Atmospheric water vapor content inversion method and device based on Sentinel-2 images

Abstract

The present application provides a method and device for retrieving atmospheric water vapor content based on Sentinel-2 imagery. The method includes: obtaining a first data set and a second data set of a target area, where the first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 imagery data of the target area; preprocessing the second data set to obtain a preprocessed second data set; constructing an inversion model for water vapor content, where the inversion model is obtained by training the first data set as an input variable of an atmospheric radiative transfer model; determining the retrieved water vapor content within the target area, and the retrieved water vapor content is obtained by applying the inversion model to the preprocessed second data set. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the retrieved atmospheric water vapor content is relatively accurate.

Description

Technical Field

[0001] The present application relates to the field of atmospheric remote sensing, and more particularly, to a method, apparatus, and computer-readable storage medium for retrieving atmospheric water vapor content based on Sentinel-2 imagery. Background Art

[0002] Water vapor in the atmosphere is a greenhouse gas and a major factor affecting weather changes; the evaporation process of water vapor absorbs a large amount of energy, and the condensation process of water vapor releases a large amount of energy, which to a certain extent affects the atmospheric temperature change and the movement of the atmosphere. Water vapor is also a key factor in many parameter studies, such as regional radiation budget, global climate change, cloud formation, and hydrological cycle. Therefore, accurately estimating the distribution of water vapor content is of great significance for the effective utilization of water resources and climate research.

[0003] Currently, monitoring of atmospheric water vapor content is carried out using radiosondes or ground-based GNNS network remote sensing atmospheric water vapor monitoring technologies. However, these devices are expensive and have few deployed stations, making it difficult to achieve large-scale and full-coverage spatial mapping of atmospheric water vapor content.

[0004] In recent years, satellite remote sensing imagery has also been used to retrieve water vapor in the atmosphere. Commonly used satellites include the Moderate-resolution Imaging Spectroradiometer (MODIS) launched by NASA in the United States, the Visible and Infrared Radiometer (VIRR) of China's Fengyun series satellites, etc. These satellites can all provide water vapor data with high temporal resolution, but currently the spatial resolution of the water vapor data is low and cannot meet the requirements of high-resolution and fine monitoring. Summary of the Invention

[0005] The main object of the present application is to provide a method, apparatus, and computer-readable storage medium for retrieving atmospheric water vapor content based on Sentinel-2 imagery, so as to solve the problem of low spatial resolution of the monitored water vapor content in the prior art.

[0006] According to one aspect of the embodiments of the present invention, a method for retrieving atmospheric water vapor content based on Sentinel-2 imagery is provided, including: obtaining a first dataset and a second dataset of a target area, where the first dataset is data related to the meteorological parameters of the target area, and the second dataset is Sentinel-2 imagery data of the target area; preprocessing the second dataset to obtain the preprocessed second dataset; constructing an inversion model for the water vapor content, where the inversion model is trained by using the first dataset as an input variable of an atmospheric radiative transfer model; and determining the retrieved water vapor content within the target area, where the retrieved water vapor content is obtained by applying the inversion model to the preprocessed second dataset.

[0007] Optionally, constructing an inversion model for the water vapor content includes: inputting the first dataset into the atmospheric radiative transfer model to determine a first apparent reflectance and a second apparent reflectance, where the first apparent reflectance refers to the apparent reflectance in the water vapor absorption band, and the second apparent reflectance refers to the apparent reflectance in the atmospheric window band; constructing a reflectance relationship based on the first apparent reflectance and the second apparent reflectance; using the reflectance relationship to determine the atmospheric transmittance, where the atmospheric transmittance is the ratio of the electromagnetic radiation flux after atmospheric attenuation to the incident electromagnetic radiation flux; and constructing the inversion model with the atmospheric transmittance as the independent variable.

[0008] Optionally, using the reflectance relationship to determine the atmospheric transmittance includes: using the reflectance relationship: to determine the atmospheric transmittance, where τ represents the atmospheric transmittance, ρ * B9 represents the first apparent reflectance, and ρ * B8A represents the second apparent reflectance.

[0009] Optionally, constructing the inversion model with the atmospheric transmittance as the independent variable includes: obtaining a first fitting coefficient and a second fitting coefficient, where the first fitting coefficient and the second fitting coefficient are determined by numerical fitting of a set of water vapor content and the atmospheric transmittance; and constructing the inversion model: where W represents the water vapor content, α represents the first fitting coefficient, B represents the second fitting coefficient, and τ represents the atmospheric transmittance.

[0010] Optionally, obtaining a first data set and a second data set of the target area includes: calling the first data set stored in the target weather station, where the target weather station is used to monitor the meteorological data of the target area; downloading the second data set from the target database of the target server, where the target database of the target server is used to store the Sentinel-2 image data of the target area.

[0011] Optionally, preprocessing the second data set to obtain a preprocessed second data set includes: adjusting the spatial resolution of different types of data in the second data set to a unified spatial resolution; exporting the second data set in a target format to obtain the preprocessed second data set.

[0012] Optionally, after determining the retrieved water vapor content in the target area, the method further includes: obtaining the actual atmospheric water vapor content, where the actual atmospheric water vapor content is detected by a sensor; verifying the retrieved water vapor content with the actual atmospheric water vapor content.

[0013] Optionally, verifying the retrieved water vapor content with the actual atmospheric water vapor content includes: comparing the actual atmospheric water vapor content with the retrieved water vapor content; determining that the verification result is passed when the difference between the actual atmospheric water vapor content and the retrieved water vapor content is less than or equal to a difference threshold; determining that the verification result is not passed when the difference between the actual atmospheric water vapor content and the retrieved water vapor content is greater than the difference threshold.

[0014] According to another aspect of the embodiments of the present invention, there is also provided an apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images, including: a first obtaining unit, configured to obtain a first data set and a second data set of the target area, where the first data set is data related to the meteorological parameters of the target area, and the second data set is the Sentinel-2 image data of the target area; a processing unit, configured to preprocess the second data set to obtain a preprocessed second data set; a constructing unit, configured to construct an inversion model of the water vapor content, where the inversion model is trained by using the first data set as an input variable of an atmospheric radiative transfer model; a determining unit, configured to determine the retrieved water vapor content in the target area, where the retrieved water vapor content is obtained by applying the inversion model to the preprocessed second data set.

[0015] According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and the program executes any one of the methods described above.

[0016] In an embodiment of the present invention, first, a first data set and a second data set of a target area are obtained. Then, the second data set is preprocessed to obtain a preprocessed second data set. After that, an inversion model of water vapor content is constructed. Finally, the inverted water vapor content in the target area is determined. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the inverted atmospheric water vapor content is relatively accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0018] Figure 1 FIG. shows a schematic flow chart of a method for inverting atmospheric water vapor content based on Sentinel-2 images according to an embodiment of this application;

[0019] Figure 2 FIG. shows a schematic diagram of the relationship between the water vapor content of the atmosphere and the atmospheric transmittance;

[0020] Figure 3 FIG. shows a schematic diagram of the inversion result and the measured value;

[0021] Figure 4 FIG. shows a schematic diagram of the water vapor distribution of an embodiment;

[0022] Figure 5 FIG. shows a schematic structural diagram of a device for inverting atmospheric water vapor content based on Sentinel-2 images according to an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will describe this application in detail with reference to the drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0025] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so as to implement the embodiments of the present application described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element can be directly on the other element, or there can also be intermediate elements. Moreover, in the description and claims, when an element is described as "connected" to another element, the element can be "directly connected" to the other element, or "connected" to the other element through a third element.

[0027] As described in the background art, the spatial resolution of the water vapor content monitored in the prior art is relatively low. To solve the above problems, in an embodiment of the present application, a method, device and computer-readable storage medium for retrieving the atmospheric water vapor content based on Sentinel-2 images are provided.

[0028] Figure 1 It is a flowchart of retrieving the atmospheric water vapor content based on Sentinel-2 images according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0029] Step S101, obtaining a first data set and a second data set of a target area, where the first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area;

[0030] Specifically, Sentinel-2 is a group of satellites launched by the European Space Agency, including two satellites in the same group, 2A launched in 2015 and 2B launched in 2017. The carried Multi-Spectral Instrument (MSI) has an atmospheric window band (B8A, spatial resolution 20m) and a water vapor absorption band (B9, spatial resolution 60m), providing the possibility for large-scale and high-spatial-resolution fine water vapor detection.

[0031] Step S102, preprocessing the second data set to obtain a preprocessed second data set;

[0032] Step S103: Construct an inversion model for water vapor content. The inversion model is obtained by training the first dataset as the input variable of the atmospheric radiative transfer model.

[0033] Step S104: Determine the inverted water vapor content within the target area. The inverted water vapor content is obtained by applying the inversion model to the preprocessed second dataset.

[0034] In the above method, first, the first dataset and the second dataset of the target area are obtained. Then, the second dataset is preprocessed to obtain the preprocessed second dataset. After that, an inversion model for water vapor content is constructed. Finally, the inverted water vapor content within the target area is determined. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the inverted atmospheric water vapor content is relatively accurate.

[0035] In another embodiment of the present application, obtaining the first dataset and the second dataset of the target area includes: calling the first dataset stored in the target weather station, where the target weather station is used to monitor the meteorological data of the target area; downloading the second dataset from the target database of the target server, where the target database of the target server is used to store the Sentinel-2 image data of the target area. In this embodiment, both the first dataset and the second dataset can be directly obtained. The first dataset can be directly obtained from the target weather station, and the second dataset can be directly downloaded from the target database of the target server. Of course, it is not limited to this method, and the first dataset and the second dataset can also be obtained by any other feasible method.

[0036] Specifically, the second dataset can be the L1C-level product of Sentinel-2 downloaded through the Copernicus Science Hub of the European Space Agency.

[0037] In practical applications, Shenmu City, Yulin City, Shaanxi Province can be selected as the target area. Two scenes of images passing by on August 1, 2021 (Sentinel-2A) and August 6, 2021 (Sentinel-2B) are downloaded through the Copernicus Science Hub of the European Space Agency. At the same time as the satellite passes by, GPS receivers are deployed, and the actual water vapor content is calculated using the software GAMIT10.6 to verify the accuracy of the water vapor content inverted by Sentinel-2.

[0038] In a specific embodiment of the present application, the above-mentioned second data set is preprocessed to obtain a preprocessed second data set, including: adjusting the spatial resolutions of different types of data in the second data set to a unified spatial resolution; exporting the second data set in a target format to obtain the preprocessed second data set. In this embodiment, the second data set is exported in a target format, which can ensure that the file can be processed by various software.

[0039] Specifically, the spatial resolution of Sentinel-2 remote sensing image data can be adjusted through the Sentinel Application Platform (abbreviated as SNAP). For Sentinel-2 image data, there are a total of 13 bands, and different bands correspond to three spatial resolutions of 10m, 20m, and 60m. Taking two bands used in the present invention as an example, the spatial resolution of the B9 band is 60m, and the spatial resolution of the B8A band is 20m. For the convenience of calculation, the spatial resolutions of the two bands are unified to 60m. In addition to spectral data, the observation zenith angle, observation azimuth angle, solar zenith angle, and solar azimuth angle required for calculation are also exported. The exported file format is selected as "ENVI", which can be processed by various software such as ENVI and MATLAB. Image upsampling can enlarge the original image and display a higher spatial resolution, but it will affect the quality of the image. Therefore, the resolution of B8A (20m) is downsampled to be consistent with the resolution of B9 (60m).

[0040] In an embodiment of the present application, an inversion model for water vapor content is constructed, including: inputting the above-mentioned first data set into the above-mentioned atmospheric radiative transfer model to determine a first apparent reflectance and a second apparent reflectance, where the first apparent reflectance refers to the apparent reflectance in the water vapor absorption band, and the second apparent reflectance refers to the apparent reflectance in the atmospheric window band; constructing a reflectance relationship based on the first apparent reflectance and the second apparent reflectance; using the reflectance relationship to determine the atmospheric transmittance, where the atmospheric transmittance refers to the ratio of the electromagnetic radiation flux after atmospheric attenuation to the incident electromagnetic radiation flux; constructing the above-mentioned inversion model with the atmospheric transmittance as the independent variable. In this embodiment, by constructing the reflectance relationship, the atmospheric transmittance of the target area can be determined more accurately, and then an inversion model for water vapor content can be constructed based on the atmospheric transmittance. Subsequently, the water vapor content of the target area can be determined more accurately according to the water vapor content inversion model.

[0041] Specifically, the theoretical basis for retrieving atmospheric water vapor content using Sentinel-2 imagery is as follows: In the near-infrared band range, for a homogeneous Lambertian surface, if the influence of the adjacency effect is not considered, the radiance received by the satellite sensor mainly includes the solar radiation (path radiance) directly reflected by the surface and scattered by the atmosphere, which can be expressed by Equation 1. Equation 1: L(λ) = L sun (λ)τ(λ)ρ(λ) + L path (λ), where L(λ) represents the radiance received by the sensor, L sun (λ) represents the solar irradiance at the top of the atmosphere, τ(λ) represents the atmospheric transmittance, ρ(λ) represents the surface reflectance, and L path (λ) represents the path radiance. When the sky is clear and visibility is high, the aerosol optical depth is very small. Therefore, the path radiance in the near-infrared band can be neglected compared to the reflected radiation from the surface, and Equation 1 can be simplified to Equation 2. Equation 2: L(λ) = L sun (λ)τ(λ)ρ(λ). The apparent reflectance of the satellite can be expressed by Equation 3. Equation 3: ρ * represents the apparent reflectance. The radiative transfer equation of Equation 2 can be transformed into Equation 4. Equation 4: ρ * (λ) = τ(λ)ρ(λ). Assuming that the surface reflectances in the atmospheric window and water vapor absorption bands are equal, the ratio of the atmospheric transmittances can be expressed by Equation 5. Equation 5: λ1 represents the water vapor absorption band, and λ2 represents the atmospheric window band. Since τ(λ2) ≈ 1, it can also be transformed into Equation 6. Equation 6: After that, the atmospheric radiation transfer model MODTRAN 5 can be used to simulate the relationship between the atmospheric transmittance and the atmospheric water vapor content, and determine the inversion model for the atmospheric water vapor content.

[0042] In a specific embodiment of the present application, the above reflectance relationship is used to determine the atmospheric transmittance, including: using the above reflectance relationship: to determine the above atmospheric transmittance, where τ represents the above atmospheric transmittance, and ρ * B9 represents the above first apparent reflectance, and ρ * B8A represents the above second apparent reflectance. In this embodiment, the atmospheric transmittance can be determined more accurately through the reflectance relationship, and the inversion model can be constructed more efficiently subsequently. The above formulas are only exemplary, and any deformation falls within the protection scope of the present application.

[0043] In a specific embodiment of the present application, constructing the above inversion model with the above atmospheric transmittance as the independent variable includes: obtaining a first fitting coefficient and a second fitting coefficient, where the first fitting coefficient and the second fitting coefficient are determined by fitting a set of water vapor content and the above atmospheric transmittance values; constructing the above inversion model: Wherein, W represents the above water vapor content, α represents the above first fitting coefficient, B represents the above second fitting coefficient, and τ represents the above atmospheric transmittance. In this embodiment, the inversion water vapor content of the target area can be further accurately determined through the inversion model. The above formula is only exemplary, and any deformation falls within the protection scope of the present application.

[0044] In one embodiment, the input parameters of the atmospheric radiation transfer model MODTRAN 5 can be set according to the parameters listed in Table 1. Only the main parameters affecting the model establishment are listed in this table, and the default values of the software are used for the parameters with less influence (such as atmospheric profile mode, scattering mode, etc.).

[0045] Table 1: Parameter Table

[0046]

[0047]

[0048] The format of the input file is as follows:

[0049] CARD1 ts 2 2 2 1 0 0 0 0 0 0 1 0 0.298.150 0.25

[0050] CARD1A tT 8 5 365.0000g 2.8 1.0F F T 0.000 0.0000 0.00000 0.000000.00000 0

[0051] CARD2 1 0 1 10 0 40.0000 5.0000 5.0000 0.00000 1.20000

[0052] CARD3 786.00000 1.20000 176.79605 0.000 0.000 0.000 0 0.0000

[0053] CARD3A1 12 2 213 0

[0054] CARD3A2 140.390 25.9291 0.000 0.000 3.370 0.000 0.000 0.000

[0055] CARD4 837.000 959.000 1.000 2.000RN run_tp5NGAA 0 0.00

[0056] End symbol 0

[0057] Among them, the parameters of CARD1 (the first line) control the main radiative transfer driving of the relational expression (mainly including MODEL, ITYPE, IEMSCT, TPTEMP, IMULT); CARD1A (the second line) is a supplement to the parameters of CARD1, including the setting of scattering parameters, common gas content, etc. (mainly including H2OSTR); the parameters of CARD2 (the third line) are responsible for describing the aerosol mode, cloud and rain mode, and other related atmospheric profile models (mainly including IHAZE, WSS, WHH); CARD3 (the fourth line) mainly describes the geometric conditions of the observation (mainly including H1, H2, ANGLE); CARD3A1 (the fifth line) and CARD3A2 (the sixth line, mainly including PARM1, PARM2) are both supplements to CARD3; CARD4 (the seventh line) represents spectral information (mainly including LLFLTNM), and 0 in the eighth line represents the end symbol.

[0058] The first to tenth lines of the output file are the relevant information in the input file, and the eleventh line represents the relevant variables of the output. Among them, "TOTAL_RAD" represents the total radiance received by the sensor, and the apparent reflectance can be calculated according to Formula 3. The functional relationship between the atmospheric water vapor content and the atmospheric transmittance is as Figure 2 shown, and the specific form of the calculated relational expression is: Among them, -0.0167 is the first fitting function, and 0.7384 is the second fitting function. The above formula is only exemplary, and any deformation falls within the protection scope of this application.

[0059] In order to verify the accuracy of the retrieved water vapor content obtained by Sentinel-2, in another specific embodiment of this application, after determining the retrieved water vapor content in the above target area, the above method further includes: obtaining the actual atmospheric water vapor content, and the above actual atmospheric water vapor content is detected by a sensor; verifying the retrieved water vapor content with the above actual atmospheric water vapor content.

[0060] In yet another specific embodiment of the present application, the above-mentioned retrieved water vapor content is verified using the above-mentioned actual atmospheric water vapor content, including: comparing the above-mentioned actual atmospheric water vapor content with the above-mentioned retrieved water vapor content; when the difference between the above-mentioned actual atmospheric water vapor content and the above-mentioned retrieved water vapor content is less than or equal to the difference threshold, determining that the verification result is passed; when the difference between the above-mentioned actual atmospheric water vapor content and the above-mentioned retrieved water vapor content is greater than the above-mentioned difference threshold, determining that the above-mentioned verification result is not passed. In this embodiment, the accuracy of the retrieved water vapor content can be verified more precisely.

[0061] Specifically, the water vapor content measured by ground-based GPS is used to verify the accuracy of the Sentinel-2 results. In order to make the spatial ranges measured by the two methods consistent, the average value of water vapor retrieval of 5×5 pixels around the GPS observation point corresponding to the image can be selected for comparison with the measured value. The comparison results are as Figure 3 shown (the ordinate is the average retrieval value, and the abscissa is the measured value). The determination coefficient (R 2 ), root mean square error (RMSE), and bias (Bias) between the retrieval result of the present application and the measured water vapor content are 0.8771, 0.2631 cm, and 0.1711 cm respectively, meeting the requirements of quantitative remote sensing. This solution also provides a water vapor distribution map, as Figure 4 shown, which is the spatial distribution map of the water vapor content on August 1, 2021. It can be determined that the atmospheric water vapor content in the target area is in the range of 0.303 - 5.785 cm. The spatial distribution of water vapor is affected by topography and land use types, and generally shows a trend that the atmospheric water vapor content decreases with the increase in elevation. The embodiment of the present application also provides an apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images. It should be noted that the apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images in the embodiment of the present application can be used to execute the method for retrieving atmospheric water vapor content based on Sentinel-2 images provided by the embodiment of the present application. The following introduces the apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images provided by the embodiment of the present application.

[0062] Figure 5 is a schematic diagram of the apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images according to the embodiment of the present application. As Figure 5 shown, the apparatus includes:

[0063] A first acquisition unit 10, configured to acquire a first data set and a second data set of a target area, where the first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area;

[0064] A processing unit 20, configured to preprocess the above-mentioned second data set to obtain a preprocessed second data set;

[0065] A construction unit 30, configured to construct an inversion model for water vapor content, wherein the above-mentioned inversion model is obtained by training the above-mentioned first data set as an input variable of an atmospheric radiative transfer model;

[0066] A determination unit 40, configured to determine the inversion water vapor content in the above-mentioned target area, where the above-mentioned inversion water vapor content is obtained by applying the above-mentioned inversion model to the above-mentioned preprocessed second data set.

[0067] In the above-mentioned device, the first acquisition unit acquires the first data set and the second data set of the target area, the processing unit preprocesses the second data set to obtain a preprocessed second data set, the construction unit constructs an inversion model for water vapor content, and the determination unit determines the inversion water vapor content in the target area. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the retrieved atmospheric water vapor content is relatively accurate.

[0068] In another embodiment of the present application, the first acquisition unit includes a call module and a download module. The call module is configured to call the above-mentioned first data set stored in the target weather station, and the target weather station is used to monitor the meteorological data of the above-mentioned target area; the download module is configured to download the above-mentioned second data set from the target database of the target server, and the target database of the above-mentioned target server is used to store the above-mentioned Sentinel-2 image data of the above-mentioned target area. In this embodiment, both the first data set and the second data set can be directly obtained. The first data set can be directly obtained from the target weather station, and the second data set can be directly downloaded from the target database of the target server. Of course, it is not limited to this method, and the first data set and the second data set can also be obtained by any other feasible method.

[0069] In a specific embodiment of the present application, the processing unit includes an adjustment module and an export module. The adjustment module is configured to adjust the spatial resolutions of different types of data in the above-mentioned second data set to a unified spatial resolution; the export module is configured to export the above-mentioned second data set in a target format to obtain the above-mentioned preprocessed second data set. In this embodiment, the second data set is exported in a target format, which can ensure that the file can be processed by various software.

[0070] In an embodiment of the present application, the construction unit includes a first determination module, a first construction module, a second determination module, and a second construction module. The first determination module is configured to input the above-mentioned first data set into the above-mentioned atmospheric radiative transfer model to determine a first apparent reflectance and a second apparent reflectance. The first apparent reflectance refers to the apparent reflectance in the water vapor absorption band, and the second apparent reflectance refers to the apparent reflectance in the atmospheric window band. The first construction module is configured to construct a reflectance relationship based on the first apparent reflectance and the second apparent reflectance. The second determination module is configured to determine the atmospheric transmittance using the reflectance relationship. The atmospheric transmittance refers to the ratio of the electromagnetic radiation flux after atmospheric attenuation to the incident electromagnetic radiation flux. The second construction module is configured to construct the inversion model with the atmospheric transmittance as the independent variable. In this embodiment, by constructing the reflectance relationship, the atmospheric transmittance of the target area can be determined more accurately, and then the inversion model of the water vapor content can be constructed based on the atmospheric transmittance. Subsequently, the water vapor content of the target area can be determined more accurately according to the water vapor content inversion model.

[0071] In a specific embodiment of the present application, the second determination module includes a determination sub-module, and the determination sub-module is configured to use the above-mentioned reflectance relationship: to determine the above-mentioned atmospheric transmittance, where τ represents the above-mentioned atmospheric transmittance, and ρ * B9 represents the above-mentioned first apparent reflectance, and ρ * B8A represents the above-mentioned second apparent reflectance. In this embodiment, the atmospheric transmittance can be determined more accurately through the reflectance relationship, and the inversion model can be constructed more efficiently subsequently. The above formula is only exemplary, and any deformation falls within the protection scope of the present application.

[0072] In a specific embodiment of the present application, the second construction module includes an acquisition sub-module and a construction sub-module. The acquisition sub-module is configured to acquire a first fitting coefficient and a second fitting coefficient, and the first fitting coefficient and the second fitting coefficient are determined by fitting a set of numerical values of water vapor content and the above-mentioned atmospheric transmittance. The construction sub-module is configured to construct the above-mentioned inversion model: where W represents the above-mentioned water vapor content, α represents the above-mentioned first fitting coefficient, B represents the above-mentioned second fitting coefficient, and τ represents the above-mentioned atmospheric transmittance. In this embodiment, the inversion water vapor content of the target area can be determined more accurately through the inversion model. The above formula is only exemplary, and any deformation falls within the protection scope of the present application.

[0073] In order to verify the accuracy of the retrieved water vapor content obtained by Sentinel-2, in another specific embodiment of the present application, the above device further includes a second acquisition unit and a verification unit. The second acquisition unit is used to acquire the actual atmospheric water vapor content after determining the retrieved water vapor content in the above target area, and the actual atmospheric water vapor content is detected by a sensor; the verification unit is used to verify the retrieved water vapor content with the actual atmospheric water vapor content.

[0074] In still another specific embodiment of the present application, the verification unit includes a comparison module, a third determination module, and a fourth determination module. The comparison module is used to compare the actual atmospheric water vapor content with the retrieved water vapor content; the third determination module is used to determine that the verification result is passed when the difference between the actual atmospheric water vapor content and the retrieved water vapor content is less than or equal to the difference threshold; the fourth determination module is used to determine that the verification result is not passed when the difference between the actual atmospheric water vapor content and the retrieved water vapor content is greater than the difference threshold. In this embodiment, the accuracy of the retrieved water vapor content can be verified more precisely.

[0075] The above device for retrieving atmospheric water vapor content based on Sentinel-2 images includes a processor and a memory. The above first acquisition unit, processing unit, construction unit, determination unit, etc. are all stored in the memory as program units, and the corresponding functions are implemented by the processor executing the program units stored in the memory.

[0076] The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set, and the water vapor content can be retrieved precisely by adjusting the kernel parameters.

[0077] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip.

[0078] An embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the above method for retrieving atmospheric water vapor content based on Sentinel-2 images is implemented.

[0079] An embodiment of the present invention provides a processor, and the above processor is used to run a program, wherein when the program runs, the above method for retrieving atmospheric water vapor content based on Sentinel-2 images is executed.

[0080] An embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, at least the following steps are implemented:

[0081] Step S101: Obtain a first data set and a second data set of a target area. The first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area.

[0082] Step S102: Preprocess the second data set to obtain a preprocessed second data set.

[0083] Step S103: Construct an inversion model for water vapor content. The inversion model is trained by using the first data set as an input variable of an atmospheric radiative transfer model.

[0084] Step S104: Determine the inverted water vapor content in the target area. The inverted water vapor content is obtained by applying the inversion model to the preprocessed second data set.

[0085] The device in this article can be a server, a PC, etc.

[0086] This application also provides a computer program product. When executed on a data processing device, it is adapted to execute a program initialized with at least the following method steps:

[0087] Step S101: Obtain a first data set and a second data set of a target area. The first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area.

[0088] Step S102: Preprocess the second data set to obtain a preprocessed second data set.

[0089] Step S103: Construct an inversion model for water vapor content. The inversion model is trained by using the first data set as an input variable of an atmospheric radiative transfer model.

[0090] Step S104: Determine the inverted water vapor content in the target area. The inverted water vapor content is obtained by applying the inversion model to the preprocessed second data set.

[0091] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the above-mentioned division of units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0093] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0095] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in each embodiment of the present invention. The foregoing storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs and other various media that can store program codes.

[0096] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0097] 1), The atmospheric water vapor content retrieval method based on Sentinel-2 images in this application first obtains the first dataset and the second dataset of the target area, then preprocesses the second dataset to obtain the preprocessed second dataset, then constructs an inversion model for the water vapor content, and finally determines the retrieved water vapor content within the target area. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the retrieved atmospheric water vapor content is relatively accurate.

[0098] 2), The atmospheric water vapor content retrieval device based on Sentinel-2 images in this application, the first acquisition unit acquires the first dataset and the second dataset of the target area, the processing unit preprocesses the second dataset to obtain the preprocessed second dataset, the construction unit constructs an inversion model for the water vapor content, and the determination unit determines the retrieved water vapor content within the target area. In this solution, the water vapor content inversion model obtained using the atmospheric radiative transfer model has a certain physical mechanism, and the retrieved atmospheric water vapor content is relatively accurate.

[0099] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A method for retrieving atmospheric water vapor content based on Sentinel-2 imagery, characterized in that, Including: Obtain a first data set and a second data set of a target area, where the first data set is data related to the meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area; Preprocess the second data set to obtain a preprocessed second data set; Construct an inversion model for water vapor content, where the inversion model is trained by using the first data set as an input variable of an atmospheric radiative transfer model; Determine the inversion water vapor content in the target area, where the inversion water vapor content is obtained by applying the inversion model to the preprocessed second data set; Preprocess the second data set to obtain a preprocessed second data set, including: adjusting the spatial resolutions of different types of data in the second data set to a unified spatial resolution; exporting the second data set in a target format to obtain the preprocessed second data set, where the spatial resolution of the B9 band is 60m, the spatial resolution of the B8A band is 20m, and unifying the spatial resolutions of the B9 band and the B8A band to 60m; Construct an inversion model for water vapor content, including: inputting the first data set into the atmospheric radiative transfer model to determine a first apparent reflectance and a second apparent reflectance, where the first apparent reflectance is the apparent reflectance in a water vapor absorption band, and the second apparent reflectance is the apparent reflectance in an atmospheric window band; constructing a reflectance relationship based on the first apparent reflectance and the second apparent reflectance; using the reflectance relationship to determine the atmospheric transmittance, where the atmospheric transmittance is the ratio of the electromagnetic radiation flux after atmospheric attenuation to the incident electromagnetic radiation flux; constructing the inversion model with the atmospheric transmittance as an independent variable; Using the reflectance relationship to determine the atmospheric transmittance, including: using the reflectance relationship: Determine the atmospheric transmittance, where represent the atmospheric transmittance represent the first apparent reflectance represent the second apparent reflectance Constructing the inversion model with the atmospheric transmittance as the independent variable includes: obtaining a first fitting coefficient and a second fitting coefficient, where the first fitting coefficient and the second fitting coefficient are determined by numerical fitting of a set of water vapor contents and the atmospheric transmittance; constructing the inversion model: , where represents the water vapor content, represents the first fitting coefficient, represents the second fitting coefficient, represents the atmospheric transmittance.

2. The method according to claim 1, characterized in that, Obtain a first data set and a second data set of a target area, including: Call the first data set stored in a target weather station, where the target weather station is used to monitor the meteorological data of the target area; Download the second data set from a target database of a target server, where the target database of the target server is used to store the Sentinel-2 image data of the target area.

3. The method according to claim 1, wherein After determining the inversion water vapor content in the target area, the method further includes: Obtain the actual atmospheric water vapor content, where the actual atmospheric water vapor content is detected by using a sensor; Verify the inversion water vapor content by using the actual atmospheric water vapor content.

4. The method according to claim 3, characterized in that, Verifying the inversion water vapor content by using the actual atmospheric water vapor content, including: Compare the actual atmospheric water vapor content with the inversion water vapor content; When the difference between the actual atmospheric water vapor content and the inversion water vapor content is less than or equal to a difference threshold, determine that the verification result is passed; When the difference between the actual atmospheric water vapor content and the inversion water vapor content is greater than the difference threshold, determine that the verification result is not passed.

5. An apparatus for retrieving atmospheric water vapor content based on Sentinel-2 images, which is based on the method for retrieving atmospheric water vapor content based on Sentinel-2 images according to any one of claims 1 to 4, characterized in that, Including: A first acquisition unit, configured to acquire a first data set and a second data set of a target area, where the first data set is data related to meteorological parameters of the target area, and the second data set is Sentinel-2 image data of the target area; A processing unit, configured to preprocess the second data set to obtain a preprocessed second data set; A construction unit, configured to construct an inversion model for water vapor content, where the inversion model is trained by using the first data set as an input variable of an atmospheric radiative transfer model; A determination unit, configured to determine the inverted water vapor content in the target area, where the inverted water vapor content is obtained by applying the inversion model to the preprocessed second data set; The processing unit includes an adjustment module and an export module. The adjustment module is configured to adjust the spatial resolutions of different types of data in the second data set to a unified spatial resolution; the export module is configured to export the second data set in a target format to obtain the preprocessed second data set, where the spatial resolution of the B9 band is 60m, the spatial resolution of the B8A band is 20m, and the spatial resolutions of the B9 band and the B8A band are unified to 60m; The construction unit includes a first determination module, a first construction module, a second determination module, and a second construction module. The first determination module is configured to input the first data set into the atmospheric radiative transfer model to determine a first apparent reflectance and a second apparent reflectance, where the first apparent reflectance is the apparent reflectance of a water vapor absorption band, and the second apparent reflectance is the apparent reflectance of an atmospheric window band; the first construction module is configured to construct a reflectance relationship formula according to the first apparent reflectance and the second apparent reflectance; the second determination module is configured to determine an atmospheric transmittance by using the reflectance relationship formula, where the atmospheric transmittance is the ratio of the electromagnetic radiation flux after atmospheric attenuation to the incident electromagnetic radiation flux; the second construction module is configured to construct the inversion model with the atmospheric transmittance as an independent variable; The second determination module includes a determination sub-module, and the determination sub-module is configured to use the reflectance relationship: to determine the atmospheric transmittance, where represents the atmospheric transmittance, represents the first apparent reflectance, represents the second apparent reflectance The second construction module includes an acquisition sub-module and a construction sub-module. The acquisition sub-module is used to acquire a first fitting coefficient and a second fitting coefficient, and the first fitting coefficient and the second fitting coefficient are determined by numerical fitting of a set of water vapor contents and the atmospheric transmittance; the construction sub-module is used to construct the inversion model: , where represents the water vapor content, represents the first fitting coefficient, represents the second fitting coefficient, represents the atmospheric transmittance.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, where the program executes the method according to any one of claims 1 to 4.

Images