FY4A AGRI surface temperature-based mountain air temperature direct reduction rate space-time distribution estimation method

Abstract

The invention discloses an FY4A AGRI surface temperature-based mountain air temperature direct reduction rate spatial-temporal distribution estimation method, which comprises the following steps of: acquiring FY4A AGRI thermal infrared remote sensing surface temperature and influence factor data thereof, and preprocessing to obtain data under a 4km resolution ratio; performing spatial dimension reconstruction on the surface temperature missing data by adopting a random forest regression algorithm, and performing time dimension smoothing on a spatial dimension reconstruction result by adopting an S-G filter to obtain a surface temperature space-time dimension reconstruction result; constructing a surface temperature downscaling model by using a random forest regression algorithm, and reducing the resolution ratio of the surface temperature from 4km to 1km; the influence of the altitude on the surface temperature is separated, and the standardized surface temperature is calculated; and establishing a linear regression relationship between the standardized surface temperature and the altitude by adopting a sliding window method, and calculating a regression coefficient, so as to obtain an air temperature direct reduction rate. And necessary data and technical support are provided for related research fields such as weather forecast and climate model application.

Description

Technical Field

[0001] The invention relates to a method for estimating the temporal and spatial distribution of temperature lapse rate in a mountain area based on FY4A AGRI surface temperature, and belongs to the technical field of temperature lapse rate estimation. Background Art

[0002] Temperature Lapse Rate (TLR) refers to the rate at which atmospheric temperature changes with altitude, also known as the vertical temperature gradient. In meteorology, TLR can be used to assess the stability of the atmosphere, plays an important role in the generation, development and change of atmospheric motion, plays a key role in estimating the spatial distribution of temperature and correcting temperature forecasts, and is also one of the important indicators for studying climate change and verifying climate models.

[0003] The temperature lapse rate is closely related to many factors, such as terrain undulation, surface type and coverage, and local solar incidence angle, and varies dramatically over time and space. It is impossible to accurately express the spatiotemporal variation characteristics of the temperature lapse rate in complex areas such as mountainous areas, which affects the accurate correction of mountain temperature forecasts and other related fields. Therefore, it is of great significance to obtain the spatiotemporally continuous near-surface temperature lapse rate.

[0004] The most commonly used method is to calculate the temperature lapse rate using meteorological station observation data. This method uses temperature observations and heights at multiple stations at different altitudes for regression fitting to estimate the temperature lapse rate. However, the calculation of the temperature lapse rate using station observation data is affected by many factors, including the density of observation station layout, the geographical area covered by the station, and the spatial representativeness of the station. In addition, only one temperature lapse rate data can usually be calculated in one area. Therefore, this method is not suitable for obtaining the spatial distribution of temperature lapse rate in mountainous areas with complex terrain and underlying surface conditions.

[0005] Remote sensing technology has the advantage of continuous spatial monitoring. Due to the high correlation between surface temperature and air temperature, the instantaneous surface temperature obtained by thermal infrared remote sensing inversion is used to establish a regression relationship between it and the digital elevation model (DEM), which can indirectly express the TLR characteristics. However, the commonly used thermal infrared remote sensing data is limited by factors such as the satellite regression cycle and weather conditions, and cannot provide surface temperature data with high spatial resolution and strong temporal continuity, which affects the estimation of the temperature lapse rate with high temporal and spatial resolution.

[0006] A comprehensive analysis of the current status of research at home and abroad shows that the research on using remote sensing technology to estimate the temperature lapse rate is still in its infancy. Most studies estimate the temperature lapse rate as the average value over a period of time in a region. Some studies only use clear sky remote sensing images to estimate the spatial distribution of TLR, and the research on the continuous spatiotemporal distribution of TLR needs to be further carried out. So far, there has been no research on the estimation of temperature lapse rate with high spatiotemporal resolution all day and all weather at home and abroad. Therefore, how to use the characteristics and advantages of remote sensing technology to achieve continuous temporal and spatial estimation of temperature lapse rate is an urgent need to meet the needs of meteorological forecasting and series applications in mountainous areas with large terrain fluctuations. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI surface temperature, which can obtain hourly temperature lapse rate data with a spatial resolution of 1 km, and provide necessary data and technical support for related fields such as weather forecasting and climate model application.

[0008] The present invention adopts the following technical solutions to solve the above technical problems: A method for estimating the temporal and spatial distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI land surface temperature includes the following steps: Step 1: Obtain the FY4A AGRI thermal infrared remote sensing surface temperature data of the mountain area to be estimated, as well as the surface temperature influencing factor data, and preprocess all the data to obtain data at a spatial resolution of 4 km; Step 2: Based on all data at a spatial resolution of 4 km, the random forest regression algorithm is used to reconstruct the missing data of surface temperature under the influence of cloud cover in spatial dimension, and the spatial dimension reconstruction result of the surface temperature is obtained. The SG filter is used to smooth the spatial dimension reconstruction result in time dimension, and the spatiotemporal dimension reconstruction result of the surface temperature is obtained. Step 3: Based on step 2, a random forest regression algorithm is used to construct a surface temperature downscaling model to reduce the surface temperature from 4 km spatial resolution to 1 km spatial resolution, and obtain the downscaled 1 km spatial resolution surface temperature; Step 4: Based on the downscaled 1 km spatial resolution surface temperature, the random forest regression algorithm is used to separate the effect of altitude on the surface temperature, and the standardized surface temperature is calculated. The sliding window method is used to establish a linear regression relationship between the standardized surface temperature and altitude, and the regression coefficient is calculated to obtain the temperature lapse rate.

[0009] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects: In view of the current situation that there is a lack of all-day and all-weather high-temporal and spatial resolution temperature lapse rate estimation methods at home and abroad, this paper innovatively proposes a method for estimating the temporal and spatial distribution of temperature lapse rate in mountainous areas based on FY4A AGRI surface temperature by constructing a hierarchical progressive technical system of "reconstruction of missing surface temperature data, downscaling of surface temperature, and estimation of spatial distribution of temperature lapse rate". This method aims to generate hourly temperature lapse rate data with a spatial resolution of 1km. The surface cumulative net radiation flux reanalysis data is added to the influencing factors for the first time, and the reconstruction of the missing reconstruction data of FY4A AGRI hourly surface temperature is realized. Then, the FY4A AGRI surface temperature with a resolution of 4km is downscaled to 1km based on the machine learning method, and finally the sliding window method is used to carry out the estimation of the spatial distribution of temperature lapse rate. The present invention can effectively obtain the temporal and spatial distribution of temperature lapse rate through remote sensing means, and can provide technical support for related research such as temperature forecast correction and climate model application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of the temporal and spatial distribution estimation method of the mountain temperature lapse rate based on the FY4A AGRI surface temperature of the present invention; Figure 2 is the FY4A surface temperature reconstruction result (4km resolution) of Chongli District at two times on February 20 in the embodiment of the present invention, where (a) is 10:00 and (b) is 22:00; Figure 3 It is the downscaling result of the surface temperature at two times on February 20 in Chongli District of the embodiment of the present invention (1 km resolution), where (a) is 10:00 and (b) is 22:00; Figure 4 1 is the estimation result of the temperature lapse rate at two times on February 20 in Chongli District according to the embodiment of the present invention, where (a) is 10:00 and (b) is 22:00. DETAILED DESCRIPTION

[0011] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0012] The FY4A satellite is China's second-generation geostationary meteorological satellite series. Its multi-channel scanning radiometric imager (AGRI) has 14 channels and can generate a full-disk observation image every 15 minutes, with high temporal resolution and high precision. The surface temperature inverted by the AGRI sensor has a high temporal resolution. After spatial downscaling, it has the basis for estimating the temperature lapse rate with high temporal and spatial resolution.

[0013] Aiming at the problem of estimating the spatiotemporal distribution of the all-day and all-weather temperature lapse rate TLR, this paper proposes a method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI surface temperature. This method is based on the reconstruction of the missing FY4A AGRI surface temperature data, and downscales it to 1km based on the machine learning method. Then, the sliding window method is used to carry out the temperature lapse rate estimation research, and the temperature lapse rate data with hourly and 1km spatial resolution are obtained. Figure 1 The specific plan is as follows: 1) Obtain FY4A AGRI thermal infrared remote sensing surface temperature data and data related to surface temperature influencing factors, and perform necessary data preprocessing.

[0014] Firstly, the acquired FY4A AGRI surface temperature data and surface temperature influencing factor data, including the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Climate Fifth Generation Atmospheric Reanalysis (ERA5) data, Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model (DEM) data, were converted to ensure the uniformity of data format; secondly, the FY4A AGRI data were geometrically corrected, and the surface temperature data and its influencing factor data were clipped using the study area vector file, and then the clipped data were spatially registered to ensure the consistency of spatial range; The cropped data were resampled to 4 km using the aggregation averaging method to ensure consistent spatial resolution of the data.

[0015] 2) Using FY4A AGRI land surface temperature remote sensing data, the spatial dimension of the land surface temperature is first reconstructed based on the random forest algorithm. Secondly, the SG filter is used to smooth the temporal dimension of each pixel for the preliminary reconstruction result of the spatial dimension of the land surface temperature to achieve the spatiotemporal reconstruction of the land surface temperature.

[0016] The core idea of ​​land surface temperature reconstruction is to explore the relationship between LST and the predicted variables. Due to the complex relationship between LST and its influencing factors, it is difficult to construct a suitable numerical model to reflect this relationship. Therefore, the random forest regression model in machine learning is used to describe the nonlinear relationship between LST and the predicted variables, and to construct a LST spatial reconstruction model. At a spatial resolution of 4 km, the surface temperature of the FY4A clear sky pixel is used as the dependent variable, and the surface cumulative net radiation flux, altitude, slope, NDVI and NDWI of the corresponding pixel position are used as independent variables. The reconstruction model is trained using the random forest regression algorithm to establish a nonlinear regression relationship between the surface temperature and the independent variables. The regression model can be expressed by formula (1): (1) In the formula, represents the surface temperature; represents the accumulated net radiation flux on the surface; stands for Normalized Difference Vegetation Index; stands for Normalized Difference Water Index; Represents the surface elevation; represents the surface slope; Represents a random forest regression model.

[0017] After the LST spatial reconstruction model training is completed, the surface cumulative net radiation flux, altitude, slope, NDVI and NDWI values ​​corresponding to the missing pixels of surface temperature under the influence of cloud cover are input into the model as independent variables, and the corresponding surface temperature under the cloud is calculated to fill the missing values.

[0018] LST reconstruction based on random forest algorithm is a spatial dimension LST reconstruction method. Although it can fill in the missing values, this method only considers the spatial distribution characteristics of LST, but does not consider the temporal variation characteristics of LST, making it difficult to ensure the continuity and variation characteristics of the reconstructed LST in the temporal dimension. In order to ensure that the reconstruction result is the true surface temperature under the cloud, the preliminary reconstruction result of LST needs to be smoothed in the temporal dimension to deal with the abnormal values ​​of the spatial dimension reconstruction. The Savitzky-Golay (SG) filter is a filtering method based on local polynomial least squares fitting in the time domain. The surface temperature reconstruction results are statistically analyzed in the time dimension and smoothed using SG filtering, which can keep the shape of the surface temperature time series unchanged while filtering out the noise. The SG filtering process can be expressed by formula (2): (2) In the formula, Reconstruct the dataset for smoothed land surface temperatures; is the reconstructed surface temperature data; is the filter coefficient; is the number of data in the sliding window ( ), is the window width.

[0019] 3) Using the reconstructed FY4A AGRI seamless surface temperature data, a relationship model between surface temperature and its influencing factors was constructed based on the random forest algorithm to downscale the surface temperature data.

[0020] When establishing a land surface temperature (LST) downscaling model, it is necessary to consider the complex relationship between land surface temperature and multiple influencing factors such as solar radiation (SR), vegetation index (NDVI), and surface albedo. The main steps of downscaling model establishment are as follows: Using 4km resolution data as the training set, we define the loss function (such as root mean square error RMSE) and select a suitable optimization algorithm to train the surface temperature-influencing factor relationship model. The model form is as follows: (3) in, is the surface temperature at 4 km resolution, It is the surface and terrain influencing factors at a resolution of 4km.

[0021] The influencing factor data with a resolution of 1 km are input into the trained model, and residual correction is performed to obtain the surface temperature at a resolution of 1 km.

[0022] The surface temperature downscaling formula can be expressed as: (4) in, is the downscaled 1km resolution surface temperature, is the factor affecting the 1km resolution, It is the mapping function obtained by the corresponding data with a resolution of 4 km; is the fitting residual of formula (3), which is resampled to 1 km spatial resolution by bilinear interpolation method.

[0023] 4) Using the downscaled FY4A AGRI seamless surface temperature data, the random forest model is first used to separate the effect of altitude on the surface temperature, and the surface temperature other than the surface characteristics based on altitude is obtained, so as to calculate the standardized surface temperature NLST; secondly, the sliding window method is used to use windows of different sizes (such as The linear regression relationship between the standardized surface temperature and the altitude is established, and the regression coefficient is calculated to obtain the temperature lapse rate result; finally, the window with the best effect is selected to obtain the temperature lapse rate with the best accuracy.

[0024] First, at a spatial resolution of 1 km, a relationship model between AGRI LST and surface characteristic parameters was established: (5) in, is the downscaled 1km resolution surface temperature, It is a nonlinear relationship model. (surface slope), (position), (Topographic Position Index), (Normalized Difference Vegetation Index), (local angle of incidence of the sun) and (Altitude above sea level) is the surface feature that affects LST at a spatial resolution of 1 km.

[0025] Secondly, separate the effect of altitude on land surface temperature and obtain the standardized land surface temperature NLST: (6) (7) in, It is the LST model value other than the altitude feature calculated by the random forest regression model. is the LST after removing the effect of altitude, It is a nonlinear fitting model.

[0026] Finally, using the sliding window method, in sliding windows of different sizes (such as ) A linear regression model between LST and elevation was established. The slope of the regression model was used as the temperature lapse rate of the central pixel in the window and compared with the TLR calculated from the measured data to determine the optimal sliding window size, and then calculate the spatial distribution of the temperature lapse rate TLR in the study area. The formula is expressed as: (8) In formula (8), is the normalized LST value for each 1 km resolution pixel, and is the regression coefficient, is the estimated TLR of the center pixel of the moving window.

[0027] The TLR was calculated using the station observation temperature data to verify the results of different sliding window calculations. The sliding window with the highest accuracy is taken, and the TLR value of each pixel position in the entire area is obtained by sliding calculation. Example

[0028] Based on the reconstruction of the missing reconstruction data of FY4A AGRI surface temperature, the present invention downscales it to 1km based on the machine learning method, and then uses the sliding window method to carry out the temperature lapse rate estimation research, and finally obtains the temperature lapse rate data with hourly and 1km spatial resolution. The present invention has been applied in Chongli District, Hebei Province, China, and the results show that the method proposed in the present invention can obtain the high-precision temporal and spatial distribution of temperature lapse rate.

[0029] 1) Experimental area selection: Chongli District, Zhangjiakou City, Hebei Province was selected as the experimental area, with a total area of ​​about 2,334 square kilometers, between the east longitude and to ,north latitude to , belonging to the semi-arid monsoon climate zone. Chongli District is located in the northwest of Hebei Province, in the transition zone between the Inner Mongolia Plateau and the North China Plain, backed by the Inner Mongolia grassland to the north and adjacent to the central urban area of ​​Zhangjiakou City to the south. 80% of Chongli District is mountainous, with a forest coverage rate of 52.38%. The territory is the northwestern mountainous area of ​​Hebei, mostly in the northeast-southwest and east-west directions. The altitude range of Chongli District is 813-2174 m, with a maximum altitude difference of 1361 m and an average altitude of 1485 m. Its geomorphic features are "mountains connected to mountains, continuous, gullies within gullies, countless". Due to the undulating terrain in the area, it is an ideal area for verifying the temperature lapse rate estimation method.

[0030] 2) Considering the reconstruction principle of surface temperature, the relationship model between surface temperature and downscaling factors, and the influencing factors and temporal and spatial variation characteristics of the temperature lapse rate, surface temperature data, as well as solar radiation (SR), vegetation index (NDVI), surface albedo, and topography were selected as independent variables affecting surface temperature. FY4A AGRI surface temperature data were downloaded from the Fengyun Satellite Remote Sensing Data Service Network. MODIS surface temperature data and surface reflectivity data for the same period were downloaded from the Google Earth Engine official website. ASTER DEM version 3 data with a resolution of 30 meters was downloaded from the ASTER Global Digital Elevation Model website. Hourly temperature observation data from 74 automatic weather stations in Chongli District were collected. With the help of ENVI 5.3 software and ArcMap 10.8 software, the collected data were subjected to image resampling, image cropping, spatial registration, and other operations to obtain various remote sensing images required for temperature lapse rate estimation.

[0031] 3) Reconstruction of missing data of FY4A AGRI surface temperature: At a spatial resolution of 4 km, the surface temperature of clear sky pixels in the study area was used as the dependent variable, and the cumulative net radiation flux, altitude, slope, NDVI and NDWI values ​​of the corresponding pixels after preprocessing were used as independent variables. The number of decision trees in the hyperparameters of the random forest algorithm was set to 500, the minimum leaf tree was set to 10, and the training set partition ratio was set to 80%. The random forest surface temperature reconstruction model (Equation (1)) was obtained by training. Then, various radiation factors, spectral factors and terrain factor parameters corresponding to cloud-covered pixels were input to obtain the surface temperature values ​​of the corresponding cloud-covered pixels. Since the reconstruction results of the random forest algorithm only consider the influence of the spatial dimension, there may be outliers. Therefore, the SG filter is used to process the outliers of the reconstruction results of cloud-covered pixels in the time dimension. First, the reconstruction results of each pixel are statistically analyzed according to the time series, and then each pixel is smoothed in the time dimension using equation (2) to process the surface temperature anomalies in the reconstruction results. Figure 2 (a) and (b) are the reconstruction results of FY4A surface temperature at 10:00 and 22:00, respectively.

[0032] 4) FY4A AGRI surface temperature downscaling: At a spatial resolution of 4 km, the reconstructed surface temperature of the study area was used as the dependent variable, and the surface temperature and various influencing factors such as solar radiation (SR), vegetation index (NDVI), and surface albedo (Albedo) were used as independent variables. The number of decision trees in the hyperparameters of the random forest algorithm was set to 500, the minimum leaf tree was set to 10, and the training set partition ratio was set to 80%. The random forest surface temperature-influencing factor relationship model was obtained through training (Equation (3)). Then, the influencing factor data with a resolution of 1 km were input into the trained model, and residual correction was performed (Equation (4)) to obtain the surface temperature at a resolution of 1 km. Figure 3 (a) and (b) are the surface temperature downscaling results at 10:00 and 22:00, respectively.

[0033] 5) Estimation of the temporal and spatial distribution of the temperature lapse rate: First, the downscaled surface temperature at 1 km was used as the dependent variable, and Slope, Aspect, TPI (Topographic Position Index), NDVI (Normalized Difference Vegetation Index), (local solar incidence angle) and DEM (digital elevation model) as independent variables, the number of decision trees in the hyperparameters of the random forest algorithm was set to 500, the minimum leaf tree was set to 10, and the training set division ratio was set to 80%. The relationship model between AGRI LST and surface characteristics was established (Equation (5)). Secondly, the influence of influencing parameters other than DEM on surface temperature was calculated (Equation (6)) to obtain the standardized LST (Equation (7)). Finally, the sliding window method was used to calculate the influence of influencing parameters other than DEM on surface temperature within sliding windows of different sizes (such as ) A linear regression model between LST and elevation was established (Equation (8)). The slope of the regression model was used as the temperature lapse rate of the central pixel in the window. By comparing it with the TLR calculated from the measured data, the optimal sliding window size was determined, and then the spatial distribution of the temperature lapse rate TLR in the study area was calculated.

[0034] 6) Experimental results analysis: The estimated temperature lapse rate of Chongli District at all hours on February 20 was compared with the temperature lapse rate calculated from the measured temperature of representative ground meteorological stations, and the determination coefficient R was calculated. 2 , mean absolute error MAE and root mean square error RMSE. After experimental comparison, the estimated temperature lapse rate results are more accurate when the window size is 13×13. Taking 10:00 and 22:00 on February 20 as an example, Figure 4 The results of the estimated temperature lapse rate at two moments with a window size of 13×13 are given. The temperature lapse rate calculated using the station observed temperature is used as a reference. The root mean square error of the temperature lapse rate TLR at 10:00 and 22:00 decreased by 15.3% and 34.2% respectively. Figure 4(a) At 10 a.m., the temperature lapse rate in Chongli District is generally high in the east and low in the west. The central and western parts are valley areas. At this time, the solar radiation is strong, and the radiation heating in the valley area is more obvious, resulting in a faster temperature rise. At this time, the ground is heated by solar radiation, and the heat begins to transfer to the atmosphere. However, since heat transfer takes time, the air temperature near the ground rises rapidly, and as the altitude increases, the air temperature drops, forming a lower temperature lapse rate. According to the results of the present invention, the temperature lapse rates in the central and western parts of Chongli District at this moment are distributed in arrive The eastern region is distributed between arrive Between. According to Figure 4 (b), at 22:00, the ground loses the heat of solar radiation and begins to release long-wave radiation to the atmosphere. The ground gradually cools through radiation and the temperature drops. Since the radiation cooling effect in the valley area is weak at night, the air temperature near the ground drops slowly, and as the altitude increases, the air temperature drops faster, so the temperature lapse rate is relatively high. According to the results of the present invention, the temperature lapse rate in the western part of Chongli District at 22:00 is distributed in arrive The eastern region is distributed between arrive It can be seen that the temperature lapse rate in most areas is The fixed values ​​of the data are quite different, and even some areas have temperature inversion, so it cannot be used. A fixed value is used to replace the lapse rate of temperature in mountainous areas.

[0035] 7) Experimental conclusion: The results of the present invention fully demonstrate that the method based on FY4A AGRI surface temperature reconstruction, surface temperature downscaling, and sliding window estimation of temperature lapse rate can effectively obtain hourly, 1km resolution temperature lapse rate data with high accuracy, which can provide technical support for related research such as weather forecasting and climate model application.

[0036] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned method for estimating the spatiotemporal distribution of the lapse rate of air temperature in mountainous areas based on the FY4A AGRI surface temperature are implemented.

[0037] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the aforementioned method for estimating the spatiotemporal distribution of the mountain air temperature lapse rate based on the FY4A AGRI surface temperature are implemented.

[0038] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0039] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0042] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI surface temperature, characterized in that: The steps include: Step 1: Obtain the FY4A AGRI thermal infrared remote sensing surface temperature data of the mountain area to be estimated, as well as the surface temperature influencing factor data, and preprocess all the data to obtain data at a spatial resolution of 4 km; Step 2: Based on all data at a spatial resolution of 4 km, the random forest regression algorithm is used to reconstruct the missing data of surface temperature under the influence of cloud cover in spatial dimension, and the spatial dimension reconstruction result of the surface temperature is obtained. The SG filter is used to smooth the spatial dimension reconstruction result in time dimension, and the spatiotemporal dimension reconstruction result of the surface temperature is obtained. Step 3: Based on step 2, a random forest regression algorithm is used to construct a surface temperature downscaling model to reduce the surface temperature from 4 km spatial resolution to 1 km spatial resolution, and obtain the downscaled 1 km spatial resolution surface temperature; Step 4: Based on the downscaled 1 km spatial resolution surface temperature, the random forest regression algorithm is used to separate the effect of altitude on the surface temperature and calculate the standardized surface temperature; The sliding window method was used to establish a linear regression relationship between the standardized surface temperature and altitude, and the regression coefficient was calculated to obtain the temperature lapse rate.

2. The method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI surface temperature according to claim 1 is characterized in that: The specific process of step 1 is as follows: Step 1.1, convert the obtained FY4A AGRI thermal infrared remote sensing surface temperature data of the mountainous area to be estimated, as well as the surface temperature influencing factor data, so that all data formats are unified; the surface temperature influencing factor data include the fifth generation atmospheric reanalysis data of the European Centre for Medium-Range Weather Forecasts, remote sensing data of the Moderate Resolution Imaging Spectrometer, and digital elevation model data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer; Step 1.2, based on step 1.1, geometrically correct the land surface temperature data, clip the land surface temperature data and its influencing factor data, and then spatially register the clipped land surface temperature data and land surface temperature influencing factor data; Step 1.3: Based on step 1.2, the spatially registered surface temperature data and its influencing factor data are resampled to 4 km using the aggregation averaging method to obtain data at a spatial resolution of 4 km.

3. The method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI surface temperature according to claim 1 is characterized in that: The specific process of step 2 is as follows: Step 2.1, based on the surface temperature data and its influencing factor data at a spatial resolution of 4 km obtained by preprocessing in step 1, the surface temperature of the clear sky pixel is used as the dependent variable, and the surface cumulative net radiation flux, altitude, slope, normalized difference vegetation index and normalized difference water index corresponding to the clear sky pixel position are used as independent variables. A nonlinear regression model between the surface temperature and the independent variables is established and trained to obtain a trained surface temperature spatial dimension reconstruction model; Among them, the nonlinear regression model between surface temperature and independent variables is expressed as follows: ， In the formula, represents the surface temperature, represents the accumulated net radiation flux on the surface, represents the Normalized Difference Vegetation Index, represents the normalized difference water index, represents the surface elevation, represents the surface slope, represents the random forest regression model; Step 2.2, the surface cumulative net radiation flux, altitude, slope, normalized difference vegetation index and normalized difference water index corresponding to the missing pixels of surface temperature under the influence of cloud cover are input as independent variables into the trained surface temperature spatial dimension reconstruction model to obtain the surface temperature spatial dimension reconstruction result; Step 2.3: For the spatial dimension reconstruction result of the surface temperature, statistics are performed in the time dimension, and the SG filter is used for smoothing to obtain the spatial and temporal dimension reconstruction result of the surface temperature; the smoothing process of the SG filter is as follows: ， In the formula, Reconstruct the data for the smoothed surface temperature, Reconstruct data for the spatial dimension of land surface temperature, is the filter coefficient, is the number of data in the sliding window, , is the window width.

4. The method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI land surface temperature according to claim 1 is characterized in that: The specific process of step 3 is as follows: Step 3.1: Use the average aggregation method to resample the surface temperature influencing parameters at 1 km spatial resolution to 4 km resolution. The surface temperature influencing parameters include normalized difference vegetation index, surface albedo, solar radiation, altitude and latitude. Based on the relationship between the surface temperature and the influencing parameters at 4 km spatial resolution, use the random forest regression algorithm to construct and train the surface temperature downscaling model to obtain the trained surface temperature downscaling model. The surface temperature downscaling model is expressed as follows: ， In the formula, Represents the surface temperature at 4 km spatial resolution, Represents the normalized difference vegetation index at 4 km spatial resolution, Represents the surface albedo at 4 km spatial resolution, Represents 4km resolution solar radiation, Represents the altitude at 4km resolution, represents 4km spatial resolution latitude, represents the random forest regression model; Step 3.2: Calculate the fitting residual using the predicted values ​​and actual values ​​of the 4km spatial resolution surface temperature model : ， In the formula, Represents the actual value of AGRI surface temperature at 4 km spatial resolution after reconstruction, Represents the surface temperature with a spatial resolution of 4 km predicted by the surface temperature downscaling model; Step 3.3: Input the normalized difference vegetation index, surface albedo, solar radiation, altitude and latitude at a spatial resolution of 1 km into the trained surface temperature downscaling model and use the bilinear interpolation method to convert Resample to 1 km spatial resolution and perform fitting residual correction to obtain the surface temperature at 1 km spatial resolution; the surface temperature at 1 km spatial resolution is expressed as follows: ， In the formula, represents the downscaled 1km spatial resolution surface temperature, Represents the normalized difference vegetation index at 1km spatial resolution, Represents the surface albedo with a spatial resolution of 1 km, Represents solar radiation at 1km spatial resolution, Represents the altitude at 1km spatial resolution, represents 1km spatial resolution latitude, is the fitting residual of 1 km spatial resolution after resampling.

5. The method for estimating the spatiotemporal distribution of the temperature lapse rate in mountainous areas based on the FY4A AGRI land surface temperature according to claim 1 is characterized in that: The specific process of step 4 is as follows: Step 4.1, establish the relationship model between the downscaled 1km spatial resolution surface temperature and surface characteristic parameters: ， In the formula, represents the downscaled 1km spatial resolution surface temperature, Represents the surface slope at 1km spatial resolution, Represents 1km spatial resolution direction, Represents the terrain position index of 1km spatial resolution, Represents the normalized difference vegetation index at 1km spatial resolution, Represents the local solar incidence angle at 1km spatial resolution, Represents the altitude at 1km spatial resolution, represents the random forest regression model; Step 4.2, separate the effect of altitude on surface temperature, establish the relationship model between surface characteristic parameters other than altitude and surface temperature with 1 km spatial resolution, and obtain the standardized surface temperature: ， In the formula, is based on the model value of the surface temperature in addition to the altitude parameter, represents the normalized surface temperature after removing the effect of altitude, represents a nonlinear fitting model; Step 4.3, using the sliding window method, establish a linear regression model between the standardized surface temperature and altitude in sliding windows of different sizes: ， In the formula, and is the regression coefficient, The value of is the estimated value of the temperature lapse rate of the central pixel of the sliding window; Calculate the corresponding sliding windows of different sizes The error between the estimated value of the temperature lapse rate and the actual observed value under sliding windows of different sizes is calculated. The sliding window size corresponding to the minimum error is selected, and the temperature lapse rate of each pixel position in the entire mountainous area is obtained by sliding calculation.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for estimating the spatiotemporal distribution of the lapse rate of air temperature in mountainous areas based on the FY4A AGRI land surface temperature are implemented as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for estimating the spatiotemporal distribution of the lapse rate of air temperature in mountainous areas based on the FY4A AGRI land surface temperature are implemented as described in any one of claims 1 to 5.

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