CN117540634B - All-weather earth surface uplink long-wave radiation inversion method and device for stationary satellite - Google Patents
All-weather earth surface uplink long-wave radiation inversion method and device for stationary satellite Download PDFInfo
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Abstract
The invention discloses an all-weather earth surface uplink long-wave radiation inversion method and device for a stationary satellite, and relates to the technical field of satellite remote sensing. Comprising the following steps: acquiring cloud mask data of a stationary satellite, and judging a sunny condition or a cloudy condition according to the cloud mask data; estimating the surface long wave uplink radiation SLUR under a sunny condition by adopting a developing mixing method based on a first target database to obtain a sunny SLUR estimation result; based on a second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result; and obtaining the inversion result of all-weather earth surface uplink long wave radiation of the stationary satellite according to the calculated SLUR under the conditions of sunny days and cloudy days. The invention develops a method and a system for estimating FY-4A/AGRI all-weather SLUR, comprising a new mixing method for estimating clear sky SLUR and a machine learning algorithm for estimating cloud sky SLUR, which are used for estimating AGRI all-weather SLUR and generating products thereof.
Description
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to an all-weather earth surface uplink long-wave radiation inversion method and device for a static satellite.
Background
The earth's surface absorbs radiation from the sun, thereby heating the earth's surface and causing it to emit long wave radiation. This energy is then redistributed by the atmosphere and the ocean and is radiated mainly into space with infrared radiation in the electromagnetic spectrum having a wavelength of about 4 to 100. Thus, SLUR (Surface Longwave Upward Radiation, surface uplink long wave radiation) is an important component of surface radiation budget, an important parameter of hydrologic and climate models, and is closely related to variables such as evapotranspiration, soil moisture and topography. Therefore, in order to describe the hydrologic, ecological and bio-geochemical processes of the earth's surface, it is necessary, and also scientifically urgent, to accurately estimate SLUR and understand its spatiotemporal dynamics.
The surface upward long wave radiation is the sum of the thermal radiation emitted by the surface itself and the reflection of the surface downward long wave radiation. According to this definition, methods for estimating SLUR using remote sensing techniques can be divided into two categories: methods and hybrid methods based on LST-BBE (Land Surface Temperature-Broad Band Emissivity ). The LST-BBE method is straightforward and has been widely used to calculate SLUR, but requires LST, BBE and SLDR (Surface Longwave Downward Radiation, surface downlink long wave radiation reflection) products. Because of the large uncertainty in the remote sensing products of LST, BBE and SLDR, the accuracy of estimating SLUR is not ideal. Considering that the clear sky TOA (Top of Atmosphere, atmospheric top) radiance contains information of LST, BBE and SLDR, the mixing method directly links SLUR and clear sky TOA radiance by using a linear or nonlinear model on the basis of radiation transmission simulation, and bypasses uncertainty of LST and BBE separation. In many cases, therefore, the mixing method can achieve an accurate estimate of clear sky SLUR.
Currently, four all-weather global SLUR satellite products are publicly available: international satellite cloud climate project-flux data (ISCCP-FD, -280 km), global energy and water circulation experimentation-surface radiation budget (GEWEX-SRB, -10 km), cloud and earth radiation energy system-grid radiation flux and cloud (CERES FSW, -100 km), and gloss (Global LAnd Surface Satellite ) surface long wave radiation products (1 km). The ISCCP-FD, GEWEX-SRB and CERES FSW SLUR products are not only coarse in spatial resolution, but also very low in accuracy. Guide et al (2010) used ground measurement data from 15 stations in north america and china in 2003 for these three productsIt was evaluated that under all sky conditions, the standard deviations (std) were 39.4, 29.7 and 29.9W/m, respectively 2 . The GLASS SLUR product has higher spatial resolution and better global precision, and has a standard deviation of 19.5W/m under the condition of full sky through 181 site verification 2 . However, its time resolution is low, and there are 4 crossing times per day in low and medium latitude areas. Geostationary satellites can make up for the lack of time resolution and provide continuous coverage between 60 ° latitude north and south. For example, the 1 hour, 3 hours, and day-averaged earth surface long wave radiation products of the NASA Cloud and Earth Radiant Energy System (CERES) Synchronization (SYN) satellite products were obtained from five stationary satellites (e.g., GOES, meteosat, stationary meteorological satellites, etc.) by time interpolation. Thus, geostationary satellites have a high time resolution and are capable of providing a large amount of observations for accurate computation of the SLUR observations both on a daily basis, and even monthly and yearly.
A Fengyun No. A star (Fengyun-4A, FY 4A) is a new generation of weather stationary satellites in China, and is provided with an AGRI (Advanced Geostationary Radiation Imager, advanced stationary radiation imager) for acquiring high-frequency thermal infrared measurement data (40 scenes/day) in east Asia. Up to now, the AGRI only provides a clear sky SLUR product, and the product is underestimated according to the verification result; in addition, AGRI does not have cloud-sky LST and SLDR products, and thus the LST-BBE method cannot be used to estimate cloud-sky AGRI SLUR.
The AGRI SLUR product provided by the national weather satellite center has two problems: (1) The phenomenon of underestimated SLUR on sunny days is verified that the deviation (bias) is-8.43W/m 2 Root Mean Square Error (RMSE) of 19.45W/m 2 The method comprises the steps of carrying out a first treatment on the surface of the (2) The SLUR product space coverage is incomplete, and the cloud SLUR product is not existed.
Disclosure of Invention
The invention provides the method for solving the problem that the SLUR product is not completely covered in space due to underestimation of the AGRI SLUR product provided by the national meteorological satellite center and the lack of the cloud SLUR product.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a method for inverting all-weather earth surface uplink long-wave radiation of a stationary satellite, which is realized by electronic equipment and comprises the following steps:
s1, acquiring cloud mask data of a stationary satellite, and judging whether the cloud mask data is a sunny condition or a cloudy condition according to the cloud mask data.
S2, based on the first target database, developing a mixing method, and estimating the surface uplink long-wave radiation SLUR under the sunny condition to obtain a sunny SLUR estimation result.
And based on the second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
S3, obtaining an all-weather earth surface uplink long wave radiation inversion result of the stationary satellite according to the sunny SLUR estimation result and the cloudy SLUR estimation result.
Optionally, the acquiring process of the first target database in S2 includes:
acquiring global atmospheric profile data and emissivity spectrum data, and determining a first target database based on the global atmospheric profile data and the emissivity spectrum data, wherein the target data comprises: the thermal infrared band of the stationary satellite is the top radiation brightness of the atmosphere layer and the surface uplink long wave radiation data.
The process for acquiring the second target database comprises the following steps:
acquiring static satellite cloud parameters, atmospheric analysis data and ground surface broadband emissivity data, and determining a second target database;
wherein the static satellite cloud parameters comprise cloud cover, cloud top temperature and cloud top height; the atmospheric analysis data includes stationary satellite cloud parameter data, total atmospheric moisture content and 2m air temperature.
Optionally, the mixing method in S2 includes:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of the AGRI-TOA channel on the top of the air layer of the advanced stationary radiation imager-sunny day.
S22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition.
Optionally, performing radiation transmission simulation on the advanced stationary radiation imager-the sunny atmosphere top AGRI-TOA channel radiance and SLUR in S21 includes:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data includes advanced stationary radiation imager-clear day atmosphere top AGRI-TOA channel radiance and SLUR.
S212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula.
S213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition.
S214, according to the weather atmospheric profile data and the emissivity spectrums, calculating to obtain the weather atmospheric layer top radiance of the advanced static radiation imager AGRI 11-14 channel by using a convolution spectrum response function and an atmospheric layer top radiance formula.
S215, generating a plurality of simulation samples for each atmospheric profile and each satellite observation zenith angle according to the plurality of weather atmospheric profile data and the plurality of emissivity spectrums.
Optionally, a spectrum SLUR calculation formula in S212 is shown in the following formula (1):
wherein d SLUR Representing the spectrum SLUR, d λ Representing spectral separation, ε λ Represents the spectral emissivity lambda, B at wavelength λ Representing the planck function of the device, xs represents the surface temperature d SLDR Representing the spectrum SLDR.
Optionally, the SLUR calculation formula in S212 is as shown in the following formula (2):
wherein epsilon represents the emissivity of the earth surface broadband, T s Representing surface temperature, SLDR represents surface uplink and downlink long wave radiation.
Optionally, the formula of the top radiance of the atmosphere in S214 is shown in the following formula (3):
wherein L is TOA，i Represents the top radiance, lambda of the atmosphere layer of AGRI channel i 1 Spectral wavelength 4, lambda representing a specific thermal infrared band 2 Spectral wavelength 100, epsilon (lambda) representing the specific thermal infrared band, and emissivity at wavelength lambda, B (lambda, T s ) Represents Planck constant, λ represents wavelength, T s Represent LST, L ↓ Represents SLDR, τ (λ) represents transmittance at wavelength, L ↑ Representing path radiation, SRF i Representing the spectral response function of the AGRI channel i.
Optionally, determining a relationship between the SLUR under the simulated sunny condition and the radiation brightness of the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel according to the SLUR under the simulated sunny condition and the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel radiation brightness data in S22, and establishing a SLUR nonlinear statistical model under the sunny condition, including:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13.
S222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of the AGRI 14 channel under a simulated sunny condition.
S223, establishing an SLUR nonlinear statistical model under a sunny condition based on the AGRI 11-14 channels according to the linear correlation and the relation of the cubic polynomials.
Optionally, based on the second target database, developing a machine learning method in S2 to estimate the surface uplink long-wave radiation SLUR under the cloud condition, so as to obtain a cloud SLUR estimation result, including:
and (3) developing a machine learning method based on broadband emissivity, near-surface air temperature, total water vapor content, cloud top temperature, cloud top height and cloud amount data, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
In another aspect, the present invention provides an apparatus for inverting all-weather earth surface uplink long-wave radiation of a stationary satellite, where the apparatus is applied to implement an all-weather earth surface uplink long-wave radiation inversion method of a stationary satellite, the apparatus includes:
the acquisition module is used for acquiring the cloud mask data of the stationary satellite and judging whether the cloud mask data is a sunny condition or a cloudy condition according to the cloud mask data.
The estimating module is used for developing a mixing method based on the first target database, estimating the surface uplink long-wave radiation SLUR under the sunny condition, and obtaining a sunny SLUR estimating result.
And based on the second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
And the output module is used for obtaining the all-weather earth surface uplink long-wave radiation inversion result of the stationary satellite according to the clear day and the cloudy day SLUR estimation result.
Optionally, the estimation module is further configured to:
acquiring global atmospheric profile data and emissivity spectrum data, and determining a first target database based on the global atmospheric profile data and the emissivity spectrum data, wherein the target data comprises: the thermal infrared band of the stationary satellite is the top radiation brightness of the atmosphere layer and the surface uplink long wave radiation data.
An estimation module, further configured to:
acquiring static satellite cloud parameters, atmospheric analysis data and ground surface broadband emissivity data, and determining a second target database;
wherein the static satellite cloud parameters comprise cloud cover, cloud top temperature and cloud top height; the atmospheric analysis data includes stationary satellite cloud parameter data, total atmospheric moisture content and 2m air temperature.
Optionally, the mixing method comprises:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of the AGRI-TOA channel on the top of the air layer of the advanced stationary radiation imager-sunny day.
S22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition.
Optionally, the estimation module is further configured to:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data includes advanced stationary radiation imager-clear day atmosphere top AGRI-TOA channel radiance and SLUR.
S212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula.
S213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition.
S214, according to the weather atmospheric profile data and the emissivity spectrums, calculating to obtain the weather atmospheric layer top radiance of the advanced static radiation imager AGRI 11-14 channel by using a convolution spectrum response function and an atmospheric layer top radiance formula.
S215, generating a plurality of simulation samples for each atmospheric profile and each satellite observation zenith angle according to the plurality of weather atmospheric profile data and the plurality of emissivity spectrums.
Alternatively, the spectrum SLUR calculation formula is shown as the following formula (1):
wherein d SLUR Representing the spectrum SLUR, d λ Representing spectral separation, ε λ Represents the spectral emissivity lambda, B at wavelength λ Representing the Planck function, T s Represents the surface temperature d SLDR Representing the spectrum SLDR.
Alternatively, the SLUR calculation formula is shown in the following formula (2):
wherein epsilon represents the emissivity of the earth surface broadband, T s Representing surface temperature, SLDR represents surface uplink and downlink long wave radiation.
Optionally, the formula of the atmospheric top radiance is shown in the following formula (3):
wherein L is TOA，i Represents the top radiance, lambda of the atmosphere layer of AGRI channel i 1 Spectral wavelength 4, lambda representing a specific thermal infrared band 2 Spectral wavelength 100, epsilon (lambda) representing the specific thermal infrared band, and emissivity at wavelength lambda, B (lambda, T s ) Represents Planck constant, λ represents wavelength, T s Represents the surface temperature, L ↓ Represents SLDR, τ (λ) represents transmittance at wavelength, L ↑ Representing path radiation, SRF i Representing the spectral response function of the AGRI channel i.
Optionally, the estimation module is further configured to:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13.
S222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of the AGRI 14 channel under a simulated sunny condition.
S223, establishing an SLUR nonlinear statistical model under a sunny condition based on the AGRI 11-14 channels according to the linear correlation and the relation of the cubic polynomials.
Optionally, the estimation module is further configured to:
and (3) developing a machine learning method based on broadband emissivity, near-surface air temperature, total water vapor content, cloud top temperature, cloud top height and cloud amount data, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
In one aspect, an electronic device is provided, the electronic device including a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to implement the above-described stationary satellite all-weather earth surface uplink long wave radiation inversion method.
In one aspect, a computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement the above-described stationary satellite all-weather earth surface uplink long wave radiation inversion method is provided.
Compared with the prior art, the technical scheme has at least the following beneficial effects:
by the scheme, high-precision remote sensing estimation of the AGRI SLUR and the cloud SLUR on sunny days can be realized, and therefore remote sensing inversion of the AGRI all-weather SLUR is realized. The invention improves the underestimation phenomenon of the official SLUR of the AGRI on sunny days and provides a method support for the remote sensing estimation of the AGRI SLUR on cloudy days. In addition, the cloud machine learning method developed by the invention can make up for the space deficiency of the AGRI official SLUR product, and fills the space blank of the SLUR.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for inverting all-weather earth surface uplink long-wave radiation of a stationary satellite provided by an embodiment of the invention;
FIG. 2 is a diagram of an all-weather SLUR estimation framework provided by an embodiment of the present invention;
FIG. 3 is a graph of accuracy of estimating a sunny day SLUR provided by an embodiment of the present invention;
FIG. 4 is a graph of accuracy of estimating the cloud sky SLUR provided by an embodiment of the present invention;
FIG. 5 is a graph of accuracy of estimating a sunny day SLUR provided by an embodiment of the present invention;
FIG. 6 (a) is a first spatial distribution diagram of an estimated all-weather SLUR provided by an embodiment of the present invention;
FIG. 6 (b) is a second spatial distribution diagram of an estimated all-weather SLUR according to an embodiment of the present invention;
FIG. 7 is a block diagram of an all-weather earth surface uplink long-wave radiation inversion device of a stationary satellite provided by an embodiment of the invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
As shown in fig. 1, the embodiment of the invention provides an all-weather earth surface uplink long-wave radiation inversion method of a stationary satellite, which can be realized by electronic equipment. The process flow of the method can comprise the following steps:
s1, acquiring static satellite cloud mask observation data, and judging whether the cloud mask observation data is a sunny condition or a cloudy condition according to cloud mask identification data.
In a possible implementation manner, the data acquisition method may be to acquire high-frequency thermal infrared measurement data and cloud parameter data according to the wind cloud number four a star. The flow of the all-weather AGRI SLUR inversion is shown in fig. 2.
S2, based on the first target database, developing a mixing method, and estimating the surface uplink long-wave radiation SLUR under the sunny condition to obtain a sunny SLUR estimation result.
In a possible embodiment, the invention proposes a novel mixing method for estimating the AGRI SLUR on sunny days, which takes into account the contribution of carbon dioxide absorption. The method comprises two steps: radiation transmission simulation and model construction.
Further, global Seebor atmospheric profile data, ASTRE and USGS emissivity spectrum data are acquired, a target database is constructed based on the data, then an SLUR estimation model is developed based on the target database, and finally a hybrid algorithm is constructed to realize estimation of the surface uplink long-wave radiation SLUR under the condition of sunny days.
Optionally, the mixing method in S2 includes S21-S22:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of the AGRI-TOA channel on the top of the air layer of the advanced stationary radiation imager-sunny day.
Optionally, the step S21 may include the following steps S211 to S215:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data includes advanced stationary radiation imager-clear day atmosphere top AGRI-TOA channel radiance and SLUR.
In a possible embodiment, first, 2283 Seebor clear sky atmospheric profile data and 84 emissivity spectra are selected for simulating clear sky SLUR. For each atmospheric profile, the spectra SLUR and SLDR, thermal path emissivity, TOA transmittance at 8 fixed sensor observation zenith angles (0 °, 11.6 °, 26.1 °, 40.3 °, 53.7 °, 60 °, 65 °, and 70 °) were simulated using medium resolution atmospheric transfer software (MODTRAN 5.2), respectively. LST is assigned by shifting the near-surface Ta from-15K to 20K, with a step size of 5K.
S212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula.
Alternatively, theoretically, SLUR includes an integral of the surface thermal infrared radiation and SLDR reflection at wavelengths of 4-100 meters. In view of the isotropy of the heat radiation, the spectrum SLUR can be represented by the following formula (1):
wherein d SLUR Representing the spectrum SLUR, d λ Representing spectral separation, ε λ Represents the spectral emissivity at wavelength λ, B λ Representing the Planck function, T s Represents the surface temperature d SLDR The spectrum SLDR is represented, and a constant (pi) is used instead of the directional integration. Finally, SLUR may be calculated as in equation (2):
wherein epsilon represents the emissivity of the earth surface broadband, T s Representing surface temperature, SLDR represents subsurface long wave radiation.
The clear sky atmosphere top radiation contains surface temperature, broad band emissivity and SLDR information. In practice, the hybrid approach assumes that the surface is lambertian, inverting the SULR from the sunny atmosphere top radiation of the thermal infrared sensor avoids separate estimation and error propagation of the three variables in equation (2), thereby estimating the SLUR more accurately.
S213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition.
S214, according to the weather atmospheric profile data and the emissivity spectrums, calculating to obtain the weather atmospheric layer top radiance of the advanced static radiation imager AGRI 11-14 channel by using a convolution spectrum response function and an atmospheric layer top radiance formula.
Optionally, the formula of the atmospheric top radiance is shown in the following formula (3):
wherein L is TOA，i Represents the top radiance, lambda of the atmosphere layer of AGRI channel i 1 Spectral wavelength 4, lambda representing a specific thermal infrared band 2 Spectral wavelength 100, epsilon (lambda) representing the specific thermal infrared band, and emissivity at wavelength lambda, B (lambda, T s ) Represents Planck constant, λ represents wavelength, T s Represents the surface temperature, L ↓ Represents SLDR, τ (λ) represents transmittance at wavelength, L ↑ Representing the path radiation, SRFx represents the spectral response function of the AGRI channel i.
S215, generating a plurality of simulation samples aiming at each atmospheric profile and each satellite observation zenith angle according to the atmospheric zenith radiation of a sunny day.
In a possible implementation, 1,534,176 (2283 profiles x 8 surface temperatures x 84 emissivity spectra) simulated samples are generated for each satellite observation zenith angle as a training dataset for hybrid method development.
S22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition.
Optionally, the step S22 may include the following steps S221 to S223:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13.
S222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of the AGRI 14 channel under a simulated sunny condition.
S223, establishing an SLUR nonlinear statistical model under a sunny condition based on the AGRI 11-14 channels according to the linear correlation and the relation of the cubic polynomials.
In one possible implementation, a nonlinear statistical model is established to derive a sunny SLUR by analyzing the relationship between simulated sunny SLUR and the AGRI TOA channel radiance. According to the analysis, there is a linear correlation of up to 0.98 between the atmospheric top radiance of the sunny day SLUR and the AGRI channels 11, 12 and 13, and an obvious cubic polynomial relation is provided with the atmospheric top radiance of the AGRI channel 14, and finally, a nonlinear statistical model of the sunny day SLUR is constructed based on the AGRI channels 11 to 14.
And based on the second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
In a possible implementation, a machine learning algorithm is developed to calculate the cloud sky SLUR based on cloud parameters, analysis data and broadband emissivity provided by the AGRI.
Specifically, cloud parameters, atmospheric analysis data and ground surface broadband emissivity data are acquired, and a second target database is determined; the cloud parameters comprise cloud quantity, cloud top temperature and cloud top height; the atmospheric analysis data included the total atmospheric moisture content and the 2m air temperature.
Optionally, the step S3 may include:
and (3) developing a machine learning method based on broadband emissivity, near-surface air temperature, total water vapor content, cloud top temperature, cloud top height and cloud amount data, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
In a possible embodiment, the present invention selects a LightGBM (Gradient Boosting Machine, gradient elevator) model to estimate the AGRI clouds SLUR. The GLASS broadband emissivity BBE is selected as one of the inputs to the cloud SLUR. The surface temperature LST and the near-surface air temperature have a significant linear relationship (R2 > 0.9), so that in estimating the cloud sky SLUR, the near-surface air temperature of ERA5 is used to replace the cloud sky LST; in addition, the cloud SLDR is mainly affected by near-surface air temperature, humidity, and cloud characteristics. Therefore, the AGRI vapor product, ERA5 Ta, and total vapor content data are employed to reflect near-ground heat radiation, and the AGRI cloud top temperature, cloud top height, and cloud cover are selected to account for cloud heat radiation contributions. Finally, a machine learning method based on the broadband emissivity, the near-surface air temperature, the total water vapor content, the cloud top temperature, the cloud top height and the cloud cover is constructed.
S3, obtaining the inversion result of all-weather earth surface uplink long-wave radiation of the stationary satellite according to the clear day and the cloudy day SLUR estimation result.
The invention realizes the remote sensing estimation of the all-weather earth surface uplink long wave radiation of the stationary satellite and the generation of the product thereof. The accuracy of estimating the SLUR on sunny days and cloudy days based on the invention is shown in figures 3-5, and the spatial distribution of estimating the SLUR on all-weather based on the invention is shown in figures 6 (a) and 6 (b).
According to the embodiment of the invention, the high-precision remote sensing estimation of the AGRI SLUR and the cloud SLUR in sunny days can be realized, so that the remote sensing inversion of the AGRI all-weather SLUR is realized. The invention improves the underestimation phenomenon of the official SLUR of the AGRI on sunny days and provides a method support for the remote sensing estimation of the AGRI SLUR on cloudy days. In addition, the cloud machine learning method developed by the invention can make up for the space deficiency of the AGRI official SLUR product, and fills the space blank of the SLUR.
As shown in fig. 7, an embodiment of the present invention provides an apparatus 700 for inverting all-weather earth surface uplink long-wave radiation of a stationary satellite, where the apparatus 700 is applied to implement an all-weather earth surface uplink long-wave radiation inversion method of a stationary satellite, and the apparatus 700 includes:
the acquiring module 710 is configured to acquire cloud mask data of the stationary satellite, and determine a sunny condition or a cloudy condition according to the cloud mask data.
The estimating module 720 is configured to develop a mixing method based on the first target database, and estimate the surface uplink long-wave radiation SLUR under a sunny condition, so as to obtain a sunny SLUR estimation result.
And based on the second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
And the output module 730 is configured to obtain an all-weather earth surface uplink long-wave radiation inversion result of the stationary satellite according to the clear day and the cloudy day SLUR estimation result.
Optionally, the estimation module 720 is further configured to:
acquiring global atmospheric profile data and emissivity spectrum data, and determining a first target database based on the global atmospheric profile data and the emissivity spectrum data, wherein the target data comprises: the thermal infrared band of the stationary satellite is the top radiation brightness of the atmosphere layer and the surface uplink long wave radiation data.
An estimation module 720, further configured to:
acquiring static satellite cloud parameters, atmospheric analysis data and ground surface broadband emissivity data, and determining a second target database;
wherein the static satellite cloud parameters comprise cloud cover, cloud top temperature and cloud top height; the atmospheric analysis data includes stationary satellite cloud parameter data, total atmospheric moisture content and 2m air temperature.
Optionally, the mixing method comprises:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of the AGRI-TOA channel on the top of the air layer of the advanced stationary radiation imager-sunny day.
S22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition.
Optionally, the estimation module 720 is further configured to:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data includes advanced stationary radiation imager-clear day atmosphere top AGRI-TOA channel radiance and SLUR.
S212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula.
S213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition.
S214, according to the weather atmospheric profile data and the emissivity spectrums, calculating to obtain the weather atmospheric layer top radiation brightness of the advanced static radiation imager AGRI 11-14 channels by using an atmospheric layer top radiation brightness formula through a convolution spectrum response function.
S215, generating a plurality of simulation samples for each atmospheric profile and each satellite observation zenith angle according to the plurality of weather atmospheric profile data and the plurality of emissivity spectrums.
Alternatively, the spectrum SLUR calculation formula is shown as the following formula (1):
wherein d sLUR Representing the spectrum SLUR, d λ Representing spectral separation, ε λ Represents the spectral emissivity lambda, B at wavelength λ Representing the Planck function, T s Represents the surface temperature d SLDR Representing the spectrum SLDR.
Alternatively, the SLUR calculation formula is shown in the following formula (2):
wherein epsilon represents the emissivity of the earth surface broadband, T s Representing surface temperature, SLDR represents subsurface long wave radiation.
Optionally, the formula of the atmospheric top radiance is shown in the following formula (3):
wherein L is TOA，i Represents the top radiance, lambda of the atmosphere layer of AGRI channel i 1 Spectral wavelength 4, lambda representing a specific thermal infrared band 2 Spectral wavelength 100, epsilon (lambda) representing the specific thermal infrared band, and emissivity at wavelength lambda, B (lambda, T s ) Represents Planck constant, λ represents wavelength, T s Represents the surface temperature, L ↓ Represents SLDR, τ (λ) represents transmittance at wavelength, L ↑ Representing path radiation, SRF i Representing the spectral response function of the AGRI channel i.
Optionally, the estimation module 720 is further configured to:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13.
S222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of the AGRI 14 channel under a simulated sunny condition.
S223, establishing an SLUR nonlinear statistical model under a sunny condition based on the AGRI 11-14 channel radiance according to the linear correlation and the relation of the cubic polynomial.
Optionally, the estimation module 720 is further configured to:
and (3) developing a machine learning method based on broadband emissivity, near-surface air temperature, total water vapor content, cloud top temperature, cloud top height and cloud amount data, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
According to the embodiment of the invention, the high-precision remote sensing estimation of the AGRI SLUR and the cloud SLUR in sunny days can be realized, so that the remote sensing inversion of the AGRI all-weather SLUR is realized. The invention improves the underestimation phenomenon of the official SLUR of the AGRI on sunny days and provides a method support for the remote sensing estimation of the AGRI SLUR on cloudy days. In addition, the cloud machine learning method developed by the invention can make up for the space deficiency of the AGRI official SLUR product, and fills the space blank of the SLUR.
Fig. 8 is a schematic structural diagram of an electronic device 800 according to an embodiment of the present invention, where the electronic device 800 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 801 and one or more memories 802, where at least one instruction is stored in the memories 802, and the at least one instruction is loaded and executed by the processor 801 to implement the following method for performing all-weather earth surface uplink long wave radiation inversion of a stationary satellite:
s1, acquiring cloud mask data of a stationary satellite, and judging whether the cloud mask data is a sunny condition or a cloudy condition according to the cloud mask data.
S2, based on the first target database, developing a mixing method, and estimating the surface uplink long-wave radiation SLUR under the sunny condition to obtain a sunny SLUR estimation result.
And based on the second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
S3, obtaining the inversion result of all-weather earth surface uplink long-wave radiation of the stationary satellite according to the clear day and the cloudy day SLUR estimation result.
In an exemplary embodiment, a computer readable storage medium, such as a memory, comprising instructions executable by a processor in the terminal to perform the above-described stationary satellite all-weather earth-surface uplink long-wave radiation inversion method is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (7)
1. An all-weather earth surface uplink long-wave radiation inversion method of a stationary satellite, which is characterized by comprising the following steps:
s1, acquiring cloud mask data of a stationary satellite, and judging a sunny condition or a cloudy condition according to the cloud mask data;
s2, based on a first target database, developing a mixing method, and estimating the surface uplink long-wave radiation SLUR under a sunny condition to obtain a sunny SLUR estimation result;
based on a second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result;
s3, obtaining an all-weather earth surface uplink long-wave radiation inversion result of the stationary satellite according to the sunny day and cloudy day SLUR estimation results;
the mixing method in the S2 comprises the following steps:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of an advanced stationary radiation imager-an AGRI-TOA channel on the top of a sunny atmosphere;
s22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition;
the simulating radiation transmission of the advanced stationary radiation imager-the sunny atmosphere top AGRI-TOA channel radiance and SLUR in S21 includes:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data comprise the irradiation brightness and the spectrum SLUR of an advanced stationary radiation imager-an AGRI-TOA channel on the top of a sunny atmosphere;
s212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula;
s213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition;
s214, calculating to obtain the top radiation brightness of the sunny atmosphere layer of the AGRI 11-14 channel of the advanced stationary radiation imager by utilizing a top radiation brightness formula of the atmosphere through a convolution spectrum response function according to the data of the sunny atmosphere profile and the emissivity spectrums;
s215, generating a plurality of simulation samples aiming at each atmospheric profile and each satellite observation zenith angle according to the weather atmospheric profile data and the emissivity spectrums;
in S22, according to the simulated radiation brightness data of the SLUR under the sunny condition and the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel, determining the relationship between the SLUR under the sunny condition and the radiation brightness of the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel, and establishing a SLUR nonlinear statistical model under the sunny condition, including:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13;
s222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of an AGRI 14 channel under a simulated sunny condition;
s223, establishing an SLUR nonlinear statistical model under the sunny condition based on the AGRI 11-14 channels according to the linear correlation and the relation of the cubic polynomial.
2. The method according to claim 1, wherein the process of obtaining the first target database in S2 includes:
acquiring global atmospheric profile data and emissivity spectrum data, and determining a first target database based on the global atmospheric profile data and the emissivity spectrum data, wherein the target data comprises: the top radiation brightness and the surface uplink long wave radiation data of the atmospheric layer in the thermal infrared band of the stationary satellite;
the process for obtaining the second target database comprises the following steps:
acquiring static satellite cloud parameters, atmospheric analysis data and ground surface broadband emissivity data, and determining a second target database;
wherein the stationary satellite cloud parameters include cloud cover, cloud top temperature, and cloud top height; the atmospheric analysis data includes stationary satellite cloud parameter data, total atmospheric moisture content and 2m air temperature.
3. The method of claim 1, wherein the spectral SLUR calculation formula in S212 is represented by the following formula (1):
wherein d SLUR Representing the spectrum SLUR, d λ Representing spectral separation, ε λ Represents the spectral emissivity lambda, B at wavelength λ Representing the Planck function, T s Represents the surface temperature d SLDR Representing the spectrum SLDR.
4. The method of claim 1, wherein the SLUR calculation formula in S212 is represented by the following formula (2):
wherein ε represents the broadband emissivity, σ represents the Boltzmann function, T s Representing land surface temperature, SLDR represents subsurface long wave radiation.
5. The method according to claim 1, wherein the formula of the atmospheric top radiance in S214 is shown in the following formula (3):
wherein L is TOA,i Represents the top radiance, lambda of the atmosphere layer of AGRI channel i 1 Spectral wavelength 4, lambda representing a particular AGRI thermal infrared channel 2 Representing the spectral wavelength 100 of a particular AGRI thermal infrared channel, ε (λ) represents the emissivity at wavelength λ, B (λ, T) s ) Represents the Planck function, λ represents the wavelength, τ (λ) represents the transmittance, T s Represents the surface temperature, L ↓ Representing subsurface long wave radiation, L ↑ Representing heat path emissivity, SRF i Representing the spectral response function of the AGRI channel i.
6. The method of claim 1, wherein the estimating the surface uplink long wave radiation SLUR under the cloud environment condition based on the second target database in S2 by developing a machine learning method to obtain a cloud environment SLUR estimation result comprises:
and (3) developing a machine learning method based on broadband emissivity, near-surface air temperature, total water vapor content, cloud top temperature, cloud top height and cloud amount data, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result.
7. An all-weather earth surface uplink long-wave radiation inversion device for a stationary satellite, which is characterized by comprising:
the acquisition module is used for acquiring cloud mask data of the stationary satellite and judging whether the cloud mask data is a sunny condition or a cloudy condition according to the cloud mask data;
the estimating module is used for developing a mixing method based on the first target database, estimating the surface uplink long-wave radiation SLUR under the sunny condition, and obtaining a sunny SLUR estimating result;
based on a second target database, developing a machine learning method, and estimating the surface uplink long-wave radiation SLUR under the cloud environment condition to obtain a cloud environment SLUR estimation result;
the output module is used for obtaining the all-weather earth surface uplink long-wave radiation inversion result of the stationary satellite according to the sunny day and cloudy day SLUR estimation results;
the mixing method comprises the following steps:
s21, performing radiation transmission simulation on the radiation brightness and SLUR of an advanced stationary radiation imager-an AGRI-TOA channel on the top of a sunny atmosphere;
s22, determining the relation between the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance of the advanced stationary radiation imaging instrument-sunny atmosphere top according to the SLUR under the simulated sunny condition and the AGRI-TOA channel radiance data of the advanced stationary radiation imaging instrument-sunny atmosphere top, and establishing a SLUR nonlinear statistical model under the sunny condition;
the simulating radiation transmission of the advanced stationary radiation imager-the sunny atmosphere top AGRI-TOA channel radiance and SLUR in S21 includes:
s211, acquiring input data in a simulation process, wherein the input data are used for simulating SLUR under a sunny condition; the input data in the simulation process comprises a plurality of weather atmospheric profile data and a plurality of emissivity spectrums; the simulation data comprise the irradiation brightness and the spectrum SLUR of an advanced stationary radiation imager-an AGRI-TOA channel on the top of a sunny atmosphere;
s212, obtaining an SLUR calculation formula according to the spectrum SLUR calculation formula;
s213, according to the simulation data and the SLUR calculation formula, obtaining the SLUR under the simulated sunny condition;
s214, calculating to obtain the top radiation brightness of the sunny atmosphere layer of the AGRI 11-14 channel of the advanced stationary radiation imager by utilizing a top radiation brightness formula of the atmosphere through a convolution spectrum response function according to the data of the sunny atmosphere profile and the emissivity spectrums;
s215, generating a plurality of simulation samples aiming at each atmospheric profile and each satellite observation zenith angle according to the weather atmospheric profile data and the emissivity spectrums;
in S22, according to the simulated radiation brightness data of the SLUR under the sunny condition and the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel, determining the relationship between the SLUR under the sunny condition and the radiation brightness of the advanced stationary radiation imager-sunny atmosphere top AGRI-TOA channel, and establishing a SLUR nonlinear statistical model under the sunny condition, including:
s221, obtaining linear correlation between SLUR under simulated sunny conditions and the top radiance of the atmosphere of the channels of the advanced static radiation imagers AGRI11, 12 and 13;
s222, acquiring a cubic polynomial relation between SLUR and atmosphere layer top radiance of an AGRI 14 channel under a simulated sunny condition;
s223, establishing an SLUR nonlinear statistical model under the sunny condition based on the AGRI 11-14 channels according to the linear correlation and the relation of the cubic polynomial.
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