CN113447137B - Surface temperature inversion method for unmanned aerial vehicle broadband thermal imager - Google Patents
Surface temperature inversion method for unmanned aerial vehicle broadband thermal imager Download PDFInfo
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Abstract
The invention discloses a surface temperature inversion method for a broadband thermal imager of an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle remote sensing surface temperature inversion. The method collects the atmosphere profile data of a measuring area, and obtains the atmosphere uplink radiation, the atmosphere downlink radiation and the atmosphere transmittance through the constructed radiation transmission simulation; constructing a functional relation between the atmospheric parameters and the near-surface meteorological observation data; meanwhile, obtaining NDVI data of the measured area by using multispectral data obtained by synchronous observation of the unmanned aerial vehicle, and obtaining the surface emissivity of the measured area by using an NDVI threshold method; and finally, performing atmospheric correction by using a radiation transmission equation algorithm, and performing inversion by combining the obtained surface emissivity data and a lookup table method to obtain the surface temperature. According to the method, the model is established by utilizing the atmospheric profile data of the measurement area, and atmospheric correction is carried out by combining the easily obtained near-surface conventional meteorological observation data, so that the inversion precision is improved; and a lookup table of temperature and spectral radiance is established, so that the uncertainty of temperature inversion is reduced.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle remote sensing earth surface temperature inversion, and particularly relates to an earth surface temperature inversion method for an unmanned aerial vehicle broadband thermal imager.
Background
Surface Temperature (LST) is a fundamental parameter in the earth's surface and atmospheric systems that can be used to describe the intrinsic physical processes of energy and material exchange. The earth surface temperature provides the space-time change information of the earth surface equilibrium state, so that the method is widely applied to the fields of earth surface evapotranspiration estimation, climate change research, environment monitoring, urban heat island effect research and the like. Through decades of development, the satellite thermal infrared remote sensing has been widely applied to research in various fields due to the advantages of wide observation range, high timeliness and data comprehensiveness; many scholars have also developed a series of earth surface temperature inversion algorithms for different satellite sensors. However, at the same time, satellite thermal infrared remote sensing also has some limitations, such as: the remote sensing image is easily shielded by cloud layers, which causes discontinuity of available remote sensing data in time; the resolution of the thermal infrared image is generally tens of meters to hundreds of meters, and the requirements of the fields of precision agriculture, target detection and the like cannot be met. Although passive microwave remote sensing can obtain all-weather surface temperature data by the characteristic that the passive microwave remote sensing can penetrate through cloud layers, the defect of thermal infrared remote sensing under the condition of non-clear air can be overcome to a certain extent; however, the spatial resolution is generally lower than 1 km, and the requirements of many fields cannot be met. In recent years, an unmanned aerial vehicle becomes a new remote sensing platform, and compared with satellite remote sensing, the unmanned aerial vehicle has the characteristics of low cost, high resolution and high maneuverability; compared with the remote sensing by man and machines, the remote sensing system has higher cost performance and higher mobility. The unmanned aerial vehicle remote sensing platform can be used for obtaining a high-resolution bright temperature image in a research area, however, a thermal imager carried by the unmanned aerial vehicle is usually a single-channel wide-band sensor, and if a ground surface temperature inversion algorithm suitable for a satellite sensor is directly used for thermal infrared data of the unmanned aerial vehicle, great uncertainty can be caused.
Disclosure of Invention
The invention provides an unmanned aerial vehicle wide-waveband thermal imager-oriented earth surface temperature inversion method, which is used for reducing uncertainty in the earth surface temperature inversion process so as to obtain earth surface temperature products with high spatial resolution and high precision.
The technical scheme of the invention is as follows:
an unmanned aerial vehicle broadband thermal imager-oriented earth surface temperature inversion method comprises the following steps:
determining a range of values [ T ] for surface temperature min ,T max ]Wherein, T min 、T max Respectively representing the minimum value and the maximum value of the surface temperature; setting step interval delta T of the surface temperature;
the atmospheric profile data of survey district is gathered, carries out the radiation transmission simulation based on the spectral response function of sensor to obtain near-surface atmospheric parameters, includes: atmospheric uplink radiation, atmospheric downlink radiation and atmospheric transmittance; establishing a mapping relation between the earth surface temperature and the spectrum radiance based on a Planck equation to obtain a lookup table of the earth surface temperature and the spectrum radiance;
acquiring NDVI data of the measured area through multispectral data obtained by synchronous observation of an unmanned aerial vehicle, and acquiring the surface emissivity of the measured area based on an NDVI threshold method;
and obtaining the earth surface temperature of the measurement area according to the lookup table and the earth surface emissivity by inversion:
according to the formula
Calculating the earth surface temperature T s Broadband spectral radiance & lt/EN & gt of homothermal black body>
Wherein L is channel Representing the radiance, ε, of a broad band spectrum of the sensor channel Represents the broadband surface emissivity>
Denotes the equivalent wavelength, τ channel Represents the wide-band atmospheric transmittance>
Represents broadband atmospheric up radiation>
Representing broadband atmospheric downlink radiation;
looking up and in the look-up table
The serial number of the nearest spectrum radiance value L is based on the corresponding earth surface temperature value T of the serial number i The surface temperature of the examined area is obtained, wherein>
The number of the nearest spectral radiance value L is:
n denotes the number in the look-up table, L n Representing the surface temperature value with the serial number n in the lookup table.
Further, the earth surface emissivity of the survey area obtained based on the NDVI threshold method is as follows:
wherein ε represents the emissivity of the pixel, ε s Showing the broadband emissivity, epsilon, of the bare soil pixels v Broadband emissivity, P, representing purely vegetated pixels v Representing vegetation coverage, d ε representing a terrain correction parameter, NDVI min Expressing the normalized vegetation index, NDVI, corresponding to the pure bare soil pixel max Expressing the normalized vegetation index corresponding to the pure vegetation pixel; and the terrain correction parameter d epsilon is: d ε = (1- ε) v )(1-P v )Fε v And F denotes a shape parameter.
Further, the radiation transmission simulation comprises a function relation between the atmosphere uplink radiation and the near-surface meteorological element, a function relation between the atmosphere downlink radiation and the near-surface meteorological element, and a function relation between the atmosphere transmittance and the near-surface meteorological element:
τ channel ＝a 1 T a 2 +a 2 RH 2 +a 3 T a ·RH+a 4 T a +a 5 RH+a 6 ；
wherein RH represents relative humidity, P represents atmospheric pressure, and T represents a Represents the near-surface air temperature, a 1 ～a 6 、b 1 ～b 6 、c 1 ～c 6 Representing the equation coefficients in each functional relationship.
The technical scheme provided by the invention at least has the following beneficial effects:
according to the method, the model is established by utilizing the atmospheric profile data of the measurement area, and atmospheric correction is carried out by combining the easily obtained near-surface conventional meteorological observation data, so that the inversion precision is improved; meanwhile, the invention establishes a lookup table of temperature and spectral radiance, and reduces the uncertainty of temperature inversion.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic processing flow diagram of an earth surface temperature inversion method for a broadband thermal imager of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is an uncertainty analysis plot (compared to an MODIS sensor) generated by temperature inversion of a broadband sensor (WIRIS Pro Sc) using a conventional algorithm in an embodiment of the present invention, wherein (a) an uncertainty comparison plot generated by solving the surface temperature for the planck equation with the equivalent wavelength of the broadband sensor (WIRIS Pro Sc); (b) Solving a map of uncertainty contrast generated by the surface temperature for substituting a narrow band sensor (MODIS) equivalent wavelength into Planckian equation; (c) Is a spectral response function of WIRIS Pro Sc and a spectral response function of MODIS 31 th wave band; (d) And respectively substituting the equivalent wavelength of WIRIS Pro Sc and the equivalent wavelength of MODIS 31 channel into the Planckian equation under the same spectral radiance, and inverting an error contrast diagram generated by temperature.
Fig. 3 is a scatter diagram between the estimated atmospheric parameters and the MODTRAN simulation results using the measured atmospheric profile data in the past year (taking black river midstream and downstream as an example), using the near-surface meteorological observation data, wherein (a) is the scatter diagram between the estimated atmospheric uplink radiation and the simulated atmospheric uplink radiation; (b) A scatter diagram between the estimated atmospheric downlink radiation and the simulated atmospheric downlink radiation is obtained; (c) A scatter plot between the estimated atmospheric transmittance and the simulated atmospheric transmittance; (d) Is the difference between the spectral radiance corresponding to the estimated surface temperature and the spectral radiance corresponding to the simulated surface temperature.
FIG. 4 is a plot of the surface temperature inversion error (calculated from the rate of change of surface temperature at about 10 ℃ with spectral radiance) resulting from the combined effect of estimated and simulated atmospheric parameter errors using measured atmospheric profile data from the past year, where (a) is an error bar; and (b) is an error frequency distribution diagram.
FIG. 5 is a scattergram between estimated atmospheric parameters and MODTRAN simulation results using ERA5 (fifth generation ECMWF (European Centre for Medium-Range Weather means)) atmospheric re-analysis global climate data using near-surface meteorological observation data, wherein (a) is the scattergram between estimated atmospheric upstream radiation and simulated atmospheric upstream radiation; (b) A scatter diagram between the estimated atmospheric downlink radiation and the simulated atmospheric downlink radiation is obtained; (c) A scatter plot between the estimated atmospheric transmittance and the simulated atmospheric transmittance; (d) Is the difference between the spectral radiance corresponding to the estimated surface temperature and the spectral radiance corresponding to the simulated surface temperature.
FIG. 6 is a plot of the surface temperature inversion errors (calculated from the rate of change of surface temperature with spectral radiance at around 10 ℃ C.) resulting from the combination of the estimated and simulated errors in various atmospheric parameters using ERA5 atmospheric profile data, where (a) is an error bar; and (b) is an error frequency distribution diagram.
Fig. 7 is a thermal infrared bright temperature image of the drone, wherein (a) - (c) are partial images of different geographical locations of the drone which are more than full station in 7, month and 13 of 2020; (d) - (e) partial images of different geographical positions of the wetland station at 7, 14 months in 2020; (f) - (g) partial images of different geographical locations which are more than 20 months and more than 8 months in 2020; (h) - (j) partial images of different geographical locations of the copy substation from 8/20/2020; (k) And (l) partial images of different geographical positions of the wetland station of 8-21 days in 2020.
Fig. 8 is a partial NDVI (normalized vegetation index) image used, where (a) is the partial region of the grand station of 7, 13 months of 2020; (b) is a partial region of the Zizania sub-station of 8-month-20-year-2020; and (c) is the wetland station partial area of 8-21 months in 2020.
FIG. 9 is a surface temperature image obtained by inversion from thermal infrared bright temperature image data, wherein (a) - (c) are partial images of different geographical locations of a 7-month 13-day old full station in 2020; (d) - (e) partial images of different geographical positions of the wetland station in 7, 14 days of 2020; (f) - (g) partial images of different geographical locations which are more than 20 months and more than 8 months in 2020; (h) - (j) partial images of different geographical positions of the Zizhai substation from 8/20/2020; (k) And (l) partial images of different geographical positions of the wetland station of 8-21 days in 2020.
Fig. 10 is a scatter plot between the ground surface temperature of the unmanned aerial vehicle and the ground station measured data obtained by inversion.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The temperature data directly acquired by the unmanned aerial vehicle broadband thermal imager does not consider the influence of earth surface emissivity and atmosphere, and because the spectral response range of the sensor is wide, if the traditional earth surface temperature inversion algorithm applicable to the satellite narrow-band sensor is directly applied to the broadband sensor, great uncertainty can be generated; the temperature product can not meet the requirements of related fields on high-precision ground surface temperature products. In order to solve the problem, the embodiment of the invention provides an earth surface temperature inversion method for a broadband thermal imager of an unmanned aerial vehicle, which is a method for generating earth surface temperature with high precision and high spatial resolution by combining survey area atmospheric profile data and multispectral data based on a radiation transmission equation and a lookup table method. On one hand, the method reduces the uncertainty error caused by applying the earth surface temperature inversion algorithm suitable for the satellite narrow-band sensor to the wide-band sensor; on the other hand, the method uses the atmospheric profile data and the near-surface conventional meteorological observation data of the survey area to carry out atmospheric correction, and uses multispectral data synchronously acquired by the unmanned aerial vehicle to obtain the surface emissivity of the survey area, thereby improving the surface temperature inversion precision. The verification result shows that the earth surface temperature obtained by inversion of the method has higher precision, can provide data support for other related fields (such as evapotranspiration estimation, climate change research, vegetation monitoring and the like), and has great practical value.
The technical scheme of the ground surface temperature inversion method for the unmanned aerial vehicle broadband thermal imager provided by the embodiment of the invention is as follows:
firstly, collecting atmosphere profile data of a measuring area, formulating a proper simulation scheme by combining with the actual geographic condition of the measuring area, and performing radiation transmission simulation by using a MODTRAN (model Spectral Resolution adaptive transmission) model to obtain corresponding atmosphere parameters, wherein the simulation scheme comprises the following steps: atmospheric uplink radiation, atmospheric downlink radiation and atmospheric transmittance; training by using the obtained parameters to obtain a functional relation between the atmospheric parameters and the near-surface meteorological observation data; then, acquiring NDVI data of the measurement area by using multispectral data obtained by synchronous observation of an unmanned aerial vehicle, and acquiring the surface emissivity of the measurement area by using an NDVI threshold method (NDVI threshold method); and finally, performing atmospheric correction by using a radiation transfer equation algorithm (radial transfer equation), and performing inversion by combining the obtained surface emissivity data and a look-up table method (look-up table method) to obtain the surface temperature.
(1) The embodiment of the invention discloses a basic theory used in a surface temperature inversion method.
The radiation transport equation is of the form shown in equation (1):
in the formula, L λ Represents the spectral radiance of the light received by the sensor at a wavelength of lambda and has a unit of W.m -2 ·sr -1 ·μm -1 (ii) a λ represents wavelength in μm; epsilon λ Representing the surface emissivity; t is s Represents the surface temperature in K; b (lambda, T) s ) Denotes the wavelength λ and the surface temperature T s Spectral radiance of time, unitIs W.m -2 ·sr -1 ·μm -1 ；τ λ The atmospheric transmittance at a wavelength λ is represented;
represents the upward radiation of the atmosphere at a wavelength of λ, with the unit of W · m -2 ·sr -1 ·μm -1 ；/>
Represents the downward radiation of the atmosphere at a wavelength of λ, with the unit of W.m -2 ·sr -1 ·μm -1 。
Since the quantities in equation (1) all represent values at a particular wavelength and the sensor has a range of bands, a band weighted average is used, and the radiation transmission equation is then of the form shown in equation (2):
in the formula, L channel Represents the radiance of the broadband spectrum of the sensor and has the unit of W.m -2 ·sr -1 ·μm -1 ；ε channel The broadband surface emissivity is shown;
denotes a temperature of T s The corresponding broadband spectral radiance of the black body is W.m -2 ·μm -1 ·sr -1 ；/>
The equivalent wavelength is expressed in the unit of mum and can be calculated by the formula (3); tau is channel Representing the broadband atmospheric transmittance; />
Represents broadband atmospheric uplink radiation with the unit of W.m -2 ·μm -1 ·sr -1 ；/>
Represents the broadband atmospheric downlink radiation with the unit of W.m -2 ·μm -1 ·sr -1 。
In the formula of lambda 1 、λ 2 Respectively representing the lower limit and the upper limit of the wavelength of the spectral response function of the sensor, and the unit is mum; f (λ) represents the spectral response function of the sensor.
Under the condition that the spectral radiance, the surface emissivity, the atmospheric transmittance and the atmospheric up-and-down radiation of the sensor are determined, the broadband spectral radiance of the black body with the same temperature as the surface temperature can be obtained
Substituting into Planck's equation (as shown in equation (4)) to calculate the surface temperature T s The calculation expression is shown in formula (5).
In the formula, c 1 Represents the first radiation constant and takes the value of 1.191 multiplied by 10 8 W·m -2 ·sr -1 ·μm 4 ；c 2 The second radiation constant is 14388 μm · K.
Because the spectral response function range of the unmanned aerial vehicle thermal imager is wide, if the earth surface temperature is directly calculated by the formulas (1) - (5), non-negligible uncertainty errors can be brought, and the influence of the uncertainty errors needs to be reduced; a better way is to establish a lookup table of temperature and spectral radiance based on the planck equation, and the establishment process and the use mode of the lookup table are as follows:
1) According to the actual situation of a survey area during flight operation, determining the approximate range T epsilon [ T ] of the earth surface temperature change min ,T max ]In the embodiment of the present invention, default T is set min ＝0℃，T max =100 ℃; a certain step interval delta T is set as required, and in the embodiment of the invention, the default delta T =0.1 ℃;
2) Selecting 1 piece of atmosphere profile data (considering that the relation between temperature and spectral radiance is irrelevant to atmospheric conditions, the profile can be selected at will), formulating a MODTRAN simulation scheme by combining a spectral response function of a sensor, and performing radiation transmission simulation;
3) Extracting a program operation result to obtain a corresponding relation between the temperature (the meaning of the temperature is the earth surface temperature at the moment, and the temperature is practically irrelevant to whether the temperature is the earth surface temperature) and the spectrum radiance L; then within a set temperature range T ∈ [ T ] min ，T max ]There is always a one-to-one correspondence of spectral radiance L e [ L ∈ [ [ L ] min ,L max ]Namely, the mapping relation of the T → L exists;
4) Calculating the broadband spectrum radiance of the black body with the same temperature as the earth surface temperature according to the formula (2)
Thereafter, it is not necessary to assert the equivalent wavelength->
Substituting the formula (5) for solving; it is only necessary to find and->
The number of the closest spectral radiance value L; the corresponding temperature value under the serial number is the surface temperature T s As shown in equation (6); since the value of delta T in the lookup table is small, the obtained T s Is more accurate.
In which i represents a group of
And L n When the difference is smallest, L n Corresponding serial numbers in the lookup table; n represents a sequence number in the lookup table; t is a unit of i When the serial number is i, the corresponding temperature value in the lookup table is obtained.
(2) The embodiment of the invention discloses a method for determining relevant parameters in a surface temperature inversion method.
Because the unmanned aerial vehicle thermal imager records a Digital value (DN), the Digital value can be converted into a brightness temperature value according to a user manual, and the conversion mode is as shown in a formula (7):
T b ＝a×DN+b (7)
in the formula, T b Expressing the brightness temperature value of the pixel, and the unit is K; a represents the conversion coefficient for the sensor, which can be obtained from the user manual; DN represents a digital value of the pixel; b denotes the conversion coefficient for the sensor, which can be obtained from the user manual, in units of K.
In consideration of the strong correlation between the near-surface meteorological parameters and the atmospheric parameters at the flying height of the unmanned aerial vehicle, in the embodiment of the invention, the atmospheric profile data of the measurement area is utilized, a MODTRAN model is used for radiation transmission simulation, and a functional relation between the atmospheric parameters and the near-surface meteorological parameters is constructed, so that the broadband atmospheric transmittance and the broadband atmospheric uplink and downlink radiation required in the formula (2) are obtained (because the atmospheric profile data of the measurement area measured in real time is difficult to obtain and cannot be directly utilized for radiation transmission simulation to estimate the atmospheric parameters, the atmospheric profile data measured in the measurement area in the past year is used for establishing a model to estimate the atmospheric parameters, and through experimental tests, the embodiment of the invention also recommends using the atmospheric profile data in ERA5 reanalysis data to establish the model to estimate the atmospheric parameters, which can also obtain higher theoretical precision), as shown in the formulas (8) - (10):
τ channel ＝f 1 (RH,P,T a ,...) (8)
in the formula, f 1 、f 2 、f 3 Respectively representing the functional relations of the broadband atmospheric transmittance, the broadband atmospheric uplink radiation and the broadband atmospheric downlink radiation and the near-surface meteorological parameters, wherein a quadratic polynomial function is used by default in the embodiment of the invention; RH, P, T a ,.. Near surface meteorological parameters are expressed, where RH represents relative humidity in%; p represents atmospheric pressure in hPa; t is a The temperature near the surface is expressed in K; in the embodiment of the invention, the default selected meteorological parameters are RH and T a (ii) a The specific solution form is converted into equations (11) to (13):
τ channel ＝a 1 T a 2 +a 2 RH 2 +a 3 T a ·RH+a 4 T a +a 5 RH+a 6 (11)
in the formula, a 1 -a 6 、b 1 -b 6 、c 1 -c 6 Representing the coefficients of the equation.
In order to obtain the surface emissivity of the survey area by using the NDVI thresholding method, the NDVI is calculated by using the multispectral reflectivity data, as shown in formula (14):
in the formula (I), the compound is shown in the specification,NDVI denotes the normalized vegetation index; rho nir Representing the reflectivity of the near infrared band; ρ is a unit of a gradient red Indicating the reflectivity in the red band.
After the NDVI image of the measurement area is generated, the surface emissivity of the research area (measurement area) can be obtained by using an NDVI threshold value method; the general expression is shown in formula (15):
in the formula, epsilon represents the pixel emissivity obtained by calculation; epsilon v The broadband emissivity of the pure vegetation pixel is represented and can be obtained by calculation of an emissivity curve provided by actual measurement or a spectrum library; p v Representing vegetation coverage; epsilon s The broadband emissivity of the pure bare soil pixel is represented and can be obtained by actual measurement or calculation of an emissivity curve provided by a spectrum library; d epsilon represents a terrain correction parameter; NDVI min The normalized vegetation index corresponding to the pure bare soil pixel is expressed, and can be 0.2 generally; NDVI max The normalized vegetation index corresponding to the pure vegetation pixel is expressed and can be 0.5 generally. The calculation mode of the broadband emissivity of the sensor is shown as an equation (16):
in the equation, T represents an approximate LST, and since the LST is about 300K in most cases and the influence of temperature change on the calculation result is small, T is set to a fixed value of 300K at the time of calculation. Because the spectral response range of the selected sensor has little difference with the emissivity curve range, and the emissivity changes more smoothly in a thermal infrared band, the emissivity exceeding the emissivity curve range adopts the emissivity close to the wavelength when calculating the emissivity.
The vegetation coverage is calculated as shown in formula (17):
the terrain correction parameter is calculated as shown in equation (18) and is negligible on a flat surface.
dε＝(1-ε v )(1-P v )Fε v (18)
In the formula, F represents a shape parameter, and is usually 0.55.
Substituting the calculated parameters into the formula (2), and combining the established lookup table to obtain the surface temperature.
The experimental observation data of the unmanned aerial vehicle related to the embodiment are obtained in a midstream HiWATER test area of a black river basin in northwest China. The black river basin is the second continental river basin in China and has extremely arid climate; the test area of the midstream is positioned in artificial oasis of Zhangye and peripheral areas thereof, and the main ground object coverage type of the oasis is corn land; the northern part of the oasis is a wetland, and the main vegetation type is reed; the southern part of the oasis is gobi, the main type of ground cover is sparse grass. The test area had an elevation of about 1400m, an annual precipitation of about 260mm and an annual evaporation of about 1750mm. Under the framework of the HiWATER test, a plurality of test stations are erected in the test area; in this embodiment, unmanned aerial vehicle's flight zone has covered 3 stations in the test area respectively, is respectively: big full stations (100.3722E, 38.8555N, elevation 1556 m), compact substations (100.3201E, 38.7659N, elevation 1731 m) and wetland stations (100.4464E, 38.9751N, elevation 1460 m).
The data specifically adopted in this embodiment includes: (1) unmanned aerial vehicle remote sensing data, including: thermal infrared data (spatial resolution of about 0.4 m) and multispectral data (spatial resolution of about 0.2 m); (2) Synchronously observing data (the data recording interval is 5 s) by a ground SI-111 infrared radiometer; (3) Real-time meteorological observation data in the flight area (data recording interval is 10 min); (4) atmospheric profile data comprising: actually measuring atmospheric profile data (2012 and 2013) in the middle and downstream of the black river in the past year, and measuring area ERA5 atmospheric profile data; (5) JHU spectral library surface emissivity data.
Referring to fig. 1, for an application scenario, a specific processing procedure of the ground surface temperature inversion method for the unmanned aerial vehicle broadband thermal imager provided by the embodiment of the invention is as follows:
1. and (4) preprocessing data.
(1) After the abnormal data is removed, the obtained thermal infrared image and the multispectral image are spliced by using Pix4D software (in order to better verify and show the temperature inversion result, in this embodiment, only multispectral images are spliced, a single thermal infrared image is used for temperature inversion, and the original thermal infrared data needs to be converted into bright temperature data by using a formula (7), in this embodiment, a =0.025, and b = 173.15);
(2) Carrying out band operation by using ENVI software (Environment for visualization Images, a remote sensing image processing platform) in combination with a formula (14) to obtain an NDVI image in a tif format;
(3) Resampling the NDVI image to the resolution of the thermal infrared image by utilizing ArcMap software (mapping software), and then registering the thermal infrared image and the NDVI image to obtain data with space matching and the same resolution;
(4) Extracting original data of the atmospheric profile by using a Matlab language, extracting useful information such as altitude, air temperature, air pressure, relative humidity and the like in the original data, and outputting the useful information into a txt file;
(5) And (4) arranging conventional meteorological observation data, and extracting data such as air temperature, air pressure and relative humidity during the flight of the unmanned aerial vehicle.
2. And (4) radiation transmission simulation and model construction.
(1) By using an IDL (Interface description language), combining a spectral response function (WIRIS Pro Sc in this example) of the broadband sensor and the extracted atmospheric profile data (including data actually measured in the past year and ERA5 reanalysis data), and considering the geographic condition of the flight area and the flying height of the unmanned aerial vehicle (300 m in this example), making a simulation scheme and generating a tp5 file;
(2) Radiation transmission simulations were performed using MODTRAN software (version MODTRAN 5.2.2 used in this example). In the step, profile data with relative humidity greater than 85% is removed;
(3) Extracting a radiation transmission simulation result by using an IDL language, and performing corresponding calculation to finally obtain data such as atmospheric uplink and downlink radiation, atmospheric transmittance and the like;
(4) Modeling the data extracted in the step (3) by utilizing Matlab language, and solving fitting coefficients of formulas (11) - (13); and respectively establishing a functional relation between the atmospheric up-and-down radiation, the atmospheric transmittance and the near-surface meteorological elements for subsequently estimating the atmospheric parameters during flight operation.
3. And (5) unmanned aerial vehicle remote sensing earth surface temperature inversion.
(1) Constructing a look-up table between temperature and spectral radiance for subsequent use by the steps described previously;
(2) Combining an emissivity curve of a JHU spectrum library (a surface feature spectrum library of John Hopkins university, USA) and NDVI data obtained by data preprocessing, and acquiring a ground surface emissivity image of a flight area through formulas (15) - (18);
(3) Calculating the atmospheric up-and-down radiation and atmospheric transmittance during flight operation by combining the near-surface meteorological parameters actually measured during flight operation through the functional relation determined by the formulas (11) - (13);
(4) Converting the surface brightness temperature image into a spectrum radiance image through a lookup table, combining the emissivity and the atmospheric parameters obtained in the steps (2) and (3), and solving the spectrum radiance image through a formula (2)
The surface temperature can be inverted from the established look-up table.
And (3) processing results:
taking the part of flight data of 7 and 8 months in 2020 as an example, the inversion method provided by the embodiment of the invention is used for obtaining the remote sensing surface temperature of the unmanned aerial vehicle. From the results of fig. 2, it can be found that for a wide-band sensor (WIRIS Pro Sc), if uncertainty issues arising from inverting the surface temperature without taking into account a too wide band range, non-negligible errors may result; in the range of 273.15K-373.15K of the surface temperature, the root mean square error between the surface temperature inverted by directly substituting the equivalent wavelength into the Planck equation and the actual surface temperature can reach about 2K, and the average error can reach about-1.92K, as shown in (a) and (d) in FIG. 2; for comparison, a narrow-band sensor (e.g., MODIS, both of which have spectral response functions as shown in (c) of fig. 2) is used, and the root mean square error of the temperature inversion result from the actual surface temperature is about 0.02K, and the average error is about-0.02K, which can be ignored, as shown in (b), (d) of fig. 2.
FIG. 3 illustrates a comparison between estimated atmospheric parameters and simulated atmospheric parameters based on measured atmospheric profile data from the past year; as shown in FIG. 3 (a), the root mean square error between the estimated atmospheric uplink radiation and the simulated atmospheric uplink radiation is 0.0217 W.m -2 ·sr -1 ·μm -1 Average error of 0.0011 W.m -2 ·sr -1 ·μm -1 (ii) a As shown in FIG. 3 (b), the root mean square error between the estimated atmospheric downlink radiation and the simulated atmospheric downlink radiation is 0.2215 W.m -2 ·sr -1 ·μm -1 Average error of 0.0255 W.m -2 ·sr -1 ·μm -1 (ii) a As shown in fig. 3 (c), the root mean square error of the estimated atmospheric transmittance from the simulated atmospheric transmittance is 0.0020, and the average error is-0.0026; the spectral radiance calculation error corresponding to the earth surface temperature caused by the combined action of the 3 atmospheric parameters is shown in (d) in FIG. 3, and the root mean square error is 0.0330 W.m -2 ·sr -1 ·μm -1 Average error of-0.0021 W.m -2 ·sr -1 ·μm -1 (ii) a The result shows that the atmospheric parameter calculation model established by the measured atmospheric profile data in the past year has higher precision and can be used for estimating atmospheric parameters in flight.
FIG. 4 shows the effect of the atmospheric parameter estimation error calculated based on the measured atmospheric profile data of the past year on the inversion of the earth's surface temperature (calculated by using the earth's surface temperature at about 10 ℃ and the rate of change of the earth's surface temperature with the spectral radiance), as shown in FIG. 4 (a) is an error bar graph with a root mean square error of 0.26K and an average error of-0.02K; as shown in (b) of fig. 4, the error frequency distribution histogram, which substantially exhibits a normal distribution; the method shows that the inversion error of the earth surface temperature finally caused by the atmospheric parameter calculation model established by actually measured atmospheric profile data in the past year is small, and the accuracy is high.
FIG. 5 illustrates the estimated atmospheric parameters and simulated atmospheric parameters based on the ERA5 atmospheric profile dataComparing the two; as shown in FIG. 5 (a), the root mean square error between the estimated atmospheric uplink radiation and the simulated atmospheric uplink radiation is 0.0535 W.m -2 ·sr -1 ·μm -1 Mean error of 0.0034 W.m -2 ·sr -1 ·μm -1 (ii) a As shown in FIG. 5 (b), the root mean square error between the estimated atmospheric downlink radiation and the simulated atmospheric downlink radiation is 0.2959 W.m -2 ·sr -1 ·μm -1 Average error of 0.0020 W.m -2 ·sr -1 ·μm -1 (ii) a As shown in fig. 5 (c), the root mean square error of the estimated atmospheric transmittance from the simulated atmospheric transmittance is 0.0046, and the average error is 0.0003; the spectral radiance calculation error corresponding to the surface temperature caused by the comprehensive action of the 3 atmospheric parameters is shown in (d) in fig. 5, and the root mean square error is 0.0705 W.m -2 ·sr -1 ·μm -1 Average error of-0.0026 W.m -2 ·sr -1 ·μm -1 (ii) a This indicates that the calculation model of the atmospheric parameters established by the ERA5 atmospheric profile data also has higher precision (although the precision is lower than that of the model established by the measured atmospheric profile data), and can be used for estimating the atmospheric parameters in flight.
FIG. 6 shows the effect of the calculated atmospheric parameter estimation error based on the ERA5 atmospheric profile data on the inversion of the earth's surface temperature (calculated using the earth's surface temperature around 10 ℃ and the rate of change of the earth's surface temperature with the spectral radiance), as shown in FIG. 6 (a) is an error bar graph with a root mean square error of 0.56K and an average error of-0.02K; fig. 6 (b) shows an error frequency distribution histogram, which basically exhibits a normal distribution; the result shows that the earth surface temperature inversion error finally caused by the atmospheric parameter calculation model established by using the ERA5 atmospheric profile data is also smaller, and the accuracy is higher (although the accuracy is lower than that of the model established by the actually measured atmospheric profile data).
Fig. 7 shows the unmanned aerial vehicle hot infrared bright temperature images of randomly selected parts of 7 and 8 months in the embodiment, and it can be seen that there is a great difference between bright temperatures of different watch types.
Fig. 8 shows NDVI images of partial areas of 3 stations, and it can be seen that in a large-full station and a wetland station, the NDVI values of the images are generally high due to large vegetation coverage areas; while the emulational substation is mainly desert covered, the NDVI value of the emulational substation is generally lower.
Fig. 9 shows an inverted surface temperature image (here, the atmospheric parameters used in the inversion are estimated using modeling of the measured atmospheric profile from the past year), showing that the surface temperature is lower in vegetation-covered areas and higher in bare-soil areas.
Fig. 10 shows a scatter diagram of the ground surface temperature obtained by inversion in this embodiment and the ground surface temperature actually measured by the ground infrared radiometer, where the root mean square error of the ground surface temperature and the ground surface temperature is 2.16K, the average error is-0.46K, the correlation coefficient reaches 0.87, and the consistency of the ground surface temperature and the ground surface temperature is high; therefore, the surface temperature inverted by the method has higher precision.
The ground surface temperature inversion method for the unmanned aerial vehicle broadband thermal imager has the core that the uncertainty error of the ground surface temperature inversion of the broadband sensor is reduced by adopting a lookup table mode; and establishing a relational expression of the near-surface actual measurement meteorological data and the atmospheric parameters to carry out atmospheric correction. In consideration of the fact that the earth surface emissivity is also a key parameter influencing earth surface temperature inversion, the embodiment of the invention estimates the earth surface emissivity of the measured area by adopting an NDVI threshold value method, and the emissivity precision estimated by the method is more reliable in the areas with earth surface types of mostly bare soil and vegetation. Through verification of the ground surface temperature actually measured by the ground infrared radiometer, the method provided by the embodiment of the invention can obtain the unmanned aerial vehicle remote sensing ground surface temperature data with higher precision. In an area with complex ground surface coverage types, the multispectral image can be used for ground object classification, and each ground object type is endowed with a determined emissivity value, so that the obtained ground surface emissivity is more reliable than the ground surface emissivity calculated by an NDVI threshold method, and the inversion accuracy of the remote sensing ground surface temperature of the unmanned aerial vehicle is further improved.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
What has been described above are merely some of the embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention.
Claims (2)
1. An unmanned aerial vehicle broadband thermal imager-oriented earth surface temperature inversion method is characterized by comprising the following steps:
determining a range of values [ T ] for surface temperature min ,T max ]Wherein, T min 、T max Respectively representing the minimum value and the maximum value of the surface temperature; setting step interval delta T of the earth surface temperature;
collecting atmospheric profile data of a measuring area, and carrying out radiation transmission simulation based on a spectral response function of a sensor: utilize MODTRAN model to carry out radiation transmission simulation to obtain near-surface atmospheric parameters, include: atmospheric uplink radiation, atmospheric downlink radiation and atmospheric transmittance; and training by using the obtained parameters to obtain a functional relation between the near-surface atmospheric parameters and the near-surface meteorological observation data, wherein the functional relation comprises the following steps: the functional relation between the atmospheric transmittance and the near-surface meteorological elements, the functional relation between the atmospheric uplink radiation and the near-surface meteorological elements and the functional relation between the atmospheric downlink radiation and the near-surface meteorological elements are as follows:
τ channel ＝a 1 T a 2 +a 2 RH 2 +a 3 T a ·RH+a 4 T a +a 5 RH+a 6 ；
wherein, tau channel Represents the atmospheric transmittance of a wide band of wavelengths,
represents broadband atmospheric uplink radiation>
Representing broadband atmospheric downlink radiation, RH representing relative humidity, T a Represents the near-surface air temperature, a 1 ～a 6 、b 1 ～b 6 、c 1 ～c 6 Expressing equation coefficients in the respective functional relationships;
establishing a mapping relation between the earth surface temperature and the spectrum radiance based on a Planck equation to obtain a lookup table of the earth surface temperature and the spectrum radiance;
acquiring NDVI data of the measured area through multispectral data obtained by synchronous observation of an unmanned aerial vehicle, and acquiring the surface emissivity of the measured area based on an NDVI threshold method;
and obtaining the surface temperature estimated value of the measured area according to the lookup table and the surface emissivity by inversion:
according to the formula
Calculating and measuring the surface temperature T of the area s Broadband spectral radiance & lt/EN & gt of homothermal black body>
Wherein L is channel Representing the radiance, ε, of a broad band spectrum of the sensor channel Represents the broadband surface emissivity>
Represents an equivalent wavelength;
looking up and in the look-up table
The serial number of the nearest spectrum radiance value L is based on the earth surface temperature value T corresponding to the serial number i Obtaining an estimate of the surface temperature in the test area, where and->
The number of the nearest spectral radiance value L is: />
n denotes the number in the look-up table, L n Indicating the spectral radiance value with the number n in the lookup table.
2. The method of claim 1, wherein the surface emissivity of the survey area obtained based on the NDVI thresholding is:
wherein epsilon represents the emissivity of the pixel, epsilon s Showing the broadband emissivity, epsilon, of the bare soil pixels v Broadband emissivity, P, representing purely vegetated pixels v Representing vegetation coverage, d ε representing a terrain correction parameter, NDVI min Expressing the normalized vegetation index, NDVI, corresponding to the pure bare soil pixel max Expressing the normalized vegetation index corresponding to the pure vegetation pixel; and the terrain correction parameter d epsilon is: d ε = (1- ε) v )(1-P v )Fε v And F denotes a shape parameter.
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