CN107608939A - TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image - Google Patents
TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image Download PDFInfo
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- CN107608939A CN107608939A CN201710700311.2A CN201710700311A CN107608939A CN 107608939 A CN107608939 A CN 107608939A CN 201710700311 A CN201710700311 A CN 201710700311A CN 107608939 A CN107608939 A CN 107608939A
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Abstract
The present invention relates to a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image, this method obtains a variety of satellite meteorolo-gy data from official channels first, by Leave one out cross validation methods, corrects TRMM remote sensing precipitation datas；Further according to the factor for influenceing precipitation data, by step Regressive selection variables, and hysteresis quality of the precipitation to vegetation is considered, and the spatial coherence of vegetation, multi-spatial scale model is established, selects optimal models to carry out NO emissions reduction to precipitation data.This method process is simple, and it is accurate that precipitation is predicted.
Description
Technical field
The present invention relates to weather prognosis technical field, specially a kind of TRMM precipitation numbers based on high resolution satellite remote sensing image
According to NO emissions reduction method.
Background technology
Precipitation is the important component of the processes such as each ring layer mass exchange of global seismic, hydrologic cycle.Precipitation data is
The mostly important data basis in field such as the hydrology, meteorology, ecology are studied, for understanding water cycle process, water resources management, alleviation
Shortage of water resources etc. has facilitation.At present, the approach for obtaining precipitation data mainly has：Remote sensing skill is surveyed and utilized to precipitation station
Art inverting.Traditional Regional Precipitation data are normally based on observation station data, are obtained by the method for space interpolation, in rainfall
Website is distributed less area, and limited observation data can only reflect a range of precipitation in website position, simultaneously
Influenceed by underlying surface, it is difficult to accurately reflect the spatial variations of precipitation.Meanwhile existing numerous studies show the drop based on website
For water number according to that can not reflect precipitation spatial variations well, the inverting of precipitation data is carried out using remote sensing technology turns into precipitation space
The important channel of Changeement.
Remotely-sensed data is used for the meteorological model process study in the large scales such as whole world area at present, in Regional Precipitation research
On, also have that data resolution is relatively low, the not high many deficiencies of precision.Have scholar both at home and abroad and carry out TRMM for different regions
Remote sensing precipitation data NO emissions reduction is studied, to further promoting remotely-sensed data in Small-scale space using significant.
The exponential relationship that Immerzeel etc. is established between TRMM and NDVI, by TRMM spatial resolution by 0.25 ° of raising
To 1km, and find that the precision of TRMM precipitation datas also increases；Guan etc. introduces terrain factor and establishes regression model, will
Next Generation Radar (NEXRAD) data spatial resolution brings up to 4km by 16km；Xu etc. establishes TRMM numbers on 4 kinds of space scales
According to the relation between NDVI data, survey region TRMM data spatial resolutions are improved to 1km.These researchs are in certain journey
Solve the problems, such as TRMM data spatial resolution deficiencies on degree, but there is also certain defect, such as the only consideration such as Immerzeel
Influences of the NDVI to precipitation, ignores the effect of Terrain on Precipitation；Guan etc. considers the influence of orographic factor, but is only divided
Resolution is 4km × 4km precipitation data；Jia etc. considers the influence of NDVI and orographic factor, does not consider but climatic factor to drop
The influence of water, and ignore hysteresis quality of the precipitation to vegetation.
Shi Zhou et al. carries out TRMM satellite precipitation data NO emissions reductions by M5-Local method, and its specific method is as follows：
1km the environmental variance factor such as vegetation index, digital elevation model, surface temperature on daytime, evening surface temperature, landform humidity
8 index, the gradient, slope aspect, length of grade data carry out polymerization calculating and arrive 25km, as independent variable, corresponding 25km resolution ratio
TRMM data using M5-LocalR Method Modelings, and predict 1km spatial resolution regression modeling equations as dependent variable
Slope Parameters corresponding to intercept and each envirment factor variable, by the way that 1kmTRMM rainfall values are calculated.This method is also without examining
Consider hysteresis quality of the vegetation to precipitation, and due to the inconsistent situation in vegetation index space caused by underground water, irrigation etc., may
There can be certain error.
The content of the invention
The technical problems to be solved by the invention are to be directed to drawbacks described above, there is provided the invention discloses one kind to be based on high score
The TRMM precipitation data NO emissions reduction methods of resolution satellite data, this method process is simple, and it is accurate that precipitation is predicted.
The present invention uses following technical scheme to solve above-mentioned technical problem：
A kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image, this method is first from official channels
A variety of satellite meteorolo-gies and terrain data are obtained, and data are pre-processed, by Leave-one-out cross validation methods,
Select appropriate model correction remote sensing precipitation data；And the hysteresis quality in view of precipitation to vegetation, and the spatial coherence of vegetation,
According to the factor for influenceing precipitation data, by step Regressive selection variables, NO emissions reduction analysis finally is carried out to precipitation data.
Methods described is specially：
Step 1, each official's satellite meteorolo-gy data are obtained, and data are pre-processed；
Step 2, year precipitation data is surveyed as independent variable using each website of the first satellite meteorolo-gy data, corresponding annual precipitation
For dependent variable, regression equation is established, model of fit is selected by Leave-One-Out cross validations, school is carried out to precipitation data
Just；
Step 3, precipitation data of the second satellite meteorolo-gy data per ten days and the data of different lag periods are analyzed, led to
Cross R2With RMSE indexes as index, lag period of this area vegetation to precipitation is determined；
Step 4, the NDVI data in area are studied by local not blue exponent pair and analyzed, obtain studying NDVI in area
Spatial autocorrelation distribution map；
Step 5, selected section shows in all multivariables being had an impact using TRMM of the multiple stepwise regression after to correction
Variable is write as independent variable, dependent variable is forecast with building optimal regression equation；
Step 6, by various spatial data resamplings to four kinds of space scales, model is established, selects optimal model to join
Number, NO emissions reduction analysis is carried out to treated data；
Step 7, by calculating R2, RMSE and Bias values verify to NO emissions reduction result.
Further, the step 1 is specially：
Described official's satellite meteorolo-gy data include：To be derived from China of National Climate information centre terrestrial climate data day
Value Data collection, digital complex demodulation data are SRTM 90m × 90mSRTM3 data, temperature
Data are MOD11A1 day surface temperature data, vegetation index SPOT-NDVI data are by alliance of European Union committee member
The earth monitoring system of the national joint development such as meeting, France obtains, and it is TRMM satellites the 7th edition that rain task TRMM data are surveyed in the torrid zone
Moon precipitation data；
All data intercept the data in research area by ARCGIS spatial visualizations platform, and pass through dem data
Calculate and obtain Gradient, longitude and latitude degrees of data can directly obtain.
Further, the step 2 is specially：
To TRMM Data corrections：Using each website actual measurement year precipitation data as dependent variable, corresponding TRMM annual precipitations are certainly
Variable, regression equation is established, fitting effect best model is selected by Leave-One-Out (LOO) cross validation, then selected
Best model is corrected to TRMM precipitation datas；
The LOO cross validation methods are as follows：
In formula, CV is prediction error quadratic sum, YiFor actual TRMM values, XiTo survey precipitation station precipitation value,To deduct
The outer model established by least square method of i-th pair measured value.
Further, the step 3 is specially：
The NDVI data of precipitation data and different lag periods to per ten days are analyzed, and pass through R2It is used as and refers to RMSE indexes
Mark, determines lag period of this area vegetation to precipitation, its calculation formula is as follows：
Wherein, R2For the goodness of fit of regression model, RMSE is the root-mean-square error of model, and Y is actual NDVI values,For
Actual NDVI average,For NDVI predicted value.
Further, the step 4 is specially：
The NDVI data in research area are analyzed by local not blue index M oran ' s I, obtain studying in area
NDVI spatial autocorrelation distribution map, its calculation formula are as follows：
In formula, IiFor local space autocorrelation exponent value；XiIt is measured value of a certain variable on mikey i；It is to become
The average of amount；ZiIt is the standardized value of the observation on space cell i；A is the total number of observation；WijIt is mikey i and j
Between space weight coefficient；B is the space cell total number adjacent with mikey i.
Vegetation data are moved forward to the hysteresis quality of precipitation, make plant by the vegetation obtained using the analysis of the step 3
Current precipitation information can be reflected by index；Using the analysis of the step 4, space-independent vegetation index is deleted, then
Carry out space interpolation.
Further, the step 5 is specially：
Stepwise regression analysis showed is carried out for the various data after correction, it is as follows to establish archetype：
TRMM~NDVI+DEM+NDVI2+Lat+Lon+Temday+Temnight+Slope
NDVI represents vegetation in formula, and DEM represents elevation, and Lat represents latitude, and Lon represents longitude, and Temday represents temperature on daytime
Degree, Temnight represent night temperatures, and Slope represents the gradient；
Then AIC information rule testing model performances are used, by stepwise regression analysis, when AIC values are minimum, are shown
Model is optimal, and is calculated as follows：
AIC=2k-2ln (L)
In formula：K is the quantity of Model Parameter, and L is the maximum of model likelihood function.
Further, the step 6 is specially：
NO emissions reduction analysis is carried out using following steps：TRMM after correction is added up into precipitation and progressively returned by polynary
Return each independent variable resampling of determination to different space scales, the TRMM data established respectively under different spaces yardstick with becoming certainly
The multivariate regression models of amount, model parameter is obtained by least square method；The best model of fitting effect is selected, by each becoming
Precipitation data under its affiliated definition case of amount data prediction；The precipitation data of prediction is subtracted with TRMM data, obtains precipitation
Residual values, precipitation data is added with precipitation residual values, that is, obtains annual precipitation data final in survey region.
The step 7 is specially：
Checking to NO emissions reduction result：The TRMM data of each website and the TRMM data after NO emissions reduction are extracted, calculate two
R between sets of data and actual measurement station data2, RMSE and Bias values, wherein Bias calculation formula is as follows：
Wherein, Bias is the deviation of predicted value and actual value, and Y is actual NDVI values,For NDVI predicted value, N is the time
The length of sequence, i.e. time.
The present invention compared with prior art, has following technique effect using above technical scheme：
(1) different regions TRMM remote sensing precipitation has different relations from actual measurement precipitation, and we pass through leave-ont-out
The suitable model of method choice of cross validation, correct TRMM data.
(2) consider that response of the vegetation to precipitation has hysteresis quality, the first precipitation data to per ten days and different lag periods
NDVI data analyzed, index is used as by R2 and RMSE indexes, determines lag period of this area vegetation to precipitation, then
The NDVI of certain lag period is added up, annual NDVI indexes is obtained, as independent variable, establishes multivariate regression models.
(3) the NDVI data in research area are analyzed by local not blue index (Moran ' s I), obtains studying area
Interior NDVI spatial autocorrelation distribution map, because NDVI may be affected by other factors, such as underground water, lake etc.,
Local point of the not blue index less than 0 is removed, interpolation is then carried out by conventional linear interpolation.
Brief description of the drawings
Fig. 1 is actual measurement precipitation data and curve matching figure of the TRMM data based on different models；
Fig. 2 is the hysteresis quality analysis chart of precipitation and vegetation index；
Fig. 3 is vegetation index spatial autocorrelation analysis figure；
Fig. 4 is fianalysis tting degree figures of the TRMM with more independents variable of different year different spaces yardstick；
Fig. 5 is the TRMM of more annuals and predicts comparison diagram of the precipitation data in different scale；
Fig. 6 is the TRMM after the correction that NO emissions reduction processing procedure figure (a) spatial resolution is 0.25 ° × 0.25 °；(b) it is empty
Between resolution ratio be 0.25 ° of prediction precipitation；(c) spatial resolution is 0.25 ° of precipitation residual values；(d) spatial discrimination after interpolation
Rate is 1km precipitation residual values；(e) spatial resolution is 1km prediction precipitation；(f) spatial resolution is 1km precipitation number
According to；
Fig. 7 is the R of each website2, comparison diagram of RMSE, B index before and after NO emissions reduction；
Fig. 8 is the particular flow sheet of the inventive method.
Embodiment
Technical scheme is described in further detail below in conjunction with the accompanying drawings：
A kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image provided by the invention, from existing skill
From the point of view of in art, some defects during NO emissions reduction be present,
1st, consider that the Rainfall Influence factor is not comprehensive during NO emissions reduction, or the possible factor of influence of worry about, this
The method choice meteorological factor relevant with precipitation as complete as possible and topographic(al) feature, and there may be in view of different variables multiple
Synteny, returned and selected by step.
2nd, for the final data resolution ratio that most of NO emissions reduction methods obtain at present still than relatively low, this method passes through selection point
The higher meteorological element data of resolution carry out NO emissions reduction, finally obtain 1km × 1km precipitation data
3rd, response of the plant to precipitation has hysteresis quality, studies the article of NO emissions reduction method at present seldom in view of vegetation
Hysteresis quality, this method select lag period of the vegetation to precipitation by different indexs, it is contemplated that the factor of hysteresis quality
4th, due to different regions TRMM remote sensing precipitation from actual measurement precipitation there are different relations, we pass through leave-ont-
The suitable model of method choice of out cross validations, TRMM data are corrected, then carry out NO emissions reduction analysis.
In view of above mentioned problem, as shown in figure 8, the present invention provides a kind of TRMM precipitation based on high resolution satellite remote sensing image
Data NO emissions reduction method, this method obtain a variety of satellite meteorolo-gies and terrain data from official channels first, and data are carried out pre-
Processing, by Leave-one-out cross validation methods, select appropriate model correction remote sensing precipitation data；And consider precipitation
To the spatial coherence of the hysteresis quality of vegetation, and vegetation, according to the factor for influenceing precipitation data, pass through step Regressive selections
Variable, NO emissions reduction analysis finally is carried out to precipitation data.
Methods described is specially：
Step 1, each official's satellite meteorolo-gy data are obtained, and data are pre-processed；
Step 2, year precipitation data is surveyed as independent variable using each website of the first satellite meteorolo-gy data, corresponding annual precipitation
For dependent variable, regression equation is established, model of fit is selected by Leave-One-Out cross validations, school is carried out to precipitation data
Just；
Step 3, precipitation data of the second satellite meteorolo-gy data per ten days and the data of different lag periods are analyzed, led to
Cross R2With RMSE indexes as index, lag period of this area vegetation to precipitation is determined；
Step 4, the NDVI data in area are studied by local not blue exponent pair and analyzed, obtain studying NDVI in area
Spatial autocorrelation distribution map；
Step 5, linear regression is established to multivariable can preferably be fitted dependent variable, and multiple stepwise regression is used for from right
Selected section variable is entered as independent variable with building optimal regression equation to dependent variable in all multivariables that dependent variable has an impact
Row forecast；
Step 6, the NO emissions reduction that treated data are carried out with different spaces yardstick are analyzed；
Step 7, NO emissions reduction result is verified by calculating R2, RMSE and Bias value.
The inventive method specific embodiment detailed process is as follows：
(1) acquisition of data and processing early stage：The station data of actual measurement in survey region, to be derived from National Climate information
State's terrestrial climate data earning in a day data set (http in the heart://data.cma.cn/)；Dem data is that (spatial resolution is SRTM3
90m × 90m) data (http://srtm.csi.cgiar.org/), the spatial dimension of 60 ° of north latitude of covering to 56 ° of south latitude；Temperature
Data are MOD11A1 day surface temperature data (http://www.gscloud.cn/), spatial resolution is 1km × 1km；
SPOT-NDVI data (1km × 1km) are obtained by the earth monitoring system of the national joint development such as the committee of alliance of European Union, France
Obtain (http://www.vito-eodata.be/collections/srv/eng/main.home), spatial resolution be 1km ×
1km；TRMM data are the moon precipitation datas of TRMM satellites the 7th edition, and horizontal resolution is 0.25 ° × 0.25 °, and coverage is
50 ° of south latitude arrives 50 ° of north latitude；In view of the uniformity of survey region, all data are intended to put down by ARCGIS spatial visualizations
Data in platform interception research area, and by dem data calculate and obtain the gradient and slope aspect data.
(2) TRMM Data corrections：Using each website actual measurement year precipitation data as dependent variable, corresponding TRMM annual precipitations are certainly
Variable, regression equation is established, fitting effect best model is selected by Leave-One-Out (LOO) cross validation, then selected
Best model is corrected to TRMM precipitation datas, as shown in Figure 1.
LOO cross validations are special circumstances of LPO (Leave-P-Out) cross validations in p=1, are tested because LOO intersects
Each model is that estimated result is reliable and stable, so it is to use by the use of almost all of sample as training set in card method
Most commonly used cross validation method.LOO cross-validation methods are used to select each precipitation station annual rainfall right with it in our current research
The model for answering position TRMM to add up between annual precipitation, and TRMM data are corrected with selected model, it is closest to obtain
The Precipitation estimation value of " true value ".LOO methods are as follows：
In formula, CV is prediction error quadratic sum, YiFor actual TRMM values, XiTo survey precipitation station precipitation value,To deduct
The outer model established by least square method of i-th pair measured value.
(3) the NDVI data of the precipitation data to per ten days and different lag periods are analyzed, and pass through R2Make with RMSE indexes
For index, lag period of this area vegetation to precipitation is determined, Fig. 2 is the result that selected research area calculates, and its calculation formula is as follows：
Wherein, R2For the goodness of fit of regression model, RMSE is the root-mean-square error of model, and Y is actual NDVI values,For
Actual NDVI average,For NDVI predicted value.
(4) the NDVI data in research area are analyzed by local not blue index (Moran ' s I), obtains studying area
Interior NDVI spatial autocorrelation distribution map, as shown in Figure 3.Local space autocorrelation exponent is used to characterize vegetation index NDVI and existed
The aggregation of spatial distribution or off-note, Moran ' I index variations scope are -1~1, when its value is -1~0, representation space
Negative correlation, show that the variable has space diversity in a certain distance.When its value is 0~1, representation space positive correlation, table
The bright variable has similitude；Show space non-correlation when its value is 0, then point of the value less than 0 is removed, passes through interpolation
Method filled up, its calculation formula is as follows：
In formula, IiFor local space autocorrelation exponent value；XiIt is measured value of a certain variable on mikey i；It is to become
The average of amount；ZiIt is the standardized value of the observation on space cell i；A is the total number of observation；WijIt is mikey i and j
Between space weight coefficient；B is the space cell total number adjacent with mikey i.
(5) linear regression is established to multivariable can preferably be fitted dependent variable, but may bring multicollinearity
The problem of, multiple stepwise regression is used for from all multivariables being had an impact to dependent variable selected section variable as independent variable,
Dependent variable is forecast with building optimal regression equation, it calculates contribution of each independent variable to dependent variable by mathematics change
Rate, and whether model is introduced into determining some independent variable according to the size of contribution rate or is rejected from built formwork erection type.Herein
Using AIC information rule testing model performances, by successive Regression, when AIC values are minimum, show that model is optimal.Meter
Calculate as follows：
AIC=2k-2ln (L)
In formula：K is the quantity of Model Parameter, and L is the maximum of model likelihood function.
(6) NO emissions reduction analysis is carried out using following steps：By after correction TRMM add up precipitation and by it is polynary by
Step returns each independent variable resampling determined to four different space scales：0.25°×0.25°、0.50°×0.50°、
0.75°×0.75°、1.00°×1.00°；The TRMM data established respectively under different spaces yardstick and the multiple regression of independent variable
Model, model parameter is obtained by least square method, as shown in Figure 4 and Figure 5；The present embodiment be for 0.25 ° of resolution ratio ×
0.25 °, spatial resolution 1km × 1km data be predicted using model, select the best model of fitting effect, pass through
The precipitation data that each argument data prediction spatial resolution that resolution ratio is 0.25 ° × 0.25 ° is 0.25 ° × 0.25 °；With
0.25 ° × 0.25 ° of TRMM data subtract the precipitation data of prediction, obtain the precipitation residual error that resolution ratio is 0.25 ° × 0.25 °
Value, and it is 1km × 1km to be resampled to spatial resolution；With identical regression model, using spatial resolution as 1km × 1km
Argument data based on, obtain prediction resolution ratio be 1km × 1km grid annual rainfall；It is by the resolution ratio of acquisition
1km × 1km precipitation data is added with precipitation residual values, that is, obtains annual precipitation data final in survey region, resolution ratio
For 1km × 1km, as shown in Figure 6.
(7) to the checking of NO emissions reduction result：The TRMM data of each website and the TRMM data after NO emissions reduction are extracted, are calculated
R2, RMSE and Bias value between two sets of data and actual measurement station data, its spatial distribution is as shown in fig. 7, wherein R2And RMSE
Related to before the computational methods of index, wherein Bias calculation formula is as follows：
Wherein, Bias is the deviation of predicted value and actual value, and Y is actual NDVI values,For NDVI predicted value, N is the time
The length of sequence, i.e. time.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not limited to this hair
It is bright, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., it should be included in the present invention
Protection domain within.
Claims (10)
- A kind of 1. TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image, it is characterised in that this method is first A variety of satellite meteorolo-gies and terrain data are obtained from official channels, and data are pre-processed, are intersected by Leave-one-out Verification method, select appropriate model correction remote sensing precipitation data；And the hysteresis quality in view of precipitation to vegetation, and the sky of vegetation Between correlation, according to influence precipitation data factor, by step Regressive selection variables, drop chi finally is carried out to precipitation data Degree analysis.
- 2. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 1, its It is characterised by, methods described is specially：Step 1, each official's satellite meteorolo-gy data are obtained, and data are pre-processed；Step 2, year precipitation data is surveyed as independent variable using each website of the first satellite meteorolo-gy data, corresponding annual precipitation be because Variable, regression equation is established, model of fit is selected by Leave-One-Out cross validations, precipitation data is corrected；Step 3, precipitation data of the second satellite meteorolo-gy data per ten days and the data of different lag periods are analyzed, pass through R2With RMSE indexes determine lag period of this area vegetation to precipitation as index；Step 4, the NDVI data in area are studied by local not blue exponent pair and analyzed, obtain studying the sky of NDVI in area Between auto-correlation distribution map；Step 5, selected section significantly becomes in all multivariables being had an impact using TRMM of the multiple stepwise regression after to correction Amount is used as independent variable, and dependent variable is forecast with building optimal regression equation；Step 6, by various spatial data resamplings to four kinds of space scales：0.25 ° × 0.25 °, 0.5 ° × 0.5 °, 0.75 ° × 0.75 °, 1 ° × 1 °, model is established, selects optimal model parameter, NO emissions reduction analysis is carried out to treated data；Step 7, the TRMM values before and after NO emissions reduction corresponding to website are extracted, by calculating R2, RMSE and Bias values are to NO emissions reduction knot Fruit is verified.
- 3. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 2, its It is characterised by, the step 1 is specially：Described official's satellite meteorolo-gy data include：To be derived from China of National Climate information centre terrestrial climate data earning in a day number According to collection, digital complex demodulation data are SRTM 90m × 90mSRTM3 data, temperature record For MOD11A1 day surface temperature data, vegetation index SPOT-NDVI data are by the committee of alliance of European Union, method The earth monitoring system of the national joint development such as state is obtained, and the moon drop that rain task TRMM data are TRMM satellites the 7th edition is surveyed in the torrid zone Water number evidence；All data intercept the data in research area by ARCGIS spatial visualizations platform, and are carried out by dem data Calculate and obtain Gradient, longitude and latitude degrees of data can directly obtain.
- 4. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 2, its It is characterised by, the step 2 is specially：To TRMM Data corrections：Using each website actual measurement year precipitation data as dependent variable, corresponding TRMM annual precipitations are independent variable, Regression equation is established, fitting effect best model is selected by Leave-One-Out LOO cross validations, then selects optimal mould Type is corrected to TRMM precipitation datas；The LOO cross validation methods are as follows：<mrow> <mi>C</mi> <mi>V</mi> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>&lsqb;</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mover> <mi>f</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mo>-</mo> <mi>i</mi> <mo>)</mo> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> </mrow>In formula, CV is prediction error quadratic sum, YiFor actual TRMM values, XiTo survey precipitation station precipitation value,To deduct i-th To the model established outside measured value by least square method.
- 5. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 2, its It is characterised by, the step 3 is specially：The NDVI data of precipitation data and different lag periods to per ten days are analyzed, and pass through R2With RMSE indexes as index, really Determine lag period of this area vegetation to precipitation, its calculation formula is as follows：<mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msup> <mrow> <mo>(</mo> <mover> <mi>Y</mi> <mo>^</mo> </mover> <mo>-</mo> <mover> <mi>Y</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mrow> <mi>&Sigma;</mi> <msup> <mrow> <mo>(</mo> <mi>Y</mi> <mo>-</mo> <mover> <mi>Y</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow><mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> <mo>=</mo> <msqrt> <mfrac> <mrow> <mi>&Sigma;</mi> <msup> <mrow> <mo>(</mo> <mover> <mi>Y</mi> <mo>^</mo> </mover> <mo>-</mo> <mi>Y</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mi>N</mi> </mfrac> </msqrt> </mrow>Wherein, R2For the goodness of fit of regression model, RMSE is the root-mean-square error of model, and Y is actual NDVI values,For reality NDVI average,For NDVI predicted value.
- 6. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 5, its It is characterised by, the step 4 is specially：The NDVI data in research area are analyzed by local not blue index M oran ' s I, obtain studying NDVI in area Spatial autocorrelation distribution map, its calculation formula are as follows：<mrow> <msub> <mi>I</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>Z</mi> <mi>i</mi> </msub> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>B</mi> </munderover> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&CenterDot;</mo> <msub> <mi>Z</mi> <mi>i</mi> </msub> </mrow><mrow> <msub> <mi>Z</mi> <mi>i</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>X</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>/</mo> <msqrt> <mrow> <mfrac> <mn>1</mn> <mi>A</mi> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>A</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>X</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow>In formula, IiFor local space autocorrelation exponent value；XiIt is measured value of a certain variable on mikey i；It is variable Average；ZiIt is the standardized value of the observation on space cell i；A is the total number of observation；WijIt is between mikey i and j Space weight coefficient；B is the space cell total number adjacent with mikey i.
- 7. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 6, its It is characterised by,Vegetation data are moved forward to the hysteresis quality of precipitation, refer to vegetation by the vegetation obtained using the analysis of the step 3 Number can reflect current precipitation information；Using the analysis of the step 4, the space-independent vegetation lattice point of vegetation index is deleted, Then space interpolation is carried out.
- 8. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 7, its It is characterised by, the step 5 is specially：Stepwise regression analysis showed is carried out for the various data after correction, it is as follows to establish archetype：TRMM~NDVI+DEM+NDVI2+Lat+Lon+Temday+Temnight+SlopeNDVI represents vegetation in formula, and DEM represents elevation, and Lat represents latitude, and Lon represents longitude, and Temday represents day temperature, Temnight represents night temperatures, and Slope represents the gradient；Then AIC information rule testing model performances are used, by stepwise regression analysis, selects that there is precipitation and significantly affects Meteorology and terrain factor.When AIC values are minimum, show that model is optimal, be calculated as follows：AIC=2k-2ln (L)In formula：K is the quantity of Model Parameter, and L is the maximum of model likelihood function.
- 9. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 2, its It is characterised by, the step 6 is specially：NO emissions reduction analysis is carried out using following steps：TRMM after correction is added up into precipitation and true by multiple stepwise regression Fixed each independent variable resampling to different space scales, the TRMM data established respectively under different spaces yardstick and independent variable Multivariate regression models, model parameter is obtained by least square method；The best model of fitting effect is selected, passes through respective variable number It is predicted that the precipitation data in the case of its low resolution；The precipitation data of prediction is subtracted with TRMM data, obtains precipitation residual values, It is interpolated into high-resolution data, by the precipitation residual values phase after the precipitation data and interpolation predicted in the case of high accuracy Add, that is, obtain annual precipitation data final in survey region.
- 10. a kind of TRMM precipitation data NO emissions reduction methods based on high resolution satellite remote sensing image according to claim 5, its It is characterised by, the step 7 is specially：Checking to NO emissions reduction result：The TRMM data of each website and the TRMM data after NO emissions reduction are extracted, calculate two tricks According to the R between actual measurement station data2, RMSE and Bias values, compare the precision of TRMM data before and after NO emissions reduction；Wherein Bias calculation formula is as follows：<mrow> <mi>B</mi> <mi>i</mi> <mi>a</mi> <mi>s</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <mover> <mi>Y</mi> <mo>^</mo> </mover> <mo>-</mo> <mi>Y</mi> </mrow> <mi>N</mi> </mfrac> </mrow>Wherein, Bias is the deviation of predicted value and actual value, and Y is actual NDVI values,For NDVI predicted value, N is time series Length, i.e. time.
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