CN106019408B - A kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data - Google Patents
A kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data Download PDFInfo
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- CN106019408B CN106019408B CN201610307332.3A CN201610307332A CN106019408B CN 106019408 B CN106019408 B CN 106019408B CN 201610307332 A CN201610307332 A CN 201610307332A CN 106019408 B CN106019408 B CN 106019408B
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Abstract
The invention discloses a kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data.The 1km environmental variance factor is included 9 vegetation index, digital elevation model, earth's surface temperature on daytime, evening earth's surface temperature, Topographic Wetness Index, the gradient, roughness of ground surface, Reflectivity for Growing Season and the lowest point flattening index data aggregates first and calculates and arrive 25km by the present invention, as independent variable, the TMPA 3B43 v7 precipitation datas of corresponding 25km resolution ratio are modeled as dependent variable, and the model of foundation is applied in the 1km environmental variance factors of corresponding geographic area, finally draw 1km high-precision Prediction of Precipitation data.The present invention is based on multi- source Remote Sensing Data data, it is proposed that a kind of high resolution ratio satellite remote-sensing evaluation method, finally gives the Prediction of Precipitation value of 1km spatial resolutions.This method precision of prediction is higher, and method is simple.
Description
Technical field
The present invention relates to a kind of high-precision drafting algorithm of remote sensing data, and in particular to based on TMPA
3B43 v7 high-precision precipitation data modeling and forecasting algorithm.
Technical background
Precipitation has served as key player in fields such as hydrology, meteorology, ecology and agricultural researches, particularly the whole world
One of yardstick Exchange of material and energy main drive.Surface-based observing station is a kind of widely used Rainfall estimation means, and is had
There is the characteristics of precision height and technology maturation.But the precipitation 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 large-area precipitation distribution characteristics, it is especially sparse in surface-based observing station cloth reticular density
Highlands.And satellite remote sensing technology can provide the precipitation data compared with high-spatial and temporal resolution, covering spatial dimension is wider, very
The good limitation for overcoming surface precipitation observation station and rain detection radar, strong data supporting is provided for Global Precipitation 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 U.S. torrid zone Rainfall estimation satellite (Tropical Rainfall Measuring Mission) Precipitation Products TMPA
3B43 v7.TMPA precipitation satellite provides the precipitation data in the region within 50 ° of S~50 ° N covering the whole world.But TRMM satellites
Original resolution it is relatively low (spatial resolution be 0.25 °, about 25km), there is certain office in terms of the yardstick precipitation of estimation range
Sex-limited and deviation, it is therefore desirable to the raising of spatial resolution is carried out for TMPA data, so as to obtain the higher precipitation of resolution ratio
Measured value.
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 multi- source Remote Sensing Data data
High resolution ratio satellite remote-sensing evaluation method.
The concrete technical scheme of the present invention is as follows：
A kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data, comprises the following steps：
Step 1) data acquisition：Obtain TMPA 3B43 v7 precipitation datas, the MODIS satellite remote-sensing image numbers in region to be measured
According to this and ASTER GDEM satellite remote-sensing image data, while the intra day ward observation of ground observation website in region to be measured is collected
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 TMPATMPA 3B43 v7 precipitation 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 extracting data, polymerization calculate the gradient for respectively obtaining 1km and 25km, Topographic Wetness Index, Barrier facility, the flat finger in the lowest point
Number, roughness of ground surface and Reflectivity for Growing Season data；
Step 3) is modeled and parameter calibration：By the 25kmTMPATMPA 3B43 v7 precipitation datas after step 2) processing
As dependent variable, using spatial resolution as 25km vegetation index, digital elevation model, earth's surface temperature on daytime, evening earth's surface temperature,
9 shape humidity index, the gradient, roughness of ground surface, Reflectivity for Growing Season and the lowest point flattening index data are modeled as independent variable
And parameter calibration.
The high-precision precipitation data prediction drawing of step 4)：The model established based on step 3) under 25km spatial resolutions should
Use in the environmental variance that spatial resolution is 1km and be modeled prediction, so as to obtain 1km high-precision precipitation data；Simultaneously
It is 1km that precipitation residual values that spatial resolution is 25km, which are carried out resampling to obtain spatial resolution, and by itself and spatial discrimination
Rate is that 1km surface precipitations amount predicts that Value Data is added, and obtains the high-precision precipitation data that spatial resolution is 1km.
In described step 1), the spatial resolution of TMPA 3B43 v7 precipitation datas is 0.25 ° × 0.25 °, the time point
Resolution is the moon；The spatial resolution of described ASTER GDEM satellite remote-sensing image data is 90m；Described MODIS satellites are distant
The spatial resolution of sense image data is 1km, and temporal resolution is 8 days.
The common version of parameter estimation models is used by being modeled in described step 3)：
Wherein, N represents independent variable number in parameter estimation models；anRepresent the coefficient of n-th of environmental variance；a0Represent mould
The constant term coefficient of shape parameter；ynRepresent prediction of precipitation value；xnRepresent n-th of environmental variance；
a0And anCalculation formula it is as follows：
Wherein:K represents ground observation website number；xinRepresent n-th of environmental variance of i-th of ground observation website
Value, yiWhat is represented is the intra day ward observation of i-th of ground observation website,The average of n-th of environmental variance factor is represented,Represent the average of the intra day ward observation of all ground observation websites.
Model in heretofore described step 3) after parameter calibration is：
Yprecip=210.088+0.102 × Xdem-20.7×Xlst_day+40.4×Xlst_night-8.4×Xslope+0.54×
Xrug+1201×Xndvi-23×Xtwi+0.0005×Xrad+31×Xmrvbf
Wherein YprecipIt is 1km ground precipitation predicting value, XdemWhat is represented is the grid point value of 1km digital elevation models,
Xlst_dayWhat is represented is 1km surface temperature on daytime grid point values, Xlst_nightWhat is represented is 1km evening surface temperature grid point values, Xslope
What is represented is 1km gradient grid point values, XndviWhat is represented is 1km vegetation index grid point values, XtwiWhat is represented is that 1km landform humidity refers to
Number grid point value, XrugWhat is represented is 1km roughness of ground surface, XradWhat is represented is 1km Reflectivity for Growing Season, XmrvbfWhat is represented is 1km paddy
Bottom flattening index.
The present invention is based on multi- source Remote Sensing Data data, it is proposed that a kind of high resolution ratio satellite remote-sensing evaluation method, finally gives 1km
The Prediction of Precipitation value of spatial resolution.This method precision of prediction is higher, and method is simple.
Embodiment
With reference to specific embodiment, the present invention is further described.
Choose China and be used as survey region, high-precision forecast drawing research is carried out to 2008-2012 moon rainfall, most
The Prediction of Precipitation value of 1km spatial resolutions is obtained eventually.
Step 1) data acquisition：Obtain TMPA 3B43 v7 precipitation datas, the MODIS satellite remote-sensing image numbers in region to be measured
According to this and ASTER GDEM satellite remote-sensing image data, while the intra day ward observation of ground observation website in region to be measured is collected
Value；Wherein MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products；TMPA 3B43 v7
The spatial resolution of precipitation data is 0.25 ° × 0.25 °, and temporal resolution is the moon；Described ASTER GDEM satellite remote sensing shadows
As the spatial resolution of data is 90m；The spatial resolution of described MODIS satellite remote-sensing image data is 1km, time resolution
Rate is 8 days.
Step 2) data prediction：At the temporal resolution for the TMPATMPA 3B43 v7 precipitation 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 extracting data, polymerization calculate the gradient for respectively obtaining 1km and 25km, Topographic Wetness Index, Barrier facility, the flat finger in the lowest point
Number, roughness of ground surface and Reflectivity for Growing Season data；
Step 3) is modeled and parameter calibration：By the 25kmTMPATMPA 3B43 v7 precipitation datas after step 2) processing
As dependent variable, using spatial resolution as 25km vegetation index, digital elevation model, earth's surface temperature on daytime, evening earth's surface temperature,
9 shape humidity index, the gradient, roughness of ground surface, Reflectivity for Growing Season and the lowest point flattening index data are modeled as independent variable
And parameter calibration.
Parameter estimation models form is used by modeling：
Wherein, N represents independent variable number in parameter estimation models；anRepresent the coefficient of n-th of environmental variance；a0Represent mould
The constant term coefficient of shape parameter；ynRepresent prediction of precipitation value；xnRepresent n-th of environmental variance；
a0And anCalculation formula it is as follows：
Wherein:K represents ground observation website number；xinRepresent n-th of environmental variance of i-th of ground observation website
Value, yiWhat is represented is the intra day ward observation of i-th of ground observation website,The average of n-th of environmental variance factor is represented,Represent the average of the intra day ward observation of all ground observation websites.
Model after parameter calibration of the present invention is：
Yprecip=210.088+0.102 × Xdem-20.7×Xlst_day+40.4×Xlst_night-8.4×Xslope+0.54×
Xrug+1201×Xndvi-23×Xtwi+0.0005×Xrad+31×Xmrvbf
Wherein YprecipIt is 1km ground precipitation predicting value, XdemWhat is represented is the grid point value of 1km digital elevation models,
Xlst_dayWhat is represented is 1km surface temperature on daytime grid point values, Xlst_nightWhat is represented is 1km evening surface temperature grid point values, Xslope
What is represented is 1km gradient grid point values, XndviWhat is represented is 1km vegetation index grid point values, XtwiWhat is represented is that 1km landform humidity refers to
Number grid point value, XrugWhat is represented is 1km roughness of ground surface, XradWhat is represented is 1km Reflectivity for Growing Season, XmrvbfWhat is represented is 1km paddy
Bottom flattening index.
The high-precision precipitation data prediction drawing of step 4)：The model established based on step 3) under 25km spatial resolutions should
Use in the environmental variance that spatial resolution is 1km and be modeled prediction, so as to obtain 1km high-precision precipitation data；Simultaneously
It is 1km that precipitation residual values that spatial resolution is 25km, which are carried out resampling to obtain spatial resolution, and by itself and spatial discrimination
Rate is that 1km surface precipitations amount predicts that Value Data is added, and obtains the high-precision precipitation data that spatial resolution is 1km.
The precision analysis of step 5) precipitation predicted value：Using surface precipitation eyeball to the 1km spaces in step 4) point
The precipitation predicted 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.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
Surface precipitation observes the average value of station data,It is the average value in the model predication value of all websites.
Finally, coefficient R2For 0.676, root-mean-square error RMSE is 37.928mm, and mean absolute error MEA is
28.654mm。
Claims (2)
1. a kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data, it is characterised in that comprise the following steps：
Step 1) data acquisition：Obtain TMPA 3B43 v7 precipitation datas, the MODIS satellite remote-sensing images data in region to be measured with
And ASTERGDEM satellite remote-sensing image data, while collect the intra day ward observation of ground observation website in region to be measured；Its
Middle MODIS satellite remote-sensing images data include MOD11A2 data products and MOD13A2 data products；
Step 2) data prediction：The temporal resolution processing for the TMPA 3B43 v7 precipitation datas that step 1) is obtained is the moon；
ASTER GDEM satellite remote-sensing images data are subjected to polymerization calculating and respectively obtain the DEM numbers that spatial resolution is 1km and 25km
According to；Surface temperature on daytime and evening surface temperature parameter are extracted from MOD11A2 data products, and is obtained respectively by polymerizeing to calculate
To spatial resolution it is 1km and 25km surface temperature data and spatial resolution is 1km and 25km evening earth's surface on daytime
Temperature data；Vegetation index parameter is extracted from MOD13A2 data products, after abnormality value removing is handled, is counted by polymerizeing
Calculate the vegetation index data for respectively obtaining that spatial resolution is 1km and 25km；From ASTER GDEM satellite remote-sensing image data
Extraction, polymerization calculate the gradient, Topographic Wetness Index, Barrier facility, the lowest point flattening index, the earth's surface for respectively obtaining 1km and 25km
Roughness and Reflectivity for Growing Season data；
Step 3) is modeled and parameter calibration：25kmTMPA 3B43 v7 precipitation datas after step 2) is handled are as because becoming
Amount, refers to by 25km vegetation index, digital elevation model, earth's surface temperature on daytime, evening earth's surface temperature, landform humidity of spatial resolution
Number, the gradient, roughness of ground surface, 9 data of Reflectivity for Growing Season and the lowest point flattening index are modeled as independent variable and parameter rate
It is fixed；
Used parameter estimation models form is：
Wherein, N represents independent variable number in parameter estimation models；anRepresent the coefficient of n-th of environmental variance；a0Represent model ginseng
Several constant term coefficients；ynRepresent prediction of precipitation value；xnRepresent n-th of environmental variance；
a0And anCalculation formula it is as follows：
Wherein:K represents ground observation website number；xinRepresent the value of n-th of environmental variance of i-th of ground observation website, yi
What is represented is the intra day ward observation of i-th of ground observation website,The average of n-th of environmental variance factor is represented,Generation
The average of the intra day ward observation of all ground observation websites of table；
Model after parameter calibration is：
Yprecip=210.088+0.102 × Xdem-20.7×Xlst_day+40.4×Xlst_night-8.4×Xslope+0.54×Xrug+
1201×Xndvi-23×Xtwi+0.0005×Xrad+31×Xmrvbf
Wherein YprecipIt is 1km ground precipitation predicting value, XdemRepresent be 1km digital elevation models grid point value, Xlst_dayRepresent
Be 1km surface temperature on daytime grid point values, Xlst_nightWhat is represented is 1km evening surface temperature grid point values, XslopeRepresent be
1km gradient grid point values, XndviWhat is represented is 1km vegetation index grid point values, XtwiWhat is represented is 1km Topographic Wetness Index grid point values,
XrugWhat is represented is 1km roughness of ground surface, XradWhat is represented is 1km Reflectivity for Growing Season, XmrvbfWhat is represented is the flat finger in 1km the lowest point
Number；
The high-precision precipitation data prediction drawing of step 4)：The model established based on step 3) under 25km spatial resolutions is applied to
Spatial resolution is to be modeled prediction in 1km environmental variance, so as to obtain 1km high-precision precipitation data；Simultaneously by sky
Between resolution ratio be 25km precipitation residual values carry out resampling to obtain spatial resolution be 1km, and be by itself and spatial resolution
1km surface precipitations amount prediction Value Data is added, and obtains the high-precision precipitation data that spatial resolution is 1km.
2. a kind of high resolution ratio satellite remote-sensing evaluation method based on multi- source Remote Sensing Data data as claimed in claim 1, its feature
It is, in described step 1), the spatial resolution of TMPA 3B43 v7 precipitation datas is 0.25 ° × 0.25 °, temporal resolution
For the moon；The spatial resolution of described ASTER GDEM satellite remote-sensing image data is 90m；Described MODIS satellite remote sensing shadows
As the spatial resolution of data is 1km, temporal resolution is 8 days.
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| CN106776481B (en) * | 2016-11-29 | 2020-05-05 | 河海大学 | Downscaling correction method for satellite precipitation data |
| CN106918394B (en) * | 2017-01-24 | 2019-01-29 | 中国科学院地理科学与资源研究所 | A kind of effective MODIS surface temperature angle correction method |
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| CN108009392B (en) * | 2017-10-19 | 2022-01-04 | 桂林航天工业学院 | Remote sensing reflectivity model construction and calibration application method for dense vegetation surface |
| CN108268735A (en) * | 2018-01-29 | 2018-07-10 | 浙江大学 | Soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data |
| CN109447325A (en) * | 2018-09-30 | 2019-03-08 | 广州地理研究所 | Precipitation data detection method, device and electronic equipment based on random forests algorithm |
| CN109472393B (en) * | 2018-09-30 | 2021-01-15 | 广州地理研究所 | Spatial downscaling precipitation data detection method and device and electronic equipment |
| CN110427995B (en) * | 2019-07-24 | 2023-11-17 | 中国科学院空天信息创新研究院 | Bayesian soil moisture estimation method based on multi-source remote sensing data |
| CN116310847A (en) * | 2023-05-19 | 2023-06-23 | 中国工商银行股份有限公司 | Farmland analysis method and device, processor and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103810376A (en) * | 2014-01-17 | 2014-05-21 | 浙江大学 | Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging |
| CN104820754A (en) * | 2015-05-13 | 2015-08-05 | 南京信息工程大学 | Space statistical downscaling rainfall estimation method based on geographical difference analysis method |
| CN105160192A (en) * | 2015-09-17 | 2015-12-16 | 浙江大学 | TRMM (Tropical Rainfall Measuring Mission) satellite rainfall data downscaling method based on M5-Local |
| CN105550423A (en) * | 2015-12-09 | 2016-05-04 | 浙江大学 | CMORPH satellite precipitation data downscaling method based on Fuzzy-OLS (Ordinary Least Squares) |
-
2016
- 2016-05-10 CN CN201610307332.3A patent/CN106019408B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103810376A (en) * | 2014-01-17 | 2014-05-21 | 浙江大学 | Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging |
| CN104820754A (en) * | 2015-05-13 | 2015-08-05 | 南京信息工程大学 | Space statistical downscaling rainfall estimation method based on geographical difference analysis method |
| CN105160192A (en) * | 2015-09-17 | 2015-12-16 | 浙江大学 | TRMM (Tropical Rainfall Measuring Mission) satellite rainfall data downscaling method based on M5-Local |
| CN105550423A (en) * | 2015-12-09 | 2016-05-04 | 浙江大学 | CMORPH satellite precipitation data downscaling method based on Fuzzy-OLS (Ordinary Least Squares) |
Non-Patent Citations (1)
| Title |
|---|
| 中高纬度地区TRMM卫星降雨数据的精度评价;蔡研聪 等;《应用生态学报》;20141130;第25卷(第11期);全文 * |
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