CN116611352A - General method and system for earth surface temperature time comparability correction of polar orbit satellite - Google Patents
General method and system for earth surface temperature time comparability correction of polar orbit satellite Download PDFInfo
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Abstract
The invention belongs to the technical field of remote sensing application, and relates to a general method and a general system for correcting earth surface temperature time comparability of polar orbit satellites. The method comprises the following steps: acquiring surface temperature data of a clear sky state in a transit period of a polar orbit satellite; analyzing time change characteristics of the earth surface temperature in a clear sky state in the transit period of the polar orbit satellite, and constructing an earth surface temperature time change rate model; acquiring a model time comparability correction parameter data set; acquiring a corresponding effective characteristic variable data set, and constructing a prediction model of a time comparability correction parameter; performing integrated regression model training to obtain a trained time comparability correction parameter prediction model; determining actual time comparability correction parameters of the polar orbit satellite; and determining the earth surface temperature after the polar orbit satellite time comparability correction. According to the earth surface temperature time comparability correction method and the earth surface temperature time comparability correction device, the earth surface temperature time comparability correction precision and applicability of the polar orbit satellite are improved through the earth surface temperature time change rate model and the time comparability correction parameter prediction model.
Description
Technical Field
The invention relates to the technical field of remote sensing application, in particular to a general method and a general system for correcting earth surface temperature time comparability of polar orbit satellites.
Background
Surface temperature (Land Surface Temperature, LST for short) is an important indicator reflecting regional or global scale surface energy balance and moisture exchange processes. The surface temperature can be effectively inverted using Thermal Infrared (TIR) data acquired from satellite observations. A variety of well-established surface temperature inversion algorithms have been developed based on which a range of surface temperature products have been generated, such as MOD/MYD LST products from the medium resolution imaging spectrometer (MODIS-resolution Imaging Spectroradiometer, abbreviated MODIS) Terra and Aqua platforms. These products have been validated by the system with an accuracy of about 1K.
However, these products have the problem of inconsistent acquisition times of the pixels along the scan line due to the design features of the satellite observation system itself and the large field of view of the polar orbit satellite sensor. In the revisit period, the observation time (local sun time) of different pixels in the same image or the same pixel in different date images can be changed significantly (up to 2 hours of time difference). The defect that the time is incomparable between earth surface temperature pixels of polar satellites limits the practical application of earth surface temperature data in various fields to a certain extent.
To solve the time incomparable problem of LST products, researchers have developed a number of algorithms. A temperature day change (Diurnal Temperature Cycle, DTC) model may be used to describe a time-of-day pattern of change in the surface temperature of a clear sky day and night. Currently, there have been studies on using DTC models for time-comparability correction of surface temperature data, such as processing united states National Ocean and Atmosphere Administration (NOAA) very high resolution scanning radiometer (Advanced Very High Resolution Radiometer, AVHRR) LST data with orbital drift effects. However, the DTC model is only suitable for the condition of clear sky throughout the day, and the LST measurement data acquired by the polar orbit satellite in one day is limited in general, so that the DTC model alone cannot be used for simulating the LST daily variation cycle mode. To solve these problems, researchers have constructed a model of ground surface temperature time normalization experience specific to the passing hours of 10:00-12:00 am, and have developed a random forest algorithm that considers the effect of accumulated solar incident radiation on ground surface temperature. Although these methods better achieve time normalization of surface temperature, LST products are primarily directed to satellite over-the-morning hours. The surface temperature in the afternoon is affected by multiple factors of more and longer duration than the surface temperature in the afternoon during the day, and its change characteristics are more complex, so it is more challenging to make time-comparability corrections.
At present, the research on a time comparability correction algorithm for the afternoon passing period of the polar orbit satellite or the time comparability correction algorithm applicable to each period of the daytime all the day is very little, and a new general polar orbit satellite earth surface temperature time comparability correction method needs to be developed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a general method and a general system for correcting the earth surface temperature time comparability of an polar orbit satellite.
In a first aspect, the present invention provides a general method for polar orbit satellite earth surface temperature time comparability correction, comprising:
acquiring earth surface temperature data of a clear sky state of a polar orbit satellite in a transit period, wherein the earth surface temperature data comprises earth surface temperature data of an observation station and a static satellite;
analyzing time change characteristics of the earth surface temperature data in a clear sky state in a transit period of a polar orbit satellite, and constructing an earth surface temperature time change rate model;
obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in a clear sky state and the earth surface temperature time change rate model;
acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameter, and constructing a prediction model of the time comparability correction parameter;
carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
Acquiring the actual observation area and time of the polar orbit satellite data, and determining the actual time comparability correction parameters of the polar orbit satellite ground surface temperature according to the trained time comparability correction parameter prediction model;
and determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
In a second aspect, the invention provides a polar orbit satellite earth surface temperature time comparability correction universal system, which comprises a first acquisition unit, a first construction unit, a first processing unit, a second construction unit, a model training unit, a second processing unit and a third processing unit;
the first acquisition unit is used for acquiring surface temperature data in a clear sky state, and comprises surface temperature data of an observation site and a static satellite in the clear sky state in a transit period of a polar orbit satellite;
the first construction unit is used for analyzing the time change characteristics of the earth surface temperature data in the clear sky state of the transit time period of the polar orbit satellite and constructing an earth surface temperature time change rate model;
the first processing unit is used for obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in a clear sky state and the earth surface temperature time change rate model;
The second construction unit acquires a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameter, and constructs a prediction model of the time comparability correction parameter;
the model training unit is used for carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
the second processing unit is used for acquiring the actual observation area and time of the polar orbit satellite data and determining the actual time comparability correction parameters of the polar orbit satellite ground surface temperature according to the trained time comparability correction parameter prediction model;
the third processing unit is used for determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
On the basis of the technical scheme, the invention can be improved as follows.
Further, obtaining surface temperature data of a clear sky state of a transit period of a polar orbit satellite includes: collecting long wave radiation data of a clear sky state observed by a station, calculating to obtain the earth surface temperature data observed by the station in the transit period of the polar orbit satellite based on a Stefan Boltzmann equation, collecting the earth surface temperature data of the stationary satellite, and screening the earth surface temperature data of the clear sky state of the observation station and the stationary satellite in the transit period of the polar orbit satellite.
Further, according to the earth surface temperature data of the stationary satellite in the clear sky state and the earth surface temperature time change rate model, a data set of time comparability correction parameters is obtained, and the method comprises the following steps:
and acquiring the earth surface temperature data of the stationary satellite at the reference moment by using the earth surface temperature data of the clear sky state of a plurality of observation times before and after the reference time and adopting a time interpolation method, and fitting the relationship between the earth surface temperature and the observation time of the stationary satellite in the clear sky state of the polar orbit satellite transit time period according to the earth surface temperature time change rate model to obtain a data set of the time comparability correction parameter.
Further, the effective feature variable corresponding to the time comparability correction parameter in the data set of the time comparability correction parameter is used as an independent variable of the prediction model, the time comparability correction parameter is used as a dependent variable of the prediction model, a training data set is constructed by using the independent variable and the dependent variable, and integrated regression model training is carried out on the prediction model.
Further, the effective characteristic variables include observation date, short wave wide band albedo, atmospheric precipitation, solar altitude, latitude, normalized vegetation index, digital ground elevation, gradient and slope.
Further, the general method for correcting the earth surface temperature time comparability of the polar orbit satellite further comprises the step of adopting a cross verification method to verify the earth surface temperature after the polar orbit satellite time comparability correction, and the method comprises the following steps:
calculating the surface temperature data of the stationary satellite with unified local sun through a time interpolation method as reference data for cross validation;
resampling and reprojection preprocessing are carried out on the reference data;
based on satellite observation angle constraint conditions, screening pixel pairs for comparison analysis after pretreatment;
and for different time difference ranges, performing cross-validation analysis on the earth surface temperature corrected for the polar orbit satellite time comparability by using the screened pixels.
The beneficial effects of the invention are as follows:
(1) The polar orbit satellite earth surface temperature time comparability correction algorithm provided by the invention belongs to a general method, and is suitable for all polar orbit satellite transit time periods in sunny states in the morning and afternoon;
(2) The general algorithm for the earth surface temperature time change rate of the polar orbit satellite provided by the invention does not need excessive input parameters, and is simple and easy to implement;
(3) The time change rate model provided by the invention can accurately capture the change characteristics of the clear sky surface temperature along with time in the transit time period of the polar orbit satellite, and has higher simulation precision;
(4) The process provided by the invention does not need to consider weather conditions except for the transit time of the polar orbit satellite in the application process, and has a wider application range;
(5) The spatial resolution of the training data set and the spatial resolution of the prediction data set applied in the process of establishing the time comparability correction parameter prediction model are similar, so that the influence of scale effect on the result is reduced as much as possible;
(6) According to the method, the time effect of the earth surface temperature under different earth surface coverage and different seasons can be effectively relieved, and the clear sky earth surface temperature data product with time comparability correction can be obtained.
Drawings
FIG. 1 is a schematic diagram of a general method for correcting earth surface temperature time comparability of an polar orbit satellite according to the embodiment 1 of the present invention;
FIG. 2 is a graph of the time rate of change of surface temperature at 7 sites;
FIG. 3 is a plot of the surface temperatures of the GOES-R16 stationary satellite with unified local sun after interpolation of MODIS surface temperatures and time before and after 4 observation date-time comparability corrections;
FIG. 4 is a distribution histogram of the surface temperature difference before correction and the surface temperature difference after correction for the date sequences 2020009, 2020114, 2020274, and 2020297;
FIG. 5 shows statistical indicators of verification results of the surface temperature and the original surface temperature after time comparability correction of different vegetation types, wherein (a) is the root mean square error of the surface temperature, and (b) is the deviation of the surface temperature;
Fig. 6 is a schematic diagram of a polar orbit satellite earth surface temperature time comparability correction general system according to embodiment 2 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
As an embodiment, as shown in fig. 1, to solve the above technical problem, the present embodiment provides a general method for correcting earth surface temperature time comparability of an polar orbit satellite, including:
acquiring earth surface temperature data of a clear sky state of a polar orbit satellite in a transit period, wherein the earth surface temperature data comprises earth surface temperature data of an observation station and a static satellite; analyzing time change characteristics of earth surface temperature data of a clear sky state in a transit period of a polar orbit satellite, and constructing an earth surface temperature time change rate model;
obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in the clear sky state and the earth surface temperature time change rate model;
Acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameters, and constructing a prediction model of the time comparability correction parameters;
carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
acquiring the actual observation area and time of the polar orbit satellite data, and predicting the actual time comparability correction parameters of the polar orbit satellite according to the trained time comparability correction parameter prediction model;
and determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
Optionally, obtaining surface temperature data of a clear sky state of a transit period of the polar orbit satellite includes: the method comprises the steps of collecting long wave radiation data observed by a site, calculating to obtain surface temperature data of the observation site based on a Stefan Boltzmann equation, collecting surface temperature data of a stationary satellite, and screening surface temperature data of the observation site and the stationary satellite in a clear sky state in a transit period of a polar orbit satellite, wherein the specific process is as follows:
1) Acquiring ground surface temperature data observed by a site: in the practical application process, collecting site long wave radiation data, and calculating site high time resolution (3 minutes) surface temperature based on a Stefan Boltzmann equation:
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,for satellite observation time atmospheric uplink long wave radiation (3-50 +.>) Units: w.m -2 ，/>Atmospheric downlink long wave radiation (3-50 +.>) Units: w.m -2 Sigma represents the Stefan Boltzmann constant )，/>Representing the emissivity of a broadband, setting the emissivity to be 0.97, wherein LST is the calculated site surface temperature;
2) Acquiring a high time resolution (5 minutes or 1 hour) LST product dataset of GOES-R16 stationary satellite;
3) Screening surface temperature data in a clear sky state: because the time comparability correction is only applicable to the surface temperature data of the clear sky state in the observation time, the surface temperature data needs to be screened to ensure that the used data is free from cloud interference. And performing preliminary screening on the clear sky surface temperature by simulating the nonlinear relation between the surface temperature and the corresponding observation time in the polar orbit transit period. In the polar orbit satellite passing period, such as the upper star passing period 9:00-12:00 or the lower star passing period 12:00-15:00, the relation between the surface temperature and time in the clear sky state can be simulated through a quadratic curve. Therefore, the earth surface temperature data set of the clear sky state of the polar orbit satellite transit time period is screened by using the constraint condition that the secondary coefficient is smaller than 0 and the pearson correlation coefficient R is larger than 0.95.
According to the data of the clear sky surface temperature of the screened site and the static satellite, further analyzing the time change characteristics of the surface temperature of the clear sky state in the transit period of the polar orbit satellite, and constructing a surface temperature time change rate model, wherein the specific process is as follows:
in order to characterize the change characteristics of the surface temperature along with time and establish a model to develop the time comparability correction of the surface temperature, two key variables are introduced, wherein the first variable is the time change rate of the surface temperatureFor representing the amplitude of the change in the surface temperature, the second variable being the time difference, i.e. the difference between the actual observation time of the polar orbit satellite and the reference time +.>For indicating a change in time. These two variables can be further expressed as:
；
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the actual observation time of each pixel of the polar orbit satellite before time comparability correction, < + >>A reference time representing a time comparability correction. The reference time is a constant at the time of the local sun, which may be set to 10:30 as before, and 13:30 as afternoon. />Ground temperature for polar satellites at actual observation time, +.>The earth surface temperature of the polar orbit satellite at the reference time, that is, the earth surface temperature after the time comparability correction is represented.
Further based on the earth surface temperature data of the clear sky state, analyzing the earth surface temperature time change rate Time differenceTaking the afternoon transit time as an example, the invention sets the reference time for time comparability correction to be 13:30 (local sun time), and the front and back 1.5 hours of the reference time are used for carrying out statistical analysis on the afternoon observation time of the polar orbit satellite. And calculating the earth surface temperature at the reference time by adopting a time interpolation algorithm through the observed values before and after the observed time 2-4, calculating the earth surface temperature time change rate by combining the earth surface temperature observed in the polar orbit satellite time, and analyzing the change relation between the earth surface temperature time change rate and the time difference. As shown in fig. 2, the time variation rate distribution diagram of the earth surface temperature of 7 sites has a horizontal axis of time difference, and the time difference is the difference of the satellite observation time minus the reference time of 13:30, and the unit is: h, the vertical axis is the time change rate of the surface temperature which is enlarged 1000 times, and the unit is: percent of the total weight of the composition. Let y be the simulated earth temperature time rate of change, x be the time difference, R be the pearson correlation coefficient, RMSE (Root Mean Square Error ) be the root mean square error, RMSE unit: k, MAE is the mean absolute error (Mean Absolute Error, abbreviated as MAE), MAE units: K. FIG. 2 (a) is the first site, < > a->Rmse=0.491, mae=0.400, r=0.990; FIG. 2 (b) is a second site, < > >Rmse=0.414, mae=0.315, r=0.995; FIG. 2 (c) is a third site, +.>Rmse=0.647, mae=0.468, r=0.980; FIG. 2 (d) is a fourth site, +.>Rmse=0.550, mae=0.428, r=0.994; FIG. 2 (e) is a fifth site, +.>，RMSE=0.502，MAE=0.385, r=0.963; FIG. 2 (f) is a sixth site, < >>Rmse=0.832, mae=0.613, r=0.977; figure 2 (g) is a seventh station,，RMSE=0.552，MAE=0.432，R=0.988。
according to the simulation result of the time difference of the earth surface temperature time change rate of the clear sky, the earth surface temperature time change rate of the earth surface satellite in the transit period of the polar orbit shows a quadratic curve shape, so the earth surface temperature time change rateCan be expressed as:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Parameters representing the empirical model. When the time difference is->At 0, the surface temperature change valueAnd the time change rate of the surface temperature->And correspondingly are all 0. Thus, the model has no intercept term. Further, the surface temperature at the time of reference after time comparability correction +.>Expressed as:
；
；.
from the above model, only parameters need to be obtainedAnd->In combination with a time comparability correction criterion +.>Actual observation time +.>And its actual observed surface temperature +.>Finally, the earth surface temperature after the polar orbit satellite time comparability correction can be calculated >。
Optionally, obtaining a time comparability correction parameter according to the earth surface temperature data of the stationary satellite in the clear sky state and the earth surface temperature time change rate modelaAndbcomprising:
according to the invention, the MODIS afternoon passing period daily surface temperature product MYD11A1 is taken as example data of time comparability correction, and the interference of satellite observation data pixel scale effect can be reduced by using GOES-R16 stationary satellite data to extract time comparability correction parameters. This is because the stationary satellite data has an observation feature of high time resolution and has a spatial resolution (0.02 °) close to that of the example data MODIS ground surface temperature data (0.01 °).
Based on the screened earth surface temperature data of the stationary satellite in the clear sky state, the earth surface temperature data of the stationary satellite in the clear sky state at the reference moment is obtained by utilizing earth surface temperature data of a plurality of observation times before and after the reference time by adopting a time interpolation method, and according to an earth surface temperature time change rate model, the relationship between the earth surface temperature of the stationary satellite in the clear sky state in the polar orbit satellite transit period and the observation time is fitted, so that a data set of time comparability correction parameters is obtained, wherein the specific process is as follows:
1) Setting a reference time for time comparability correction to be 13:30 (local sun time), wherein 1.5 hours before and after the reference time is a afternoon transit period of the polar orbit satellite, and calculating a time difference between the observed time of the polar orbit satellite and the reference time;
2) Converting the instantaneous observation time (UTC world unified time) into local sun according to the clear sky state static satellite data, acquiring the earth surface temperature of the reference time (13:30 local sun) by using earth surface temperature values of 2-4 observation stations in half an hour before and after the reference time, and calculating the earth surface temperature time change rate by combining the earth surface temperature actually observed by the polar orbit satellite;
3) And fitting the time change characteristics of the earth surface temperature change rate of the polar orbit satellite observation period according to the earth surface temperature time change rate model and combining the earth surface temperature time change rate and the time difference to obtain a time comparability correction parameter, and taking the time comparability correction parameter as a dependent variable in a prediction model training data set.
Acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameters, and constructing a prediction model of the time comparability correction parameters, wherein the specific process is as follows:
and the effective characteristic variable corresponding to the parameter is used as an independent variable in the training data set. In the practical application process, nine effective feature variables are selected after feature screening, including: observation date (the Day of the Year, DOY), short wave broadband ALBEDO (ALBEDO), atmospheric precipitation (Total Precipitable Water, TPW), solar altitude (Solar Zenith Angle, SZA), latitude (LAT), normalized vegetation index (Normalized Difference Vegetation Index, NDVI), digital ground elevation (Digital Elevation Model, DEM), grade (SLOPE), and SLOPE direction (ASPECT). For the actual MODIS afternoon day surface temperature data product MYD11A1, corresponding feature variable data are extracted from the MODIS data product, the GOES-R data product and ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model, advanced satellite-borne thermal emission and reflection radiometer global digital elevation model) data as independent variables of training data.
Normalized vegetation index NDVI data is obtained from GOES-R16 product definition and user guidance using GOES-R16 near infrared channel and red band advanced baseline imager radiation data. It should be noted that, these calculated NDVI data instantaneous values will change with the change of the observation time in one day, and the training requires daily normalization of the vegetation index NDVI values, so that the daily NDVI values are synthesized from the maximum value in the 4-hour time window from 10:00 am to 2:00 pm by adopting the mid-day time window averaging method. In addition, a secondary product of the earth surface ALBEDO of the GOES-R series earth observation satellite advanced baseline imager is selected to obtain the average value of high-frequency observation values, and the daily short-wave broadband ALBEDO is synthesized in the 4-hour time window.
The secondary atmospheric precipitation yield product of the GOES-R series earth observation satellite advanced baseline imager is utilized, and the atmospheric precipitation yield TPW variable is obtained by time interpolation of adjacent observations at a reference moment. Digital ground elevation DEM, SLOPE ASPECT and SLOPE were extracted using an ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer, advanced satellite borne thermal and reflected radiometer) digital elevation model with 30 meters spatial resolution. And the latitude, the observation date and the observation reference time are used as input data, and the solar zenith angle SZA variable is calculated according to the change rule of the solar altitude. The DOY variable may be collected from the acquired raw dataset.
In the actual application process, a time comparability correction parameter prediction model is built by building a relation between the time comparability correction parameter and an effective characteristic variable.
Optionally, the time comparability correction parameter is used as a dependent variable, the effective characteristic variable corresponding to the time comparability correction parameter is used as an independent variable, a training data set is constructed by using the independent variable and the dependent variable, and the integrated regression model training is carried out on the prediction model. GBRT (Gradient Boost Regression Tree, progressive gradient regression tree) was chosen as the integrated regression training model. And (3) carrying out integrated regression model training on the prediction model by using a training data set to obtain a trained time comparability correction parameter prediction model, wherein the specific process is as follows:
1) The GBRT model parameters are adjusted and set as follows: the number of decision trees is 500, the maximum depth of the decision trees is 4, the minimum number of samples of leaf nodes is 5, the learning rate is 0.01, and the applied loss function is the mean square error;
2) During training, 80% of the dataset was chosen as the training dataset and 20% was chosen as the test dataset. After training, parameters are establishedaSum parametersbOutputting the actual comparability correction parameters of the predicted polar orbit satellite;
3) The significance of the applied feature variable was evaluated using the percent value of the impure reduction index (Mean Decrease Impurity, MDI for short) to determine the effect of the feature variable on the time-comparability correction parameter in the stationary satellite training data set. The index of the reduction of the degree of the opacity is used as a method of high efficiency and stability commonly used in tree models, and the importance of variables can be measured. Parameters (parameters)aSum parametersbThe statistics of the feature importance percentage of the predictive model of (a) are shown in the following table:
the above table reflects the influence degree of different characteristic variables on model parameters, and analysis shows that the influence degree of different characteristic variables on model prediction results is different. For parameters ofaThe digital ground elevation DEM and the observation date DOY are the most important characteristic variables. While for parametersbThe atmospheric precipitation TPW is the most important characteristic variable, and the digital ground elevation DEM and observation date DOY variables are the next. Furthermore, the solar altitude SZA has a significant impact on both parameters, while the importance ratio of SLOPE and normalized vegetation index NDVI is relatively low. At the same time, latitude LAImportance of T and short wave broadband ALBEDO for parametersaSum parametersbThere is also a significant difference.
Acquiring the actual observation area and time of the polar orbit satellite data, and determining the actual time comparability correction parameters of the polar orbit satellite surface temperature according to the trained time comparability correction parameter prediction model, wherein the specific process is as follows:
1) For the actual daily surface temperature data MYD11A1 in the MODIS afternoon period, acquiring corresponding values of 9 effective characteristic variables from a MODIS data product, a GOES-R data product and the digital elevation model data product, wherein a MODIS vegetation index product (500 m resolution) is adopted to extract a normalized vegetation index NDVI, a MODIS 500 m spatial resolution bidirectional reflection distribution function and an ALBEDO model parameter data product are adopted to extract a short-wave broadband ALBEDO, and other variables refer to an acquisition method of the effective characteristic variables in a training data set, and combining the extracted 9 characteristic variables to generate a prediction data set;
2) In order to be consistent with the MODIS surface temperature data, all data of the predicted data set are re-projected to a WGS84 projection system and re-sampled to 0.01 degrees;
3) And (3) inputting a prediction data set by combining the time comparability correction parameter prediction model, and calculating the actual time comparability correction parameter of the MODIS surface temperature data.
And determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the polar orbit satellite actual time comparability correction parameter.
Optionally, the general method for correcting the earth surface temperature and time comparability of the polar orbit satellite further comprises a cross verification method, wherein the method for verifying the earth surface temperature after correcting the earth surface temperature and time comparability of the polar orbit satellite comprises the following steps:
calculating ground surface temperature data of a stationary satellite with unified local sun time by a time interpolation method, and taking the ground surface temperature data as reference data for cross verification;
resampling and reprojection preprocessing are carried out on the reference data;
based on satellite observation angle constraint conditions, screening pixel pairs for comparison analysis after pretreatment;
and performing cross-validation analysis on the earth surface temperature corrected for the time comparability of the polar orbit satellite by using the screened pixels according to different time difference ranges.
In the practical application process, the specific verification process is as follows:
(1) Acquiring the surface temperature of the stationary satellite with consistent time at the reference time (13:30 at the local sun) based on the surface temperature data of the stationary satellite in clear sky at the transit time of the screened polar orbit satellite, and taking the surface temperature as the reference data of cross verification;
(2) In order to screen the earth surface temperature data of the clear sky state in the afternoon observation time of the polar orbit satellite to the greatest extent, the interference of the MODIS observation pixel angle effect is reduced as much as possible, and the constraint condition that the difference of the observation angles VZA is smaller than 15 degrees and the maximum observation angle VZA is smaller than 50 degrees is adopted. In addition, the pixel pairs of GOES-R16 and MODIS with the surface temperature difference larger than 10K are removed, and the pixel pairs of the polar satellites and the GOES-R16 are screened to reduce the influence of uncorrelated factors and realize more effective verification;
(3) Comparing and verifying four indexes of a process correlation coefficient R, a root mean square error RMSE, a standard deviation STD (Standard Deviation, STD for short) and a deviation value Bias for quantitative evaluation analysis;
(4) The quantitative index analysis is completed on 4 days (009, 114, 274 and 297 days in one year) in different seasons, and the time difference between the counted pixel observation and the reference time is 0.5-h-1.0 h;
(5) In addition, surface temperature data for a total of 36 days was selected every month throughout the year for 3 days, and the results of comparability correction under different land cover types were statistically analyzed. Overall, the accuracy of the results after the comparability correction is greatly improved over different land cover types.
Optionally, according to the independent variables of the training data set, preparing the dependent variables of a prediction model for time comparability correction in corresponding MODIS data, re-projecting all the data to a WGS84 coordinate system (World Geodetic System, 1984 world geodetic coordinate system), re-sampling to 0.01 degrees, inputting the independent variables of the training data set into the prediction model, and obtaining the time comparability correction parameters of the surface temperature of the MODIS polar orbit satellite. And training the prediction model by using an integrated regression model to obtain the time comparability correction parameter prediction model.
Calculating the earth surface temperature data of the stationary satellite with unified local sun through a time interpolation algorithm, and taking the earth surface temperature data as reference data for cross verification;
resampling and reprojection preprocessing are carried out on the reference data;
based on satellite observation angle constraint conditions, screening pixel pairs for comparison analysis;
and performing cross verification analysis on earth surface temperature and stationary satellite earth surface temperature data after the time comparability correction of polar orbit satellites at different dates according to different time difference ranges.
Specifically, in the practical application process, in order to verify the time-comparably corrected ground surface temperature data, the time-comparably corrected ground surface temperature data and the GOES-R16 stationary satellite ground surface temperature data are compared and analyzed, and GOES-R16 stationary satellite ground surface temperature data (spatial resolution is 0.02 ° and temporal resolution is 5 minutes or 1 hour) of 1 month in 2020 to 12 months in 2020 are used.
The cross-validation analysis process is as follows:
(1) Acquiring GOES-R16 stationary satellite ground surface temperature data with uniform observation time (reference time 13:30 local solar time) through a time interpolation algorithm, and taking the data as reference data of cross verification;
(2) In order to minimize the difference between MODIS data and GOES-R16 ground surface temperature data caused by angle effect, pixel pairs with observed zenith angle VZA (Viewing Zenith Angle, abbreviated as VZA) angle difference smaller than 15 degrees and VZA angles smaller than 50 degrees are used, and a bilinear method is used for converging the MODIS ground surface temperature data corrected by time comparability from 0.01 degrees to 0.02 degrees so that the resolution ratio is kept consistent;
(3) Cross-validation was performed with GOES-R16 stationary satellite earth temperature. As shown in FIG. 3, the data sequences shown in the 4 observation date-time comparability correction front and rear MODIS ground surface temperatures and time interpolation are 2020009, 2020114, 2020274 and 2020297, the data sequences shown in FIG. 3 are p1-p4, the abscissa is the original MODIS ground surface temperature (unit: K), the ordinate is the time-consistent static satellite ground surface temperature (unit: K), the abscissa is the comparability corrected ground surface temperature (unit: K), the ordinate is the GOES-R16 static satellite ground surface temperature (unit: K), and the time difference is in the range of 0.5-1.0 hours. Let the number of samples be N. In h1 of fig. 3, n=733, rmse=2.33, bias= -2.08, r=0.90; in p1 of fig. 3, n=733, rmse=1.22, bias= -0.69, r=0.91; in h2 of fig. 3, n=1893, rmse=3.58, bias= -3.47, r=0.94; in p2 of fig. 3, n=1893, rmse=2.02, bias= -1.81, r=0.94; in h3 of fig. 3, n=1369, rmse=2.31, bias= -2.12, r=0.83; in p3 of fig. 3, n=1369, rmse=0.95, bias= -0.32, r=0.84; in h4 of fig. 3, n=929, rmse=2.69, bias= -2.02, r=0.70; in p4 of fig. 3, n=929, rmse=1.95, bias=0.72, r=0.68.
(4) In the case of the date sequences 2020009, 2020114, 2020274 and 2020297 shown in FIG. 4, the distribution histograms of the difference between the ground surface temperature before correction (ground surface temperature before correction minus the time-coincident ground surface temperature of the stationary GOES-R16 satellite, in K) and the ground surface temperature after correction (ground surface temperature after correction minus the time-coincident ground surface temperature of the stationary GOES-R16, in K) are in the range of 0.5-1.0 hours; the ordinate is the frequency (unit:%), the frequency of occurrence of each column value is a percentage of the total frequency, MEAN is the average value, stdev is the variance; the date sequence is 2020009, the ground surface temperature difference value MEAN before correction is= -2.08, the ground surface temperature difference value variance Stdev=1.03 before correction, the ground surface temperature difference value MEAN after correction is= -0.69, and the ground surface temperature difference value variance Stdev=1.01 after correction; the date sequence is 2020114, the ground surface temperature difference value MEAN before correction is= -3.47, the ground surface temperature difference value variance Stdev=0.86 before correction, the ground surface temperature difference value MEAN after correction is= -1.81, and the ground surface temperature difference value variance Stdev=0.89 after correction; the date sequence is 2020297, the ground surface temperature difference value MEAN before correction is= -2.12, the ground surface temperature difference value variance Stdev=0.91 before correction, the ground surface temperature difference value MEAN after correction is= -0.32, and the ground surface temperature difference value variance Stdev=0.88 after correction; the date sequence is 2020297, the average value mean= -2.02 of the surface temperature difference before correction, the variance stdev=1.77 of the surface temperature difference before correction, the average value mean=0.72 of the surface temperature difference after correction, and the variance stdev=1.81 of the surface temperature difference after correction.
(5) As shown in fig. 5, the statistical indexes of the verification results of the surface temperature and the original surface temperature after the time comparability correction of different vegetation types are shown in fig. 5 (a), the Root Mean Square Error (RMSE) of the surface temperature is shown in fig. 5 (b), the deviation Bias of the surface temperature is shown in fig. 5 (b), and the time difference is in the range of 0.5-1.0 hour. Vegetation type: 1 is evergreen conifer forest; 2 is evergreen broad-leaved forest; 3 is fallen needle leaf forest; 4 is deciduous broad-leaved forest; 5 is a mixed forest; 6 is a closed shrub forest; 7 is an open shrub; 8 is a multi-tree grassland; 9 is a thin tree grassland; 10 is grassland; 11 is a permanent wetland; 12 is farmland; 13 is town and building; 14 is a mixture of farmland and natural vegetation; 15 is ice or snow; 16 is bare land or low vegetation coverage.
By cross-validation of the GOES-R16 stationary satellite dataset consistent with time after time interpolation, it can be found that the time comparability is corrected more strongly than the stationary satellite ground surface temperature data consistent with time before correction.
The beneficial effects of the invention are as follows:
(1) The process provided by the invention belongs to a general algorithm, and is suitable for all the sunny state polar orbit satellite transit time periods in the morning and afternoon, such as 9:00-12:00 or 12:00-15:00;
(2) The general algorithm for the earth surface temperature time change rate of the polar orbit satellite does not need excessive input parameters, and the model is simple and easy to operate;
(3) The time change rate model provided by the invention can accurately capture the change characteristics of the clear sky surface temperature along with time in the transit time period of the polar orbit satellite, and has higher simulation precision;
(4) The process provided by the invention does not need to consider weather conditions except for the transit time of the polar orbit satellite in the application process, and has wider application range;
(5) The spatial resolution of the training data set and the spatial resolution of the prediction data set, which are applied in the establishment process, of the time comparability correction parameter prediction model are similar, so that the influence of scale effect on the result is reduced as much as possible;
(6) According to the method, the time effect of the earth surface temperature under different earth surface coverage and different seasons can be effectively relieved, and the clear sky earth surface temperature data product with time comparability correction can be obtained.
Example 2
Based on the same principle as the method shown in embodiment 1 of the present invention, as shown in fig. 6, there is also provided a polar orbit satellite earth surface temperature time comparability correction general system in the embodiment of the present invention, including a first acquisition unit, a first construction unit, a first processing unit, a second construction unit, a model training unit, a second processing unit, and a third processing unit;
The first acquisition unit is used for acquiring surface temperature data of a clear sky state of a transit period of the polar orbit satellite, and comprises surface temperature data of an observation station and a static satellite;
the first construction unit is used for analyzing time change characteristics of surface temperature data of a clear sky state in a transit period of the polar orbit satellite and constructing a surface temperature time change rate model;
the first processing unit is used for obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in a clear sky state and the earth surface temperature time change rate model;
the second construction unit is used for acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameter and constructing a prediction model of the time comparability correction parameter;
the model training unit is used for carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
the second processing unit is used for acquiring the actual observation area and time of the polar orbit satellite data and determining the actual time comparability correction parameters of the polar orbit satellite ground surface temperature according to the trained time comparability correction parameter prediction model;
the third processing unit is used for determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
Optionally, obtaining surface temperature data of a clear sky state of a transit period of the polar orbit satellite includes: long wave radiation data of a clear sky state observed by an observation station is collected, surface temperature data of the observation station is obtained through calculation based on a Stefan Boltzmann equation, surface temperature data of a stationary satellite is collected, and surface temperature data of the clear sky state of the station and the stationary satellite in a polar orbit satellite transit period are screened.
Optionally, obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in the clear sky state and the earth surface temperature time change rate model includes:
and (3) obtaining the earth surface temperature data of the stationary satellite at the reference moment by using earth surface temperature data of a clear sky state of a plurality of observation times before and after the reference time and adopting a time interpolation method, and fitting the relationship between the earth surface temperature of the stationary satellite in the clear sky state of the transit period of the polar orbit satellite and the observation time according to an earth surface temperature time change rate model to obtain a data set of a plurality of time comparability correction parameters.
Optionally, the effective feature variable corresponding to the time comparability correction parameter in the data set of the time comparability correction parameter is used as an independent variable of the prediction model, the time comparability correction parameter is used as a dependent variable of the prediction model, a training data set is constructed by using the independent variable and the dependent variable, and the integrated regression model training is performed on the prediction model.
Optionally, the effective characteristic variables include observation date, short wave wide band albedo, atmospheric precipitation, solar altitude, latitude, normalized vegetation index, digital ground elevation, gradient and slope.
Optionally, the polar orbit satellite earth surface temperature time comparability correction universal system further comprises a cross verification unit, wherein the cross verification unit is used for verifying the earth surface temperature after polar orbit satellite time comparability correction by adopting a cross verification method. The method adopting cross validation comprises the following steps:
calculating ground surface temperature data of a stationary satellite with unified local sun time by a time interpolation method, and taking the ground surface temperature data as reference data for cross verification;
resampling and reprojection preprocessing are carried out on the reference data;
based on satellite observation angle constraint conditions, screening pixel pairs for comparison analysis after pretreatment;
and performing cross-validation analysis on the earth surface temperature corrected for the time comparability of the polar orbit satellite by using the screened pixels according to different time difference ranges.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The general method for correcting the earth surface temperature time comparability of the polar orbit satellite is characterized by comprising the following steps:
acquiring earth surface temperature data of a clear sky state of a polar orbit satellite in a transit period, wherein the earth surface temperature data comprises earth surface temperature data of an observation station and a static satellite;
analyzing time change characteristics of the earth surface temperature data in a clear sky state in a transit period of a polar orbit satellite, and constructing an earth surface temperature time change rate model;
obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in a clear sky state and the earth surface temperature time change rate model;
acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameter, and constructing a prediction model of the time comparability correction parameter;
carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
acquiring the actual observation area and time of the polar orbit satellite data, and determining the actual time comparability correction parameters of the polar orbit satellite ground surface temperature according to the trained time comparability correction parameter prediction model;
and determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
2. The general method for correcting the earth surface temperature and time comparability of the polar orbit satellite according to claim 1, wherein the step of obtaining the earth surface temperature data of the clear sky state of the transit time period of the polar orbit satellite comprises the following steps: and collecting long wave radiation data observed by a site, calculating to obtain the surface temperature data of the observation site based on a Stefan Boltzmann equation, collecting the surface temperature data of a stationary satellite, and screening the surface temperature data of a clear sky state of the observation site and the stationary satellite in a polar orbit satellite transit period.
3. The general method for correcting earth surface temperature time comparability of polar orbit satellite according to claim 1, wherein obtaining a data set of time comparability correction parameters according to stationary satellite earth surface temperature data in clear sky state and the earth surface temperature time change rate model comprises:
and acquiring the earth surface temperature data of the stationary satellite at the reference moment by using the earth surface temperature data of the clear sky state of a plurality of observation times before and after the reference time and adopting a time interpolation method, and fitting the relationship between the earth surface temperature and the observation time of the stationary satellite in the clear sky state of the polar orbit satellite transit time period according to the earth surface temperature time change rate model to obtain a data set of the time comparability correction parameter.
4. The general method for time-comparability correction of earth surface temperature of polar orbit satellite according to claim 1, wherein the effective feature variable corresponding to the time-comparability correction parameter in the data set of time-comparability correction parameters is used as an independent variable of the prediction model, the time-comparability correction parameter is used as a dependent variable of the prediction model, a training data set is constructed by using the independent variable and the dependent variable, and integrated regression model training is performed on the prediction model.
5. The general method for polar orbit satellite ground surface temperature time comparability correction according to claim 4, wherein the effective characteristic variables comprise observation date, short wave wide band albedo, atmospheric precipitation, solar altitude, latitude, normalized vegetation index, digital ground elevation, gradient and slope.
6. The general method for correcting for earth surface temperature time comparability of a polar orbit satellite according to claim 1, further comprising the step of verifying the earth surface temperature after the polar orbit satellite time comparability correction by adopting a cross verification method, comprising the steps of:
calculating the surface temperature data of the stationary satellite with unified local sun through a time interpolation method as reference data for cross validation;
Resampling and reprojection preprocessing are carried out on the reference data;
based on satellite observation angle constraint conditions, screening pixel pairs for comparison analysis after pretreatment;
and for different time difference ranges, performing cross-validation analysis on the earth surface temperature corrected for the polar orbit satellite time comparability by using the screened pixels.
7. The polar orbit satellite earth surface temperature time comparability correction universal system is characterized by comprising a first acquisition unit, a first construction unit, a first processing unit, a second construction unit, a model training unit, a second processing unit and a third processing unit;
the first acquisition unit is used for acquiring surface temperature data of a clear sky state of a polar orbit satellite in a transit period and comprises surface temperature data of an observation station and a stationary satellite;
the first construction unit is used for analyzing the time change characteristics of the earth surface temperature data in the clear sky state of the transit time period of the polar orbit satellite and constructing an earth surface temperature time change rate model;
the first processing unit is used for obtaining a data set of time comparability correction parameters according to the earth surface temperature data of the stationary satellite in a clear sky state and the earth surface temperature time change rate model;
The second construction unit is used for acquiring a corresponding effective characteristic variable data set according to the data set of the time comparability correction parameter and constructing a prediction model of the time comparability correction parameter;
the model training unit is used for carrying out integrated regression model training on the prediction model to obtain a trained time comparability correction parameter prediction model;
the second processing unit is used for acquiring the actual observation area and time of the polar orbit satellite data and determining the actual time comparability correction parameters of the polar orbit satellite ground surface temperature according to the trained time comparability correction parameter prediction model;
the third processing unit is used for determining the earth surface temperature after the polar orbit satellite time comparability correction according to the earth surface temperature time change rate model, the earth surface temperature actually observed by the polar orbit satellite, the actual observation time of the polar orbit satellite and the actual time comparability correction parameter.
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