CN112329265A - Satellite remote sensing rainfall refinement space estimation method and system - Google Patents
Satellite remote sensing rainfall refinement space estimation method and system Download PDFInfo
- Publication number
- CN112329265A CN112329265A CN202011341530.4A CN202011341530A CN112329265A CN 112329265 A CN112329265 A CN 112329265A CN 202011341530 A CN202011341530 A CN 202011341530A CN 112329265 A CN112329265 A CN 112329265A
- Authority
- CN
- China
- Prior art keywords
- regression
- spatial
- satellite
- data
- remote sensing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000001556 precipitation Methods 0.000 claims abstract description 40
- 230000001419 dependent effect Effects 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000000611 regression analysis Methods 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims abstract description 5
- 238000012417 linear regression Methods 0.000 claims abstract description 5
- 238000012216 screening Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 28
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 6
- 230000009977 dual effect Effects 0.000 claims description 4
- 150000001875 compounds Chemical class 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 6
- 238000011160 research Methods 0.000 description 9
- 230000009467 reduction Effects 0.000 description 5
- 238000012952 Resampling Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001134 F-test Methods 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012950 reanalysis Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Abstract
The invention relates to the field of remote sensing image processing, and discloses a satellite remote sensing rainfall refinement space estimation method and system, which are used for meeting the requirements of improving the spatial resolution and maintaining the data accuracy. The method comprises the following steps: analyzing the correlation coefficient between the annual precipitation data of the satellite and each influence factor, and screening out an explanatory variable set by adopting a stepwise linear regression method; taking satellite precipitation as a dependent variable, taking a screened explanatory variable set as an explanatory variable, performing regression analysis by adopting a mixed geography weighted regression model fusing least square method global regression and geography weighted regression, and obtaining high-resolution regression trend surface space data according to a calculation result of a regression coefficient; interpolating the regression residual by adopting a common kriging interpolation method to obtain residual interpolation space data; and adding the regression trend surface and the residual interpolation spatial data, and performing downscaling conversion according to the target spatial resolution to obtain estimated satellite annual precipitation data.
Description
Technical Field
The invention relates to the field of remote sensing image processing, in particular to a satellite remote sensing rainfall refinement space estimation method and system.
Background
The precipitation is used as a key link of global atmosphere and water circulation, has important significance in the research fields of weather, agriculture, environment and the like, and can provide an effective data support means for the aspects of agricultural production, water resource management, ecological environment management, disaster prevention and reduction and the like. Spatial precipitation data is theoretically available through spatially discrete meteorological and hydrological stations, but is established on conditions where the meteorological or hydrological stations are sufficiently dense and uniformly distributed. Although china has a large number of weather and hydrological stations as the largest developing countries in the world, china is far from being full of countless areas of countryside, and many stations are distributed disorderly and are mostly densely distributed in the eastern coastal areas with high economy. In the Qinghai-Tibet plateau, Tianshan mountain and its peripheral region of China, and many mountains with complex terrain, etc., there are few sites distributed sporadically, and some regions even have no station layout at all.
The rainfall data is obtained by satellite remote sensing, and the method has the advantages of wide measurement range, continuous distribution, strong situational performance, high precision and the like. The rapid development of rainfall is monitored by satellite remote sensing, and the method is widely applied to large-scale climate prediction and hydrological models by virtue of the advantages of large range, high timeliness, easiness in acquisition and the like. At present, the wind and cloud fourth satellite inversion precipitation product is widely applied to the fields of meteorology, climate, hydrology and the like.
However, under the existing technical conditions, the remote sensing detection of precipitation by the wind and cloud four-satellite is limited by the limitations of sensor performance, cloud layer properties, inversion algorithm and the like, the quantitative error is prominent, and the application and development of the wind and cloud four-satellite in the small-scale fine research field are restricted by the coarse spatial resolution. Along with the development of the remote sensing satellite rain measuring technology, a rainfall information fusion concept is proposed by a scholars and is gradually applied to quantitative estimation of space rainfall, and an important direction is pointed out for the fine research of the space rainfall. At present, a great deal of research has been carried out by a great number of scholars at home and abroad in the field, and the field of precipitation information fusion is greatly developed, but the precipitation cause is complex, the precipitation cause is interfered by a plurality of factors such as water vapor condition, atmospheric circulation, vegetation condition and even human activities, the precipitation cause is obviously non-stable in space, the capability of carving space precipitation under the influence of complex terrain is still limited, and the exploration and research of related fields still need to be made.
Aiming at the problems, under the basic idea of rainfall information fusion, the method seeks a more optimal solution of the spatialization of the rainfall data in the complex terrain area through multi-source data such as ground, exploration, remote sensing, reanalysis data and the like.
Disclosure of Invention
The invention aims to disclose a satellite remote sensing rainfall refinement space estimation method and system, which can meet the requirements of improving the spatial resolution and maintaining the data accuracy.
In order to achieve the purpose, the invention discloses a satellite remote sensing rainfall refinement space estimation method, which comprises the following steps:
analyzing the correlation coefficient between the annual precipitation data of the satellite and each influence factor, and screening out an explanatory variable set by adopting a stepwise linear regression method;
taking satellite precipitation as a dependent variable, taking a screened explanatory variable set as an explanatory variable, performing regression analysis by adopting a mixed geography weighted regression model fusing least square method global regression and geography weighted regression, and obtaining high-resolution regression trend surface space data according to a calculation result of a regression coefficient;
interpolating the regression residual by adopting a common kriging interpolation method to obtain residual interpolation space data;
and adding the regression trend surface and the residual interpolation spatial data, and performing downscaling conversion according to the target spatial resolution to obtain estimated satellite annual precipitation data.
Optionally, the influencing factors include longitude, latitude, altitude, grade, slope, relief, and normalized vegetation index.
Preferably, the calculation of the residual interpolated spatial data comprises: by using GS+And 9, performing half-variance function fitting on the residual error of the mixed geographic weighting regression model by software, obtaining function parameters according to the fitting, and performing spatial interpolation on the regression residual error of the model by using a common kriging interpolation method, wherein the function parameters comprise step length, lump sum and variable range.
Preferably, the target spatial resolution is 1km × 1 km.
Preferably, the basic form of the hybrid geo-weighted regression model is:
in the formula, yiIs the target variable at the position i,
regression coefficient matrix, x, for geoweighted regressionk,iIs the k local variable at position i, γlRegression coefficient of global regression in constant form, xl,iIs the l-th global variable, ε, at position iiThe regression residual error at the position i is shown, m is the number of local variables, and n is the total number of explanatory variables;
in the geoweighted regression model, the regression coefficient of the explanatory variables is no longer a constant but a function related to the spatial position, position (u)i,vi) The regression coefficient of (b) is calculated by the following formula:
in the formula (I), the compound is shown in the specification,
is a regression coefficient matrix; x is an explanatory variable design matrix, XTA transposed matrix that is X; y is a dependent variable matrix; w(ui,vi)Is a spatial weight matrix formed by a spatial weight function W(i,j)To find, the spatial weight function selects a dual square kernel function as the spatial weight function, that is:
in the formula (d)ijIs the distance of samples i and j, and b is the bandwidth.
In order to achieve the above object, the present invention further discloses a satellite remote sensing precipitation refinement space estimation system, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps corresponding to the above method when executing the computer program.
The invention has the following beneficial effects:
according to the method, the rainfall data inverted by the satellite, the related geographic factors and the normalized vegetation index (NDVI) are utilized, and the mixed geographic weighted regression model is established to perform downscaling processing on the rainfall data inverted by the satellite, so that the spatial resolution of a satellite rainfall product can be effectively increased, and the accuracy of the inverted rainfall data can be improved.
The method takes the Hunan province with obvious precipitation space difference as an example, the precipitation data inverted by the wind cloud satellite No. four in the Hunan province is subjected to scale reduction research, and the results of different scale reduction methods are compared and verified through the actual measurement data of 100 meteorological sites to obtain the method, wherein the method comprises the following steps: after the scale reduction processing, the spatial resolution of precipitation data inverted by the wind and cloud fourth satellite is improved from 5km to 1km, and the fineness of the data is obviously improved; compared with the traditional resampling method Bilinear method and the global regression Crigin method based on least square regression, the mixed geographic weighted regression model has higher precision, the constructed downscaling model meets the requirements of improving the spatial resolution and maintaining the data precision, and can provide effective data support for small-scale application research of precipitation products inverted by Fengyun No. four satellites.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a satellite remote sensing precipitation refinement space estimation method in an embodiment of the invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses a rainfall refinement space estimation method based on wind and cloud four-satellite remote sensing rainfall products.
As shown in fig. 1, the method of this embodiment includes the following steps:
step 1: and (3) analyzing the correlation coefficient between the annual precipitation amount (the spatial resolution is 5km multiplied by 5km, the target variable) of the wind and cloud four satellite precipitation product in the Hunan province and each influence factor (the spatial resolution is 1km multiplied by 1km), and screening out an explanatory variable set by adopting a stepwise linear regression method. The influence factors of the embodiment include 7 influence factors such as longitude, latitude, altitude, gradient, slope direction, topographic relief degree and normalized vegetation index NDVI; besides NDVI, other influencing factors can be obtained by respectively extracting corresponding terrain factors from the DEM data of the digital elevation model.
The process of stepwise regression to select variables involves two basic steps: firstly, removing variables which are not significant through inspection from the regression model, secondly, introducing new variables into the regression model, and the commonly used stepwise regression method comprises a forward method and a backward method. The embodiment adopts a forward method, and the idea of the forward method is that the variables are increased from less to more one at a time until no variable can be introduced. The method comprises the following specific steps:
(a) establishing a unitary regression model for the p regression independent variables respectively corresponding to the dependent variable Y:
Y＝β0+βiXi+ε,i＝1,2,…,p
wherein, beta0Is a constant number, betaiIs the regression parameter for the ith sample, and ε is the random error.
Calculating variable XiThe value of the F-test statistic of the corresponding regression coefficient is recorded
Taking the maximum value therein
Namely:
for a given significance level α, the corresponding threshold value is denoted as F(1)，
Then will be
Introducing a regression model, recording I1Is selected into the variable index set.
(b) Establishing dependent variable Y and independent variable subset
The regression model (i.e., the regression elements of the regression model are binary) has p-1 total regression models. The statistical value of regression coefficient Ftest of the calculated variable is recorded as
Choose the largest one, record as
Corresponding argument pin marked i2I.e. by
For a given significance level α, the corresponding threshold value is denoted as F(2)，
Then variable
A regression model was introduced. Otherwise, the variable import process is terminated.
(c) Considering dependent variable versus subset of variables
Repeat step 2. This process is repeated, one at a time, from the independent variables that are not introduced into the regression model, until no variables are introduced as tested.
Step 2: and (3) taking rainfall of a wind and cloud fourth satellite as a dependent variable, taking the screened influence factor set as an explanatory variable, performing regression analysis by adopting a mixed geography weighted regression model, and obtaining high-resolution (1km multiplied by 1km) regression trend surface spatial data according to a calculation result of a regression coefficient.
The core of the downscaling method is a hybrid geoweighted regression model. At present, a global regression method based on a least square method is mostly adopted for regression analysis, namely, homogeneity of the relation among variables is assumed, and a regression coefficient of each explanatory variable is a constant in the whole research area. Because the space non-stationarity characteristic of the relation between certain variables is not considered, the accuracy of the analysis result is limited to a certain extent. For this purpose, the embodiment proposes a mixed geography-weighted regression method combining the least square method global regression and geography-weighted regression, and the basic form is as follows:
in the formula, yiIs the target variable at the position i,
regression coefficient matrix, x, for geoweighted regressionk,iIs the k local variable at position i, γlRegression coefficients (constants), x, for global regressionl,iIs the l-th global variable, ε, at position iiThe regression residual at position i, m is the number of local variables and n is the total number of explanatory variables.
The mixed geography weighted regression model comprises a least square method global regression part and a geography weighted regression part. In the geoweighted regression model, the regression coefficients of the explanatory variables are no longer constants but functions related to the spatial position, in the following formula, the position (u)i,vi) The regression coefficient β at (a) is calculated by the following formula:
in the formula (I), the compound is shown in the specification,
is a regression coefficient matrix; x is an explanatory variable design matrix, XTA transposed matrix that is X; y is a dependent variable matrix;
is a spatial weight matrix formed by a spatial weight function W(i，j)Finding the observed value of j sample points at different spatial positions in the quantitative measurement field relative to the regression point (u)i,vi) Degree of influence of regression coefficient estimation. In practical applications, the commonly used spatial weighting functions mainly include gaussian kernel functions and dual square kernel functions. A dual square kernel function is chosen as the spatial weight function, namely:
in the formula (d)ijIs the distance between the sampling points i and j, b is the bandwidth (window size), and the optimum bandwidth is determined by the minimum AICc (Corrected Akaike Information Criterion) method.
As shown in fig. 1, prior to constructing the hybrid geo-weighted regression model, satellite data is preferably pre-processed in a correlation manner, including but not limited to: and (4) carrying out conventional precision evaluation by combining the rainfall data observed by the ground station, and then carrying out regional difference analysis to obtain the high-resolution rainfall background field.
And step 3: and (3) interpolating the regression residual by adopting a common Krigin interpolation method to obtain residual spatial data (the spatial resolution is 1km multiplied by 1 km).
This step uses GS+And 9, performing half-variance function fitting on the residual error of the regression model by software, and performing spatial interpolation on the regression residual error of the model by using a common kriging interpolation method according to the function parameters such as step length, block gold value and variation range obtained by fitting.
And 4, step 4: and adding the regression trend surface and the residual interpolation spatial data to finally obtain a wind and cloud No. four satellite annual precipitation data product with the spatial resolution of 1km multiplied by 1km, and realizing the downscaling conversion.
In summary, in this embodiment, a wind and cloud number four satellite precipitation spatial distribution map with a spatial resolution increased to 1km × 1km through downscaling processing is obtained through steps of obtaining a regression trend surface by superimposing the influencing factor variables and the regression coefficients thereof, and superimposing the regression trend surface and a residual interpolation result.
And 5: and for comparison, the global regression kriging and resampling Bilinear methods are adopted to reduce the scale of the Fengyun No. four satellite remote sensing precipitation product to 1km multiplied by 1km, and the results of different scale reduction methods are compared and verified through measured data of 100 meteorological sites.
The method comprises the steps of obtaining each downscaling result of the positions of 100 meteorological sites in the Hunan province area through an ArcGIS software value extraction to point tool, calculating 5 indexes including an average absolute error, a root mean square error, an average absolute percentage error, a root mean square relative error and a correlation coefficient according to site measured values and downscaling values, and comparing and verifying downscaling precision of different models.
According to the rainfall refinement space estimation method disclosed by the embodiment, after the downscaling processing, the spatial resolution of rainfall data inverted by the wind and cloud four-satellite is improved from 5km to 1km, and the refinement degree of the data is obviously improved. Compared with the traditional resampling method Bilinear method and the global regression Crigin method based on least square regression, the mixed geographic weighted regression model has higher precision, the constructed downscaling model meets the requirements of improving the spatial resolution and maintaining the data precision, and can provide effective data support for small-scale application research of precipitation products inverted by Fengyun No. four satellites.
Example 2
The embodiment discloses a satellite remote sensing precipitation refined space estimation system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the following steps:
and S1, analyzing the correlation coefficient between the annual precipitation data of the satellite and each influence factor, and screening out an explanatory variable set by adopting a stepwise linear regression method. The influencing factors include longitude, latitude, altitude, grade, slope, topographic relief and normalized vegetation index.
And step S2, taking the satellite precipitation as a dependent variable, taking the screened interpretation variable set as an interpretation variable, performing regression analysis by adopting a mixed geography weighted regression model fusing least square method global regression and geography weighted regression, and obtaining high-resolution regression trend surface space data according to the calculation result of the regression coefficient. The mixed geography weighted regression model refers to the embodiment of the method, and details are not described.
And step S3, interpolating the regression residual error by adopting a common Krigin interpolation method to obtain residual error interpolation space data. As above, this step can be accomplished by using GS+9, performing half-variance function fitting on the residual error of the mixed geographic weighted regression model by software, obtaining function parameters according to the fitting, and respectively performing spatial interpolation on the regression residual error of the model by using a common kriging interpolation method, wherein the function parameters comprise the steps ofLong, lump value and variation.
And step S4, adding the regression trend surface and the residual interpolation space data, and performing downscaling conversion according to the target space resolution to obtain estimated satellite annual precipitation data.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A satellite remote sensing precipitation refined space estimation method is characterized by comprising the following steps:
analyzing the correlation coefficient between the annual precipitation data of the satellite and each influence factor, and screening out an explanatory variable set by adopting a stepwise linear regression method;
taking satellite precipitation as a dependent variable, taking a screened explanatory variable set as an explanatory variable, performing regression analysis by adopting a mixed geography weighted regression model fusing least square method global regression and geography weighted regression, and obtaining high-resolution regression trend surface space data according to a calculation result of a regression coefficient;
interpolating the regression residual by adopting a common kriging interpolation method to obtain residual interpolation space data;
and adding the regression trend surface and the residual interpolation spatial data, and performing downscaling conversion according to the target spatial resolution to obtain estimated satellite annual precipitation data.
2. The satellite remote sensing precipitation refined space estimation method according to claim 1, wherein the influencing factors include longitude, latitude, altitude, gradient, slope direction, topographic relief degree and normalized vegetation index.
3. The satellite remote sensing precipitation refinement space estimation method according to claim 1 or 2, wherein the calculation of the residual interpolation space data comprises:
by using GS+And 9, performing half-variance function fitting on the residual error of the mixed geographic weighting regression model by software, obtaining function parameters according to the fitting, and performing spatial interpolation on the regression residual error of the model by using a common kriging interpolation method, wherein the function parameters comprise step length, lump sum and variable range.
4. The satellite remote sensing precipitation refinement spatial estimation method according to claim 3, wherein the target spatial resolution is 1km x 1 km.
5. The satellite remote sensing precipitation refinement spatial estimation method according to claim 1 or 2, characterized in that the mixed geography weighted regression model has a basic form:
in the formula, yiIs the target variable at the position i,
regression coefficient matrix, x, for geoweighted regressionk,iIs the k local variable at position i, γlRegression coefficient of global regression in constant form, xl,iIs the l-th global variable, ε, at position iiThe regression residual error at the position i is shown, m is the number of local variables, and n is the total number of explanatory variables;
in the geoweighted regression model, the regression coefficient of the explanatory variables is no longer a constant but a function related to the spatial position, position (u)i,vi) The regression coefficient of (b) is calculated by the following formula:
in the formula (I), the compound is shown in the specification,
is a regression coefficient matrix; x is an explanatory variable design matrix, XTA transposed matrix that is X; y is a dependent variable matrix;
is a spatial weight matrix formed by a spatial weight function W(i,j)To find, the spatial weight function selects a dual square kernel function as the spatial weight function, that is:
in the formula (d)ijIs the distance of samples i and j, and b is the bandwidth.
6. A satellite remote sensing precipitation refinement spatial estimation system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011341530.4A CN112329265A (en) | 2020-11-25 | 2020-11-25 | Satellite remote sensing rainfall refinement space estimation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011341530.4A CN112329265A (en) | 2020-11-25 | 2020-11-25 | Satellite remote sensing rainfall refinement space estimation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112329265A true CN112329265A (en) | 2021-02-05 |
Family
ID=74308325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011341530.4A Pending CN112329265A (en) | 2020-11-25 | 2020-11-25 | Satellite remote sensing rainfall refinement space estimation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112329265A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113553697A (en) * | 2021-06-24 | 2021-10-26 | 中国矿业大学(北京) | Long-time-sequence multi-source data-based vegetation disturbance analysis method for coal mining |
| CN115270386A (en) * | 2022-04-22 | 2022-11-01 | 水利部交通运输部国家能源局南京水利科学研究院 | Quantitative evaluation method and system for beach tank evolution main control factor weight |
| CN116228046A (en) * | 2023-05-09 | 2023-06-06 | 成都信息工程大学 | Mountain area space precipitation estimation method based on satellite remote sensing and geographic data |
| CN116680548A (en) * | 2023-08-03 | 2023-09-01 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106776481A (en) * | 2016-11-29 | 2017-05-31 | 河海大学 | A kind of NO emissions reduction bearing calibration for acting on satellite precipitation data |
| CN109544690A (en) * | 2018-10-18 | 2019-03-29 | 华南理工大学 | Shared bicycle trip influence factor recognition methods, system and storage medium |
| CN111177652A (en) * | 2019-11-27 | 2020-05-19 | 中国科学院遥感与数字地球研究所 | Spatial downscaling method and system for remote sensing precipitation data |
-
2020
- 2020-11-25 CN CN202011341530.4A patent/CN112329265A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106776481A (en) * | 2016-11-29 | 2017-05-31 | 河海大学 | A kind of NO emissions reduction bearing calibration for acting on satellite precipitation data |
| CN109544690A (en) * | 2018-10-18 | 2019-03-29 | 华南理工大学 | Shared bicycle trip influence factor recognition methods, system and storage medium |
| CN111177652A (en) * | 2019-11-27 | 2020-05-19 | 中国科学院遥感与数字地球研究所 | Spatial downscaling method and system for remote sensing precipitation data |
Non-Patent Citations (2)
| Title |
|---|
| 任萍等: "用逐步线性回归方法分析预测我国纸和纸板消费量", 《纸和造纸》 * |
| 覃文忠等: "混合地理加权回归模型算法研究", 《武汉大学学报(信息科学版)》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113553697A (en) * | 2021-06-24 | 2021-10-26 | 中国矿业大学(北京) | Long-time-sequence multi-source data-based vegetation disturbance analysis method for coal mining |
| CN113553697B (en) * | 2021-06-24 | 2022-02-01 | 中国矿业大学(北京) | Long-time-sequence multi-source data-based vegetation disturbance analysis method for coal mining |
| CN115270386A (en) * | 2022-04-22 | 2022-11-01 | 水利部交通运输部国家能源局南京水利科学研究院 | Quantitative evaluation method and system for beach tank evolution main control factor weight |
| CN115270386B (en) * | 2022-04-22 | 2023-09-12 | 水利部交通运输部国家能源局南京水利科学研究院 | Quantitative evaluation method and system for beach evolution main control factor weight |
| CN116228046A (en) * | 2023-05-09 | 2023-06-06 | 成都信息工程大学 | Mountain area space precipitation estimation method based on satellite remote sensing and geographic data |
| CN116228046B (en) * | 2023-05-09 | 2023-07-18 | 成都信息工程大学 | Mountain area space precipitation estimation method based on satellite remote sensing and geographic data |
| CN116680548A (en) * | 2023-08-03 | 2023-09-01 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
| CN116680548B (en) * | 2023-08-03 | 2023-10-13 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11333796B2 (en) | Spatial autocorrelation machine learning-based downscaling method and system of satellite precipitation data | |
| CN112329265A (en) | Satellite remote sensing rainfall refinement space estimation method and system | |
| CN112905560B (en) | Air pollution prediction method based on multi-source time-space big data deep fusion | |
| CN109840553B (en) | Extraction method and system of cultivated land crop type, storage medium and electronic equipment | |
| CN110222911B (en) | Rainfall station network optimization layout method based on satellite remote sensing and ground data cooperation | |
| CN103810376A (en) | Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging | |
| Stein et al. | Integrating spatial statistics and remote sensing | |
| Arowolo et al. | Comparison of spatial interpolation techniques to generate high‐resolution climate surfaces for Nigeria | |
| Song et al. | An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression | |
| CN112861072A (en) | Satellite-ground multi-source rainfall self-adaptive dynamic fusion method | |
| Fan et al. | A comparative study of four merging approaches for regional precipitation estimation | |
| CN113281749A (en) | Time sequence InSAR high-coherence point selection method considering homogeneity | |
| CN113704693A (en) | High-precision effective wave height data estimation method | |
| Chew et al. | Flooding and inundation maps using interpolated CYGNSS reflectivity observations | |
| CN112285808B (en) | Method for reducing scale of APHRODITE precipitation data | |
| El Kenawy et al. | Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques | |
| Song et al. | Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model | |
| Serrano‐Notivoli et al. | From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets | |
| Tang et al. | CYGNSS Soil Moisture Estimations Based on Quality Control | |
| Cui et al. | A new fusion algorithm for simultaneously improving spatio-temporal continuity and quality of remotely sensed soil moisture over the Tibetan Plateau | |
| Aparecido et al. | Spatial interpolation techniques to map rainfall in southeast Brazil | |
| CN115639979A (en) | High-resolution SPEI data set development method based on random forest regression model | |
| Paparrizos et al. | A downscaling technique for climatological data in areas with complex topography and limited data | |
| CN113807409B (en) | Coastal zone classification method based on discriminant analysis | |
| Du Plessis et al. | The applicability of the use of radar data to develop areal reduction factors in South Africa |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210205 |