CN101634711A - Method for estimating temperature of near-surface air from MODIS data - Google Patents
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Abstract
The invention relates to a method for estimating the temperature of near-surface air near from MODIS data, which can be used for remote sensing application departments, i.e. weather, environmental monitoring, land management, agricultural monitoring, disaster monitoring, and the like. The method comprises three steps: step1: utilizing the near-surface temperature and the emission rate of each pixel of a MODIS data product and the water vapour content value of atmosphere as priori knowledge and input parameters of atmosphere radiation transmission module simulation software MODTRAN4, carrying out forward direction simulation in different areas and seasons aiming at a 29th thermal infrared band, a 31st thermal infrared band and a 32nd thermal infrared band of each pixel of the obtained remote sensing data MODIS and establishing a training and testing database; step2: utilizing a neural network to repeatedly training and testing a training and testing dataset; step3: carrying out back calculation for practical image data of the MODIS to obtain the temperature distribution condition of the air near the earth's surface of an objective area of the earth's surface. The method can be used for weather prognosis, the environmental monitoring, the agricultural monitoring, the disaster monitoring, and the like.
Description
Technical field
The present invention relates to a kind of method of utilizing the ground level heat radiation information estimation near surface air themperature that the MODIS sensor obtains on the earth observation satellite, broken through the limitation of traditional ground observation.Can be applied in remote sensing departments such as meteorology, agricultural, environmental monitoring and damage caused by a drought monitoring.
Background technology
The near surface air themperature is meant the temperature about about 2 meters high apart from the face of land, it is very important parameter [Sun, Y.J., Wang J.F. in the climate change research, Zhang, R.H., Gillies, R.R., Xue Y.andBo Y.C., Air temperature retrieval from remote sensing data based onthermodynamics.Theoretical and Applied Climatology, 2005,80:37-48.].Because the near surface air themperature is subjected to time and space, and the influence of face of land situation, does not also have a kind of method can estimate the space distribution of near surface air themperature well so far.At present, known three kinds of methods that obtain the near surface air themperature in climate change research, one is based on the physical method of energy equilibrium.Physical method needs aerodynamic resistance, and ground table status (comprising water, the state of soil and vegetation etc.), this Several Parameters is very difficult to obtain [Sun, Y.J., Wang J.F., Zhang, R.H., Gillies, R.R., Xue Y.and Bo Y.C., Air temperature retrieval from remote sensing data based on thermodynamics.Theoretical and Applied Climatology, 2005,80:37-48.]; The another one method is exactly empirical method [Boyer, D.G., Estimation of daily temperature means using elevation and latitudein mountainous terrain, Water Resource Bull 4,1984,583-588.].The near surface air themperature that empirical method utilizes GIS (Geographic Information System) meteorological site to obtain is exactly carried out the distribution plan that interpolation obtains the near surface air themperature.When meteorological site is not that the result that interpolation obtains is not very good when much and not being even distribution (particularly in the mountain area).The 3rd, Mao Kebiao etc. utilize the surface temperature of ASTER data and emissivity as priori, inverting near surface air themperature from the ASTER data, but ASTER data wave band resolution is difficult to satisfy present demand than higher.Be exactly that band setting gets and is not very reasonable in addition, precision remains further to be improved [Mao Kebiao, Huajun Tang, Xiufeng Wang, Qingbo Zhou, Daolong Wang, Near-Surface Air Temperature Estimation FromASTER Data Using Neural Network, International Journal ot Remote Sensing, 2008,29 (20): 6021-6028.].But because the restriction of various conditions, that generally adopt at present is empirical method [Burrough, P.A.and Mc Donnell R.A., Principles of geographical informationsystems.New York:Oxford University Press, 1998.].
The MODIS remote sensor carried the earth observation satellite successful launch in 1999 and 2002, for global and region resource environmental dynamic monitor have been opened up another new approach.MODIS is an intermediate-resolution remote sensing system (see figure 1) that has 36 wave bands, can obtain one time the global observation data in per 1～2 day, its flight and sun synchronization, every day, the same area can obtain two scape images round the clock at least, and be free reception, the region resource environmental dynamic monitor of large scale in therefore being very suitable for.In 36 wave bands of MODIS, there are 8 to be the thermal infrared wave band,, thereby most suitablely analyze in the face of land of regional scale heat spatial diversity as table 1.At present at the Surface Temperature Retrieval algorithm of MODIS remotely-sensed data many [Wan, Z.M.and Li, Z.L., Aphysics-based algorithm for retrieving land-surface emissivity and temperature fromEOS/MODIS data.IEEE Transactions on Geoscience and Remote Sensing, 1997,35 (4): 980-996; Mao Kebiao is at the Surface Temperature Retrieval method research of MODIS data, master thesis, Nanjing University, 2004.5.; Mao Kebiao, Qin Zhihao executes and builds up, the palace roc is split window algorithm research, Wuhan University's journal (information science version), 2005 (8): 703-708. at the MODIS data], the inversion accuracy of surface temperature and emissivity has had certain guarantee.At present also do not utilize the method for MODIS data estimation near surface air themperature to deliver.
Table 1MODIS remote sensor technical parameter
[MODIS?Level?1B?Product?User’s?Guide，For?Level?1B?Version?4.2.0(Terra)and?Version?4.2.1(Aqua).]
Summary of the invention
The object of the present invention is to provide a kind of method from remotely-sensed data MODIS estimation near surface air themperature, to overcome the practical difficulty that existing near surface air themperature utilizes physical method to be difficult to obtain, and the meteorological site interpolation is difficult to guarantee the shortcoming of precision, particularly be difficult to guarantee ageing shortcoming in the backcountry, and can also further improve the estimation precision of near surface small scale air themperature and face of land evapotranspiration.
For achieving the above object, the method from remotely-sensed data MODIS estimation near surface air themperature provided by the invention is:
The first step, set up the simulated database of radiance temperature on MODIS remote sensor the 29th, 31, the 32 wave band stars
1-1) atmospheric profile pattern, air path, radiation mode and the backscatter mode of the location of the selection image that obtains are as input parameter;
1-2) read surface temperature (LST), the emissivity (ε of corresponding each pixel of MODIS product
i) and the value of atmosphere vapour content (w), for each pixel, LST-2K≤LST≤LST+2K, ε
i-0.03≤ε
i≤ ε
i+ 0.03, w-0.13w≤w≤w+0.13w is input to MODTRAN4[Berk, A. as the priori of each pixel, Bemstein, L.S.and Roberttson, D.C., MODTRAN:Amodetate resolution model for LOWTRAN.Burlington, MA, Spectral Science, Inc., Rep.AFGL-TR-87-0220.1987.] in simulate and set up the training and testing database;
1-3) read in the surface temperature and the emissivity value of MODIS data correspondence, according to 1-2) in the variation range that limits, simulation surface temperature and near surface air themperature may variation;
1-4) read in atmosphere vapour content initial value, according to 1-2) middle limit error variation range, atmosphere vapour content in the simulation process;
Other parameters such as 1-5) input MODIS satellite sensor height, and acquiescence atmospheric aerosol, carbon dioxide;
1-6) carry out simulation according to the wavelength coverage of MODIS data the 29th, 31,32 wave bands, and radiance on output MODIS data the 29th, 31, the 32 wave bands simulation star;
1-7) will simulate at every turn and obtain that radiance converts brightness temperature on the star, at each pixel and and the surface temperature and the emissivity of each analog input, and atmosphere vapour content is set up corresponding database together.
Second step, neural metwork training and test
2-1) simulated database in the first step is divided into two groups, one group is training dataset; One group is test data set;
2-2) brightness temperature and atmosphere vapour content are as the input node of neural network on the star of MODIS the 29th, 31,32 wave bands that training data is concentrated, and the near surface air themperature is trained as output node;
2-3) the neural network that the input of brightness temperature on the star of test data set and atmosphere vapour content is trained, output near surface air themperature;
2-4) with the near surface air themperature and corresponding near surface air themperature contrast exported among the 2-3.
The 3rd step, inverting near surface air themperature
3-1) read the 29th, 31,32 wave bands and the atmosphere vapour content data of MODIS remote sensing image data;
3-2) brightness transition Cheng Xing on the star of the 29th, 31,32 wave bands of MODIS data is gone up brightness temperature (T29, T31, T32) and the corresponding atmosphere vapour content W of extraction;
3-3) T29, T31, T32, W among the 3-2 are input to second and go on foot in the neural network that trains, output near surface air themperature (NSAT);
3-4) according to the face of land of image correspondence be correlated with the checking and applied analysis.
Described method, wherein, among the 1-3 of the first step, near surface air temperature variations scope is NSAT≤LST+15K, step change amplitude is 2K in the simulation process.
Described method, wherein, among the 1-4 of the first step, atmosphere vapour content initial value is the atmosphere vapour content (w) that reads in, and limited range is w-0.13w≤w≤w+0.13w, and step change amplitude is 0.2g/cm in the simulation process
2
Described method, wherein, among the 1-5 of the first step, input MODIS satellite sensor height is 705KM.
Described method, wherein, second the step 2-4 in, near surface air themperature standard error all adds 5 greater than 2K with two-layer implicit node, the repetition 2-2 proceed training and testing, near surface air themperature standard error less than 2K.
The invention has the beneficial effects as follows, utilize MODIS surface temperature and emissivity and atmosphere vapour content as priori, there is linear relationship in emissivity between the proximity thermal infrared band, there is relation between transmitance and the atmosphere vapour content, utilize atmospheric radiation transmission to simulate and to utilize these potential information well, reduce unknown number effectively and solve the not enough difficult problem of equation in the ill inverting.Improved inversion accuracy and computing time, overcome in the past directly from the not enough shortcoming of satellite data estimation near surface air themperature quantity of information.Be climate change research, weather forecast, evapotranspiration, agricultural feelings monitoring and disaster monitoring etc. provide effective means and technical support.Its operation practicality must be simple than traditional ground meteorological watch website interpolation of utilizing, and precision wants high on the face.In fact, the surface weather observation station also is the data important supplement source that this method further improves precision, and the two is in conjunction with the estimation precision of near surface air themperature on the zone will be provided greatly.
Description of drawings
The present invention is further described below in conjunction with drawings and Examples.
Description of drawings
Fig. 1 MODIS remote sensor.
Fig. 2 is a main flow synoptic diagram of the present invention.
Fig. 3 is the schematic flow sheet that the present invention sets up the simulated database of radiance temperature on MODIS remote sensor the 29th, 31, the 32 wave band stars.
Fig. 4 is the multilayer neural network structural representation that the present invention adopts.
Fig. 5 is neural metwork training of the present invention and testing process synoptic diagram.
Fig. 6 is an estimation near surface air themperature schematic flow sheet of the present invention.
Fig. 7 adopts the face of land measured data that the present invention obtains and the comparison diagram of inversion result.
Embodiment
Top temperature and emissivity inverting are based on the heat radiation transmission equation, and general expression formula is [Mao Kebiao is at the Surface Temperature Retrieval method research of MODIS data, master thesis, Nanjing University, 2004.5.] as the formula (1):
B
i(T
i)=ε
i(θ) τ
i(θ) B
i(T
s)+[1-τ
i(θ ')] [1-ε
i(θ) τ
i(θ) (formula 1)
B
i(T
ia)+[1-τ
i(θ)]B
i(T
ia)
T in the formula
sThe expression surface temperature, T
iExpression passage i brightness temperature on the star that sensor height obtains, τ
i(θ) expression passage i is at the atmospheric transmittance of observed ray θ, the direction of the downward bright temperature radiation of θ ' expression atmosphere, ε
i(θ) expression passage i is in the face of land emissivity at observed ray θ place.B
i(T
i) be the radiation intensity that sensor receives, B
i(T
s) radiation intensity on the face of land, T
IaThe effective atmosphere mean effort temperature of expression passage i.Effective atmosphere mean effort temperature (T
Ia) change with wavelength variations, it is mainly by atmosphere vapour content and near surface air themperature decision [Mao Kebiao, Huajun Tang, Xiufeng Wang, Qingbo Zhou, Daolong Wang, Near-Surface Air Temperature Estimation From ASTER Data Using Neural Network, International Journal of Remote Sensing, 2008,29 (20): 6021-6028.].
T
Ia=A
i+ B
iT
0(formula 2)
T in the formula
0Be 2 meters high left and right sides near surface air themperatures, A
iBe constant, B
iPassage i coefficient.The near surface air themperature also is subjected to the influence of surface temperature.In a given place, also there are the relation as formula 2 near surface air themperature and surface temperature, but this relation is not very stable, and it changes and change with the place in time.In equation 1, three unknown numbers (emissivity, surface temperature and near surface air themperature) are arranged, this is a typical ill-conditioning problem.If do not construct other condition, system of equations does not have and separates.In addition, the transmitance of each thermal infrared wave band (τ (θ)) also is a unknown number, and it is the function (formula 3) of atmosphere vapour content and other gas.
τ
i(θ)=f (W, O) (formula 3)
W is an atmosphere vapour content, and O represents other gas (carbon dioxide, nitrogen monoxide, ozone, methane, carbon monoxide etc.), and the relative atmosphere vapour content of these gases is stable, and its influence can obtain by the simulation of normal atmosphere section.Thermal infrared wave band transmitance is very responsive to steam, split window algorithm and be exactly and utilize two thermal infrared wave bands that the different susceptibility of steam are eliminated the influence of steam, thereby Inversion Calculation obtains surface temperature.For different atural object at different wave bands, emissivity almost is a constant, Mao et al. (2008) [Mao, K., Shi J., Tang H., Li Z.L., Wang X.and Chen K., A Neural Network Technique forSeparating Land Surface Emissivity and Temperature from ASTER Imagery, IEEETransactions on Geoscience and Remote Sensing, 2008,46 (1): 200-208.] propose to utilize between the contiguous band emission rate local line's sexual intercourse to reduce unknown number and overcome ill-conditioning problem.Equation can be described as suc as formula 4.
ε
i(θ)=C
i+ D
iε
j(θ) (formula 4)
ε
i(θ) and ε
j(θ) be different-waveband (i, j) emissivity when observation angle θ.C
iBe constant, D
iIt is the coefficient of passage i.For with a kind of type of ground objects, the emissivity of different-waveband can be represented with the emissivity of a wave band, thereby the emissivity of different-waveband is reduced to 1.Owing to be difficult to all wave bands accurately be described with several functions, this potential information [Mao that is not fully utilized, K., Shi J., Tang H., LiZ.L., Wang X.and Chen K., A Neural Network Technique for Separating LandSurface Emissivity and Temperature from ASTER Imagery, IEEE Transactions onGeoscience and Remote Sensing, 2008,46 (1): 200-208.].The linear simplifiation of nonlinear function (such as Planck function) also can produce error in addition.
In order to overcome the shortcoming that traditional inversion algorithm need be calculated the expensive time.The present invention utilizes neural network (NN) not need to know relation between the input and output parameter, can pass through atmospheric radiation transmission (MODTRAN4) simulated training data set, directly determines input and imports relation between the data by the simulated data training.
This example realizes that (method) mainly comprises three steps, as Fig. 2.
First step is surface temperature, emissivity and the atmosphere vapour content that reads each pixel from MODIS surface temperature and emissivity product, atmosphere vapour content product, with they input parameters as MODTRAN4.Give an example, surface temperature such as a pixel is 300K, wave band 29,31 and 32 emissivity is respectively 0.95,0.96,0.98, atmosphere vapour content is 1.2g/cm2, and with the known input parameter of these product values as MODTRAN, near surface air temperature variations scope is that 300K is to 315K, employing is the mid latitude atmosphere section, and simulation process as shown in Figure 3;
Second step is to utilize neural network software, and neural network and traditional method are different, and it does not need to know exactly inversion algorithm (rule).Because neural network possesses from complexity and coarse extracting data information, so neural network can be used to extract model prediction [Hornik K.M., Stinchcombe M., andWhite H., Multilayer feedforward networks are universal approximators, NeuralNetwork, 1989,4 (5): 359-366], as shown in Figure 4.Present embodiment adopts dynamic learning neural network (DL) that the database of setting up in the first step is carried out training and testing.The ability of nonlinear problem is understood in speed of convergence when dynamic neural network has used Kaman's filtering to increase training and raising, and each node weights of neural network is initialized to the random number between (1,1).Kaman's filtering is the process that root mean square is estimated iteration, and the renewal of each network weight is that it is separate that the weight of output node is upgraded on the basis of new input data set based on previous weight study.Because the dynamic learning neural network based on Kaman's filtering only needs two iterative process just to reach desired root mean square threshold value, and inversion result is very stable, so root-mean-square error is set at 10e usually
-3, iterations is 2.More see also [Tzeng Y.C., Chen K.S., Kao W.L., and Fung A.K., A Dynamic learning nerual network for remote sensingapplications, IEEE Trans.Geosci.Remote Sensing, 1994,32 (5): 1096-1102.] introduction.Whole simulation and training process are as shown in Figure 5.Simulated data is divided into training and testing two parts: training dataset 89 and test data set 51.After repetition training and test, T near surface air themperature inversion result such as the table 1
0' shown in.As can be seen from Table 1, inversion result is very good, and average error and standard error are approximately 0.8K and 0.9K respectively.The main cause that precision improves has been that surface temperature and emissivity and atmosphere vapour content are as priori; The inversion error of considering surface temperature and emissivity is approximately respectively ± 1K and ± 0.015[Wan, Z., Y.Zhang, Y.Q.Zhang, and Z.L.Li, Validation of the landsurface temperature products retrieved from Moderate Resolution ImagingSpectroradiometer data, Remote Sens.Environ., 2002,83:163-180.], the error of atmosphere vapour content is ± 13%[Kaufman Y.J., Gao B.C., Remote sensing of water vapor in thenear-IR from EOS/MODIS.IEEE Trans.Geosci.Rem.Sens., 1992,30,871-884.].Here our inversion error of considering surface temperature is ± 0.03 at ± 2K and emissivity error.For each pixel, LST-2K≤LST≤LST+2K, ε
i-0.03≤ε
i≤ ε
i+ 0.03, w-0.13w≤w≤w+0.13w is input to as the priori of each pixel and simulates and to set up the training and testing data among the MODTRAN4.Surface temperature such as a pixel is 300K, and wave band 29,31 and 32 emissivity are respectively 0.95,0.96,0.98, and atmosphere vapour content is 1.2g/cm2.Surface temperature (297-303K), the emissivity 0.92-0.97 of wave band 29,31,32,0.93-0.99,0.96-1, atmosphere vapour content 1-1.5g/cm2 is input among the MODTRAN4 as priori.Training dataset is 836, and test data set is 392.After repetition training and test, the near surface air themperature is as table 1T
0" shown in.Average error and standard deviation are respectively 1.5K and 1.8K, and this still can meet the demands in present application.
Table 1 inversion error table
R: related coefficient; SD: standard deviation .T
0', T
0" be the near surface air themperature
Third step is to utilize the neural network that trains in second module that remote sensing image data MODIS is carried out practical inversion.For the ease of comparing with Practical Meteorological Requirements research station point data, we have selected the MODIS data of two meteorological site of Xiao Tang mountain and Hailaer (2004), according to longitude and latitude with brightness temperature on the star of MODIS the 29th, 31, the 32 wave band correspondences of corresponding single-point, surface temperature, emissivity, atmosphere vapour content extract, set up tranining database (process such as step 1 respectively, as Fig. 3), carry out the concrete training and testing flow process of reality then as shown in Figure 6.The comparative result that obtains such as Fig. 7, average error
Approximately be 1.6K.
Claims (1)
1, from MODIS data estimation near surface air themperature method, the steps include:
The first step, set up the simulated database of radiance temperature on MODIS remote sensor the 29th, 31, the 32 wave band stars
1-1) atmospheric profile pattern, air path, radiation mode and the backscatter mode of the location of the selection image that obtains are as input parameter;
1-2) read surface temperature (LST), the emissivity (ε of corresponding each pixel of MODIS product
i) and the value of atmosphere vapour content (w), for each pixel, LST-2K≤LST≤LST+2K, ε
i-0.03≤ε
i≤ ε
i+ 0.03, w-0.13w≤w≤w+0.13w is input to as the priori of each pixel and simulates and to set up the training and testing database among the MODTRAN4;
1-3) read in the surface temperature and the emissivity value of MODIS data correspondence, according to 1-2) in the variation range that limits, simulation surface temperature and near surface air themperature may variation;
1-4) read in atmosphere vapour content initial value, according to 1-2) middle limit error variation range, atmosphere vapour content in the simulation process;
Other parameters such as 1-5) input MODIS satellite sensor height, and acquiescence atmospheric aerosol, carbon dioxide;
1-6) carry out simulation according to the wavelength coverage of MODIS data the 29th, 31,32 wave bands, and radiance on output MODIS data the 29th, 31, the 32 wave bands simulation star;
1-7) will simulate at every turn and obtain that radiance converts brightness temperature on the star, at each pixel and and the surface temperature and the emissivity of each analog input, and atmosphere vapour content is set up corresponding database together.
Second step, neural metwork training and test
2-1) simulated database in the first step is divided into two groups, one group is training dataset; One group is test data set;
2-2) brightness temperature and atmosphere vapour content are as the input node of neural network on the star of MODIS the 29th, 31,32 wave bands that training data is concentrated, and the near surface air themperature is trained as output node;
2-3) the neural network that the input of brightness temperature on the star of test data set and atmosphere vapour content is trained, output near surface air themperature;
The 3rd step, inverting near surface air themperature
3-1) read the 29th, 31,32 wave bands and the atmosphere vapour content data of MODIS remote sensing image data;
3-2) brightness transition Cheng Xing on the star of the 29th, 31,32 wave bands of MODIS data is gone up brightness temperature (T29, T31, T32) and the corresponding atmosphere vapour content W of extraction;
3-3) T29, T31, T32, W among the 3-2 are input to second and go on foot in the neural network that trains, output near surface air themperature (NSAT);
3-4) according to the face of land of image correspondence be correlated with the checking and applied analysis.
Described method, wherein, among the 1-3 of the first step, near surface air temperature variations scope is NSAT≤LST+15K, step change amplitude is 2K in the simulation process.
Described method, wherein, among the 1-4 of the first step, atmosphere vapour content initial value is the atmosphere vapour content (w) that reads in, and limited range is w-0.13w≤w≤w+0.13w, and step change amplitude is 0.2g/cm in the simulation process
2
Described method, wherein, among the 1-5 of the first step, input MODIS satellite sensor height is 705KM.
Described method, wherein, second the step 2-4 in, near surface air themperature standard error all adds 5 greater than 2K with two-layer implicit node, the repetition 2-2 proceed training and testing, near surface air themperature standard error less than 2K.
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