CN114169215B - Surface temperature inversion method coupling remote sensing and regional meteorological model - Google Patents
Surface temperature inversion method coupling remote sensing and regional meteorological model Download PDFInfo
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Abstract
Cloud pollution can interfere with the transmission of thermal infrared in the atmosphere, and the potential of a satellite thermal infrared sensor for providing complete space-time coverage for earth surface temperature is limited. In order to solve the problem, the invention discloses a three-step inversion model (TRM), wherein (i) the WRF model is used for simulating the surface temperature which is not polluted by cloud and has the resolution of 1 KM; (ii) Fitting the simulated earth surface temperature into a similar MODIS cloudless earth surface temperature by taking the MODIS data as reference and using an optimal machine learning model; (iii) And optimizing by combining a median filtering algorithm, and removing salt and pepper noise points in the fitted image so as to obtain the surface temperature which has the precision similar to that of the MODIS surface temperature and has no cloud pollution. The constructed model can provide a MODIS cloud-free time sequence earth surface temperature image in a region with frequent cloud coverage, and the defect that the satellite thermal infrared image is polluted by cloud and cannot obtain the complete earth surface temperature is overcome.
Description
Technical Field
The invention relates to the technical field of atmospheric environment and atmospheric remote sensing, in particular to a surface temperature inversion method of a coupling remote sensing and regional meteorological model.
Background
The surface temperature is one of important indexes for monitoring resource environment and climate change, and plays an important role in surface research. Surface temperature is widely used in various fields including climatic weather, agroforestry, ecological environment, geology and exploration, etc., and is considered one of the high priority parameters of the international territory and biosphere program. The accurate surface temperature inversion is not only beneficial to evaluating the surface energy, hydrologic balance, thermal inertia and soil humidity, but also beneficial to detecting climate change and mastering the change rule of long-term climate. In recent years, remote sensing technology has been developed rapidly, and surface temperature inversion by using thermal infrared has become the mainstream direction. Due to the complexity of the process of receiving earth surface radiation energy by a satellite and the direct influence of the atmospheric environment, the earth surface temperature reflected by a satellite remote sensing image under a cloudy condition is different from the true value, unfortunately, in the global actual daytime weather, the cloudy weather dominates half of the time, so that the application of an operable earth surface temperature product for space-time analysis is limited, and an effective model needs to be designed to derive the earth surface temperature under the cloudy condition at present.
Many studies have attempted to construct reconstruction methods to recover data lost due to cloud contamination, and these methods can be classified into three types. The first method is based on a surface energy balance method, which needs to assume or know meteorological conditions, and deduces the earth surface temperature under a cloudy condition by inputting the quality of short-wave radiation and other complex regional parameters, so that the parameterization has great uncertainty, and the other disadvantage is that the surface energy balance method cannot be applied at night when the input short-wave radiation cannot dominate the spatial variation of the earth surface temperature; the second method is a gap filling method, which recovers the earth surface temperature by means of space-time information, extracts clear adjacent earth surface temperature pixels from different satellites on the same day or space-time information of other earth surface temperature data, and deduces the earth surface temperature under a cloud condition by replacing or interpolating, the premise of using the method is that the earth surface temperature under the cloud should be similar to the earth surface temperature under the clear air of adjacent geographic areas or adjacent time images, so that only the assumed clear air earth surface temperature can be deduced, but not the earth surface temperature under a cloudy condition, and multi-step convolution and iteration are needed to obtain the pixels with large-area cloud pollution, which results in large accumulated errors, so more information about weather and earth surface characteristics is needed to improve the method, and extreme or abnormal earth surface temperature values in the empty area during capturing are obtained; the third method is a passive microwave measurement method, which can acquire earth surface temperature in cloudy conditions since microwaves can be transmitted through clouds and other atmospheric interference sources, however, estimating earth surface temperature by microwaves is challenging because the microwave signal varies significantly with earth surface characteristics, and the technique needs to take into account spatial and temporal variations in microwave emissivity, and in addition, there are some obstacles in integrating the thermal infrared and passive microwaves to generate all-weather earth surface temperature products, the first problem is that the spatial resolution of earth surface temperature for passive microwave measurements is much coarser than that for thermal infrared measurements, typically on the order of tens of kilometers over the frequency of interest, and the second problem is that most passive microwave satellite sensors have bands that result in band gaps between two adjacent orbits, and thus passive microwave measurements are often used as a supplemental information for thermal infrared or other ancillary data to reverse earth surface temperature.
WRF is a new generation mesoscale weather research and forecast model, has perfect physical schemes to describe various complex weather phenomena, can be used as a regional weather simulation model, and has five modularized physical schemes with many options including micro-physics, short wave radiation, long wave radiation, near-ground layer and boundary layer. The method is characterized in that the urban canopy is closely related to the height, density, trend and the like of a building, the variation of urban surface energy can be deeply explored through the combined application of a mesoscale meteorological model and an urban canopy model, the WRF and the urban canopy model can be used for describing processes such as surface radiation, energy transfer and the like more carefully, and the surface temperature under a fine day or cloudy condition can be accurately simulated.
Disclosure of Invention
The invention aims to solve the problems that the transmission of thermal infrared in the atmosphere is interfered by cloud pollution, and the satellite thermal infrared sensor is limited to provide complete space-time coverage potential for the earth surface temperature. In order to solve the problem, a three-step inversion model (TRM) is invented, wherein (i) the WRF model is used for simulating the surface temperature which is not polluted by cloud and has the resolution of 1 KM; (ii) Fitting the simulated earth surface temperature into a similar MODIS cloudless earth surface temperature by taking the MODIS data as reference and using an optimal machine learning model; (iii) And optimizing by combining a median filtering algorithm, and removing salt and pepper noise points in the fitted image so as to obtain the surface temperature which has the precision similar to that of the MODIS surface temperature and has no cloud pollution.
The purpose of the invention is realized by the following technical scheme: a surface temperature inversion method coupling remote sensing and a regional meteorological model comprises the following steps:
(1) And selecting an optimal atmosphere WRF model scheme.
The WRF model is a model for simulating the surface temperature in the research, and the model is configured into three unidirectional nested domains, the spatial resolution is respectively set to be 9km,3km and 1km, and the pixels of the three domains are respectively 124 × 124, 160 × 160 and 205 × 205. The WRF is combined with the urban canopy model, so that the surface temperature of the urban area can be simulated more accurately. According to the relevant research, 3 schemes are selected, and 3 × 2 WRF configuration schemes are selected together because static land use data is also taken as an input parameter, and an optimal WRF configuration scheme is selected from the schemes as shown in table 1.
TABLE 1 WRF model configuration selection scheme
Errors between the surface temperature simulated by various schemes and the MODIS image need to be compared through the test points, and root mean square errors and correlation coefficients between the surface temperature simulated by various schemes and the MODIS image are used as the quality standard of the judgment schemes. And then simulating the surface temperature free from cloud pollution by using the optimal scheme.
(2) And (3) creating a positive sphere used by the WRF model through programming, and converting pixels of the positive sphere into a WGS84 coordinate system to enable the pixels to correspond to the pixels of the MODIS image one by one.
(3) And respectively fitting the data by using a linear regression model, a neural network model and a random forest model, selecting an optimal model from the three models, and selecting a good and bad standard which is still the root mean square error and the correlation coefficient. When the MODIS image is less polluted by cloud, establishing a relation between the MODIS image and the WRF simulation image in the earth surface temperature of a clear sky pixel on the same date by using an optimal machine learning model, and applying the model to the simulated earth surface temperature to obtain an earth surface temperature image similar to the MODIS, wherein when the MODIS image is more polluted by cloud, and no enough pixels are used for training the machine learning model, the fitted earth surface temperature image is not highly correlated with the earth surface temperature observed by the MODIS (the correlation coefficient is less than 0.8), the image is considered to be completely polluted by the cloud, and the earth surface temperature image similar to the MODIS is obtained by using the model adjacent in time and the WRF image on the same day, and a specific calculation formula is as follows:
in the formula, LST c Surface temperature image similar to MODIS, f i For the time-neighboring training model, N is the number of neighboring images used to estimate the earth's surface temperature, which in this study is 7.R is the correlation coefficient between time-adjacent MODIS image and analog image, T is the number of days between image dates, LST cWRF For the current day WRF image, cor is the current day MODIS image and LST cWRF ω is the weight of the image.
(4) The method is characterized in that a two-dimensional sliding window with a certain structure is used, pixels in the window are sorted according to the pixel values, and a monotonously rising (or falling) two-dimensional data sequence is generated.
g(x,y)＝med{f(x-k,y-l),(k,l∈W)} (4)
Wherein g (x, y) is the output image of two-dimensional median filtering, f (x, y), g (x, y) are the original image and the processed image respectively, and W is a two-dimensional 3 x 3 window.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method utilizes WRF and combines a city canopy model at the same time, and inverses the surface temperature through a machine learning model and a median filtering algorithm, instead of inversing the surface temperature under the assumed condition like the prior algorithm, the real surface temperature is obtained.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 optimal WRF scheme selection test point
FIG. 3 is similar to the MODIS surface temperature acquisition process under the condition of complete cloud pollution or insufficient pixel training.
FIG. 4 shows simulated root mean square error of surface temperature for various land use types and the total for the WRF configuration.
FIG. 5 shows the comparison and correlation between the WRF simulation results and the weather station air temperature.
And 6, the surface temperature simulated by the scheme 6 is obtained by fitting a linear regression, a neural network and a random forest model.
FIG. 7 MODIS surface temperature and the correlation coefficient and root mean square error between the surface temperature and the surface temperature fit by linear regression, neural networks and random forest models.
Fig. 8 shows the MODIS image and the TRM simulation image corresponding to the date under the condition of less cloud pollution, and the associated thermodynamic diagrams of the earth surface temperature before and after optimization.
FIG. 9 shows the root mean square error and other rates of change before and after optimization of the surface temperature under conditions of low cloud pollution.
Fig. 10 shows the MODIS image and the TRM simulation image on the corresponding date under the cloud pollution multi-condition, and the associated thermodynamic diagrams of the earth surface temperature before and after optimization.
FIG. 11 shows the root mean square error and other rates of change before and after optimization of the surface temperature under various cloud pollution conditions.
Fig. 12 absolute error and correlation coefficient between TRM surface temperature and MODIS surface temperature per soil utilization type.
Fig. 13TRM model was inverted to obtain the root mean square error and correlation coefficient of the surface temperature in 3 months of 2020.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
The present embodiment takes the results of tests conducted in Beijing as the research area as an example, and describes the process of utilizing the present invention to reverse the cloudless surface temperature. According to the method flowchart shown in fig. 1, the method comprises the steps of:
1: the method comprises the following steps of firstly, preprocessing work, including processing of land utilization data and software development of converting NC format files into GeoTiff format files. Replacing WRF default geographical static field data with MOD11Q1 land utilization data; under the Qt platform, C + + language is used for developing format conversion software, a GDAL library is used for creating a positive sphere, and pixels of the WRF result are converted into a WGS84 coordinate system through ellipsoid conversion.
2: WRF scheme selection and performance, using three-day images which are less polluted by cloud in 3 months and can cover the test points as scheme test data, wherein the three days are respectively 3 months 19 days, 3 months 20 days and 3 months 21 days, and the corresponding time is 2:30 (UTC), and fig. 3 shows the root mean square error of the simulation values of various land utilizations and the total ground surface temperature corresponding to the 6 WRF configuration schemes, and from the root mean square error of the total ground surface temperature of various schemes, the ground surface temperature simulated by the scheme 6 is closest to the MODIS ground surface temperature, the accuracy is highest, and the root mean square errors are 1.84 ℃, 3.31 ℃ and 3.73 ℃, respectively, so the scheme 6 is selected as the optimal scheme. To further verify the reliability of the results of the scheme 6, the earth surface temperature images of 19 days, 20 days and 21 days were simulated by using the scheme 6, and the 144 time point comparison results (fig. 4) were obtained by using the temperature data provided by the 454110 meteorological site, and the results show that the simulated temperature and the actual temperature have the same variation trend, and the simulation results have reliability.
3: the selection and performance of the machine learning model are that three machine learning models, namely a linear regression model, a neural network and a random forest are used, three earth surface temperature images of three days 2 (UTC) of 3, 5, 14 and 23 days in 2020 are respectively simulated, and the result is as shown in fig. 5, visually, the earth surface temperature images fitted by the three machine learning models and the earth surface temperature observed by the MODIS have a similar spatial distribution pattern, the correlation coefficient between the fitted earth surface temperature images and the earth surface temperature images observed by the MODIS on the corresponding date is calculated from the root mean square error, and the scatter diagram of fig. 6 is obtained.
And 4, generating and verifying a cloudless ground surface temperature image, when the MODIS image is little polluted by clouds, establishing the relation between the ground surface temperatures of the MODIS image and the WRF simulation image in clear sky pixels on the same date by using an optimal machine learning model, and applying the model to the simulated ground surface temperature to obtain the cloudless ground surface temperature image. Because the result image has salt and pepper noise, in order to optimize the surface temperature image, the third step of the TRM model, namely median filtering denoising, is executed. Considering the median filtering optimization effect, the correlation between the images before and after optimization and the MODIS observation image corresponding to the date is compared, as can be seen from the thermodynamic correlation diagram (fig. 7), the correlation is larger after optimization than before optimization, fig. 8 shows the root mean square error value and the change rate thereof before and after optimization, which indicates that after optimization, the precision of the earth surface temperature is improved, the root mean square error between the earth surface temperature image inverted by the TRM model and the MODIS observation image is about 1.2 ℃, the correlation is larger than 0.9, and when the MODIS image is little polluted by cloud, the earth surface temperature can be better inverted.
When the MODIS image is heavily polluted by cloud, the model with adjacent time and the WRF image of the current day are used to obtain the surface temperature image similar to the MODIS, the technical process is shown in fig. 9, and the specific calculation formula is (1-3), with the calculation formula being 2 for two days, i.e. 3/9/18/2020: 30 (UTC) image as an example (fig. 10), the thermodynamic correlation diagram (fig. 10) shows the correlation coefficient of the surface temperature before and after optimization, with the correlation coefficient increasing after optimization, and fig. 11 presents the values of the root mean square error before and after optimization and its rate of change, indicating that the root mean square error is overall decreasing after optimization. The method shows that the accuracy is improved after the inverted ground surface temperature is optimized under the condition of complete cloud pollution, the root mean square error between the ground surface temperature image inverted by the TRM model and the MODIS observation image is about 1.8 ℃, the correlation is greater than 0.9, and when the MODIS image is completely polluted by the cloud, the TRM can also invert the ground surface temperature with higher accuracy.
5: and (3) analyzing the error of the TRM model, namely comparing the surface temperature inverted by the TRM model with the surface temperature observed by the corresponding MODIS, wherein the absolute error can more intuitively show the performance of the model, so that the absolute error and a correlation coefficient are used for analyzing the error of the TRM model. Fig. 12 shows absolute errors and correlation coefficients between the MODIS surface temperature and the TRM surface temperature for each land use of 3, 4, 9, 14, 18, and 23 days of 2020, where the surface temperature inverted by the TRM model has a smaller error and a higher correlation with the surface temperature observed by the MODIS, which indicates that the constructed TRM model can invert a higher-precision MODIS-like surface temperature image and can make up for the defects of the cloud images of the MODIS.
6: in order to illustrate the reliability of our results, the earth surface temperature was inverted for all 3 months in 2020, and from the dot line graph (fig. 13) of the correlation coefficient and the root mean square error, it can be seen that the root mean square error was less than 2 ℃, the average value of the overall root mean square error was 1.23 ℃, and the average value of the correlation coefficient was 0.93, so our study results were reliable.
The above examples are preferred embodiments of the present invention, but the present invention is not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and they are included in the scope of the present invention.
Claims (4)
1. A surface temperature inversion method coupling remote sensing and a regional meteorological model is characterized by comprising the following steps: the method comprises the following steps:
A. simulating the surface temperature without cloud pollution by using an optimal scheme based on an atmospheric WRF model, wherein in the optimal scheme, the micro-physics is set to be WSM6, the short wave radiation is set to be RRTMG, the long wave radiation is set to be RRTMG, the ground surface is set to be MOJ, the boundary layer is set to be YSU, the city canopy layer is set to be UCM, the land utilization is set to be default, the spatial resolution of simulated surface temperature data is 1KM, and the time resolution is 1 day;
B. converting the simulated earth surface temperature data to a WGS84 coordinate system to enable the earth surface temperature data to correspond to the MODIS image pixels one by one;
C. respectively fitting the data by using a linear regression model, a neural network model and a random forest model, selecting an optimal model as an optimal machine learning model, and fitting the simulated earth surface temperature with a coordinate system into the similar MODIS cloud-free earth surface temperature;
D. and optimizing by combining a median filtering algorithm, and removing salt and pepper noise points in the fitted image, thereby obtaining the earth surface temperature which has the precision similar to that of the MODIS earth surface temperature and is free from cloud pollution.
2. The method for inverting the earth's surface temperature by coupling remote sensing with the regional meteorological model according to claim 1, wherein the step B specifically comprises:
b1: a perfect sphere used by the WRF model is created, and the pixels of the perfect sphere are transformed into the WGS84 coordinate system.
3. The method for inverting the earth's surface temperature by coupling remote sensing with the regional meteorological model according to claim 1, wherein the step C specifically comprises:
c1: when the MODIS image is little polluted by cloud, establishing a relation between the earth surface temperatures of the MODIS image and the WRF simulation image in the clear sky pixels on the same date by using an optimal machine learning model, and applying the model to the simulated earth surface temperature to obtain an earth surface temperature image similar to the MODIS;
c2: when the MODIS image is polluted by cloud and sufficient pixels are not available for training the machine learning model, the correlation coefficient of the fitted surface temperature image and the surface temperature observed by the MODIS is smaller than 0.8, namely the fitting image is not highly correlated, the image is considered to be completely polluted by the cloud, the time-adjacent model and the WRF image of the day are used for acquiring the surface temperature image similar to the MODIS, and the specific calculation formula is as follows:
in the formula, LST c Surface temperature image similar to MODIS, f i For the time-adjacent training model, N is the number of adjacent images for estimating the earth surface temperature, N =7, R is the correlation coefficient of the time-adjacent MODIS image and the simulation image, T is the number of days between the image dates, and LST cWRF For the current day WRF image, cor is the current day MODIS image and LST cWRF ω is the weight of the image.
4. The method for inverting the earth's surface temperature by coupling remote sensing with the regional meteorological model according to claim 1, wherein the step D specifically comprises:
d1: optimizing by using a median filtering algorithm, and removing salt and pepper noise points in the fitted image;
the method uses a two-dimensional sliding window to sort pixels in the window according to the pixel values, and generates a two-dimensional data sequence which is monotonically increased or decreased;
wherein x and y are the number of rows and columns of image pixels, g (x, y) is the output image of two-dimensional median filtering, f (x, y), g (x, y) are the original image and the processed image, and W is a two-dimensional 3 × 3 window.
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