CN110096743B - Method for estimating surface water vapor pressure based on remote sensing data and elevation information - Google Patents
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Abstract
The invention discloses a method for estimating surface water vapor pressure based on remote sensing data and elevation information, which can acquire reliable surface water vapor pressure data of an observation-free area, avoids acquiring intermediate data by an indirect method, and improves the estimation precision of the surface water vapor pressure of a region with complex terrain by directly establishing a model among the surface water vapor pressure, the remote sensing data and the elevation information.
Description
Technical Field
The invention relates to the field of digital image data processing, in particular to a method for estimating surface water vapor pressure based on remote sensing data and elevation information.
Background
Surface vapor pressure (surface vapor pressure) is a basic data of meteorological observation and research, and has high time-space variability. In the conventional meteorological observation, a ground meteorological station is used for acquiring surface water vapor pressure data, but the data of the ground conventional meteorological station is limited, and particularly in mountainous areas, the arrangement of stations is limited by conditions such as terrain, social economy and the like, and the distribution is uneven. When the distance between the stations is greater than 20km, the spatial interpolation method is difficult to calculate reliable data for the observation-free area. Under the condition, the satellite remote sensing observation has space advantages and can make up for the defects of ground conventional observation.
The satellite passive remote sensing algorithm for estimating the water vapor content is based on various spectral channel data, and a near infrared channel near 1 mu m is widely adopted. Researchers at home and abroad have made a great deal of effective research on calculating the amount of atmospheric water (total degradable water) by using near infrared images. The existing methods for estimating the water vapor pressure of the earth surface by using a near-infrared remote sensing model are mainly summarized as two methods: indirect and direct processes. An indirect method: the method comprises the steps of firstly inverting (or acquiring) the atmospheric water-reducing product according to a near-infrared image, and then estimating the surface water vapor pressure based on the atmospheric water-reducing product. The direct method comprises the following steps: the method is characterized in that a statistical model between the water vapor pressure and the water vapor channel of the clear sky surface is directly established, the method considers that the water vapor pressure and the water vapor channel data of the clear sky surface have statistical relation similar to the water reducible quantity of the clear sky atmosphere.
Neither the existing indirect method nor the direct method considers the elevation information, so that the accuracy of the surface steam pressure estimated by the existing method in a region with complex terrain is low.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for estimating surface water vapor pressure based on remote sensing data and elevation information, which solves the problem of low accuracy of surface water vapor pressure estimated in a complicated terrain area by an indirect method and a direct method.
The technical scheme is as follows: the invention relates to a method for estimating surface water vapor pressure based on remote sensing data and elevation information, which is characterized by comprising the following steps:
step 1: collecting and preprocessing data;
collecting data including but not limited to remote sensing data, elevation information data, and ground observation data of surface water vapor pressure;
the remote sensing data processing mode includes but is not limited to geometric correction, calculation processing of radiance value and reflectivity;
the elevation information data is provided by a digital elevation model, the elevation information data processing mode comprises but is not limited to projection transformation and resampling, and the elevation information data ensures that the projection and the resolution ratio are the same as those of adopted remote sensing data;
the ground observation data are acquired by a ground observation station, and the ground observation data are processed in a projection transformation mode including but not limited to projection transformation so that the ground observation data, the remote sensing data and the elevation data have the same projection;
step 2: constructing a surface water vapor pressure estimation model based on remote sensing water vapor channel data and elevation information:
e 0 ＝a+bK i +clnh (1)
in the above formula e 0 Is the surface water vapor pressure, a, b and c are regression coefficients, h is the elevation value, K i The vapor transmission rate of the ith vapor absorption channel relative to the vapor window channel is determined;
and step 3: performing model calculation on the data, wherein the data required by the model calculation includes but is not limited to ground observation data at the time of remote sensing imaging used for model calculation, extracting an elevation value at a ground observation position from a digital elevation model, and calculating and extracting the water vapor relative transmittance K at the ground observation position from the remote sensing data i ；K i Using radiance value ratio G i ＝L i /L 2 Or the reflectivity ratio T i ＝ρ i /ρ 2 And expressing, respectively establishing models, substituting the data into a formula (1) to establish an equation set, and solving regression coefficients a, b and c by adopting a least square method to obtain the established specific model.
By adopting the technical scheme, the data are brought into the established surface water vapor pressure estimation model based on the remote sensing water vapor channel data and the elevation information by collecting and preprocessing the ground observation data of the remote sensing data, the elevation information data and the surface water vapor pressure to obtain the regression coefficients a, b and c, so that the established concrete model is obtained. And the surface water vapor pressure of the target area can be estimated by substituting the known data into the model. The accuracy of the result obtained by the method for estimating the surface water vapor pressure based on the remote sensing data and the elevation information is higher than that of an indirect method without considering the elevation information.
Has the advantages that: according to the method, reliable surface water vapor pressure data of the non-observation area are obtained by directly establishing a model between the surface water vapor pressure and the remote sensing data and the elevation information, so that the acquisition of intermediate data in an indirect method is avoided, and the estimation precision of the surface water vapor pressure of the area with complex terrain is improved.
Drawings
FIG. 1 is an embodiment DEM and meteorological site distribution;
FIG. 2 is a graph of indirect surface water vapor pressure estimation;
FIG. 3 is a plot of a surface water vapor pressure estimation of the method of the present invention;
FIG. 4 is a MODIS near-infrared water vapor correlation channel list;
FIG. 5 is a table showing the equations and the fitting results of the direct method model without consideration of terrain factors and the model established by the present invention.
FIG. 6 is a table comparing the error of the method of the present invention with that of the indirect method.
Detailed Description
A method for estimating surface water vapor pressure based on remote sensing data and elevation information is characterized by comprising the following steps:
step 1: collecting and preprocessing data; collecting data including but not limited to remote sensing data, elevation information data, and ground observation data of surface water vapor pressure;
collecting data including but not limited to remote sensing data, elevation information data, and ground observation data of surface water vapor pressure;
the remote sensing data processing mode includes but is not limited to geometric correction, and calculation processing of radiance value and reflectivity;
the elevation information data is provided by a digital elevation model, the elevation information data processing mode comprises but is not limited to projection transformation and resampling, and the elevation information data ensures that the projection and the resolution ratio are the same as those of adopted remote sensing data;
the ground observation data are acquired by a ground observation station, and the ground observation data are processed according to the acquired ground observation data in a mode including but not limited to projection transformation processing, so that the ground observation data, the remote sensing data and the elevation data have the same projection.
The method takes the Guizhou province of China as a research target, the Guizhou province is a typical mountain environment, the terrain is complex, and the difference of the surface water vapor pressure space is large.
The method comprises the following steps of using MODIS remote sensing data, SRTM DEM data and surface observation data of surface steam pressure, wherein the remote sensing data need window channel data and absorption channel data of steam, and more than one steam window or absorption channel can be used. The remote sensing data needs geometric correction and calculation processing of radiance values and reflectivity; the embodiment adopts MODIS remote sensing data of 25 NASA Terra satellite transit Guizhou province in Beijing time 2017, 5, 13, 11, and the specific data is MOD02 data downloaded from a NASA EOS data center, and the spatial resolution of the data is 1km. The near infrared moisture dependent channel of MOD02, as shown in fig. 4, in this embodiment uses the 2 nd channel as the moisture window channel.
And performing geometric correction and projection transformation on the remote sensing data by using remote sensing professional software ENVI, and calibrating MODIS data to obtain radiance value data and reflectivity data.
Elevation information data is usually provided by a Digital Elevation Model (DEM), and according to different adopted data, processing possibly required comprises projection transformation and resampling so as to ensure that the adopted remote sensing data has the same projection and resolution; the SRTM DEM data resolution is 90 meters, the SRTM DEM data resolution is matched with the remote sensing data resolution, the resampling is 1km, the remote sensing data projection is projected, the research area DEM is shown in figure 1, the average altitude is 1100m, and 92.5% of the area is mountainous and hilly.
The ground observation data is usually obtained by a ground observation station, and projection transformation processing may be required according to different obtained data so as to have the same projection as the remote sensing data and the elevation data; the ground observation data is divided into two parts, wherein one part is more data used for model calculation, and the other part is less data used for model precision evaluation. The ground observation data of the surface water vapor pressure is provided by the meteorological data center of the China meteorological office, and the meteorological data center has 55 meteorological sites in total, and the sites are distributed as shown in figure 1. In order to make the satellite-ground quasi-synchronous observation, the earth surface water vapor pressure observation data at the integral point time (11 and 12 points) is linearly interpolated to the imaging time of the remote sensing image. In this embodiment, 41 sites are used as calculation sites for modeling and calculation, and the other 14 sites are used as verification sites to evaluate the accuracy of the model.
And 2, step: constructing a surface water vapor pressure estimation model based on remote sensing water vapor channel data and elevation information:
e 0 ＝a+bT 17 +clnh (1)
in the above formula e 0 Is the surface water vapor pressure, a, b and c are regression coefficients, h is the elevation value, K i The vapor transmission rate of the ith vapor absorption channel relative to the vapor window channel;
in this example, considering three vapor absorption channels (17, 18 and 19) and two representations of the channel's remote sensing values (radiance value and reflectance), 6 models were constructed as shown in fig. 5.
And step 3: performing model calculation on the data, wherein the data required by the model calculation includes but is not limited to ground observation data at the time of remote sensing imaging used for model calculation, extracting an elevation value at a ground observation position from a digital elevation model, and calculating and extracting the water vapor relative transmittance K at the ground observation position from the remote sensing data i ；K i Using spokesRatio of luminance values G i ＝L i /L 2 Or the reflectivity ratio T i ＝ρ i /ρ 2 And expressing, respectively establishing models, substituting the data into a formula (1) to establish an equation set, and solving regression coefficients a, b and c by adopting a least square method to obtain the established specific model.
In this embodiment, the coefficients and the fitting effect of each model are calculated according to the observation values of the surface water vapor pressure of the 41 ground observation stations and the corresponding elevation values and the relative water vapor transmittances, and the calculation result is shown in fig. 5.
Considering that on the one hand the satellite has at least one moisture absorbing channel and on the other hand the relative water vapor transmission rate K i The expression is expressed by radiance ratio or reflectivity ratio, so that the model established in step 3 has at least two models, and the determining coefficient R is adopted 2 Evaluating the fitting effect of each model with the residual standard deviation RSE, selecting the optimal model, and determining the coefficient R 2 The residual standard deviation RSE evaluation formula is respectively
In the above formula
Is the surface water vapor pressure model estimated value of the observation station, y i Is the surface water vapor pressure observation value of the ith observation station>
The mean value of the observed values of the surface water vapor pressure participating in modeling is shown, and n is the number of the observation stations.
According to a decision coefficient R 2 And selecting a model with a large determining coefficient and a small residual standard deviation from the established models as an earth surface water vapor pressure estimation model according to the result obtained by evaluating the residual standard deviation RSE.
According to the aboveThe calculation result of the evaluation index is shown in fig. 5, according to the model evaluation result, a model with a large coefficient of determination and a small residual standard deviation in the built model is selected as a surface steam pressure estimation model, and finally a model with a model number of 22 is selected, the model uses the reflectivity ratio of a 17 th channel and a 2 nd channel to represent the relative transmittance of steam, the coefficient of determination of fitting calculation is 0.741, the residual standard deviation is 1.098hpa, and the specific model is as follows: e.g. of a cylinder 0 ＝a+bT 17 +clnh。
For comparison with the existing direct method without considering elevation information, an exponential estimation model, a polynomial estimation model and a channel value linear combination model of the surface water vapor pressure are listed in fig. 5, three water vapor absorption channels (17, 18 and 19) are divided, and the remote sensing values of the channels are modeled by two expression (radiance value and reflectivity) distributions, and the calculation and evaluation of the models are shown in fig. 5. As can be seen from fig. 5, the maximum determination coefficient of the direct method model without considering elevation information is 0.388, and the remaining standard deviation is 1.688hpa, which obviously considers that the accuracy of the elevation information is the highest and the error is the smallest.
And substituting the digital elevation model and the water vapor relative transmittance data calculated through the remote sensing data into the selected model, thereby estimating the surface water vapor pressure of the target research area.
The result of the estimation of the surface water vapor pressure in the embodiment is shown in fig. 3, and the comparison with the DEM in the research area shows that the result of the method can highlight local details and reflect the influence of a complex terrain on the water vapor pressure.
And extracting a surface water vapor pressure estimation value at the ground observation data position for model precision evaluation, analyzing absolute errors and relative errors of the surface water vapor pressure observation value and the estimation value, and evaluating the precision of the established model.
To better compare the difference between the present invention and the existing indirect method, in this embodiment, in addition to the estimation of the surface water vapor pressure of the research area by using the method of the present invention, the surface water vapor pressure of the research area is also estimated by using the indirect method, and the error conditions of the two methods are compared according to 14 verification stations. Linear formula for indirect method (W = a + be) 0 ) Calculating the surface water vapor pressure e in a fitting manner 0 Coefficient (a and b) to atmospheric degradable water quantity W, estimation by indirect methodAs a result, in relation to the intermediate data (atmospheric water reducible product) used, the NASA EOS MOD05 atmospheric water reducible product, which is widely used at home and abroad, is used in this embodiment. As can be seen from the map of the estimation result of the surface water vapor pressure: the method and the indirect method of the invention calculate the macroscopic distribution trend of the surface water vapor pressure to be consistent, both can reflect that the surface water vapor pressure space change of the area is larger, but the result of the method of the invention can highlight local details and can reflect the influence of complex terrains on the water vapor pressure. The results of the error quantitative calculation of the verification station are shown in fig. 6, and regarding the absolute error of each station, most stations of the method of the present invention are smaller than that of the indirect method, and the mean values of the absolute errors of the method of the present invention and the indirect method are 1.234hpa and 1.806hpa, respectively. Number of stations with relative error exceeding 15%: direct method 2 stations and indirect method 5 stations. The mean values of the relative errors of the direct and indirect methods were 8.2% and 12.0%, respectively. Thus, in general, the direct method is more accurate than the indirect method. Compared with the indirect method without considering elevation information in precision, the absolute error average value of the indirect method is 1.806hpa, the absolute error average value of the model established by the method is 1.234hpa, the error is reduced by 0.572hpa, and the error is reduced by 31.67%.
Claims (5)
1. A method for estimating surface water vapor pressure based on remote sensing data and elevation information is characterized by comprising the following steps:
step 1: collecting and preprocessing data;
collecting data including remote sensing data, elevation information data and ground observation data of surface water vapor pressure;
the remote sensing data processing mode comprises geometric correction and calculation processing of radiance value and reflectivity;
the elevation information data is provided by a digital elevation model, and the elevation information data processing mode comprises projection transformation and resampling, so that the elevation information data and the adopted remote sensing data have the same projection and resolution;
the ground observation data are obtained by a ground observation station, and the ground observation data processing mode comprises projection transformation processing so that the ground observation data, the remote sensing data and the elevation data have the same projection;
step 2: constructing a surface water vapor pressure estimation model based on remote sensing water vapor channel data and elevation information:
e 0 ＝a+bK i +clnh (1)
in the above formula e 0 Is the surface water vapor pressure, a, b and c are regression coefficients, h is the elevation value, K i The vapor transmission rate of the ith vapor absorption channel relative to the vapor window channel is determined;
and step 3: performing model calculation, wherein data required by the model calculation comprise ground observation data at the time of remote sensing imaging for model calculation, extracting an elevation value at a ground observation position from a digital elevation model, and calculating and extracting the water vapor transmittance K of an ith water vapor absorption channel relative to a water vapor window channel at the ground observation position from the remote sensing data i ；K i Using radiance value ratio G i ＝L i /L 2 Or the reflectivity ratio T i ＝ρ i /ρ 2 And expressing, respectively establishing models, substituting the data into a formula (1) to establish an equation set, and solving regression coefficients a, b and c by adopting a least square method to obtain the established specific model.
2. The method of claim 1, wherein in steps 2 and 3, the satellite has at least one vapor absorption and window channel, and the relative vapor transmission rate K is determined based on the at least one vapor absorption and window channel i The expression is expressed by radiance ratio or reflectivity ratio, so that the model established in step 3 has at least two models, and the determining coefficient R is adopted 2 Evaluating the fitting effect of each model with the residual standard deviation RSE, selecting the optimal model, and determining the coefficient R 2 The residual standard deviation RSE evaluation formula is respectively
In the above formula
As estimated value, y, of the surface water vapor pressure model of the observation station i Is the surface water vapor pressure observation value of the ith observation station>
And n is the average value of the observed values of the surface water vapor pressure participating in modeling, and the number of the observation stations.
3. The method of claim 2, wherein the method further comprises estimating surface water vapor pressure based on the remote sensing data and the elevation information according to a decision factor R 2 And selecting a model with a large determining coefficient and a small residual standard deviation from the established models as an earth surface water vapor pressure estimation model according to the result obtained by evaluating the residual standard deviation RSE.
4. The method of claim 3, wherein the digital elevation model and the relative water vapor transmission data calculated from the remote sensing data are substituted into a selected model to estimate the surface water vapor pressure in the target area of interest.
5. The method of claim 2, wherein the estimated surface water vapor pressure at the location of the ground observation for accuracy evaluation of the model is extracted to analyze absolute and relative errors of the observed and estimated surface water vapor pressure values and evaluate the accuracy of the model.
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| CN104406686A (en) * | 2014-12-10 | 2015-03-11 | 西北师范大学 | Estimation method of incident radiation of solar shortwaves under complex terrain conditions |
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| CN104036086A (en) * | 2014-06-17 | 2014-09-10 | 杭州电子科技大学 | MODIS (moderate resolution imaging spectroradiometer) data based relative atmosphere humidity estimation method |
| CN104406686A (en) * | 2014-12-10 | 2015-03-11 | 西北师范大学 | Estimation method of incident radiation of solar shortwaves under complex terrain conditions |
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