Offshore cloud optical thickness estimation method and device based on satellite-borne laser radar

Abstract

The invention relates to the technical field of laser remote sensing, in particular to a maritime cloud optical thickness estimation method and device based on a spaceborne laser radar, and the method comprises the steps: constructing a passive noise model and an active laser signal model of the spaceborne laser radar based on an atmospheric transmission optical model of the spaceborne laser radar meeting a preset condition, determining a theoretical noise rate and a theoretical signal photon number under the optical thickness of the target cloud layer; and the actually-measured average noise rate and the actually-measured average signal photon number of each along-track segment are determined by utilizing actually-measured photon point cloud data of the satellite-borne laser radar, and an estimation result of the optical thickness of the offshore cloud layer is determined according to the actually-measured average noise rate, the actually-measured average signal photon number, the theoretical noise rate and the theoretical signal photon number. Therefore, the problems that in the prior art, limitation exists in inversion of different cloud layer thicknesses, environmental factors are not considered in an existing satellite-borne laser radar signal and noise model, and the estimation precision of the optical thickness of the offshore cloud layer is reduced are solved.

Description

Technical Field

[0001] The present application relates to the field of laser remote sensing technology, and in particular to a method and device for estimating the optical thickness of marine clouds based on a satellite-borne laser radar. Background Art

[0002] Clouds play a key role in the global climate system. Their formation, development and disappearance have a direct impact on weather changes, radiation balance and climate change. The optical characteristics of clouds, such as cloud optical thickness, are key parameters for understanding the impact of clouds on radiation transfer and climate feedback.

[0003] In the field of laser remote sensing technology, due to the significant impact of clouds on satellite-borne laser radar signals and background noise, obtaining the temporal and spatial distribution of clouds and understanding the microphysical characteristics of clouds are of great significance for the analysis, screening and processing of satellite-borne laser radar data. With the development of remote sensing technology, especially the application of satellite-borne photon counting laser radar in recent years, the observation capabilities of the atmosphere, land and ocean have been significantly improved, showing the characteristics of high detection sensitivity and high temporal and spatial resolution, and effectively enhancing the ability to obtain the optical characteristics of clouds.

[0004] At present, the cloud optical thickness inversion algorithm for spaceborne lidar mainly relies on passive background noise (solar background noise) or active laser signals. However, the existing methods are restricted when the cloud layer is too thin or too thick, resulting in limitations in the inversion of different cloud thicknesses. In addition, the existing spaceborne lidar signal and noise model does not consider environmental factors, which leads to the introduction of deviations in the estimation of cloud optical thickness and reduces the estimation accuracy of the optical thickness of marine clouds, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for estimating the optical thickness of marine clouds based on satellite-borne laser radar, so as to solve the problems that the inversion of different cloud thicknesses in the related technology is limited, and the existing satellite-borne laser radar signal and noise model does not take environmental factors into consideration, thereby reducing the estimation accuracy of the optical thickness of marine clouds.

[0006] The first aspect of the present application provides a method for estimating the optical thickness of marine clouds based on a satellite-borne laser radar, comprising the following steps: based on an atmospheric transmission optical model of the satellite-borne laser radar that meets preset conditions, respectively constructing a passive noise model and an active laser signal model of the satellite-borne laser radar; determining a theoretical noise rate at the optical thickness of the target cloud layer according to the passive noise model, and determining a theoretical number of signal photons at the optical thickness of the target cloud layer using the active laser signal model; determining a measured average noise rate and a measured average number of signal photons for each along-track segment using the measured photon point cloud data of the satellite-borne laser radar, and determining an estimation result of the optical thickness of the marine cloud layer according to the measured average noise rate, the measured average number of signal photons, the theoretical noise rate, and the theoretical number of signal photons.

[0007] Optionally, in one embodiment of the present application, constructing the passive noise model of the satellite-borne laser radar includes: determining a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions based on the atmospheric transmission optical model; and constructing the passive noise model of the satellite-borne laser radar according to the first quantitative mapping relationship.

[0008] Optionally, in one embodiment of the present application, constructing the active laser signal model of the satellite-borne laser radar includes: determining a second quantitative mapping relationship between the optical thickness of the cloud layer and the active laser signal under the target environmental conditions based on the atmospheric transmission optical model; and constructing the active laser signal model of the satellite-borne laser radar according to the second quantitative mapping relationship.

[0009] Optionally, in one embodiment of the present application, the measured average noise rate and the measured average number of signal photons of each along-track segment are determined using the measured photon point cloud data of the satellite-borne laser radar, including: based on the measured photon point cloud data, determining the number of noise photons in the area along the flight trajectory direction and the vertical direction, and determining the cumulative interval length along the flight trajectory direction and the vertical direction, so as to determine the measured average noise rate based on the number of noise photons and the cumulative interval length; extracting the sea surface photon signal point cloud in the measured photon point cloud data, and using the sea surface photon signal point cloud to determine the measured average number of signal photons.

[0010] Optionally, in one embodiment of the present application, determining the estimated result of the optical thickness of the marine cloud layer based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number includes: determining the actual observation data pairs of the signal and noise along the track of each segment based on the measured average noise rate and the measured average signal photon number; determining the theoretical correspondence data pairs of the signal and noise under the target cloud layer optical thickness using the theoretical noise rate and the theoretical signal photon number; calculating the minimum difference between the actual observation data pair and the theoretical correspondence data pair, and determining the estimated result of the optical thickness of the marine cloud layer based on the minimum difference.

[0011] The second aspect of the present application provides an optical thickness estimation device for marine clouds based on a satellite-borne laser radar, including: a construction module, which is used to construct a passive noise model and an active laser signal model of the satellite-borne laser radar based on an atmospheric transmission optical model of the satellite-borne laser radar that meets preset conditions; a determination module, which is used to determine the theoretical noise rate under the optical thickness of the target cloud layer according to the passive noise model, and to determine the theoretical number of signal photons under the optical thickness of the target cloud layer using the active laser signal model; an estimation module, which is used to determine the measured average noise rate and the measured average signal photon number of each along-track segment using the measured photon point cloud data of the satellite-borne laser radar, and determine the estimation result of the optical thickness of the marine clouds based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number.

[0012] Optionally, in one embodiment of the present application, the construction module includes: a first determination unit, used to determine a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions based on the atmospheric transmission optical model; and a first construction unit, used to construct a passive noise model of the satellite-borne lidar according to the first quantitative mapping relationship.

[0013] Optionally, in one embodiment of the present application, the construction module includes: a second determination unit, used to determine a second quantitative mapping relationship between the optical thickness of the cloud layer and the active laser signal under the target environmental conditions based on the atmospheric transmission optical model; and a second construction unit, used to construct an active laser signal model of the satellite-borne laser radar according to the second quantitative mapping relationship.

[0014] Optionally, in one embodiment of the present application, the estimation module includes: a third determination unit, used to determine the number of noise photons in the area along the flight trajectory direction and the vertical direction based on the measured photon point cloud data, and determine the cumulative interval length along the flight trajectory direction and the vertical direction, so as to determine the measured average noise rate based on the number of noise photons and the cumulative interval length; an extraction unit, used to extract the sea surface photon signal point cloud in the measured photon point cloud data, and use the sea surface photon signal point cloud to determine the measured average number of signal photons.

[0015] Optionally, in one embodiment of the present application, the estimation module includes: a fourth determination unit, used to determine the actual observation data pair of the signal and noise along the track of each segment according to the measured average noise rate and the measured average signal photon number; a fifth determination unit, used to determine the theoretical correspondence data pair of the signal and noise under the optical thickness of the target cloud layer using the theoretical noise rate and the theoretical signal photon number; a calculation unit, used to calculate the minimum difference between the actual observation data pair and the theoretical correspondence data pair, and determine the estimation result of the optical thickness of the marine cloud layer based on the minimum difference.

[0016] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating the optical thickness of marine clouds based on satellite-borne laser radar as described in the above embodiment.

[0017] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, it implements the above-mentioned method for estimating the optical thickness of marine clouds based on space-borne laser radar.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for estimating the optical thickness of marine clouds based on space-borne laser radar.

[0019] The embodiments of the present application can construct the passive noise model and active laser signal model of the satellite-borne laser radar based on the atmospheric transmission optical model of the satellite-borne laser radar that meets certain conditions, and then respectively determine the theoretical noise rate and theoretical signal photon number under the optical thickness of the target cloud layer, and use the measured photon point cloud data of the satellite-borne laser radar to determine the measured average noise rate and the measured average signal photon number of each along-track segment, and then combine the theoretical noise rate and the theoretical signal photon number to determine the estimated result of the optical thickness of the sea cloud layer, effectively improving the atmospheric environment monitoring capability of the satellite-borne laser radar and improving the estimation accuracy of the optical thickness of the sea cloud layer. Thus, the limitations in the inversion of different cloud thicknesses in the related technology are solved, and the existing satellite-borne laser radar signal and noise model does not take environmental factors into account, which reduces the estimation accuracy of the optical thickness of the sea cloud layer.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A flowchart of a method for estimating optical thickness of marine clouds based on a satellite-borne laser radar according to an embodiment of the present application;

[0023] Figure 2 A flowchart of estimating the optical thickness of ocean clouds based on space-borne laser radar according to a specific embodiment of the present application;

[0024] Figure 3 A schematic diagram of cloud thickness inversion above the ocean surface according to a specific embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of a device for estimating the optical thickness of marine clouds based on a satellite-borne laser radar according to an embodiment of the present application;

[0026] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0028] The following describes the optical thickness estimation method and device of the cloud layer at sea based on the satellite-borne laser radar of the embodiment of the present application with reference to the accompanying drawings. In view of the limitations in the inversion of different cloud thicknesses in the related technology mentioned in the background technology center, and the existing satellite-borne laser radar signal and noise model does not take environmental factors into account, which reduces the estimation accuracy of the optical thickness of the cloud layer at sea, the present application provides a method for estimating the optical thickness of the cloud layer at sea based on the satellite-borne laser radar, in which the passive noise model and the active laser signal model of the satellite-borne laser radar can be constructed based on the atmospheric transmission optical model of the satellite-borne laser radar that meets certain conditions, and then the theoretical noise rate and the theoretical signal photon number under the optical thickness of the target cloud layer are determined respectively, and the measured photon point cloud data of the satellite-borne laser radar are used to determine the measured average noise rate and the measured average signal photon number of each segment along the track, and then the estimated result of the optical thickness of the cloud layer at sea is determined by combining the theoretical noise rate and the theoretical signal photon number, which effectively improves the atmospheric environment monitoring capability of the satellite-borne laser radar and improves the estimation accuracy of the optical thickness of the cloud layer at sea. Thus, the limitations of the inversion of different cloud thicknesses in the related technology are solved, and the existing satellite-borne lidar signal and noise models do not take environmental factors into consideration, thereby reducing the estimation accuracy of the optical thickness of the ocean clouds.

[0029] Specifically, Figure 1 A schematic flow chart of a method for estimating optical thickness of marine clouds based on a satellite-borne lidar provided in an embodiment of the present application.

[0030] like Figure 1 As shown, the method for estimating the optical thickness of ocean clouds based on space-borne laser radar includes the following steps:

[0031] In step S101, based on the atmospheric transmission optical model of the spaceborne laser radar that meets preset conditions, a passive noise model and an active laser signal model of the spaceborne laser radar are constructed respectively.

[0032] It is understandable that the embodiment of the present application can first construct an atmospheric transmission optical model suitable for spaceborne laser radar, taking into account the optical properties under different meteorological conditions, covering environmental parameters such as aerosol optical thickness and ozone optical thickness, so as to accurately reflect the scattering and absorption characteristics of the atmosphere to the laser signal. The embodiment of the present application can provide input parameters for the subsequent derivation of the passive solar background noise model, that is, the passive noise model and the active laser signal model through the atmospheric transmission optical model that meets the above conditions, effectively improving the feasibility of the estimation of the optical thickness of the sea cloud layer.

[0033] For example, the embodiment of the present application can use the hardware parameters of the new generation photon counting lidar ICESat-2 system and the photon point cloud data obtained by flying over a certain sea area as input samples to invert the optical thickness of the cloud layer in the area where the satellite passes. The sample selected the data packet obtained by ICESat-2 when it flew over a certain sea area on February 27, 2021 (numbered: 2021 / 02 / 27, RGT#0994, GT3R), which is specifically provided by ICESat-2 ATL03 and ATL09 products. It contains the laser point cloud data detected along the satellite flight trajectory (including signal photons and noise photons), the solar zenith angle θ at the time of transit s , laser pulse emission energy E t In addition, MOD06 C6 provides a lookup table for the cloud top reflectivity related to aerosol optical depth ρ(τ c ).

[0034] like Figure 2 As shown, the embodiment of the present application can construct an atmospheric transmission optical model suitable for satellite-borne laser radar based on the water diffuse attenuation coefficient inversion method of the underwater signal of the satellite-borne laser radar, wherein the atmospheric transmission optical model of the satellite-borne laser radar includes the calculation of direct transmittance and diffuse transmittance, and the total atmospheric direct transmittance T can be expressed as:

[0035]

[0036] Where θ is the angle of incidence (here for spaceborne lidar, it is the nadir angle of lidar observation); τ is the total optical thickness in the atmosphere, τ r is the Rayleigh optical thickness of atmospheric molecules, τ a is the aerosol optical thickness, τ c is the cloud optical thickness, τ o is the ozone optical thickness; T r , T a , T c , T o are the corresponding direct transmittance of atmospheric molecules, aerosols, clouds, and ozone. In the ocean area, the surface atmospheric pressure is close to the standard atmospheric pressure, so τ r The typical value corresponding to 0.1112 under the 532nm green light band and standard atmospheric pressure conditions can be used.

[0037] The diffuse transmittance can be expressed as:

[0038]

[0039] Among them, t r is the Rayleigh diffuse transmittance of atmospheric molecules, t a is the aerosol diffuse transmittance, t ois the ozone diffuse transmittance; ω a is the single scattering albedo of the aerosol. For marine aerosols, ω is usually used. a =0.95 as a typical value; F a is the forward scattering ratio of the aerosol, F a It can be expressed as:

[0040]

[0041] Among them, P a (ψ) is the aerosol scattering phase function with the scattering angle ψ normalized to 4π, which is usually calculated using the binomial Henyey-Greenstein (TTHG) scattering phase function.

[0042] In the embodiment of the present application, when clouds exist, since the optical thickness of atmospheric molecules and aerosols is relatively small compared to the optical thickness of clouds, the Rayleigh diffuse transmittance of atmospheric molecules t r and aerosol diffuse transmittance t a The single scattering approximation is used, but the cloud diffuse transmittance t c It is necessary to consider the multiple scattering effect and use the empirical formula: t c =A / (τ c +B) Calculation of cloud diffuse transmittance t at different cloud optical thicknesses c , where A and B are fitting constants, usually A = 6.646 and B = 6.583 can be used; τ c is the cloud optical thickness.

[0043] Optionally, in one embodiment of the present application, a passive noise model of a satellite-borne laser radar is constructed, including: determining a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions based on an atmospheric transmission optical model; and constructing a passive noise model of the satellite-borne laser radar according to the first quantitative mapping relationship.

[0044] In the actual implementation process, Figure 2 As shown, the embodiment of the present application can determine the first quantitative mapping relationship between the optical thickness of the cloud layer and the background noise under the target environmental conditions based on the atmospheric transmission optical model, such as the atmospheric noise rate (the number of noise photons per second), the ocean (including the sea surface and the seawater subsurface) noise rate and the detector dark count rate, and then construct a passive noise model suitable for the satellite-borne lidar according to the first quantitative mapping relationship, which can more accurately estimate and eliminate the background noise, thereby improving the accuracy and reliability of signal detection.

[0045] Optionally, in one embodiment of the present application, an active laser signal model of a satellite-borne laser radar is constructed, including: determining a second quantitative mapping relationship between the optical thickness of the cloud layer and the active laser signal under target environmental conditions based on an atmospheric transmission optical model; and constructing an active laser signal model of the satellite-borne laser radar according to the second quantitative mapping relationship.

[0046] In some embodiments, Figure 2 As shown, the embodiment of the present application can determine the second quantitative mapping relationship between the optical thickness of the cloud layer and the active laser signal under the target environmental conditions based on the atmospheric transmission optical model, such as the sea surface mirror reflection and white foam diffuse reflection of the active signal of the satellite-borne laser radar, thereby constructing an active laser signal model suitable for the satellite-borne laser radar according to the second quantitative mapping relationship, which can more accurately predict and analyze the intensity changes of the laser signal under different cloud optical thickness conditions, thereby improving the accuracy and reliability of signal detection.

[0047] In step S102, the theoretical noise rate at the target cloud layer optical thickness is determined according to the passive noise model, and the theoretical signal photon number at the target cloud layer optical thickness is determined using the active laser signal model.

[0048] It is understandable that the embodiment of the present application can determine the theoretical noise rate at the target cloud optical thickness according to the passive noise model, that is, the total background noise rate, for example Figure 2 As shown, the total background noise rate includes the atmospheric noise rate (number of noise photons per second), the ocean (including the sea surface and the subsurface) noise rate, and the detector dark count rate, which can be expressed as:

[0049] f n = f atm +f surf +f dark (4)

[0050] Among them, f atm is the background noise caused by backscattering of various atmospheric factors, f surf is the background noise reflected from the ocean surface and subsurface layer, f dark is the dark count noise of the detector.

[0051] It should be noted that during the day, the detector dark count noise rate is smaller than the previous two items, and given the lidar system, such as the ICESat-2 satellite-borne lidar, f dark 10KHz is a fixed value, so the model construction of the first two items is mainly discussed.

[0052] Among them, the background noise f caused by backscattering of various atmospheric factors atm It can be expressed as:

[0053]

[0054] Among them, θ v is the nadir angle of the satellite-borne lidar observation (equivalent to θ in formula 1, for the ICESat-2 system, θ v =0.38°); θ s is the solar zenith angle, provided directly by the ICESat-2 data package; f r is the backscattering of atmospheric molecules (i.e. Rayleigh scattering), f a is the aerosol backscatter, f c is the backscattering of the cloud layer, β is the ratio of air molecules below the cloud layer to the total air molecules, β has little effect on the inversion of cloud thickness and can usually be set to any value between 0.2 and 1. In this embodiment, it is set to 0.6.

[0055] For most satellite-borne lidars, such as ICESat-2, the nadir angle θ is v are all close to 0°. The following calculations are based on this assumption. Rayleigh scattering f r The induced background noise rate can be calculated using the single scattering approximation as:

[0056]

[0057] Among them, η r and η q is the receiving optical system and detector quantum efficiency, θ r is the receiving half field of view, A r is the effective area of ​​the receiving telescope, Δλ is the filter width, N λ 0 is the solar irradiance at a given wavelength, hυ is the energy of a single photon, and F is the radiation correction coefficient of the lidar system. Once the satellite-borne lidar is determined, the above parameters are all constant values ​​of the system hardware. For example, in formula (6), the Rayleigh scattering phase function P r (π-θ s )=3 / 4×[1+cos 2 (π-θ s )]. For ICESat-2 onboard lidar, η q =0.15,η r =0.4,θ r =83.5 / 2μrad, A r =0.41m 2 , Δλ=38pm, F=0.52; Since ICESat-2 uses 532nm green laser, hυ=3.7387×10 -19 J, N λ 0 =1.832w / (m 2 ·nm).

[0058] Aerosol scattering noise rate f a and the backscatter from the clouds f c Can be expressed as:

[0059]

[0060] Among them, ρ(τ c ) is the given optical thickness τ c The cloud top reflectivity can be obtained through the MODIS C6 MOD06 cloud attribute product lookup table.

[0061] Background noise reflected from the ocean surface and subsurface surf It can be expressed as:

[0062] f surf =f foam +f specular +f w (9)

[0063] Among them, f foam is the noise reflected by the sea foam, f specular is the noise caused by the reflection of the sun directly on the sea surface, f w It is the noise caused by backscattering of the subsurface layer of sea water.

[0064] Among them, the first term f foam is the noise reflected by the sea foam, which can be expressed as:

[0065]

[0066] Among them, ρ f is the equivalent reflectivity of white foam, which is a constant value of 0.22 for a wavelength of 532nm; W is the proportion of the sea surface covered by white foam. The sea surface white foam coverage is related to the wind speed U at 10 meters above the sea surface. 10 The relationship between can be expressed as W = 2.95 × 10 - 6 U 10 3.25±0.52 Specifically, the wind speed U 10 The probability distribution is approximated by the quadratic Weibull function, whose mean U can be taken as 10 =7m / s.

[0067] The second term f specular is the noise caused by the reflection of the sun directly on the sea surface, which can be approximated as:

[0068]

[0069] Among them, ρ s (θ s / 2) is the incident angle θ s / 2 is the Fresnel reflectivity of the sea surface. For most satellite-borne lidars with near-nadir incidence, this value can be approximated to 0.02; s 2 is the variance of the sea surface slope, which is determined by the wind speed U at 12.5 m above the sea surface. 12.5 The conversion relationship between the wind speed at 10m and 12.5m above the sea surface is shown in formula (12):

[0070]

[0071] For the third term f in formula (9) w represents the noise caused by backscattering of the seawater subsurface layer and can be expressed as:

[0072]

[0073] Among them, R rs It is the remote sensing reflectivity at 532nm. It is relatively small in the open sea and can be approximated by 0.0025 / sr.

[0074] Furthermore, if Figure 2 As shown, the embodiment of the present application can use the active laser signal model to determine the theoretical signal photon number under the optical thickness of the target cloud layer. For example, in an ocean observation scenario, the active signal of the satellite-borne laser radar includes not only the specular reflection of the sea surface, but also the diffuse reflection of white foam. The number of diffuse reflection laser signal photons N caused by white foam s,foam It can be expressed as:

[0075]

[0076] Among them, η r is the emission optical system efficiency; E t is the laser pulse energy. The laser pulse emission energy read by the ICESat-2 data packet has taken into account the efficiency of the emission system and corresponds to η r E t ; R h is the satellite orbit height, ICESat-2 satellite R h = 500km. Since the telescope can only receive the mirror reflection generated when the sea surface slope is equal to the tangent of the laser incident nadir angle, when the satellite-borne laser radar incident nadir angle is close to 0°, the number of laser signal photons reflected by the sea surface mirror is N s,specular It can be approximated as:

[0077]

[0078] Among them, θ t is half of the laser divergence angle, the ICESat-2 system θ t =12μrad.

[0079] The total number of signal photons N reflected by the laser radar from the sea surface s It is expressed as:

[0080]

[0081] Among them, N s,specular is the number of laser signal photons reflected by the sea surface mirror, N s,foam is the number of diffusely reflected laser signal photons.

[0082] In step S103, the measured photon point cloud data of the satellite-borne lidar is used to determine the measured average noise rate and the measured average signal photon number of each along-track segment, and the estimated result of the optical thickness of the sea cloud layer is determined based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number.

[0083] It can be understood that the embodiments of the present application can use the measured photon point cloud data of the satellite-borne laser radar to determine the measured average noise rate and the measured average signal photon number of each along-track segment, and determine the estimated result of the optical thickness of the marine cloud layer based on the measured average noise rate, the measured average signal photon number and the theoretical noise rate and the theoretical signal photon number calculated from the measured photon point cloud data in the following steps, thereby effectively improving the atmospheric environment monitoring capability of the satellite-borne laser radar and improving the estimation accuracy of the optical thickness of the marine cloud layer.

[0084] Optionally, in one embodiment of the present application, the measured photon point cloud data of the satellite-borne lidar is used to determine the measured average noise rate and the measured average number of signal photons for each along-track segment, including: based on the measured photon point cloud data, determining the number of noise photons in the area along the flight trajectory direction and the vertical direction, and determining the cumulative interval length along the flight trajectory direction and the vertical direction, so as to determine the measured average noise rate based on the number of noise photons and the cumulative interval length; extracting the sea surface photon signal point cloud in the measured photon point cloud data, and using the sea surface photon signal point cloud to determine the measured average number of signal photons.

[0085] In the actual implementation process, Figure 2 As shown, the embodiment of the present application can calculate the noise rate and the number of signal photons actually measured based on the measured point cloud of the satellite-borne laser radar. Specifically, for the calculation of the background noise rate, the photon point cloud collected in the area of ​​tens to hundreds of meters above the sea surface is the noise photons caused by the sun. According to the number of photons in the area along the flight trajectory and the vertical direction, and the cumulative interval length along the flight trajectory and the vertical direction, the measured average background noise rate can be expressed as:

[0086]

[0087] Among them, Noise_count is the total number of photons in the noise photon area, x start 、x end are the starting and ending distances along the track of the noise photon region, h start 、h end are the starting and ending elevations of the noise photon region, c is the speed of light, V is the satellite flight speed, R is the laser repetition rate, and f total is the measured background noise rate of the spaceborne lidar (the sum of the noise rates of multiple detector channels), num is the number of detector channels, and f n i is the average measured background noise rate of each detector, and i is the measured noise rate of the i-th segment along the track. 8 m / s; for ICESat-2, the satellite flight speed is V = 7km / s, the laser repetition rate R = 10KHz, and the number of detector channels num = 16; the embodiment of the present application directly uses the noise rate result f provided by the ICESat-2 ATL09 data package n i , where each segment interval is 280m long (i.e. x end -x start =280m), the noise photon statistical elevation interval is 8km (i.e. h end -h start =8km).

[0088] Next, the embodiment of the present application can calculate the number of measured signal photons. Usually, the density clustering algorithm is used to extract the sea surface photon signal point cloud. First, the density value of the satellite-borne photon counting laser radar receiving point cloud is calculated. p ,y p ) as the center and with the semi-major axis r x , semi-minor axis r y Create an ellipse, for example, with the semi-major axis r x =1.5m, semi-minor axis r y =1.5m to establish a circular domain, then the photon point q(x q ,y q ) and the ellipse center p(x p ,y p ) can be expressed as:

[0089]

[0090] When d(p,q)<1, it means that photon q is inside the ellipse. The number of photon points inside the ellipse is counted as the point cloud density N of point p. p Then, the point cloud density threshold MinPts is calculated as the extraction threshold for filtering signal points, which can be expressed as:

[0091]

[0092] Where SN1 is the expected sum of the number of photons of the signal and noise in the ellipse region, SN2 is the expected number of photons of the noise in the ellipse region, and h low 、h high SignalNoise_count is the minimum and maximum elevation coordinates within the range of the laser radar downlink distance window, and x is the maximum elevation coordinates within the range of the laser radar downlink distance window. start to x end The total number of photons in the interval segment.

[0093] When the point cloud density N of point p p >MinPts, then point p is determined as a signal. After extracting the sea surface signal photons, the measured average signal photon number is statistically calculated and can be expressed as:

[0094]

[0095] Among them, Signal_count is the total number of signal photons in the along-track interval, N s_total is the total number of photons of the measured sea surface signal of a single laser pulse of the spaceborne lidar (the sum of the noise rates of multiple detector channels), N s i is the average number of sea surface signal photons of each detector, and i represents the measured result of the signal photon number of the i-th segment along the track.

[0096] Optionally, in one embodiment of the present application, an estimation result of the optical thickness of the marine cloud layer is determined based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number, including: determining the actual observation data pairs of the signal and noise along the track of each segment based on the measured average noise rate and the measured average signal photon number; determining the theoretical correspondence data pairs of the signal and noise under the target cloud layer optical thickness using the theoretical noise rate and the theoretical signal photon number; calculating the minimum difference between the actual observation data pairs and the theoretical correspondence data pairs, and determining the estimation result of the optical thickness of the marine cloud layer based on the minimum difference.

[0097] As a possible way to achieve this, Figure 2 As shown, the embodiment of the present application can calculate the theoretical noise rate f under different cloud optical thicknesses using formula (4) based on the passive noise model of the satellite-borne laser radar and the active signal model of the satellite-borne laser radar. n (τ c ), formula (16) calculates the theoretical signal photon number N under different cloud optical thicknesses s (τ c), so as to obtain the theoretical corresponding relationship data between signal and noise under given cloud optical thickness (N s (τ c ), f n (τ c )).

[0098] Next, based on the measured photon point cloud data of the satellite-borne laser radar, the average background noise rate f of each segment is calculated. n i and the average signal photon number N s i , i represents the measured result corresponding to the i-th segment, thereby obtaining the actual observation data pair of the signal and noise along the track (N s i ,f n i ).

[0099] Finally, the embodiment of the present application can be used to calculate the actual observation data of the signal and noise along the satellite orbit of each segment (N s i ,f n i ) and the theoretical relationship data corresponding to the signal and noise levels at different cloud optical thicknesses (N s (τ c ),f n (τ c )) to calculate the error, the error function d(τ c ) is defined as:

[0100]

[0101] That is, the embodiment of the present application calculates the actual observation data pairs (N s i ,f n i ) and different cloud thickness τ c The corresponding theoretical relationship data pair (N s (τ c ),f n (τ c )) The error between c ), take the τ corresponding to the minimum error c_result is the cloud optical thickness result at the current segmented geographical location along the track, which can be expressed mathematically as:

[0102] τ c_result =argmind(τ c ),τ c ∈[ 0 , ∞] (22)

[0103] Among them, τ c_result It is the cloud optical thickness result at the current segmented geographic location along the track.

[0104] In summary, it is assumed that the embodiment of the present application uses the hardware parameters of the ICESat-2 satellite-borne photon counting lidar system, the photon point cloud data obtained by flying over the ocean, and the typical environmental parameter values ​​of the ocean scene (including the Rayleigh optical thickness τ r =0.1112, aerosol optical thickness τ a =0.2, ozone optical thickness τ o = 0.02, the cloud optical thickness empirical formula parameters A = 6.646 and B = 6.583; aerosol single scattering albedo ω a =0.95, effective reflectivity of white foam ρ f =0.22, the incident angle is θ s / 2 The Fresnel reflectivity of the sea surface is ρ s (θ s / 2)=0.02, remote sensing reflectivity R at 532nm rs =0.0025sr -1 ; The ratio of air molecules below the cloud layer to the total air molecules β = 0.6; The wind speed over the ocean U 10 =7m / s), MOD06 C6 provides a cloud top reflectivity lookup table ρ(τ c ), in the above formula, only τ c is an unknown number. By calculating various atmospheric transmittances and different aerosol optical thicknesses τ c The corresponding theoretical background noise rate of the sea surface is f atm 、f surf , dark count noise rate f dark =10KHz, and then obtain the total noise rate f n (τ c ); Then, calculate the different aerosol optical thickness τ c The corresponding theoretical signal photon number N on the sea surface s,foam 、N s,specular , and then obtain the total signal photon number N s (τ c ); Secondly, obtain the measured signal photon number N of each segment i along the track of the satellite-borne laser radar s i , noise rate f n i ; Again, calculate the actual observation data pairs of signals and noise along the track (N s i ,f n i ) and different cloud thickness τ c The corresponding theoretical relationship data pair (Ns (τ c ),f n (τ c )) time error d(τ c ), take the τ corresponding to the minimum error c_result It is the cloud optical thickness result at the current segmented geographic location along the track.

[0105] The present application embodiment uses the photon point cloud data obtained by flying the ICESat-2 satellite-borne photon counting laser radar over a certain sea area, and uses the above steps and methods to invert the following Figure 3 The inversion results are compared with the reference cloud optical thickness results provided by the Himawari-8 optical satellite. The mean absolute error (MAE) of the two is 0.785, and the coefficient of determination R 2 >0.9. Experimental results show that the embodiment of the present application can effectively estimate the optical thickness of the sea cloud layer by fusing the active signal of the space-borne laser radar and the passive noise.

[0106] According to the method for estimating the optical thickness of clouds at sea based on satellite-borne laser radar proposed in the embodiment of the present application, the passive noise model and active laser signal model of the satellite-borne laser radar can be constructed based on the atmospheric transmission optical model of the satellite-borne laser radar that meets certain conditions, and then the theoretical noise rate and theoretical signal photon number under the optical thickness of the target cloud layer can be determined respectively, and the measured photon point cloud data of the satellite-borne laser radar can be used to determine the measured average noise rate and the measured average signal photon number of each segment along the track, and then the estimated result of the optical thickness of the clouds at sea can be determined by combining the theoretical noise rate and the theoretical signal photon number, which effectively improves the atmospheric environment monitoring capability of the satellite-borne laser radar and improves the estimation accuracy of the optical thickness of the clouds at sea. Thus, the limitations in the inversion of different cloud thicknesses in the related technology and the problem that the existing satellite-borne laser radar signal and noise model do not consider environmental factors, which reduces the estimation accuracy of the optical thickness of the clouds at sea are solved.

[0107] Next, a device for estimating the optical thickness of marine clouds based on a satellite-borne laser radar according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0108] Figure 4 It is a block diagram of an apparatus for estimating optical thickness of marine clouds based on a satellite-borne laser radar according to an embodiment of the present application.

[0109] like Figure 4 As shown, the device 10 for estimating the optical thickness of marine clouds based on satellite-borne laser radar includes: a construction module 100, a determination module 200 and an estimation module 300.

[0110] Specifically, the construction module 100 is used to construct a passive noise model and an active laser signal model of the spaceborne laser radar based on the atmospheric transmission optical model of the spaceborne laser radar that meets preset conditions.

[0111] The determination module 200 is used to determine the theoretical noise rate at the target cloud layer optical thickness according to the passive noise model, and to determine the theoretical signal photon number at the target cloud layer optical thickness using the active laser signal model.

[0112] The estimation module 300 is used to determine the measured average noise rate and the measured average signal photon number of each along-track segment using the measured photon point cloud data of the satellite-borne laser radar, and to determine the estimated result of the optical thickness of the cloud layer over the sea based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number.

[0113] Optionally, in one embodiment of the present application, the construction module 100 includes: a first determining unit and a first construction unit.

[0114] Wherein, the first determination unit is used to determine a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions based on an atmospheric transmission optical model.

[0115] The first construction unit is used to construct a passive noise model of the space-borne laser radar according to the first quantitative mapping relationship.

[0116] Optionally, in one embodiment of the present application, the construction module 100 includes: a second determining unit and a second construction unit.

[0117] The second determination unit is used to determine a second quantitative mapping relationship between the cloud optical thickness and the active laser signal under target environmental conditions based on an atmospheric transmission optical model.

[0118] The second construction unit is used to construct an active laser signal model of the space-borne laser radar according to the second quantitative mapping relationship.

[0119] Optionally, in one embodiment of the present application, the estimation module 300 includes: a third determination unit and an extraction unit.

[0120] Among them, the third determination unit is used to determine the number of noise photons in the area along the flight trajectory and in the vertical direction based on the measured photon point cloud data, and determine the cumulative interval length along the flight trajectory and in the vertical direction, so as to determine the measured average noise rate according to the number of noise photons and the cumulative interval length.

[0121] The extraction unit is used to extract the sea surface photon signal point cloud from the measured photon point cloud data, and use the sea surface photon signal point cloud to determine the measured average signal photon number.

[0122] Optionally, in one embodiment of the present application, the estimation module 300 includes: a fourth determination unit, a fifth determination unit and a calculation unit.

[0123] The fourth determination unit is used to determine the actual observation data pair of the signal and noise along the track of each segment according to the measured average noise rate and the measured average signal photon number.

[0124] The fifth determination unit is used to determine a theoretical corresponding relationship data pair between the signal and the noise under the optical thickness of the target cloud layer by using the theoretical noise rate and the theoretical signal photon number.

[0125] The calculation unit is used to calculate the minimum difference between the actual observation data pair and the theoretical corresponding relationship data pair, and determine the estimated result of the optical thickness of the sea cloud layer based on the minimum difference.

[0126] It should be noted that the aforementioned explanation of the embodiment of the method for estimating the optical thickness of marine clouds based on space-borne laser radar is also applicable to the device for estimating the optical thickness of marine clouds based on space-borne laser radar in this embodiment, and will not be repeated here.

[0127] According to the device for estimating the optical thickness of clouds at sea based on satellite-borne laser radar proposed in the embodiment of the present application, the passive noise model and active laser signal model of the satellite-borne laser radar can be constructed based on the atmospheric transmission optical model of the satellite-borne laser radar that meets certain conditions, and then the theoretical noise rate and theoretical signal photon number under the optical thickness of the target cloud layer can be determined respectively, and the measured photon point cloud data of the satellite-borne laser radar can be used to determine the measured average noise rate and the measured average signal photon number of each segment along the track, and then the estimated result of the optical thickness of clouds at sea can be determined by combining the theoretical noise rate and the theoretical signal photon number, which effectively improves the atmospheric environment monitoring capability of the satellite-borne laser radar and improves the estimation accuracy of the optical thickness of clouds at sea. Thus, the limitations in the inversion of different cloud thicknesses in the related technology and the problem that the existing satellite-borne laser radar signal and noise model do not consider environmental factors, which reduces the estimation accuracy of the optical thickness of clouds at sea are solved.

[0128] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0129] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0130] When the processor 502 executes the program, the method for estimating the optical thickness of marine clouds based on space-borne laser radar provided in the above embodiment is implemented.

[0131] Furthermore, the electronic device further comprises:

[0132] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0133] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0134] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0135] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0136] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0137] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0138] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for estimating the optical thickness of marine clouds based on space-borne laser radar.

[0139] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for estimating the optical thickness of marine clouds based on space-borne laser radar.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0141] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0142] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0143] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0144] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0145] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0146] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0147] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for estimating the optical thickness of marine clouds based on space-borne laser radar, characterized in that: The following steps are involved: Based on the atmospheric transmission optical model of the spaceborne laser radar that meets the preset conditions, a passive noise model and an active laser signal model of the spaceborne laser radar are respectively constructed; Determine the theoretical noise rate at the target cloud layer optical thickness according to the passive noise model, and determine the theoretical signal photon number at the target cloud layer optical thickness using the active laser signal model; The measured photon point cloud data of the satellite-borne laser radar are used to determine the measured average noise rate and the measured average signal photon number of each along-track segment, and the estimated result of the optical thickness of the sea cloud layer is determined based on the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number.

2. The method according to claim 1, characterized in that The constructing of the passive noise model of the spaceborne laser radar comprises: Determining a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions based on the atmospheric transmission optical model; A passive noise model of the satellite-borne laser radar is constructed according to the first quantitative mapping relationship.

3. The method according to claim 2, characterized in that The constructing of the active laser signal model of the spaceborne laser radar comprises: Determining a second quantitative mapping relationship between cloud optical thickness and active laser signals under the target environmental conditions based on the atmospheric transmission optical model; An active laser signal model of the satellite-borne laser radar is constructed according to the second quantitative mapping relationship.

4. The method according to claim 1, characterized in that: The method of using the measured photon point cloud data of the satellite-borne laser radar to determine the measured average noise rate and the measured average number of signal photons of each along-track segment includes: Based on the measured photon point cloud data, determine the number of noise photons in the area along the flight trajectory direction and the vertical direction, and determine the cumulative interval length along the flight trajectory direction and the vertical direction, so as to determine the measured average noise rate according to the number of noise photons and the cumulative interval length; The sea surface photon signal point cloud in the measured photon point cloud data is extracted, and the measured average signal photon number is determined using the sea surface photon signal point cloud.

5. The method according to claim 1, characterized in that The estimating result of determining the optical thickness of the sea cloud layer according to the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number includes: Determine the actual observation data pair of the along-track signal and noise of each segment according to the measured average noise rate and the measured average signal photon number; Determine a theoretical corresponding relationship data pair of signal and noise at the target cloud layer optical thickness by using the theoretical noise rate and the theoretical signal photon number; The minimum difference between the actual observation data pair and the theoretical corresponding relationship data pair is calculated, and based on the minimum difference, an estimation result of the optical thickness of the marine cloud layer is determined.

6. A device for estimating the optical thickness of marine clouds based on space-borne laser radar, characterized in that: include: A construction module, used to construct a passive noise model and an active laser signal model of the space-borne laser radar based on an atmospheric transmission optical model of the space-borne laser radar that meets preset conditions; A determination module, used to determine the theoretical noise rate at the optical thickness of the target cloud layer according to the passive noise model, and to determine the theoretical signal photon number at the optical thickness of the target cloud layer using the active laser signal model; The estimation module is used to determine the measured average noise rate and the measured average signal photon number of each along-track segment by using the measured photon point cloud data of the satellite-borne laser radar, and to determine the estimation result of the optical thickness of the sea cloud layer according to the measured average noise rate, the measured average signal photon number, the theoretical noise rate and the theoretical signal photon number.

7. The device according to claim 6, characterized in that The building blocks include: A first determining unit, configured to determine, based on the atmospheric transmission optical model, a first quantitative mapping relationship between cloud optical thickness and background noise under target environmental conditions; The first construction unit is used to construct a passive noise model of the satellite-borne laser radar according to the first quantitative mapping relationship.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating the optical thickness of marine clouds based on a spaceborne laser radar as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for estimating the optical thickness of marine clouds based on a satellite-borne laser radar as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method for estimating the optical thickness of marine clouds based on a space-borne laser radar as described in any one of claims 1 to 5.

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