CN105160192B - TRMM satellite rainfall data NO emissions reduction methods based on M5 LocalR - Google Patents
TRMM satellite rainfall data NO emissions reduction methods based on M5 LocalR Download PDFInfo
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Abstract
The invention discloses a kind of TRMM satellite rainfall data NO emissions reduction methods based on M5 LocalR.The thought that the present invention is returned using M5 LocalR, makes full use of the high environmental stress factor of multiple spatial resolutions to improve the spatial resolution of the product.First, 1km the environmental variance factor such as 8 vegetation index, digital elevation model, earth's surface temperature on daytime, evening earth's surface temperature, Topographic Wetness Index, the gradient, slope aspect, Barrier facility data are carried out polymerization calculating and arrive 25km, as independent variable, the TRMM data of corresponding 25km resolution ratio are as dependent variable, using M5 LocalR Method Modelings, and predict 1km spatial resolution regression modeling equations intercept and each envirment factor variable corresponding to Slope Parameters, by the way that 1kmTRMM rainfall values are calculated.NO emissions reduction result based on conventional regression model will be substantially better than by the NO emissions reduction result based on M5 LocalR models.
Description
Technical field
The present invention relates to the method for TRMM rainfall data NO emissions reductions, and in particular to a kind of TRMM based on M5-LocalR
Satellite rainfall data NO emissions reduction method.
Technical background
Rainfall has served as key player, particularly material in fields such as hydrology, meteorology, ecology and agricultural researches
One important component of energy exchange conservation.Surface-based observing station is a kind of widely used rainfall measurement means, and is had
There is the characteristics of precision height and technology maturation.But the rainfall of surface-based observing station monitoring only represents earth's surface observation station and periphery is certain
The precipitation situation of distance, therefore be difficult statement widespread rain distribution characteristics, it is especially sparse in surface-based observing station cloth reticular density
Highlands.And satellite remote sensing technology can provide the rainfall data compared with high-spatial and temporal resolution, covering spatial dimension is wider, very
The good limitation for overcoming ground rainfall observation station and rain detection radar, strong data supporting is provided for global rainfall monitoring.
In recent years, as the development of meteorological satellite technology, the survey rain Satellite Product of Global Scale high-spatial and temporal resolution meet the tendency of
And give birth to, such as tropical rainfall measurement setup (Tropical Rainfall Measuring Mission, TRMM) surveys rain product 3B42
With U.S. climates prediction precipitation core integration technology Precipitation Products (Climate Prediction Center Morphing
Technique).TRMM rainfall satellites be first dedicated for quantitative observation the torrid zone, subtropical zone rainfall meteorological satellite, can
With higher spatial and temporal resolution, there is provided the rainfall data in the region within 50 ° of S~50 ° N covering the whole world.But TRMM satellites
Original resolution is relatively low (spatial resolution is 0.25 °, about 25km), has certain limitation in terms of the yardstick rainfall of estimation range
Property and deviation, it is therefore desirable to carry out spatial scaling spatially for TRMM data, surveyed so as to obtain the higher rainfall of resolution ratio
Value.
(Immerzeel W W, Rutten M M, the Droogers P.Spatial downscaling such as Immerzeel
of TRMM precipitation using vegetative response on the Iberian Peninsula[J]
.Remote Sensing of Environment,2009,113(2):362-370.) planted based on rainfall and different spaces yardstick
The capped hypothesis with correlation, it is proposed that closed using the function between the index of rainfall and normalized differential vegetation index (NDVI)
System carries out NO emissions reduction to satellite rainfall data.Jia etc. (Jia S, Zhu W,A,et al.A statistical spatial
downscaling algorithm of TRMM precipitation based on NDVI and DEM in the
Qaidam Basin of China[J].Remote sensing of Environment,2011,115(12):3069-
3079.) multiple linear regression model is based on, using NDVI and dem data, to 0.25 ° of TRMM rainfall data in the Caidamu Basin
NO emissions reduction is carried out to 1km.Duan and Bastiaanssen (Duan Z, Bastiaanssen W G M.First results
from Version 7TRMM 3B43precipitation product in combination with a new
downscaling–calibration procedure[J].Remote Sensing of Environment,2013,131:
NO emissions reduction algorithm 1-13.) is further improved using geographical difference analysis (GDA) and geographical ratio analysis (GRA) calibration method,
So as to reduce the error of rainfall data after NO emissions reduction.But current researcher thinks:Using polyfactorial NO emissions reduction algorithm
Precision can not be improved, polyfactorial precision can be less than monofactor on the contrary.Such as Xu S (Xu S, Wu C, Wang L, et
al.A new satellite-based monthly precipitation downscaling algorithm with
non-stationary relationship between precipitation and land surface
characteristics[J].Remote Sensing of Environment,2015,162:119-140) geography is weighted
The NO emissions reduction that regression analysis is applied to TRMM satellite Rainfall Products is studied, and is utilized respectively environmental variance such as DEM, NDVI and same
When returned using DEM and NDVI modelings, the results showed that only with DEM or NDVI effect than simultaneously using DEM's and NDVI
Modeling effect will get well；And other researchers also have similar conclusion.
M5-LocalR is a kind of method based on local weighted regression modeling, it combine M5 principal component analytical methods and
LocalR Geographically weighted regression procedures, traditional recurrence framework is extended, allow local rather than global parameter Estimation, extension
The parameter of model is a position k function afterwards, i.e. parameter in model is different in each regression point.
The content of the invention
It is an object of the invention to solve problems of the prior art, and provide a kind of based on M5-LocalR's
TRMM satellite rainfall data NO emissions reduction methods.Concrete technical scheme is as follows：
A kind of TRMM satellite rainfall data NO emissions reduction methods based on M5-LocalR, comprise the following steps：
Step 1) data acquisition：Obtain TRMM meteorological satellite remote sensings image data, the MODIS satellite remote sensing shadows in region to be measured
As data and ASTER GDEM satellite remote-sensing image data, while collect the daily rain amount of ground observation website in the region to be measured
Discharge observation value；Wherein MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products；
Step 2) data prediction：At the temporal resolution for the TRMM meteorological satellite remote sensing image datas that step 1) is obtained
Manage as the moon；It is 1km and 25km that ASTER GDEM satellite remote-sensing images data, which are carried out polymerizeing calculating to respectively obtain spatial resolution,
Dem data；Surface temperature on daytime and evening surface temperature parameter are extracted from MOD11A2 data products, and is counted by polymerizeing
Calculate respectively obtain spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be 1km and 25km
Evening surface temperature data；Vegetation index parameter is extracted from MOD13A2 data products, after abnormality value removing is handled, is led to
Cross polymerization and calculate the vegetation index data for respectively obtaining that spatial resolution is 1km and 25km；From ASTER GDEM satellite remote sensing shadows
As 4 the extracting data gradient, Topographic Wetness Index, Barrier facility and slope aspect parameters carry out polymerize calculating respectively obtain 1km and
25km Gradient, Topographic Wetness Index data, Barrier facility data and slope aspect data；
Step 3) carries out M5-LocalR regression modelings：By the TRMM meteorological satellite remote sensing image datas after step 2) processing
As dependent variable, with spatial resolution be 25km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data pass through as independent variable and 1km
Geographical Weighted Regression is carried out between latitude coordinate point, so as to obtain each grid point in the raster data that spatial resolution is 1km
Regression equation and spatial resolution be 25km rainfall regression residuals value；Described 1km latitude and longitude coordinates point is space minute
The latitude and longitude coordinates of each grid point in the raster data that resolution is 1km；
Step 4) NO emissions reduction is predicted：Based on each grid in the raster data that the spatial resolution obtained by step 3) is 1km
Point regression equation, using spatial resolution as 1km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data as input from becoming
Amount, is calculated, it is 1km ground prediction of precipitation Value Data to obtain spatial resolution；It is simultaneously 25km's by spatial resolution
Rainfall regression residuals value carries out resampling and obtains the rainfall regression residuals value that spatial resolution is 1km, and by itself and spatial discrimination
Rate is added for 1km ground prediction of precipitation Value Data, obtains the TRMM meteorological satellite rainfall data that spatial resolution is 1km.
Preferably, in described step 1), the spatial resolutions of TRMM meteorological satellite remote sensing image datas for 0.25 ° ×
0.25 °, temporal resolution is 3 hours；The spatial resolution of described ASTER GDEM satellite remote-sensing image data is 30m；Institute
The spatial resolution for the MODIS satellite remote-sensing image data stated is 1km, and temporal resolution is 8 days.
Preferably, in described step 2) abnormality value removing handle comprise the following steps that：By MOD13A2 data products
The vegetation index of middle extraction deletes the part that grid point value in initial vegetation index is less than 0 as initial vegetation index first, then with
11 × 11 window gliding smoothing vegetation index, the vegetation index after then being subtracted smoothly with initial vegetation index, reselection-
0.15 to 0.15 screens as threshold range to the result after subtracting each other, and casts out the grid beyond threshold range, remaining conduct
Normal vegetation index point.
Preferably, in described step 3), Geographical Weighted Regression uses M5-LocalR regression modelings, and M5-LocalR is returned
Return comprising the following steps that for modeling use：First by step 2) processing after daytime surface temperature data, evening surface temperature number
According to, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out
Principal component analysis, so as to obtain the main-control factors of each grid point；So using the main-control factors of each grid point as independent variable, with step
TRMM meteorological satellite remote sensings image data after rapid 2) processing carries out Geographical Weighted Regression as dependent variable.
As further preferably, described principal component analysis uses M5 methods.
The beneficial effects of the invention are as follows be modeled to carry out NO emissions reduction prediction, wherein M5- to TRMM data with reference to multiple-factor
LocalR is returned and is extended traditional recurrence framework, allows local rather than global parameter Estimation, the parameter of model after extension
It is the function of position, i.e., the parameter in model is different in each regression point.Therefore geography is carried out by multiple-factor to weight back
Return modeling more accurately can carry out precipitation predicting to complex area, and greatly improve the spatial resolution of precipitation predicting.
With important theory, practice significance and application value.
The present invention is based on M5-LocalR homing methods, is modeled with reference to multiple-factor and TRMM data progress NO emissions reduction is ground
Study carefully.M5-LocalR is returned and is extended traditional recurrence framework, allows local rather than global parameter Estimation, model after extension
Parameter be position k function, i.e. parameter in model is different in each regression point.Therefore carried out by multiple-factor geographical
Weighted regression modeling more accurately can carry out precipitation predicting to complex area, and greatly improve the space point of precipitation predicting
Resolution.With important theory, practice significance and application value.
Brief description of the drawings
Fig. 1 is the 25kmTRMM rainfall spatial distribution characteristic figures used in embodiment 1 or 2.
Fig. 2 is the rainfall spatial distribution based on 1km after polyfactorial Geographically weighted regression procedure NO emissions reduction in embodiment 1
Characteristic pattern.
Fig. 3 is the screening distribution map for carrying out main-control factors in embodiment 2 based on M5 methods.
Fig. 4 is the rainfall quantity space point based on 1km after polyfactorial M5-LocalR homing methods NO emissions reduction in embodiment 2
Cloth characteristic pattern.
Fig. 5 be prediction of precipitation value based on 1km after polyfactorial Geographically weighted regression procedure NO emissions reduction in embodiment 1 with
The correlation analysis figure of ground observation website.
Fig. 6 is the prediction of precipitation value based on 1km after polyfactorial M5-LocalR homing methods NO emissions reduction in embodiment 2
With the correlation analysis figure of ground observation website.
Embodiment
The present invention is further described with reference to the accompanying drawings and examples.
Embodiment 1：
NO emissions reduction prediction is carried out with Geographical Weighted Regression in the present embodiment, comprised the following steps that：
Tibet region is chosen as survey region, the moon rainfall of 2003-2009 rainy seasons (annual May-October) is entered
Row forecasting research, finally give the rainfall distribution map of monthly 1km spatial resolutions.
Step 1) data acquisition：Obtain TRMM meteorological satellite remote sensings image data, the MODIS satellite remote sensing shadows in Tibet region
As data and ASTER GDEM satellite remote-sensing image data, while collect the daily rainfall of ground observation website in the region of Tibet
Observation；Wherein MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products.Wherein：
The spatial resolution of TRMM meteorological satellite remote sensing image datas is 0.25 ° × 0.25 °, and temporal resolution is 3 hours；Described
The spatial resolution of ASTER GDEM satellite remote-sensing image data is 30m；The space of described MODIS satellite remote-sensing image data
Resolution ratio is 1km, and temporal resolution is 8 days.
Step 2) data prediction：At the temporal resolution for the TRMM meteorological satellite remote sensing image datas that step 1) is obtained
Manage as the moon, as shown in Figure 1；ASTER GDEM satellite remote-sensing images data are subjected to polymerization calculating and respectively obtain spatial resolution
For 1km and 25km dem data；Surface temperature on daytime and evening surface temperature parameter are extracted from MOD11A2 data products, and
By polymerize calculate respectively obtain spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be
1km and 25km evening surface temperature data；Vegetation index parameter is extracted from MOD13A2 data products, is picked by exceptional value
After processing, the vegetation index data that spatial resolution is 1km and 25km are respectively obtained by polymerizeing to calculate；Wherein, exceptional value
Reject comprising the following steps that for processing：Using the vegetation index extracted in MOD13A2 data products as initial vegetation index, first
The part that grid point value in initial vegetation index is less than 0 is deleted, then with 11 × 11 window gliding smoothing vegetation index, then with just
Beginning vegetation index subtract it is smooth after vegetation index, reselection -0.15 to 0.15 enters as threshold range to the result after subtracting each other
Row screening, casts out the grid beyond threshold range, remaining is as normal vegetation index point.Finally, from ASTER GDEM satellites
The gradient is extracted in remote sensing image data, 4 Topographic Wetness Index, Barrier facility and slope aspect parameters are carried out polymerizeing calculating and respectively obtained
1km and 25km Gradient, Topographic Wetness Index data, Barrier facility data and slope aspect data；
Step 3) carries out Geographical Weighted Regression modeling：TRMM meteorological satellite remote sensings image data after step 2) processing is made
For dependent variable, with spatial resolution be 25km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data pass through as independent variable and 1km
Geographical Weighted Regression is carried out between latitude coordinate point, so as to obtain each grid point in the raster data that spatial resolution is 1km
Regression equation and spatial resolution be 25km rainfall regression residuals value；Described 1km latitude and longitude coordinates point is space minute
The latitude and longitude coordinates of each grid point in the raster data that resolution is 1km；
Step 4) NO emissions reduction is predicted：Based on each grid in the raster data that the spatial resolution obtained by step 3) is 1km
Point regression equation, using spatial resolution as 1km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data as input from becoming
Amount, is calculated, it is 1km ground prediction of precipitation Value Data to obtain spatial resolution；It is simultaneously 25km's by spatial resolution
Rainfall regression residuals value carries out resampling and obtains the rainfall regression residuals value that spatial resolution is 1km, and by itself and spatial discrimination
Rate is added for 1km ground prediction of precipitation Value Data, obtains spatial resolution as 1km TRMM meteorological satellite rainfall data and leads
Enter and charted into ArcGIS, after rendering as shown in Figure 2.
The precision analysis of step 5) prediction of precipitation value：Using the method for crosscheck to the 1km spaces in step 4) point
The prediction of precipitation value of resolution be predicted precision test analysis, crosscheck from root-mean-square error, mean absolute error with
And coefficient correlation is as evaluation points.As shown in Figure 5, coefficient R2For 0.651, root-mean-square error RMSE is
39.578mm, mean absolute error MEA are 29.611mm.
The calculation formula of each index is as follows：
What MAE was represented in formula is mean absolute error, and what RMSE was represented is root-mean-square error, R2What is represented is to return correlation
Coefficient, YkIt is ground observation website k observation, OkBe by the predicted value after model NO emissions reduction at site k,It is all
The average value of ground rainfall observation station data,It is the average value in the model predication value of all websites.
Embodiment 2
Select to carry out regression modeling in M5-LocalR methods in the present embodiment, concretely comprise the following steps：
NO emissions reduction prediction is carried out with Geographical Weighted Regression in the present embodiment, comprised the following steps that：
Tibet region is chosen as survey region, the moon rainfall of 2003-2009 rainy seasons (annual May-October) is entered
Row forecasting research, finally give the rainfall distribution map of monthly 1km spatial resolutions.
Step 1) data acquisition：Obtain TRMM meteorological satellite remote sensings image data, the MODIS satellite remote sensing shadows in Tibet region
As data and ASTER GDEM satellite remote-sensing image data, while collect the daily rain amount of ground observation website in the Tibet region
Discharge observation value；Wherein MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products.Wherein：
The spatial resolution of TRMM meteorological satellite remote sensing image datas is 0.25 ° × 0.25 °, and temporal resolution is 3 hours；Described
The spatial resolution of ASTER GDEM satellite remote-sensing image data is 30m；The space of described MODIS satellite remote-sensing image data
Resolution ratio is 1km, and temporal resolution is 8 days.
Step 2) data prediction：At the temporal resolution for the TRMM meteorological satellite remote sensing image datas that step 1) is obtained
Manage as the moon；It is 1km and 25km that ASTER GDEM satellite remote-sensing images data, which are carried out polymerizeing calculating to respectively obtain spatial resolution,
Dem data；Surface temperature on daytime and evening surface temperature parameter are extracted from MOD11A2 data products, and is counted by polymerizeing
Calculate respectively obtain spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be 1km and 25km
Evening surface temperature data；Vegetation index parameter is extracted from MOD13A2 data products, after abnormality value removing is handled, is led to
Cross polymerization and calculate the vegetation index data for respectively obtaining that spatial resolution is 1km and 25km；Wherein：The tool of abnormality value removing processing
Body step is as follows：Using the vegetation index extracted in MOD13A2 data products as initial vegetation index, initial vegetation is deleted first
Grid point value is less than 0 part in index, then with 11 × 11 window gliding smoothing vegetation index, is then subtracted with initial vegetation index
Vegetation index after going smoothly, reselection -0.15 to 0.15 are screened to the result after subtracting each other as threshold range, cast out super
Go out the grid of threshold range, remaining is as normal vegetation index point.Finally, from ASTER GDEM satellite remote-sensing image data
4 the extraction gradient, Topographic Wetness Index, Barrier facility and slope aspect parameters carry out polymerization and calculate the slope for respectively obtaining 1km and 25km
Degrees of data, Topographic Wetness Index data, Barrier facility data and slope aspect data；
Step 3) carries out M5-LocalR regression modelings：By the TRMM meteorological satellite remote sensing image datas after step 2) processing
As dependent variable, with spatial resolution be 25km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data pass through as independent variable and 1km
Geographical Weighted Regression is carried out using M5-LocalR methods between latitude coordinate point, so as to obtain the grid that spatial resolution is 1km
The regression equation of each grid point and the rainfall regression residuals value that spatial resolution is 25km in data；Wherein：1km longitudes and latitudes
Coordinate points are the latitude and longitude coordinates of each grid point in the raster data that spatial resolution is 1km；
What M5-LocalR regression modelings used comprises the following steps that：First by the surface temperature on daytime after step 2) processing
Data, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and ground
Shape humidity index data carry out principal component analysis using M5 methods, so as to obtain the main-control factors of each grid point, such as Fig. 3 institutes
Show.M5 methods in the present embodiment can also use other principal component analytical methods to carry out, as long as principal component spatially can be realized
Analysis.Then using the main-control factors of each grid point as input independent variable, the TRMM meteorologies after being handled with step 2) are defended
Star remote sensing image data carries out Geographical Weighted Regression as dependent variable.
Step 4) NO emissions reduction is predicted：Based on each grid in the raster data that the spatial resolution obtained by step 3) is 1km
Point regression equation, using spatial resolution as 1km daytime surface temperature data, evening surface temperature data, vegetation index number
According to, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data as input from becoming
Amount, is calculated, it is 1km ground prediction of precipitation Value Data to obtain spatial resolution；It is simultaneously 25km's by spatial resolution
Rainfall regression residuals value carries out resampling and obtains the rainfall regression residuals value that spatial resolution is 1km, and by itself and spatial discrimination
Rate is added for 1km ground prediction of precipitation Value Data, obtains spatial resolution as 1km TRMM meteorological satellite rainfall data and leads
Enter and charted into ArcGIS, after rendering as shown in Figure 4.
The precision analysis of step 5) prediction of precipitation value：Using the method for crosscheck to the 1km spaces in step 4) point
The prediction of precipitation value of resolution be predicted precision test analysis, crosscheck from root-mean-square error, mean absolute error with
And coefficient correlation is as evaluation points.As shown in fig. 6, the coefficient R wherein obtained2For 0.844, root-mean-square error RMSE is
25.04mm, mean absolute error 17.17mm.It is obvious that improved on precision of prediction compared with example 1.
Claims (3)
- A kind of 1. TRMM satellite rainfall data NO emissions reduction methods based on M5-LocalR, it is characterised in that comprise the following steps：Step 1) data acquisition：Obtain TRMM meteorological satellite remote sensings image data, the MODIS satellite remote-sensing image numbers in region to be measured According to this and ASTER GDEM satellite remote-sensing image data, while collect the daily rainfall of ground observation website in the region to be measured and see Measured value；Wherein MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products；Step 2) data prediction：The temporal resolution for the TRMM meteorological satellite remote sensing image datas that step 1) is obtained is handled Month；ASTER GDEM satellite remote-sensing images data are subjected to polymerization calculating and respectively obtain the DEM that spatial resolution is 1km and 25km Data；Surface temperature on daytime and evening surface temperature parameter are extracted from MOD11A2 data products, and difference is calculated by polymerizeing It is 1km and 25km surface temperature data and spatial resolution is 1km and 25km evening on daytime to obtain spatial resolution Table temperature data；Vegetation index parameter is extracted from MOD13A2 data products, after abnormality value removing is handled, passes through polymerization Calculate the vegetation index data for respectively obtaining that spatial resolution is 1km and 25km；From ASTER GDEM satellite remote-sensing image data 4 the middle extraction gradient, Topographic Wetness Index, Barrier facility and slope aspect parameters carry out polymerization calculating and respectively obtain 1km's and 25km Gradient, Topographic Wetness Index data, Barrier facility data and slope aspect data；Step 3) carries out regression modeling：TRMM meteorological satellite remote sensings image data after step 2) is handled as dependent variable, with Spatial resolution be 25km daytime surface temperature data, evening surface temperature data, vegetation index data, dem data, slope Degrees of data, slope aspect data, Barrier facility data and Topographic Wetness Index data are as between independent variable and 1km latitude and longitude coordinates point Carry out Geographical Weighted Regression, so as to obtain in the raster data that spatial resolution is 1km the regression equation of each grid point and Spatial resolution is 25km rainfall regression residuals value；Described 1km latitude and longitude coordinates point is the grid that spatial resolution is 1km The latitude and longitude coordinates of each grid point in data；Described Geographical Weighted Regression uses M5-LocalR regression modelings, M5-LocalR be combine M5 principal component analytical methods and The local weighted regression modeling method of LocalR Geographically weighted regression procedures, the specific steps that M5-LocalR regression modelings use It is as follows：First by step 2) processing after daytime surface temperature data, evening surface temperature data, vegetation index data, DEM numbers According to, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out principal component analysis, it is every so as to obtain The main-control factors of individual grid point；So using the main-control factors of each grid point as independent variable, the TRMM after being handled with step 2) is meteorological Satellite remote-sensing image data carry out Geographical Weighted Regression as dependent variable；Step 4) NO emissions reduction is predicted：Based on each grid point in the raster data that the spatial resolution obtained by step 3) is 1km Regression equation, using spatial resolution as 1km daytime surface temperature data, evening surface temperature data, vegetation index data, Dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data enter as input independent variable Row calculates, and it is 1km ground prediction of precipitation Value Data to obtain spatial resolution；The rainfall that spatial resolution is 25km is returned simultaneously Return residual values to carry out resampling and obtain the rainfall regression residuals value that spatial resolution is 1km, and be 1km by itself and spatial resolution Ground prediction of precipitation Value Data is added, and obtains the TRMM meteorological satellite rainfall data that spatial resolution is 1km.
- 2. the TRMM satellite rainfall data NO emissions reduction methods based on M5-LocalR as claimed in claim 1, it is characterised in that In described step 1), the spatial resolution of TRMM meteorological satellite remote sensing image datas is 0.25 ° × 0.25 °, temporal resolution For 3 hours；The spatial resolution of described ASTER GDEM satellite remote-sensing image data is 30m；Described MODIS satellite remote sensings The spatial resolution of image data is 1km, and temporal resolution is 8 days.
- 3. the TRMM satellite rainfall data NO emissions reduction methods based on M5-LocalR as claimed in claim 1, it is characterised in that What abnormality value removing was handled in described step 2) comprises the following steps that：The vegetation index that will be extracted in MOD13A2 data products As initial vegetation index, the part that grid point value in initial vegetation index is less than 0 is deleted first, then move with 11 × 11 window Smooth vegetation index, the vegetation index after then being subtracted smoothly with initial vegetation index, reselection -0.15 to 0.15 are used as threshold value Scope is screened to the result after subtracting each other, and casts out the grid beyond threshold range, and remaining is as normal vegetation index point.
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| CN105893736A (en) * | 2016-03-23 | 2016-08-24 | 中国科学院遥感与数字地球研究所 | Passive microwave soil moisture reality check method based on multi-source data type |
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