CN103530499A - Method for building mountainous area surface temperature base line and application - Google Patents
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Abstract
The invention discloses a method for building a mountainous area surface temperature base line and application, and relates to the measurement and application of mountainous area surface temperature base lines. A transient temperature field distributed continuously in MODIS thermal infrared remote sensing image inversion spaces is combined with longitude and latitude raster data to build the annual average temperature linear estimation model of a weather station as the calibration model of the transient surface temperature field. The model corrects the transient temperature field to obtain the mountainous area surface temperature base line. In YunnanProvince with the mountain landform as the main landform, the temperature base line with a 90m*90m grid as a unit is built, the temperature distribution characteristics and rules of the area are reflected, and the average temperature base line value of a 90m*90m land parcel can be found out in a quantitative mode. The temperature base line is used as basic data to be combined with an expert scoring method to respectively build a Yunnan Province pine wilt disease occurrence risk pre-warning model and a Yunnan Province forest fire danger long-term forecast model.
Description
Technical Field
The invention belongs to the measurement of geographic temperature characteristic values, in particular to the measurement and application of a ground surface temperature base line.
Background
The surface temperature is a key factor of a plurality of ecological environment processes, and the change rule of the surface temperature is the basic content of theoretical establishment and practical application in the fields of meteorology, hydrology, ecology, forestry and the like. In the traditional method, meteorological station observation data are used as sampling point data, and a geographic information system means for temperature rasterization research is developed by an interpolation method, an air temperature direct reduction rate correction method, a multiple regression method and the like, so that the traditional method is essentially a model made by applying a statistical method and lacks the realistic meaning of physical temperature simulation. Even if a correction model based on environment gradient factors such as altitude, latitude and the like is available, the correction model is based on a purely mathematical linear regression coefficient, at most, the correction model can only be a correction coefficient under the comprehensive action of terrain, and the mechanism action of each environment gradient factor on temperature cannot be explained. With the popularization and application of the 3S technology, the method for inverting the earth surface temperature by applying the interpolation simulation of a geographic information system and based on the thermal infrared band of the remote sensing image becomes a latest means for studying mountain climate, and can acquire temperature distribution data on a large area and continuous earth surface at the same time and more accurately reflect the real distribution characteristics of the temperature. Compared with the traditional method, the method does not depend on observation data of a meteorological station, is not influenced by data representativeness, and does not need to pay a large amount of labor and time. Currently, most researchers focus on mathematical inversion algorithms and exploration of the coarse-scale spatial distribution law of surface temperature.
Compared with the research on the earth surface temperature on the global and regional scales, the refined research on the spatial distribution theory and the change rule of the mountain land temperature is relatively weak, the establishment of the spatial continuous mountain land temperature base line and the micro-pattern spatial distribution characteristic of the earth surface temperature is hindered, and the research and the forecast on aspects of mountain land agriculture, forestry, natural disasters, ecology and the like are influenced. Yunnan with mountain land morphology type as the main part is in a stepped topography hierarchical structure, not only is the province totally saved, but also each area has higher mountain lands, low dams and deep cut valleys, and complex and multi-layer natural conditions are formed. Under the comprehensive effects of terrain, mountain and river trends and the like, the regional difference and the vertical change rule of heat are very obvious, but the characteristics of complicated and changeable mountain landform and three-dimensional climate are obvious, so that the representativeness of more than one hundred conventional meteorological stations in the whole province is poor, the traditional temperature base line is often only a few isolines in the whole environment range of Yunnan, and great difficulty is caused in exploring the mountain temperature rule. At present, no yunnan surface temperature baseline data which is spatially continuous and can be widely applied to various industries exists.
In the following terminology, DEM is a short term for Digital Elevation Model, which is a solid ground Model that represents the Elevation of the ground in the form of an ordered array of values.
Disclosure of Invention
The invention aims to obtain the ground surface temperature baseline data of a main region with mountain land feature types based on a ground surface instantaneous temperature field inverted by a remote sensing image thermal infrared band, further obtain the ground surface temperature baseline data of the main region with the mountain land feature types of Yunnan province, enrich the basic work in the research aspect of mountain land climate resources of the region, and provide data support and method reference for refining the development and protection of mountain land climate resources.
The above object relates to the steps of:
(1) MODIS thermal infrared image preprocessing, including stripe noise elimination, data overlapping phenomenon removal, reflectivity calibration, sun zenith angle correction and geometric correction;
(2) the environmental gradient factor processing comprises the steps of constructing or acquiring DEM data, carrying out smooth denoising and specific convex-concave point filling, and extracting latitude and longitude data based on the DEM;
(3) an instantaneous earth surface temperature field T based on MODIS thermal infrared waveband inversion is obtained by selecting a window splitting algorithm of the memorial pornogen and the like;
the method is characterized in that:
in the algorithm, the 2 nd wave band of the MODIS image is used for calculating the atmospheric transmittance, and the spatial resolution of the MODIS image is 250m, so that the spatial resolution of the instantaneous surface temperature field T obtained by inversion is 250 m; secondly, resampling the instantaneous earth surface temperature field with the pixel size of 250m obtained by inversion by using a nearest neighbor method so as to enable the instantaneous earth surface temperature field to correspond to the pixel size of the environmental gradient factor;
(4) construction of a mathematical model of a temperature baseline B-T
Projecting the vector and the earth surface instantaneous temperature field of the conventional meteorological station in the measurement region and DEM, longitude and latitude environmental gradient factors into a consistent coordinate system, extracting earth surface instantaneous temperature inversion values and longitude and latitude values at the conventional meteorological station, establishing a sample set with annual average temperature observation data, and making the earth surface instantaneous inversion temperature be equal toLongitude is
Latitude of degree of latitudeAnnual average temperature is
Using linear regression tool in statistical analysis software to obtain regression estimation equation of annual average temperature
After the significance of the equation is determined by F test and T test of the equation and the coefficient, a mathematical model for obtaining a temperature baseline B-T is established;
(5) obtaining a temperature baseline B-T of a pixel scale
And (4) substituting the instantaneous earth surface temperature field T and longitude and latitude data in the measurement region into the mathematical model constructed in the step (4), and calculating to obtain an earth surface temperature base line B-T taking the size of the grid as a unit based on the grid calculation function of GIS image processing software.
When the Gaussian-Kruger projection method is selected in the step (2), the central meridian of the measured region needs to be determined, and then map projection is carried out, so that the minimum deformation of the mountain landform is ensured.
And (4) taking the historical 30-year average air temperature observed values of the conventional meteorological sites in the region as dependent variable samples of linear regression.
The invention also relates to an application of the surface temperature baseline method, which is characterized in that a Gaussian-Kruge projection method with the longitude of the central meridian of 102 degrees is selected in the step (2) for map projection, the average air temperature observed values of 135 conventional meteorological sites in Yunnan province in 30 years are used as a sample set in the step (4), and after abnormal points are removed, a linear regression method is used for establishing a mathematical model of the temperature baseline B-T as follows:
the model was tested with a 95% confidence statistic, where the regression equation F has a value of 108.587,
、
、
、have t values of 5.986, 12.878, -6.846 and-3.235, respectively.
The invention has the advantages that:
the invention utilizes the ground continuous instantaneous temperature field distribution data obtained by the remote sensing thermal infrared satellite image inversion technology, combines the annual average temperature observation data accumulated by the conventional meteorological sites, calculates the annual average temperature estimation regression equation by a linear regression method and establishes a calibration model of the instantaneous earth surface temperature field by hypothesis test, namely, a relation model between the remote sensing inversion instantaneous temperature and the annual average observation temperature, corrects the instantaneous temperature field with continuous change in space, thereby obtaining earth surface temperature baseline data which particularly takes mountain landforms as the main area. The temperature base line takes the size of a 90m multiplied by 90m grid as a unit, and the invention can express the spatial continuous distribution of the temperature generated by different micro-geographic environments.
The surface temperature base line of Yunnan province obtained by the method well expresses the continuous distribution characteristics and the spatial change rule of the Yunnan temperature. As shown in fig. 4, the north of the Yunnan topography is high, the south is low, and the continuous surface temperature base line is in three-stage ladder shape along with the topography from the northwest to the southeast under the action of altitude change: low-temperature region in Yunnan-northwest, middle-temperature region in Yunnan and south-east, and high-temperature region in Yunnan. The characteristic of the vertical change of the temperature is matched with the relief pattern of the topography of Yunnan province, and the characteristic of 'three-dimensional climate' in the local area is highlighted. Due to the heat preservation effect of the valley, the typical large valley has higher temperature than surrounding areas, such as the warming phenomenon of the surface temperature of micro-pattern spaces of anger river, billow river, Jinsha river valley, Yuanjiang river valley and red river valley, and the like, and the warming phenomenon is well expressed in the instantaneous surface temperature field, particularly the Yuanjiang dry and hot valley is the most prominent. The mountain body effect causes that high mountains and middle mountains with intensively distributed large mountains are generally higher than low mountains; the dam and canyon surrounded by the high mountain are generally higher than the dam and canyon which have flat and wide topography.
As one of basic data, the invention combines the surface temperature base line of Yunnan province with an expert scoring method to establish two forecasting models: the early warning model for the occurrence risk of the pine wilt disease in Yunnan province and the long-term forecasting model for the forest fire risk in Yunnan province. The application proves that the obtained ground surface temperature base line can reflect the spatial pattern change rule of the ground surface temperature in the mountainous area, the obtained ground surface temperature base line is more accurate and fine, and the method has important basic significance for agriculture, forestry production, biodiversity protection and ecological civilization construction in Yunnan.
Drawings
Fig. 1 is a projection flow chart.
Fig. 2 is a surface temperature inversion flow.
FIG. 3 is a scatter plot of the instantaneous surface temperature versus the measured year mean temperature.
Fig. 4 is a chart of a multi-year temperature base line of Yunnan province obtained by the method of the invention.
Fig. 5 is a graph showing the occurrence risk of pine wilt disease in Yunnan province with the size of the grid as a unit.
FIG. 6 is a long-term forecast of forest fire in Yunnan province.
The present invention will be further described with reference to the following detailed description.
Detailed Description
(ii) acquisition of surface temperature baseline
(1) Remote sensing image preprocessing
The remote sensing instantaneous temperature field is obtained by inversion by using MODIS images of Yunnan province obtained by TERRA at 12:11 12 at 11 months and 12 days in 2005. The MODIS image preprocessing comprises stripe noise elimination, data overlapping phenomenon removal, radiation product calculation, sun zenith angle correction and geometric correction. The following are distinguished:
and (3) eliminating stripe noise: and (3) using a neighborhood difference value method, namely replacing the gray value of the noise pixel by the gray value average value of 6 non-noise pixels around the noise pixel. And (3) assuming a noise pixel, subtracting the average value of the adjacent 6 non-noise pixels from the average value, and dividing the average value by the average value to obtain a value which is noise if the value is greater than a set threshold value T.
Judgment formula for noise:
in the formula,
is as follows
Go to the firstThe gray values of the pixels of the column,
is the average value of 6 adjacent non-noise picture elements,
is a threshold value.
The more the value approaches to 0, the pixel misjudgmentThe greater the probability of being a noise pel; if the value is too large, the noise pixel cannot be detected. The data of the MODIS spectral band 5 includes 20 detection units per scan band, so that the noise band of the image is generated repeatedly with 20 pixels as a period. Taking the modulus of all detected noise band line numbers to 20 to obtain the line number less than 20, accumulating the times of the line number, if the obtained times are the most, the line number is the first noise band line number, and usingLf, then all noise bin numbers N can be expressed as Lf plus a multiple of 20 lines of the cycle: n = Lf +20 × N. And after the row number of the noise band is determined, replacing the gray value of all pixels in the noise band by the average value of 6 adjacent pixels with intersection pixels.
Removing the data overlapping phenomenon: the MODISBowCorrection function implementation provided by the MODIStools module is used. The MODISTOLs module is written by IDL and can be embedded in the ENVI environment for use. By clicking bow-tiecording command under modiscols, selecting the image needing bow-tie processing in the pop-up window, defining the spectrum and space range needing processing, and then entering the next window to define the scan width of MODIS data. For a band of 250m resolution, the width is selected to be 40, and the corresponding resolution is selected; if the resolution is already available, the default settings are not changed.
Calculating the radiation product: the following formula is used:
here, the
Is the solairiradiance weighted by a particular detector;
is the square of the distance between the day and the earth,reflectanceis the reflectivity.
Correcting the zenith angle of the sun:this is achieved using "SolarZenith". The conventional correction formula is:
wherein
In order to correct the reflectivity before the correction,
in order to have the reflectivity after the correction,
the zenith angle of the sun. The sun zenith angle data "SolarZenith" is stored in a 1KM file, and the observed value thereof is of 16-bit unsigned integer type with a zoom factor of 0.01. For the observed DN value recorded in SDS in MODIS1B, divide it by a scaling factor of 0.01 to obtain zenith angle ө, whose correction formula for the solar zenith angle is:wherein
、
The surface reflectivity before and after the zenith angle is corrected, where the default value of the COS function parameter is provided in the IDL as radians.
Geometric correction of the image: longitude and latitude data carried in the MODIS data file are utilized. The correction principle is that every pixel in the image is corresponding to a longitude and latitude coordinate value, and the pixel is transformed to a corresponding position of a standard geographic space by projection according to the coordinate value, thereby achieving the purpose of geometric correction. In an input image and corresponding longitude and latitude data thereof, pixel coordinates and geodetic coordinates (B, L) in the image are selected according to fixed row-column intervals. The pixel coordinates in the input image are represented by (Ri, Ci), the pixel coordinates in the output image are represented by (Ro, Co), and the geometrically corrected geographical coordinates are represented by (X, Y). The flow from (Ri, Ci) to (Ro, Co) is as shown in FIG. 2.
In fig. 2, BL-XY is the projective transformation process, which can be implemented in IDL by the functions MAP _ PROJ _ INIT and MAP _ PROJ _ FORWARD.
(2) Ambient gradient factor processing
Using a smoothing tool Focalomean and a filling tool such as Fill and Zonal Fill carried by ArcGIS software of an ESRI company to carry out smoothing denoising and specific convex-concave point filling processing on the digital DEM data processing of 90m in Yunnan; and creating latitude and longitude data with 90m resolution by utilizing DEM coordinate information in a latitude and longitude format and using an Add XY Coordinates tool.
In order to ensure the minimum deformation of the map in Yunnan province, a Project Raster tool is used, and a Gaussian-Kruger projection method with the longitude of the central meridian of 102 degrees is selected for map projection.
(3) Instantaneous surface temperature field T inversion
In 2005, the memorial Hao et al proposed a surface temperature inversion algorithm suitable for MODIS data for the AVHRR windowing algorithm, and the basic parameters of the algorithm can be obtained from the MODIS data. The thermal infrared band brightness temperature inversion is calculated by using a MODIS-LOADTEMPERATURE function provided by a MODISTOLS module and calibrating a radiation band based on MOD021 KM. An algorithm formula of the Yunnan province instantaneous earth surface temperature field based on MODIS thermal infrared band inversion is as follows:
in the formula, TsIs the surface temperature, T31And T32The luminance temperatures of the MODIS bands 31 and 32, respectively. A. the0，A1And A2Is a parameter of the split window algorithm, defined as follows:
here, ,
is a constant value and is provided with a constant value,
，
，
，
(ii) a Other intermediate parameters are calculated as follows:
wherein,
is that
（
=31 ， 32) The wave band visual angle isθThe atmospheric transmittance of (c);
is thatThe band earth surface radiance, these two parameters directly influence the accuracy of the earth surface temperature that finally obtains. The algorithm flow is shown in figure 3.
The calculation of the atmospheric transmittance in the split window algorithm uses MODIS image resolution of 250m 2 nd wave band, so that instantaneous surface temperature data with 250m resolution is obtained by inversion.
In order to express the micro-terrain temperature change rule, the pixel size of the grid data with the environmental gradient factor resolution of 90m is corresponded, and then the earth surface temperature field with the resolution of 250m is resampled into the grid data with the resolution of 90m by using a nearest neighbor method. The Resampling process used the sample tool in ArcGIS software, with the sampling Technique option defined as NEAREST.
(4) Obtaining a temperature baseline B-T
Projecting the geographical coordinates of the conventional meteorological station in the measurement area into a coordinate system consistent with the instantaneous surface temperature field and the DEM, then performing point sampling on the instantaneous surface temperature field by using a Sample tool in ArcGIS software, and extracting the inversion value of the instantaneous surface temperature at the conventional meteorological station
Longitude, longitude
Latitude of degree of latitude
Observed and measured value of the average temperature in 30 yearsA statistical analysis sample set is established as shown in Table 3, and a scatter diagram of the surface instantaneous temperature inversion value and the historical measured temperature value is drawn as shown in FIG. 3. As shown in FIG. 3, the temperature inversion value has a certain line with the measured valueAnd (4) a sexual relation shows that the surface temperature field well reflects the realistic distribution pattern of the temperature.
Table 3 temperature baseline model construction partial samples
| Weather station | Latitude | Longitude (G) | Instantaneous temperature | Annual average temperature | Weather station | Latitude | Longitude (G) | Instantaneous temperature | Annual average temperature |
| Meng La | 21.48 | 101.57 | 14.6 | 21.1 | Jinggu | 23.50 | 100.70 | 17.5 | 20.2 |
| Big Meng Long | 21.58 | 100.67 | 14.3 | 21.3 | Gunn horse | 23.55 | 99.40 | 14.0 | 18.8 |
| Meng Hai | 21.92 | 100.42 | 12.7 | 18.2 | Mengding | 23.57 | 99.08 | 18.1 | 21.6 |
| Landscape flood | 22.00 | 100.80 | 17.7 | 21.9 | Yuanjiang river | 23.60 | 101.98 | 17.2 | 23.7 |
| Mongolian rose | 22.33 | 99.62 | 12.9 | 19.7 | Inkstone | 23.62 | 104.33 | 12.6 | 16.1 |
| Root of Langchanlan | 22.57 | 99.93 | 13.0 | 19.1 | Building water | 23.62 | 102.83 | 15.5 | 18.5 |
| Jiang Cheng | 22.58 | 101.85 | 11.3 | 18.2 | Funing medicine | 23.65 | 105.63 | 12.6 | 19.3 |
| West alling | 22.73 | 99.45 | 8.9 | 15.3 | Stone screen | 23.70 | 102.48 | 13.3 | 18 |
| Jin Ping | 22.78 | 103.23 | 13.4 | 17.8 | Far reaching of the head | 23.70 | 103.25 | 13.3 | 19.7 |
| Szemao | 22.78 | 100.97 | 14.8 | 17.8 | Zhen Yuan | 23.88 | 100.88 | 12.0 | 18.6 |
| Screen edge | 22.98 | 103.68 | 12.2 | 16.5 | Lincang | 23.88 | 100.08 | 13.3 | 17.3 |
| Spring green tea | 23.00 | 102.42 | 13.0 | 16.6 | Ruili (a Chinese character of' Ruili | 24.02 | 97.85 | 17.4 | 20.1 |
| Cuttle | 23.03 | 104.42 | 13.4 | 16.9 | Yongde | 24.03 | 99.23 | 8.1 | 17.4 |
| Pu' er tea | 23.03 | 101.05 | 13.2 | 18.2 | Qiu Bei | 24.05 | 104.18 | 13.7 | 16.3 |
| Hemp chestnut slope | 23.13 | 104.70 | 11.9 | 17.6 | Guannan province | 24.07 | 105.07 | 12.4 | 16.7 |
| Cangyuan | 23.15 | 99.27 | 12.6 | 17.5 | Xinping | 24.07 | 101.97 | 13.9 | 17.4 |
| Yuanyang (Yuanyang) | 23.17 | 102.75 | 12.7 | 16.4 | Zhengkang medicine | 24.07 | 98.97 | 11.0 | 18.9 |
| Red river | 23.37 | 102.43 | 15.9 | 20.3 | Tonghai (sea of China) | 24.12 | 102.75 | 14.0 | 15.6 |
| Wenshan mountain | 23.38 | 104.25 | 13.5 | 17.8 | Emei mountain | 24.18 | 102.40 | 12.4 | 15.9 |
| Old person | 23.38 | 103.15 | 14.4 | 15.9 | Huaning-a Chinese medicine for curing rheumatic arthritis | 24.20 | 102.92 | 13.8 | 15.7 |
| Mongolian medicine | 23.38 | 103.38 | 13.7 | 18.6 | Jiangchuan | 24.28 | 102.77 | 11.8 | 15.6 |
| Mojiang river | 23.43 | 101.72 | 12.0 | 17.9 | Yuxi tea | 24.35 | 103.55 | 14.2 | 15.7 |
| Western domain | 23.45 | 104.68 | 12.7 | 15.9 | Long Chuan | 24.37 | 97.95 | 13.1 | 18.9 |
| Double river | 23.47 | 99.80 | 13.9 | 19.6 | Maitreya | 24.40 | 103.45 | 10.6 | 17.4 |
In IBM SPSS Statistics software, a linear regression analysis is performed on a sample set by using an analysis-regression-linear tool, and after 1 abnormal value is removed, a regression equation is finally established:
wherein,for the inverted surface instantaneous temperature of the earth,
is the longitude of the weather station or stations,
is the latitude of the meteorological site,
for annual temperature equalization of meteorological sites, the model passes a statistical test with a 95% confidence level, and the results of the F-test for equation significance and the T-test for coefficient significance are shown in tables 1-2.
TABLE 1F checklist
TABLE 2T checklist
(5) And (5) obtaining a calibration model of the instantaneous earth surface temperature field by regression analysis in the step (4), wherein the calibration model comprises the following steps:
wherein T is inverted surface instantaneous temperature field data, EAST is longitude grid data of a measurement region, NORTH is latitude grid data of the measurement region, and G-T is annual average temperature field data, namely a temperature baseline. And (3) substituting the instantaneous earth surface temperature field obtained in the step (3) and the longitude and latitude data obtained in the step (2) into the formula by using a Raster Calculator (Raster Calculator) provided by ArcGIS software, and calculating to obtain a rasterized earth surface temperature baseline B-T, as shown in FIG. 4.
(II) surface temperature baseline application example
(1) And (3) a pine wood nematode occurrence risk early warning diagram in Yunnan province.
The Yunnan province temperature base line is used, and the early warning model for the occurrence risk of the pine wilt disease in the Yunnan province is established by combining with the scoring of experts as follows:
[ Long-term evaluation of the risk of bursaphelenchus xylophilus ] = [ forest distribution ] × 0.6 + [ synthetic index of human interference ] × 3 + [ temperature base line ] × 0.15- [ annual rainfall ]/1000 × 3- [ elevation ]/1000 + (30- [ latitude ]) × 0.2
Substituting the continuous space simulation forecasting factor into an early warning model based on a Raster simulator tool of ArcGIS software to obtain a risk early warning result, and making a to-be-issued pine wood nematode occurrence risk early warning diagram through GIS drawing, as shown in FIG. 5.
The result gives a good early warning to the occurrence of the pine wilt disease by taking the grid size as a unit, and has high accuracy, such as: in the later period, the showakentong zone bordering on Sichuan has more regular pine wood nematode disease, and the area is early warned as a high-risk area.
(2) Long-term forecast picture of forest fire danger in Yunnan province.
The soil humidity, forest distribution, latitude and longitude inverted by using the temperature base line of Yunnan province and the MODIS remote sensing image are scored by combining experts, and a long-term forecasting model of the forest fire danger of the Yunnan province is established as follows:
[ long-term prediction result of forest fire risk ] = [ temperature baseline of Yunnan province ]/25.00X 20.00 + [ soil humidity of Yunnan ]/1.3X 20 + [ forest distribution of Yunnan ]. times.60.00 + [ latitude ]/30X 50.00 + [ longitude ]/107X 40 +
As shown in fig. 6: the darker the colour, the higher the risk of forest fires and vice versa the lower the risk of fire. The fire risk indexes of the Yunnan northwest and the Yunnan middle of Yunnan province are higher, and the color is biased to black, which is consistent with the higher fire risk of the Yunnan northwest and the Yunnan middle of Yunnan province in the area. Due to the fact that the temperature base line of continuous spatial distribution and other forecasting factors are used, the forecasting result can reflect the fire risk size with the grid size as the unit, the forecasting result is more continuous than the traditional forecasting result based on meteorological sites, and the refinement degree is improved.
Claims (4)
1. A method for constructing a mountain land surface temperature baseline comprises the following steps:
(1) MODIS thermal infrared image preprocessing, including stripe noise elimination, data overlapping phenomenon removal, reflectivity calibration, sun zenith angle correction and geometric correction;
(2) the environmental gradient factor processing comprises the steps of constructing or acquiring DEM data, carrying out smooth denoising and specific convex-concave point filling, and extracting latitude and longitude data based on the DEM;
(3) selecting a window splitting algorithm of the people at the memorial porcupies and the like to obtain an instantaneous earth surface temperature field T based on MODIS thermal infrared waveband inversion, wherein the MODIS image resolution for calculating the atmospheric transmittance is 250m of the 2 nd waveband, and the instantaneous earth surface temperature data with the resolution of 250m is obtained through inversion;
the method is characterized in that:
further resampling the instantaneous earth surface temperature field with the pixel size of 250m obtained by the inversion in the step (3) by using a nearest neighbor method to enable the instantaneous earth surface temperature field to correspond to the pixel size of the environmental gradient factor;
(4) construction of a mathematical model of a temperature baseline B-T
Projecting the vector of the conventional meteorological station, the instantaneous surface temperature field and DEM, latitude and longitude environment gradient factors in the measurement region into a consistent coordinate system, extracting the inversion value and the latitude and longitude value of the instantaneous surface temperature at the conventional meteorological station, establishing a sample set with annual average temperature observation data, and making the instantaneous surface inversion temperature be
Longitude is
Latitude of degree of latitudeAnnual average temperature is
f test and T test of an equation and a coefficient are carried out to determine the significance of the equation, and then a mathematical model for obtaining a temperature baseline B-T is established;
(5) obtaining a temperature baseline B-T of a pixel scale
And (4) substituting the instantaneous earth surface temperature field T and longitude and latitude data in the measurement region into the mathematical model constructed in the step (4), and calculating to obtain an earth surface temperature base line B-T taking the size of the grid as a unit based on the grid calculation function of GIS image processing software.
2. The method as claimed in claim 1, wherein the step (2) uses gaussian-gram-luger projection method for map projection to ensure minimum deformation of the mountain landform map.
3. The method as claimed in claim 1 or 2, wherein the step (4) takes the observed value of the average temperature of 15-30 years in the history of the conventional meteorological site in the region as a modeling sample of the annual average temperature.
4. An application of the mountain area surface temperature baseline construction method as claimed in claim 3, wherein the longitude of the central meridian of the gaussian-kruge map projection in step (2) is 102 °, and in step (4), the average temperature observed values of 135 conventional meteorological sites in Yunnan province in 30 years are used as a sample set, and after the outliers are removed, a linear regression method is used to establish the mathematical model of the temperature baseline B-T as follows:
the model was tested by statistical tests with a 95% confidence level, where the regression equation F had a value of 108.587, corresponding to the regression estimation equation
、
、、
Have t values of 5.986, 12.878, -6.846 and-3.235, respectively.
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