CN106295190A - A kind of remote sensing cloudiness data NO emissions reduction method - Google Patents
A kind of remote sensing cloudiness data NO emissions reduction method Download PDFInfo
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Abstract
The present invention relates to a kind of remote sensing cloudiness data NO emissions reduction method; use brand-new design strategy; based on existing remote sensing cloud-cover observation data; by design series of steps; achieve the purpose of remote sensing cloud-cover observation data fall space scale; it is effectively increased the precision of cloud-cover observation, goes far towards policymaker and formulate the very important decision relevant to environmental conservation.
Description
Technical field
The present invention relates to a kind of remote sensing cloudiness data NO emissions reduction method, belong to remote sensing cloud-cover observation technical field.
Background technology
MODIS (Moderate Imaging Spectroradiomete) is one of main sensors of lift-launch on Terra satellite and Aqua satellite,
Two satellites cooperate the every 1-2 days whole earth surfaces of repeatable observation, obtain the observation data of 36 wave bands, these data
Will assist in the dynamic changing process in we deeply understand Global land, ocean and lower atmosphere layer, therefore, MODIS is in development
Effectively, playing an important role in the global earth system interaction model for predicting whole world change, it is accurate
Prediction will assist in policymaker and formulate the very important decision relevant to environmental conservation.MOD06 is that air 2,3 grade standard data are produced
Product, content is cloud product, Lambert projector space resolution 1 kilometer, 30 seconds spatial resolution of geographical coordinate, and every day, data were 2
DBMS product, per ten days, monthly Data Synthesis are 3 DBMS products.
The cloud amount data of high spatial resolution play vital work in application aspect such as the hydrology, meteorology, ecological environmenies
With.The at present acquisition of cloud amount data mainly has two sources: Ground Meteorological website and remote sensing technology, but substantial amounts of research shows
By the meteorological site actual measurement of tradition ground is all one point data, it is impossible to effectively reflect the Spatial Variation of cloud amount, especially
It is at research complex region；And remote sensing technology can not only improve the actual observation quantity of cloud amount, and essence relatively can be generated
True space lattice data.But, cloud amount is carried out the applied research of profound level, needs to improve the spatial resolution of cloud amount, this
Space NO emissions reduction method will be used.The method of NO emissions reduction mainly has simple NO emissions reduction method, Statistical downscaling, power NO emissions reduction
Method and power and statistics combine NO emissions reduction these four NO emissions reduction method.Simple NO emissions reduction method is through single-point and inserts
Value, but owing to being affected by factors such as study area topography and geomorphologies, cause space interpolation resultant error very big, and lack sky
Between representative.Dynamical downscaling has clear and definite physical significance, and observational data is less on its impact simultaneously, and can be used for difference
Spatial resolution, wide in precipitation is studied.In 1989, Dickinson et al. used CCMI global climate model conduct
The border of A Regional Climate Model, is simulated the weather of US West, makes the climate model spatial resolution of output by 500km
Drop to 60km.Statistics NO emissions reduction method is proposed in 1984 by Kim et al. first, follows season mainly by precipitation and temperature
The spatial distribution characteristic of ring change, develops improvement by researchers, makes the method more accurate when simulated precipitation.With power
NO emissions reduction method compares, Statistical downscaling have amount of calculation little, save machine time, it is easier to the feature of operation.At remote sensing cloud
Amount aspect, prior art is also not carried out the method for NO emissions reduction.
Summary of the invention
The technical problem to be solved is to provide a kind of employing brand-new design strategy, sees based on existing remote sensing cloud amount
Based on surveying data, it is possible to quickly realize the remote sensing cloudiness data NO emissions reduction method of NO emissions reduction cloud-cover observation.
The present invention is to solve above-mentioned technical problem by the following technical solutions: the present invention devises a kind of remote sensing cloud amount money
Material NO emissions reduction method, based on target area 5km × 5km resolution total amount of cloud, it is thus achieved that target area 1km × total cloud of 1km resolution
Amount, comprises the steps:
Step 001., for the historical time section of at least 30 years, is put down month by month according in each site history time period of target area
All total amount of cloud observation data, month by month average percentage of sunshine data, it is thus achieved that such as a in drag (1)nAnd bn, as target area
January to December each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inThe i-th of n-th month in the historical time section of expression target area
Website, InIn the historical time section of expression target area, the master station of n-th month counts；Represent in target area historical time section n-th
The moon, the monthly average total amount of cloud observation data of i-th website,Represent n-th month, i-th website in the historical time section of target area
Monthly average percentage of sunshine data；
Step 002. obtains 1km × 1km resolution percentage of sunshine of the l month in the respectively corresponding appointment time of target area
S(neigh, 1km), l, wherein, l={1 ..., L}, L represents the total moon number in the appointment time, subsequently into step 003；
Step 003. is based on 1km × 1km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh, 1km), l, it is thus achieved that 5km × 5km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh, 5km), l, subsequently into step 004；
Step 004. obtains l month 5km × 5km resolution percentage of sunshine in the most corresponding appointment time of target area
S(neigh, 5km), lWith 1km × 1km resolution percentage of sunshine S(neigh, 1km), lBetween difference DELTA Sl, Δ SlResolution be 1km
× 1km, and obtain l month 5km × 5km resolution total amount of cloud CL in the most corresponding appointment time of target areal,5km, subsequently into
Step 005；
Step 005. is according to such as drag (2):
CLL, 1km=CLl,5km-bl×ΔSl (2)
1km × 1km resolution total amount of cloud CL of the l month in the most corresponding appointment time of acquisition target areaL, 1km。
As a preferred technical solution of the present invention, described step 001 specifically includes following steps:
Step 00101. obtains in each site history time period of target area month by month mean total cloud observation data, puts down month by month
All sunshine-duration observation data and month by month average possible sunshine data, and according to each website in the historical time section of target area month by month
Ratio between average sunshine time observation data and month by month average possible sunshine data, it is thus achieved that in the historical time section of target area
Average month by month percentage of sunshine data, subsequently into step 00102；
Step 00102. is according to each website mean total cloud month by month observation data and month by month in the historical time section of target area
Average percentage of sunshine data, by multiple regression analysis method, it is thus achieved that such as a in drag (1)nAnd bn, as target area one
The moon to December each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inThe i-th of n-th month in the historical time section of expression target area
Website, InIn the historical time section of expression target area, the master station of n-th month counts；Represent in target area historical time section n-th
The moon, the monthly average total amount of cloud observation data of i-th website,Represent n-th month, i-th website in the historical time section of target area
Monthly average percentage of sunshine data.
As a preferred technical solution of the present invention, described step 002 includes operating as follows: obtain in target area each
In the most corresponding appointment time of individual net region, 1km × 1km resolution sunshine-duration and 1km × 1km resolution of the l month are geographical
Possible sunshine, and obtain accordingly in respectively corresponding appointment time the average 1km of all net regions in the l month, target area ×
1km × 1km resolution percentage at sunshine of the l month in 1km resolution percentage of sunshine, the i.e. target area the most corresponding appointment time
Rate S(neigh,1km), wherein, l={1 ..., L}, L represents the total moon number in the appointment time, subsequently into step 003.
As a preferred technical solution of the present invention, described step 002 specifically includes following steps:
Step 00201. obtains the 1km × 1km of the l month in the most corresponding appointment time of each net region in target area
Resolution sunshine-duration and 1km × 1km resolution geography possible sunshine, wherein, l={1 ..., L}, in L represents the appointment time
Always moon number；Subsequently into step 00202；
Step 00202. is differentiated according to l month 1km × 1km in the most corresponding appointment time of each net region in target area
Ratio between rate sunshine-duration and 1km × 1km resolution geography possible sunshine, it is thus achieved that in target area, each net region is divided
1km × 1km resolution percentage of sunshine of the l month in the not corresponding appointment time, and then obtain l in the most corresponding appointment time
In the moon, target area during the correspondence appointment respectively of 1km × 1km resolution percentage of sunshine of all net regions, i.e. target area
1km × 1km resolution percentage of sunshine S of the interior l month(neigh,1km), subsequently into step 003.
As a preferred technical solution of the present invention, described step 003 includes operating as follows: for target area respectively
1km × 1km resolution percentage of sunshine S of the l month in the corresponding appointment time(neigh,1km), by neighbor analysis method, according to 5 × 5
Window size, it is thus achieved that 5km × 5km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh,5km), subsequently into step 004.
Remote sensing cloudiness data NO emissions reduction method of the present invention use above technical scheme compared with prior art, have with
Lower technique effect: the remote sensing cloudiness data NO emissions reduction method designed by the present invention, uses brand-new design strategy, based on existing remote sensing
Based on cloud-cover observation data, by design series of steps, it is achieved that the cloud-cover observation of NO emissions reduction, it is effectively increased cloud amount and sees
The precision surveyed, goes far towards policymaker and formulates the very important decision relevant to environmental conservation.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of a kind of remote sensing cloudiness data NO emissions reduction method that the present invention designs；
Fig. 2 a is the remote sensing cloud amount schematic diagram before in January, 2013 NO emissions reduction of embodiment target area；
Fig. 2 b is the remote sensing cloud amount schematic diagram after in January, 2013 NO emissions reduction of embodiment target area；
Fig. 2 c is the remote sensing cloud amount schematic diagram before in July, 2013 NO emissions reduction of embodiment target area；
Fig. 2 d is the remote sensing cloud amount schematic diagram after in July, 2013 NO emissions reduction of embodiment target area；
Fig. 3 a is the remote sensing cloud amount amplification signal in embodiment target area before regional area in January, 2013 NO emissions reduction
Figure；
Fig. 3 b is the remote sensing cloud amount amplification signal in embodiment target area after regional area in January, 2013 NO emissions reduction
Figure；
Fig. 3 c is the remote sensing cloud amount amplification signal in embodiment target area before regional area in July, 2013 NO emissions reduction
Figure；
Fig. 3 d is the remote sensing cloud amount amplification signal in embodiment target area after regional area in July, 2013 NO emissions reduction
Figure.
Detailed description of the invention
Below in conjunction with Figure of description, the detailed description of the invention of the present invention is described in further detail.
As it is shown in figure 1, a kind of remote sensing cloudiness data NO emissions reduction method designed by the present invention, based on target area 5km ×
5km resolution total amount of cloud, it is thus achieved that target area 1km × 1km resolution total amount of cloud, in the middle of actual application, specifically include as
Lower step:
Step 001. is according to mean total cloud sight month by month in each year at least 30 years history time periods of each website in target area
Survey data, month by month average percentage of sunshine data, it is thus achieved that such as a in drag (1)nAnd bn, as target area January to 12
The moon each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inRepresent in target area historical time section in each year n-th month
I-th website, InRepresent that in the historical time section of target area, in each year, the master station of n-th month counts；Represent that target area is gone through
N-th month, the monthly average total amount of cloud of i-th website observation data in each year in the history time period, by MODIS Atmosphere Product MOD06
Obtain total amount of cloud observation data, anAnd bnThe coefficient of n-th month in the history one-year age of expression target area,Represent target area
N-th month, the monthly average percentage of sunshine data of i-th website in each year in historical time section.
Above-mentioned steps 001, specifically includes following steps:
Step 00101. obtain in each site history time period of target area in each year month by month mean total cloud observation data,
Average sunshine time observation data and month by month average possible sunshine data month by month, and according to each year in the historical time section of target area
Ratio between interior each website average sunshine time month by month observation data and month by month average possible sunshine data, it is thus achieved that target area
Average month by month percentage of sunshine data in each year in historical time section, subsequently into step 00102；Wherein, by anti-distance
Power Interpolation method (IDW) obtains the sunshine-duration.
Step 00102. is according to each website mean total cloud month by month observation data in each year in the historical time section of target area
Average percentage of sunshine data month by month, by multiple regression analysis method, it is thus achieved that such as a in drag (1)nAnd bn, as target
Region January to December each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inRepresent in target area historical time section in each year n-th month
I-th website, InRepresent that in the historical time section of target area, in each year, the master station of n-th month counts；Represent that target area is gone through
In history time, each year, n-th month, the monthly average total amount of cloud observation data of i-th website, obtained by MODIS Atmosphere Product MOD06
Total amount of cloud observation data,Represent in target area historical time section in each year n-th month, monthly average sunshine hundred of i-th website
Divide rate data.
Here based on a determined by above-mentioned method for designingnAnd bn, as the coefficient of moon each in the one-year age of target area, that
This has certain similarity, i.e. works as an> 0, bn< 0, embody the negative correlativing relation of cloud amount and percentage of sunshine；There is also difference simultaneously
The opposite sex, summer, coefficient was slightly larger than winter；It is thus based on the real data of above-mentioned target area historical time section, passes through multiple regression
Analytic process, it is thus achieved that anAnd bn, as the supplemental characteristic that the coefficient of moon each in the one-year age of target area, i.e. error are minimum, also it is
In meeting target area one-year age each moon practical situation supplemental characteristic.
Step 002. obtains the 1km × 1km of the l month in the most corresponding appointment time of each net region in target area and divides
Resolution sunshine-duration and 1km × 1km resolution geography possible sunshine, and obtain the l month, mesh in the most corresponding appointment time accordingly
Average 1km × 1km resolution percentage of sunshine of all net regions, the i.e. target area the most corresponding appointment time in mark region
1km × 1km resolution percentage of sunshine S of the interior l month(neigh,1km), wherein, l={1 ..., L}, in L represents the appointment time
Always moon number, subsequently into step 003.
Wherein, for possible sunshine, in actual landform, whether any point P any time in one day may be used
According to, mainly by the landform on this moment sunray projecting direction, P point is covered decision with or without causing.When sun altitude is more than
During the shield angle that P point is caused by landform, P point can get sunshine, otherwise, the most shielded, there is no sunshine.Based on this thought, fully
Consider the sky factor and the impact on actual landform possible sunshine of the local orographic condition, by setting up possible sunshine under rolling topography
Distributed computing platform, possible sunshine can be obtained.
Above-mentioned steps 002 specifically includes following steps:
Step 00201. obtains the 1km × 1km of the l month in the most corresponding appointment time of each net region in target area
Resolution sunshine-duration and 1km × 1km resolution geography possible sunshine, wherein, l={1 ..., L}, in L represents the appointment time
Always moon number；Subsequently into step 00202；Wherein, 1km × 1km resolution sunshine-duration is obtained by anti-distance weighting interpolation method.
Step 00202. is differentiated according to l month 1km × 1km in the most corresponding appointment time of each net region in target area
Ratio between rate sunshine-duration and 1km × 1km resolution geography possible sunshine, it is thus achieved that in target area, each net region is divided
1km × 1km resolution percentage of sunshine of the l month in the not corresponding appointment time, and then obtain l in the most corresponding appointment time
In the moon, target area during the correspondence appointment respectively of 1km × 1km resolution percentage of sunshine of all net regions, i.e. target area
1km × 1km resolution percentage of sunshine S of the interior l month(neigh,1km), subsequently into step 003.
When processing raster data, owing to data Pixel size is undesirable, or after carrying out raster data registration,
Pixel run-off the straight, or when multiple raster datas are analyzed, generally require and use identical raster resolution, ArcGIS
In resampling function can realize this purpose.Neighbor analysis is a kind of window analysis, is a grid meter in spatial analysis
Calculation mode, its basic ideas are exactly centered by grid cell, raster cell to be calculated, radiate out certain scope, then according to this
Extend the values of grid cell, raster cells and center pel a bit or only carry out functional operation by the value of extension pixel (i.e. analysis window), thus obtaining
New value to this pixel to be calculated.
Step 003. is for 1km × 1km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh,1km), by neighbor analysis method, according to 5 × 5 window sizes, it is thus achieved that the l month in the most corresponding appointment time of target area
5km × 5km resolution percentage of sunshine S(neigh,5km), subsequently into step 004.
Step 004. obtains l month 5km × 5km resolution percentage of sunshine in the most corresponding appointment time of target area
S(neigh, 5km), lWith 1km × 1km resolution percentage of sunshine S(neigh, 1km), lBetween difference DELTA Sl, Δ SlResolution be 1km
× 1km, and by MODIS Atmosphere Product MOD06, it is thus achieved that in the most corresponding appointment time of target area, l month 5km × 5km divides
Resolution total amount of cloud CLl,5km, subsequently into step 005.
Step 005. is according to such as drag (2):
CLL, 1km=CLl,5km-bl×ΔSl (2)
1km × 1km resolution total amount of cloud CL of the l month in the most corresponding appointment time of acquisition target areaL, 1km。
Remote sensing cloudiness data NO emissions reduction method designed by the present invention is applied in the particular embodiment, for embodiment
Middle target area carries out the observation of remote sensing cloudiness data NO emissions reduction, wherein, for target area in embodiment, analyzes cloud amount and sunshine
The dependency of percentage rate, is modeled cloud amount and percentage of sunshine, and modeling absolute error see table shown in 1.
Table 1
Application remote sensing cloudiness data NO emissions reduction method designed by the present invention, for target area in embodiment 2013 1
The total amount of cloud in the moon and in July, 2013 is analyzed, as shown in Figure 2 a, before in January, 2013 NO emissions reduction of embodiment target area
Remote sensing cloud amount schematic diagram, after the remote sensing cloudiness data NO emissions reduction method designed by the present invention, as shown in Figure 2 b, embodiment mesh
Remote sensing cloud amount schematic diagram after in January, 2013 NO emissions reduction of mark region；Equally, as shown in Figure 2 c, embodiment target area 2013
Remote sensing cloud amount schematic diagram before year July NO emissions reduction, after the remote sensing cloudiness data NO emissions reduction method designed by the present invention, as
Remote sensing cloud amount schematic diagram shown in Fig. 2 d, after in July, 2013 NO emissions reduction of embodiment target area.
In order to obtain the effect of clear resolution NO emissions reduction, it is respectively directed to embodiment target area in January, 2013 and 2013
Before year July NO emissions reduction, after NO emissions reduction, remote sensing cloud amount schematic diagram carries out partial enlargement process, it is thus achieved that in embodiment target area local
The implementation result figure in region, as shown in Figure 3 a, remote sensing before regional area in January, 2013 NO emissions reduction in embodiment target area
Cloud amount schematic diagram, after the remote sensing cloudiness data NO emissions reduction method designed by the present invention, as shown in Figure 3 b, embodiment target area
Remote sensing cloud amount schematic diagram after regional area in January, 2013 NO emissions reduction in territory；Equally, as shown in Figure 3 c, embodiment target area
Remote sensing cloud amount schematic diagram before regional area in July, 2013 NO emissions reduction in territory, provides through the remote sensing cloud amount designed by the present invention
After material NO emissions reduction method, as shown in Figure 3 d, remote sensing cloud after regional area in July, 2013 NO emissions reduction in embodiment target area
Amount schematic diagram, wherein, regional area is the boxed area in corresponding diagram 2a-Fig. 2 d.
Remote sensing cloudiness data NO emissions reduction method designed by the present invention, uses brand-new design strategy, based on existing remote sensing cloud
Based on discharge observation data, by design series of steps, it is achieved that the cloud-cover observation of NO emissions reduction, it is effectively increased cloud-cover observation
Precision, go far towards policymaker and formulate the very important decision relevant to environmental conservation.
Above in conjunction with accompanying drawing, embodiments of the present invention are explained in detail, but the present invention is not limited to above-mentioned enforcement
Mode, in the ken that those of ordinary skill in the art are possessed, it is also possible on the premise of without departing from present inventive concept
Make a variety of changes.
Claims (5)
1. a remote sensing cloudiness data NO emissions reduction method, based on target area 5km × 5km resolution total amount of cloud, it is thus achieved that target area
Territory 1km × 1km resolution total amount of cloud, it is characterised in that comprise the steps:
Step 001. was for the historical time section of at least 30 years, according to average total month by month in each site history time period of target area
Cloud-cover observation data, month by month average percentage of sunshine data, it is thus achieved that such as a in drag (1)nAnd bn, as target area January
To December each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inThe i-th station of n-th month in the historical time section of expression target area
Point, InIn the historical time section of expression target area, the master station of n-th month counts；Represent in target area historical time section n-th
The moon, the monthly average total amount of cloud observation data of i-th website,Represent n-th month, i-th website in the historical time section of target area
Monthly average percentage of sunshine data；
Step 002. obtains 1km × 1km resolution percentage of sunshine of the l month in the respectively corresponding appointment time of target area
S(neigh, 1km), l, wherein, l={1 ..., L}, L represents the total moon number in the appointment time, subsequently into step 003；
Step 003. is based on 1km × 1km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh, 1km), l, it is thus achieved that 5km × 5km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh, 5km), l, subsequently into step 004；
Step 004. obtains l month 5km × 5km resolution percentage of sunshine in the most corresponding appointment time of target area
S(neigh, 5km), lWith 1km × 1km resolution percentage of sunshine S(neigh, 1km), lBetween difference DELTA Sl, Δ SlResolution be 1km
× 1km, and obtain l month 5km × 5km resolution total amount of cloud CL in the most corresponding appointment time of target areal,5km, subsequently into
Step 005；
Step 005. is according to such as drag (2):
CLL, 1km=CLl,5km-bl×ΔSl (2)
1km × 1km resolution total amount of cloud CL of the l month in the most corresponding appointment time of acquisition target areaL, 1km。
A kind of remote sensing cloudiness data NO emissions reduction method, it is characterised in that described step 001 is concrete
Comprise the steps:
Step 00101. obtains in each site history time period of target area mean total cloud observation data, month by month average day month by month
According to time observation data and average possible sunshine data month by month and average according to each website in the historical time section of target area
Ratio between sunshine-duration observation data and month by month average possible sunshine data, it is thus achieved that in the historical time section of target area by
Monthly average percentage of sunshine data, subsequently into step 00102；
Step 00102. is according to each website mean total cloud month by month observation data and average in the historical time section of target area
Percentage of sunshine data, by multiple regression analysis method, it is thus achieved that such as a in drag (1)nAnd bn, as target area January extremely
December each moon the most corresponding coefficient sets { an, bn, subsequently into step 002；
Wherein, n={1 ..., 12}, in={ 1 ..., In, inThe i-th station of n-th month in the historical time section of expression target area
Point, InIn the historical time section of expression target area, the master station of n-th month counts；Represent in target area historical time section n-th
The moon, the monthly average total amount of cloud observation data of i-th website,Represent n-th month, i-th website in the historical time section of target area
Monthly average percentage of sunshine data.
A kind of remote sensing cloudiness data NO emissions reduction method, it is characterised in that described step 002 includes
Following operation: 1km × 1km resolution day of the l month in the most corresponding appointment time of each net region in acquisition target area
According to time and 1km × 1km resolution geography possible sunshine, and obtain the l month, target area in the most corresponding appointment time accordingly
In l in average 1km × 1km resolution percentage of sunshine of all net regions, the i.e. target area the most corresponding appointment time
1km × 1km resolution percentage of sunshine S of the moon(neigh,1km), wherein, l={1 ..., L}, L represents the total moon in the appointment time
Number, subsequently into step 003.
A kind of remote sensing cloudiness data NO emissions reduction method, it is characterised in that described step 002 is concrete
Comprise the steps:
Step 00201. obtains 1km × 1km resolution of the l month in the correspondence appointment time respectively of each net region in target area
Rate sunshine-duration and 1km × 1km resolution geography possible sunshine, wherein, l={1 ..., L}, L represents the total moon in the appointment time
Number；Subsequently into step 00202；
Step 00202. is according to l month 1km × 1km resolution day in the most corresponding appointment time of each net region in target area
According to the ratio between time and 1km × 1km resolution geography possible sunshine, it is thus achieved that in target area, each net region is the most right
Should specify 1km × 1km resolution percentage of sunshine of the l month in the time, so obtain in the most corresponding appointment time l month,
In 1km × 1km resolution percentage of sunshine of all net regions in target area, the i.e. target area the most corresponding appointment time
1km × 1km resolution percentage of sunshine S of the l month(neigh,1km), subsequently into step 003.
A kind of remote sensing cloudiness data NO emissions reduction method, it is characterised in that described step 003 includes
Following operation: for 1km × 1km resolution percentage of sunshine of the l month in the most corresponding appointment time of target area
S(neigh,1km), by neighbor analysis method, according to 5 × 5 window sizes, it is thus achieved that the l month in the most corresponding appointment time of target area
5km × 5km resolution percentage of sunshine S(neigh,5km), subsequently into step 004.
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| PANAGIOTIS SISMANIDIS ET AL: "Evaluating the Operational Retrieval and Downscaling of Urban Land Surface Temperatures", 《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 》 * |
| PETER M. ATKINSON: "Downscaling in remote sensing", 《INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION》 * |
| QIHAO WENG ET AL: "Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data", 《REMOTE SENSING OF ENVIRONMENT》 * |
| 刘双娜等: "基于遥感降尺度估算中国森林生物量的空间分布", 《生态学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108957594A (en) * | 2018-05-15 | 2018-12-07 | 北京维艾思气象信息科技有限公司 | A kind of distant satellite orbit total amount of cloud forecast modification method of quotient and update the system |
| CN108957594B (en) * | 2018-05-15 | 2021-01-15 | 北京维艾思气象信息科技有限公司 | Method and system for forecasting and correcting total cloud amount of satellite orbit |
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| Publication number | Publication date |
|---|---|
| CN106295190B (en) | 2019-01-22 |
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