ is slope of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant, φmax,cAnd φmin,cIt is respectively low
The maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient, Δ in spatial resolution image
It is obtained with γ by meteorological data.
Optionally, NO emissions reduction module 203 is specifically used for:
The evaporite ratio EF of each pixel in high spatial resolution image is calculated by following formulaf:
Wherein, SMfFor the Different Soil Water Deficits degree characteristic index of high spatial resolution image, φmax,fAnd φmin,fRespectively
For each pixel of high spatial resolution image maximum Priestley-Taylor coefficient and minimum Priestley-Taylor coefficient,
SMf=SMc；
According to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
Optionally, refering to Fig. 3, which further includes coefficients calculation block 204, and coefficients calculation block 204 is used for:
Calculate separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminWith
NDVImaxThe respectively corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage；
The maximum Priestley-Taylor coefficient φ of each pixel is calculated according to vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
The maximum Priestley-Taylor coefficient and minimum Priestley- for each pixel that coefficients calculation block 204 obtains
The calculating that Taylor coefficient can be used in index computing module 202 and NO emissions reduction module 203.
It should be noted that Remote sensing provided by the embodiment of the present application sends out NO emissions reduction device, basic principle and production
Raw technical effect is identical with above method embodiment, and to briefly describe, the present embodiment part does not refer to place, can refer to above-mentioned
Embodiment of the method in corresponding contents, this will not be repeated here.
Referring to Fig. 4, the Remote sensing of above-mentioned offer sends out drop the embodiment of the present application also provides a kind of electronic equipment 300
Two time scales approach can be applied in the electronic equipment, and electronic equipment 300 includes memory 302 and processor 301, memory 302
In be stored with program instruction, electronic equipment operation when, processor 301 is communicated with memory 302, is stored with executing in memory
Program instruction, to realize Remote sensing provided in this embodiment hair NO emissions reduction method.
Wherein, which can be local terminal device, such as computer, tablet computer, work station etc., Huo Zheke
Think server.Since the computation model of this method is relatively simple, required input parameter is less, when executing this method to electronics
The performance consumption of equipment is relatively low, therefore can also execute method provided in this embodiment for not high electronic equipment is configured,
Practicability is stronger.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the application, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part of the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that function marked in the box can also be to be different from some implementations as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each functional module in each embodiment of the application can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) execute each embodiment the method for the application all or part of the steps.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Those familiar with the art within the technical scope of the present application, can easily think of the change or the replacement, and should all contain
Lid is within the scope of protection of this application.Therefore, the protection scope of the application should be subject to the protection scope in claims.
Claims (10)
1. a kind of Remote sensing sends out NO emissions reduction method, which is characterized in that the described method includes:
The meteorological data of acquisition target area and initial remote sensing image data, according to the initial remote sensing image data acquisition
In the low spatial resolution image of target area in the remotely-sensed data of each pixel and high spatial resolution image each pixel it is distant
Feel data；
The low spatial point is calculated according to the remotely-sensed data of each pixel in the meteorological data and the low spatial resolution image
The Different Soil Water Deficits degree characteristic index of each pixel in resolution image, wherein any picture in the low spatial resolution image
The Different Soil Water Deficits degree table of pixel is corresponded in the Different Soil Water Deficits degree characteristic index and high spatial resolution image of member
It is identical to levy index；
Remotely-sensed data and the Different Soil Water Deficits using each pixel in the meteorological data, the high spatial resolution image
Degree characteristic index calculates the Remote sensing hair data of each pixel in the high spatial resolution image.
2. the method according to claim 1, wherein the meteorological data includes Air Close To The Earth Surface pressure and near-earth
Face atmospheric temperature, the remote sensing image data include the normalized differential vegetation index of each pixel, earth's surface in low spatial resolution image
The normalization vegetation of each pixel refers in net radiation, soil heat flux and Remote sensing hair data and high spatial resolution image
Number, surface net radiation and soil heat flux.
3. according to the method described in claim 2, it is characterized in that, described differentiate according to the meteorological data and the low spatial
The remotely-sensed data of each pixel calculates the Different Soil Water Deficits degree table of each pixel in the low spatial resolution image in rate image
Levy index, comprising:
The evaporite ratio EF of each pixel in low spatial resolution image is calculated by following formulac:
Wherein, LEc、Rn,cAnd GcThe Remote sensing of each pixel sends out data, surface net radiation respectively in low spatial resolution image
And soil heat flux；
According to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characteristic index SM of each pixel in low spatial resolution imagec:
Wherein, Δ is slope of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant, φmax,cAnd φmin,cRespectively low spatial point
The maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient in resolution image, Δ and γ are logical
The meteorological data is crossed to obtain.
4. according to the method described in claim 3, it is characterized in that, described calculate each pixel in the high spatial resolution image
Remote sensing send out data, comprising:
The evaporite ratio EF of each pixel in high spatial resolution image is calculated by following formulaf:
Wherein, SMfFor the Different Soil Water Deficits degree characteristic index of high spatial resolution image, φmax,fAnd φmin,fIt is respectively high
The maximum Priestley-Taylor coefficient and minimum Priestley-Taylor coefficient of each pixel of spatial resolution image, SMf
=SMc；
According to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
5. according to the method described in claim 4, it is characterized in that, according to the meteorological data and the low spatial resolution
The remotely-sensed data of each pixel calculates the Different Soil Water Deficits degree characterization of each pixel in the low spatial resolution image in image
Before index, the method also includes:
Calculate separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminAnd NDVImaxPoint
It Wei not the corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage；
The maximum Priestley-Taylor coefficient φ of each pixel is calculated according to the vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
6. a kind of Remote sensing sends out NO emissions reduction device, which is characterized in that described device includes:
Obtain module, for obtain target area meteorological data and initial remote sensing image data, according to the initial remote sensing shadow
As data obtain the remotely-sensed data of each pixel and high spatial resolution shadow in the low spatial resolution image of the target area
The remotely-sensed data of each pixel as in；
Index computing module, for the remotely-sensed data according to each pixel in the meteorological data and the low spatial resolution image
Calculate the Different Soil Water Deficits degree characteristic index of each pixel in the low spatial resolution image, wherein the low spatial point
The soil of pixel is corresponded in resolution image in the Different Soil Water Deficits degree characteristic index and high spatial resolution image of any pixel
Earth Water deficit levels characteristic index is identical；
NO emissions reduction module, for using the meteorological data, in the high spatial resolution image remotely-sensed data of each pixel and
The Different Soil Water Deficits degree characteristic index calculates the Remote sensing hair data of each pixel in the high spatial resolution image.
7. device according to claim 6, which is characterized in that the index computing module is specifically used for:
The evaporite ratio EF of each pixel in low spatial resolution image is calculated by following formulac:
Wherein, LEc、Rn,cAnd GcThe Remote sensing of each pixel sends out data, surface net radiation respectively in low spatial resolution image
And soil heat flux；
According to evaporite ratio EFcCalculate the Different Soil Water Deficits degree characteristic index SM of each pixel in low spatial resolution imagec:
Wherein, Δ is slope of the saturation vapour pressure to temperature, and γ is wet and dry bulb constant, φmax,cAnd φmin,cRespectively low spatial point
The maximum Priestley-Taylor coefficient of each pixel and minimum Priestley-Taylor coefficient in resolution image, Δ and γ are logical
The meteorological data is crossed to obtain.
8. device according to claim 7, which is characterized in that the NO emissions reduction module is specifically used for:
The evaporite ratio EF of each pixel in high spatial resolution image is calculated by following formulaf:
Wherein, SMfFor the Different Soil Water Deficits degree characteristic index of high spatial resolution image, φmax,fAnd φmin,fIt is respectively high
The maximum Priestley-Taylor coefficient and minimum Priestley-Taylor coefficient of each pixel of spatial resolution image, SMf
=SMc；
According to evaporite ratio EFfThe Remote sensing for calculating each pixel in high spatial resolution image sends out data LEf:
LEf=EFf×(Rn,f-Gf)
Wherein, Rn,f、GfThe respectively surface net radiation and soil heat flux of high spatial resolution image.
9. device according to claim 8, which is characterized in that described device further includes coefficients calculation block, the coefficient
Computing module is used for:
Calculate separately the vegetation coverage of each pixel in high spatial resolution image and low spatial resolution image:
Wherein, FvFor the vegetation coverage of each pixel, NDVI is the normalized differential vegetation index of each pixel, NDVIminAnd NDVImaxPoint
It Wei not the corresponding minimum NDVI of the exposed soil and corresponding maximum NDVI of full vegetative coverage；
The maximum Priestley-Taylor coefficient φ of each pixel is calculated according to the vegetation coveragemaxAnd minimum
Priestley-Taylor coefficient φmin, wherein φmin=1.26 × Fv, φmax=1.26.
10. a kind of electronic equipment, which is characterized in that including memory and processor, in which:
The memory, for storing program instruction；
The processor, for executing the program instruction stored in the memory, to realize described in claim any one of 1-5
Remote sensing send out NO emissions reduction method.
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