CN109632106B - Remote sensing surface temperature product angle effect correction method - Google Patents
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Abstract
A method for correcting the angle effect of a remote sensing surface temperature product comprises the following steps: calculating the brightness temperature of the bands of the pixels 31 and 32 by using MOD021KM data; obtaining the atmospheric lower boundary temperature T of the pixelairThe total atmospheric water vapor content CWV and the zenith angle theta v observed by a sensor; obtaining the coefficient C, A of the pixel sensor observation direction general wedge window algorithm1，A2，A3，B1，B2，B3(ii) a Determining the zenith emissivity (epsilon) of the picture elements0) Calculating the slope (α) and direction of the pixel
Calculating the local reflection angle (theta)0) (ii) a Obtaining the average emissivity of pure pixels with different coverage types on the earth surface between-65 DEG and + 65 DEG of the observation angle of the sensor
A data set; obtaining the difference of emittance of different observation zenith angles relative to zenith direction
A data set; an expression of emissivity variation of the pixel in the observation direction relative to the zenith direction is assumed; determining an emissivity expression of the pixel in the observation direction; calculating the surface temperature (T)s). The invention provides a pixel-level emissivity directional model with strong universality by combining the existing research, data and methods, and the model can be used for correcting the angle effect of MODIS temperature products.
Description
Technical Field
The invention relates to the field of thermal infrared remote sensing earth surface temperature inversion, in particular to an earth surface direction emissivity determining method based on various MODIS earth surface temperature emissivity products and application of the earth surface direction emissivity determining method in correcting an angle effect of the MODIS temperature products.
Background
The earth surface temperature is often used for estimating the energy balance of the earth atmospheric interface and is also an indispensable parameter in a plurality of models of ecology, climate, hydrology and the like. With the improvement of human science and technology, the productivity is improved, the carbon emission in the atmosphere is greatly increased, the greenhouse effect is intensified, the global warming is changed from a concept to a perceived fact, the global warming causes glaciers of the two poles to melt, the sea level to rise and extreme climate to frequently occur. The surface temperature is an important basis for human beings to research global warming laws and predict global warming trends, and the research result is to formulate a carbon emission policy and achieve an important theoretical basis of international climate agreements, so that the accurate measurement of the surface temperature is very important.
The advent of Remote Sensing technology has made it possible to obtain surface temperatures on a global or regional scale, and common temperature inversion algorithms require prior knowledge of surface emissivity except for temperature emissivity separation algorithms, single channel algorithms, and split window algorithms. there are many methods for surface emissivity determination, such as determining surface emissivity from NDVI (variance E, caseling V.mapping surface emissivity from NDVI: application to European, African, and South American area [ J ] removal Sensing of environmental element, 1996,57(3):167-184.), determining emissivity from TISI index (Belr F. temperature-index spectra in thermal information bases [ J ] removal Sensing of environmental element, 1990,32(1): 17-33) and determining surface emissivity using α, 855. the above mentioned methods for surface emissivity determination of surface emissivity, and the above mentioned methods for obtaining a high emissivity, low, high, low, and high emissivity (emission) based on the above the model-emission spectrum, the above mentioned methods for the emission spectrum, the emission spectrum of emission spectrum, 1990,32(1): 17-33), and the above mentioned methods for obtaining a high emission from the emission spectrum.
In order to improve the accuracy of the MODIS 11_ L2 temperature product, the emissivity directivity change rule on the earth surface pixel scale needs to be acquired. At present, the emissivity angle effect model is mainly established by a pure empirical method, a semi-empirical nuclear driving model method and a physical model method. Pure empirical models, such as those using a cosine function to fit the emissivity change law of the earth's surface, have the disadvantage that the strong heterogeneity of the earth's surface makes the model limited (A.J. land surface temporal evolution from the advanced high resolution radiometer and the imaging-tracking radiometer.II: Experimental results and evaluation of AVHRR algorithms [ J ]. Journal of geographic Research industries, 1993,99(D6): 13025-; a semi-empirical method representative empirical core driving model, Vinnikov et al, proposes that the anisotropy of the surface temperature is expressed by using an emissivity core and a solar core, however, the model is obtained by data training using six SURFRAD sites which represent limited surface coverage conditions, so that the model has certain limitations (Vinnikov KY, YuY, Goldberg MD, et al. angular anisotropy of satellite interference activities of surface environments [ J ]. geographic Research Letters,2012,39(23): 23802.); besides, some geometric optical models and radiation transmission models are provided, and these models need to accurately determine many parameters of the earth surface in order to accurately simulate the radiation transmission rule of the earth surface, and most of the parameters are difficult to determine, so these models are generally not good in universality when selecting representative samples to train the models in the earth surface or a laboratory.
Disclosure of Invention
The invention aims to solve the technical problems that the correction of the temperature angle effect in the prior art has space limitation and the influence of terrain on heat radiation is not taken into consideration, provides a pixel-level emissivity directional model with strong universality by combining the existing research, data and methods, and applies the pixel-level emissivity directional model to the angle effect correction of MODIS temperature products.
In order to solve the technical problem, the method for correcting the angle effect of the remote sensing surface temperature product provided by the invention comprises the following steps:
step 1: calculating brightness temperatures T31 and T32 of pixel MODIS31 and 32 wave bands;
step 2: obtaining the ambient temperature of the pixel under the atmosphereDegree TairThe total atmospheric water vapor content CWV and the zenith angle theta v observed by a sensor;
and step 3: using pixel atmospheric lower boundary temperature TairObtaining a general wedge window algorithm coefficient C, A of the pixel sensor in the observation direction from a wedge window algorithm coefficient lookup table by the total atmospheric water vapor content CWV and the observation zenith angle theta v of the sensor1，A2，A3，B1，B2，B3；
And 4, step 4: determining the zenith emissivity epsilon of the pixel0；
Step 5, calculating the gradient α and the slope direction of the pixel
Step 6: calculating the local reflection angle theta0；
And 7: obtaining the average emissivity of the pure pixels with different coverage types on the earth surface at each angle of 131 observation angles between-65 degrees and + 65 degrees
A data set;
and 8: all the results in step 7 are subtracted
Obtaining the difference of emittance from different observation zenith angles to zenith direction
A data set;
and step 9: an expression of emissivity variation of the pixel in the observation direction relative to the zenith direction is assumed as follows:
Vε(θ)＝a1θ+a2θ2+a3θ3+...+anθn
determining the coefficients a in a polynomial1，a2，a3，...anAnd the value of n;
step 10: determining an emissivity expression of the pixel in the observation direction;
step 11: determining the emissivity in the observation direction, and calculating the surface temperature T by combining the brightness temperature and the splitting window algorithm coefficients。
In the above method, the MOD021KM data and the Planck function approximation formula are used to calculate the brightness temperatures T31, T32 of the pixel MODIS31,32 wave bands in step 1,
T31＝1304.413871/ln(1+729.541636/b31)
T32＝1196.978785/ln(1+474.684780/b32)。
in the method, in step 2, the MOD07_ L2 data is used to obtain the ambient temperature T of the pixel atmosphereairAtmospheric total water vapor content CWV, and obtaining a sensor observation zenith angle theta v from MOD021KM data.
In the method, in step 4, ten-year-total-ten-year data of MOD21A 22001-2010 are used for calculating ten-year-history-month average emissivity of the pixels, the result is used as a surface zenith emissivity data set, and the zenith emissivity epsilon of the pixels is determined according to the acquisition months of the pixels0。
In the above method, in step 5, the slope α and the slope direction of the pel are calculated using NASA GLOBE 1 KMDEM data and the aspect and slope functions in ArcMap
In the above method, in step 6, the local reflection angle is calculated using the following formula
Wherein, in the formulasAnd thetav is the sensor observation azimuth angle and the observation zenith angle respectively.
In the method, in step 7, by using the MODIS surface coverage type data MOD12Q1 and MODIS surface emissivity data MOD11B1 from 2006 to 2010, the surface coverage data is subjected to aggregation upscaling to the same resolution as the emissivity data, and pure pixels with different coverage types on the surface are obtained at the observation angle of-65 DEG to + 65 DEG of the sensorMean emissivity per angle of 131 observation angles in between
A data set.
In the above method, in step 9, corresponding pixels are used according to their surface coverage type
Data set fitting determines the coefficients a in a polynomial1，a2，a3，...anThe value of n is determined using the leave-one-out method.
In the above method, in step 10, the expression for determining the emissivity of the pixel in the observation direction by combining the result of step 4 is:
ε(θ)＝ε(0)+Vε(θ)＝ε(0)+a1θ+a2θ2+a3θ3+...+anθn。
in the above method, in step 11, θ is determined according to the result in step 60Substituting the emissivity expression in step 10 to determine the emissivity epsilon (theta) in the observation direction0) In conjunction with the luminance temperature, the wedge algorithm coefficient calculates the surface temperature Ts using the following equation:
the remote sensing surface temperature product angle effect correction method provided by the invention combines the existing research, data and methods, provides a pixel level emissivity directional model with strong universality, and can be applied to MODIS temperature product angle effect correction. The method can effectively correct the angle effect and improve the measurement precision of the surface temperature.
Drawings
FIG. 1 is a technical flow chart of the method for correcting the angle effect of a remote sensing surface temperature product according to the invention.
FIG. 2 is a comparison of temperature angle effect before and after correction using MODIS021KM data using the method of the present invention.
FIG. 3 shows the coverage of MOD12Q1 and MOD11B1 data in step 7.
Detailed Description
According to the embodiment of the invention, the invention provides a method for correcting the angle effect of a remote sensing surface temperature product, and the technical flow chart is shown in figure 1. Specifically, the method for correcting the angle effect of the remote sensing surface temperature product comprises the following steps:
step 1: the MODIS021KM data and the Planck function approximate formula are used for calculating the brightness temperatures T31 and T32 of the pixels MODIS31 and 32 wave bands,
T31＝1304.413871/ln(1+729.541636/b31)；
t32 ═ 1196.978785/ln (1+474.684780/b 32); (formula 1)
Step 2: obtaining pixel atmospheric lower boundary temperature T by using MOD07_ L2 dataairThe total atmospheric water vapor content CWV; obtaining a sensor observation zenith angle theta v from MOD021KM data;
and step 3: using TairCWV and thetav obtain the coefficient C, A of the pixel sensor observation direction general wedge window algorithm from the coefficient lookup table of the wedge window algorithm1，A2，A3，B1，B2，B3；
And 4, step 4: using MOD21A2 emissivity products, ten-year data in 2001-2010, selecting and calculating ten-year historical month average emissivity of the pixel by data quality control, using the result as an earth surface zenith emissivity data set, and determining the zenith emissivity epsilon of the pixel according to the acquired month of the pixel0；
Step 5, calculating the slope α and the slope direction of the pixel by using NOAA GLOBE 1km Digital Elevation (DEM) data and aspect and slope functions in ArcMap
Step 6: the local reflection angle θ is calculated using the following equation0；
φsθ v is the sensor observation azimuth angle and zenith angle, respectively;
and 7: using MODIS surface coverage type data MOD12Q1(2006-2010) and MODIS surface emissivity data MOD11B1(2006-2010), fig. 3 shows the coverage of MOD12Q1 and MOD11B1 data in this step, and the surface coverage data is subjected to aggregation upscaling to the same resolution as the emissivity data, so as to obtain the average emissivity of 131 observation angles each between-65 ° and + 65 ° of the surface pure image elements of different coverage types
A data set;
and 8: all the results in step 7 are subtracted
Obtaining the difference of the emittance of different observation zenith angles relative to the zenith direction
A data set;
and step 9: an expression of the emissivity variation of the pixel in the observation direction relative to the zenith direction is assumed as follows:
Vε(θ)＝a1θ+a2θ2+a3θ3+...+anθn(ii) a (formula 3)
According to the type of surface coverage of the picture element, using the corresponding
Data set fitting determines the coefficients a in a polynomial1，a2，a3，...an(ii) a To avoid overfitting, the value of n is determined using Leave-One-Out Cross Validation (LOO-CV);
step 10: and (4) determining the emissivity expression of the pixel in the observation direction by combining the result of the step 4 as follows:
ε(θ)＝ε(0)+Vε(θ)＝ε(0)+a1θ+a2θ2+a3θ3+...+anθn(ii) a (formula 4)
Step 11: according to the result theta in step 60Substituting into (equation 4) to determine the surface emissivity e (theta) of the observation direction0) Combining the brightness temperature in the step 1 and the splitting window algorithm coefficient in the step 3 to calculate the surface temperature Ts
The invention selects 16 days in 7 months and 26 days in 2010: 45 (UTC) included an image inversion temperature from four SURFRAD observation sites to verify the effectiveness of the method. Fig. 2, where (a) is the MODIS 11_ L2 temperature before the angle effect correction, and (b) is the temperature after the correction. It has been concluded that MODIS temperatures are relatively low, especially in arid and semi-arid regions, reaching 3-5K. In general, there is an increase in temperature after the angle effect correction, which indicates an increase in temperature accuracy after the angle effect correction. From a detailed perspective, the details of the surface appear after the correction of the angle effects, which is consistent with the actual distribution of the surface temperature.
Selecting pixels positioned at SURFRAD sites according to the temperature inversion result after the angle effect is eliminated, calculating the real surface temperature by using the uplink and downlink long-wave radiation at the SURFRAD sites, and checking the temperature after the MODIS angle effect is eliminated, wherein the specific result is shown in a table 1: .
TABLE 1
The results of temperature verification were examined from two indicators of Bias (Bias) and Root Mean Square Error (RMSE), 1555 samples were taken at the four sites for verification, and it was found that Bias (Bias) decreased from-1.47K to-0.82K and Root Mean Square Error (RMSE) decreased from 2.13K to 1.55K, both reflecting the effectiveness of the method.
The remote sensing surface temperature product angle effect correction method provided by the invention combines the existing research, data and methods, provides a pixel level emissivity directional model with strong universality, and can be applied to MODIS temperature product angle effect correction. The method can effectively correct the angle effect and improve the measurement precision of the surface temperature.
Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for correcting the angle effect of a remote sensing surface temperature product is characterized by comprising the following steps:
step 1: calculating brightness temperatures T31 and T32 of pixel MODIS31 and 32 wave bands;
step 2: obtaining the atmospheric lower boundary temperature T of the pixelairThe total atmospheric water vapor content CWV and the zenith angle theta v observed by a sensor;
and step 3: using pixel atmospheric lower boundary temperature TairObtaining the general wedge window algorithm coefficient C, A of the pixel sensor in the observation direction from a wedge window algorithm coefficient lookup table by the total atmospheric water vapor content CWV and the observation zenith angle theta v of the sensor1，A2，A3，B1，B2，B3；
And 4, step 4: determining the zenith emissivity epsilon of the pixel0；
Step 5, calculating the gradient α and the slope direction of the pixel
Step 6: calculating the local reflection angle theta0；
And 7: obtaining the average emissivity of the pure pixels with different coverage types on the earth surface at each angle of 131 observation angles between-65 degrees and + 65 degrees
A data set;
and 8: all the results in step 7 are subtracted
Obtaining the difference of emittance from different observation zenith angles to zenith direction
A data set;
and step 9: an expression of the emissivity variation of the pixel in the observation direction relative to the zenith direction is assumed as follows:
Vε(θ)＝a1θ+a2θ2+a3θ3+...+anθn
use of
Data set determination of coefficients a in a polynomial1，a2，a3，...anAnd the value of n;
step 10: determining an emissivity expression of the pixel in the observation direction;
step 11: determining the emissivity of the observation direction, and calculating the surface temperature T by combining the brightness temperature and the splitting window algorithm coefficients。
2. The method of claim 1, wherein the MOD021KM data and the Planck function approximation formula are used to calculate the brightness temperatures T31, T32 of the MODIS31,32 bands of the pixels in step 1,
T31＝1304.413871/ln(1+729.541636/b31)
T32＝1196.978785/ln(1+474.684780/b32)。
3. the method of claim 1, wherein in step 2, the pixel atmospheric lower boundary temperature T is obtained by using MOD07_ L2 dataairAtmospheric total water vapor content CWV, and obtaining a sensor observation zenith angle theta v from MOD021KM data.
4. The method of claim 1, wherein in step 4, ten years of data of MOD21A22001 to 2010 are used to calculate the ten years historical monthly average emissivity of the pels, the result is used as a surface zenith emissivity data set, and the zenith emissivity epsilon of the pels is determined according to the acquisition months of the pels0。
5. The method of claim 1, wherein in step 5, slope α and slope of the pel are calculated using NOAA GLOBE 1 KMDEM data and aspect and slope functions in ArcMap software
6. The method of claim 1, wherein in step 6, the local reflection angle is calculated using the following formula
Wherein, in the formulasAnd thetav is the sensor observation azimuth angle and the observation zenith angle respectively.
7. The method as claimed in claim 1, wherein in step 7, using MODIS surface coverage type data MOD12Q1 and MODIS surface emissivity data MOD11B1 from 2006 to 2010, the surface coverage data is gatheredThe synthetic scale is the resolution ratio same with the emissivity data, and the average emissivity of 131 observation angles of the pure pixels with different coverage types on the earth surface between-65 degrees and + 65 degrees is obtained
A data set.
8. Method according to claim 1, characterized in that in step 9, according to the type of surface coverage of the picture element, a corresponding is used
Data set fitting determines the coefficients a in a polynomial1，a2，a3，...anThe value of n is determined using the leave-one-out method.
9. The method of claim 1, wherein in step 10, ε of step 4 is combined0And determining the emissivity expression of the pixel in the observation direction as follows:
ε(θ)＝ε(0)+Vε(θ)＝ε(0)+a1θ+a2θ2+a3θ3+...+anθn。
10. method according to claim 1, characterized in that in step 11, θ is obtained from the result in step 60Substituting into the expression in step 9 to determine the emissivity epsilon (theta) of the observation direction0) In conjunction with the luminance temperature, the wedge algorithm coefficient calculates the surface temperature Ts using the following equation:
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