A method for remotely sensing and reversing concentration of black carbon in snow

Abstract

The present application relates to the technical field of snow remote sensing modeling, and particularly relates to a snow black carbon concentration remote sensing inversion method. Remote sensing terrain data of a target region is acquired and terrain correction is performed; reflectivity of a snow area in the terrain-corrected remote sensing terrain data is acquired; a pure snow optical model is established according to the reflectivity of the snow area using an analytical asymptotic radiative transfer theory; ice-black carbon composite particle absorption characteristics are introduced to the pure snow optical model to construct a dirty snow optical model, and a rigorous radiative transfer model fitting result is used to optimize and solve the dirty snow optical model; and the black carbon concentration of the snow area is inverted according to the optimized dirty snow optical model. The snow remote sensing inversion method proposed in the present application can effectively reduce the influence of terrain shadows, model the black carbon absorption coefficient as a function of wavelength based on a snow radiative transfer model, greatly reduce the calculation complexity, and improve the calculation efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of snow cover remote sensing modeling technology, specifically to a method for remote sensing inversion of snow cover black carbon concentration. Background Technology

[0002] Snow, a key component of the Earth system, possesses a high albedo, reflecting 80%–90% of solar radiation back into the atmosphere. It plays an irreplaceable role in regulating heat exchange between the Earth and the atmosphere and maintaining global energy balance. However, black carbon aerosols, ubiquitous in the atmosphere, are transported across borders via atmospheric circulation and settle onto the snow surface or penetrate the snow layer via dry / wet deposition, significantly altering the optical properties of snow. At a microscopic scale, due to the increased probability of interaction with black carbon particles, photon scattering in snow is more frequent than in air, leading to snow darkening. Factors such as black carbon content, source composition, particle size distribution, and ice-black carbon particle mixing modes can all affect the reflectivity of snow by altering the penetration depth of solar radiation within the snow layer. These microscopic changes ultimately lead to significant alterations in the optical properties of snow at a macroscopic scale, thereby impacting regional and even global climate systems.

[0003] Studies have shown that the presence of black carbon in the cryosphere not only enhances solar radiation absorption, accelerates snowmelt, and alters snow albedo, but also generates positive radiative forcing, triggering a series of complex climate feedback mechanisms. When black carbon is deposited on snow and glacier surfaces, it significantly reduces snow / ice albedo, increases solar radiation absorption, and induces positive radiative forcing, thereby further triggering snow / ice albedo feedback, leading to more solar radiation energy being absorbed by the snow / ice layer and accelerating the melting process. More importantly, climate change caused by radiative forcing induced by black carbon, in turn, affects the transport pathways of black carbon and the dry and wet deposition rates in the atmosphere, further influencing the concentration of black carbon deposition in snow / ice, forming a complex cyclical feedback mechanism. When combined with dust storms, the snow albedo can decrease by up to 0.423, and this significant change in albedo directly affects the regional energy balance.

[0004] The presence of black carbon in snow not only exacerbates snowmelt but also significantly shortens snow cover duration, increases peak flood flow, alters regional water cycle and runoff characteristics, and may induce a series of secondary disasters, posing a potential threat to regional ecological security and socio-economic development. Specifically, the impact of black carbon on snow cover is mainly reflected in three aspects: changes in albedo, snowmelt processes, and radiative forcing. Studies have shown that the snowmelt effect of black carbon in some regions even exceeds the impact of greenhouse gases. When these light-absorbing particles (LAPs) are deposited in the cryosphere, they can significantly influence the global climate change process. On a temporal scale, related studies have shown that, whether at the watershed or global scale, black carbon significantly accelerates snowmelt and shortens snow cover duration on timescales ranging from several days to several weeks.

[0005] Considering that the refractive index, morphology, particle size, and mixing state of black carbon with other particles vary with emission sources and duration, it is difficult to make cross-sectional comparisons of black carbon samples from different times and spaces. Therefore, snow black carbon concentration has become a common indicator for quantifying the black carbon-induced albedo effect. Currently, methods for obtaining snow black carbon concentration mainly include three categories: field surveys, numerical simulations, and remote sensing inversion. Among them, traditional field surveys can accurately obtain snow black carbon content and optical property data at the point scale, and, combined with various aerosol source tracing models, clarify a series of mechanisms such as the source, migration, deposition, and aging of black carbon, providing important data support for numerical simulations and remote sensing inversions. However, continuous field surveys require a large amount of resource investment, and the sampling scale is limited (usually 10). 1 ~10 2 While large-scale time-series monitoring is difficult due to the sheer volume of data, numerical simulations of climate models can more systematically reveal the transport, evolution, and removal mechanisms of black carbon aerosols. Their advantages in large-scale research are unmatched by traditional single-point observations. However, the driving data resolution of climate models is relatively coarse, and their degree-based grid simulation results cannot reflect the subtle changes in snow black carbon concentration under complex terrain. Given the significant impact of black carbon on snow and ice albedo and radiation intensity, a high-precision remote sensing inversion algorithm for snow and ice albedo feedback mechanisms in climate change research is urgently needed. Currently, most studies are still in the single-point sampling stage, lacking a mature remote sensing inversion algorithm system for snow black carbon concentration. The key technical challenges in current snow black carbon remote sensing inversion include: first, the high complexity of the snow medium and the multiple scattering characteristics of black carbon particles make accurate simulation of the radiation transfer process difficult; second, the diverse mixing modes of ice-black carbon composite particles, with significant differences in optical properties under different mixing states, pose challenges to the accurate calculation of extinction characteristics; and third, factors such as topographic effects and surface heterogeneity in actual observations increase the uncertainty of the inversion process. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a remote sensing inversion method for snow cover black carbon concentration. The method includes: acquiring remote sensing topographic data of the target area and performing topographic correction; acquiring the reflectance of the snow-covered area in the topographically corrected remote sensing topographic data; establishing a pure snow optical model based on the reflectance of the snow-covered area using analytical asymptotic radiative transfer theory; introducing the absorption characteristics of ice-black carbon composite particles into the pure snow optical model to construct a dirty snow optical model, and optimizing the dirty snow optical model using the fitting results of a rigorous radiative transfer model; and inverting the black carbon concentration of the snow-covered area based on the optimized optical model. The snow cover remote sensing inversion method proposed in this invention can effectively reduce the influence of topographic shadows. By modeling the black carbon absorption coefficient as a function of wavelength based on the snow radiative transfer model, it significantly reduces computational complexity and improves computational efficiency and accuracy.

[0007] This invention employs the following technical solution: a remote sensing inversion method for snow black carbon concentration, comprising:

[0008] Acquire remote sensing topographic data of the target area and perform topographic correction;

[0009] Obtain the reflectance of snow-covered areas in the terrain-corrected remote sensing topographic data;

[0010] Based on the reflectivity of the snow-covered area, an analytical asymptotic radiative transfer model is used to establish a pure snow optical model;

[0011] The absorption characteristics of ice-black carbon composite particles were introduced into the pure snow optical model to construct the dirty snow optical model, and the fitting results of the rigorous radiative transfer model were used to optimize and solve the dirty snow optical model.

[0012] The black carbon concentration in the snow-covered area was inverted based on the optimized optical model.

[0013] Furthermore, remote sensing topographic data of the target area is acquired and topographic correction is performed, including:

[0014] The remote sensing topographic data is corrected using an improved SCS+C correction model. The corrected reflectance is expressed as follows:

[0015]

[0016] c snow,i =b snow,i / a snow,i

[0017] Where, ρ COR ρ represents the surface reflectance after SCS+C topographic correction. L2 The surface reflectance is L2 level satellite, cosi is the cosine of the local solar incidence angle, and θ is the surface reflectance. s Where S is the solar zenith angle and S is the surface slope. The azimuth of the sun. c represents the slope aspect of the terrain. snow,i Let a be the empirical parameter of the linear regression equation between the reflectance of the i-th band pixel in the snow-covered area and the cosine of the solar zenith angle. snow,i and b snow,i These are the intercept and slope of the regression equation for the i-th band in the snow-covered area, respectively.

[0018] Furthermore, the reflectance of snow-covered areas in the terrain-corrected remote sensing terrain data is obtained, including:

[0019] Obtain the normalized snow cover index of each pixel in the remote sensing terrain data, and determine the snow cover area in the remote sensing terrain data based on the normalized snow cover index of each pixel.

[0020] When the normalized snow index is greater than a set threshold, the pixel is determined to be a snow-covered pixel. All pixels are determined in turn to obtain the snow-covered area in the remote sensing terrain data.

[0021] Furthermore, the normalized snow cover index of each pixel in the remote sensing terrain data is obtained and represented as:

[0022] NDSI=(ρ COR,GREEN -ρ COR,NIR ) / (ρ COR,GREEN +ρ COR,NIR )

[0023] Where, ρ COR,GREEN and ρ COR,NIR These represent the surface reflectance in the green band and near-infrared band of the remote sensing topographic data after topographic correction.

[0024] Furthermore, based on the reflectivity of the snow-covered area, an optical model of the snow-covered area is established using an analytical asymptotic radiative transfer model, including:

[0025] Based on the analytical asymptotic radiative transfer model, the relationship between snow reflectivity, spherical albedo, and Lambertian reflectivity in snow-covered areas is established as follows:

[0026]

[0027] Where R is the snow reflectance, r s R0 is the spherical albedo, μ is the Lambert reflectance, and μ0 and μ1 are the cosines of the solar zenith angle and the observed zenith angle, respectively. u(μ0) and u(μ) are escape functions, which characterize the angular distribution of photons escaping when the light source is located at an infinite depth inside the medium. Together, they form the angular function f.

[0028] Furthermore, the optical model also includes:

[0029] The optical model is simplified based on the spherical albedo in the optical model:

[0030] The method for obtaining the spherical albedo is as follows:

[0031] r s =e -y ,

[0032] Where ω0 is the single-scattering albedo, g is the asymmetry factor, β is the probability of photon absorption, and y is an intermediate variable. When the snow in the snow-covered area does not contain black carbon, its calculation method is as follows:

[0033] k abs =Bαc,

[0034] Where, k abs K is the snow mass absorption coefficient. ext λ is the extinction coefficient, α is the absorption coefficient of ice, λ is the wavelength, c is the snow density, χ is the imaginary part of the complex refractive index of ice particles at wavelength λ, B is the shape factor used to simulate the shape of snow particles, and the effective absorption length l is expressed as:

[0035] l=ξd,

[0036] Where ξ is the morphology-dependent parameter, d is the snow particle size, and l is the effective absorption length;

[0037] In summary, the simplified optical model is as follows:

[0038]

[0039] Where λ1 and λ2 are wavelengths of 865 nm and 1020 nm, respectively, R(λ2) is the satellite reflectivity at a wavelength of 1020 nm, u(μ0) and u(μ) are escape functions, and R(λ i α(λ) is the snow reflectivity at wavelength i. i R is the absorption coefficient of ice, and R0 is the Lambert reflectance.

[0040] Furthermore, ice-black carbon composite particles are introduced into the optical model, specifically:

[0041] When the particle size distribution follows a Jungian power law distribution, the following is adopted: The absorption coefficient of pure black carbon is simplified by using an index, expressed as:

[0042]

[0043] k0≡k bc (λ0), λ0=1μm

[0044]

[0045] Where m represents BC absorption The coefficient, μ, represents the cosine of the solar zenith angle. c represents the absorption coefficient of snow containing black carbon. ice c represents the volume concentration of ice particles. bc k represents the volume concentration of black carbon. bc k represents the volume absorption coefficient of pure black carbon. ice α represents the volume absorption coefficient of pure ice. ice λ represents the absorption coefficient of pure ice, k0 represents the volume absorption coefficient of pure black carbon at 1 μm, λ is the wavelength, and λ0 is 1 μm. and F bc n is an intermediate variable. pol =1.75, χ pol =0.47;

[0046] Based on the analytical asymptotic radiative transport model, the probability of a photon being struck by an ice-black carbon composite particle is expressed as:

[0047]

[0048] in, β represents the probability that a photon is absorbed by an ice particle. pol Characterizes the probability of photons being absorbed by black carbon.

[0049] The reflectance and spherical albedo of snow containing black carbon are expressed as follows:

[0050]

[0051] Where R is the snow reflectance, r s R0 is the snow spherical albedo, R0 is the snow Lambertian reflectance, x is the angular function, α is the pure ice absorption coefficient, l is the effective absorption length, and f and As an intermediate variable,

[0052] Furthermore, the dirty snow optical model is optimized and solved using the fitting results of a rigorous radiative transfer model, and the expression is:

[0053]

[0054] A = 1.247, B = 1.186, C = 5.157

[0055] p(θ)=11.1exp(-0.087θ)+1.1exp(-0.014θ)

[0056] cosθ=-cosθ0cosθv +sinθ0sinθ v cosφ r

[0057] Where R0 is the Lambertian reflectance, μ and μ0 are the cosines of the solar zenith angle and the observed zenith angle, respectively, p(θ) is the phase function, cosθ is an intermediate variable, AC is a constant, θ0 is the solar zenith angle, and θ v Indicates the observed zenith angle, φ r Relative azimuth

[0058] The beneficial effects of this invention are as follows: This invention proposes a terrain correction algorithm specifically for snow remote sensing inversion, which can effectively reduce the influence of terrain shadows; in addition, by introducing snow area ratio data, it improves the secondary error generated in the correction process due to neglecting the high reflectivity of snow. These improvements significantly reduce the error of snow black carbon concentration inversion, thereby improving the accuracy of inversion.

[0059] Compared with conventional field surveys, the present invention significantly reduces costs while providing large-scale snow black carbon concentration distribution that can be continuously monitored even when no field surveys are available. Compared with climate model simulations, the present invention does not require a large amount of driving data, has a faster computation speed, higher spatial resolution, and can obtain more detailed spatial distribution maps of snow black carbon. In addition, remote sensing retrieval results can more accurately reflect the actual concentration of black carbon deposited in snow, rather than the concentration of black carbon aerosols suspended in the atmosphere. Therefore, the present invention shows significant advantages in comparison with traditional climate models.

[0060] This invention is based on the snow radiative transfer model, which models the black carbon absorption coefficient as a function of wavelength, significantly reducing computational complexity and improving computational efficiency and accuracy. This invention also considers the impact of poor satellite imaging quality on inversion accuracy, and uses a rigorous radiative transfer model to fit the results and optimize the snow Lambertian reflectance, thereby improving inversion accuracy. Compared with traditional empirical models, this algorithm has a solid physical foundation and stronger generalization ability, thus having greater advantages in application. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of a remote sensing inversion method for snow black carbon concentration according to an embodiment of the present invention.

[0063] Figure 2 This is a schematic diagram of the accuracy verification result of remote sensing inversion of snow black carbon according to an embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Black carbon in snow can induce a strong extinction effect, and the extinction intensity varies greatly at different wavelengths. Clarifying the differences in the extinction effect caused by black carbon on snow reflectivity at different wavelengths is a fundamental condition for remote sensing inversion of snow black carbon. To clarify the reflectivity of snow and the extinction effect induced by black carbon, it is necessary to select appropriate reflectivity bands for snow black carbon inversion. Based on this, by making assumptions about the optical thickness, heterogeneity, close packing, and mixing state of snow, the extinction term of black carbon is introduced into the scattering characteristics of ice particles. Ultimately, the optical characteristics of snow containing black carbon can be simulated. The reverse solution in the above process is the basic principle of snow black carbon remote sensing inversion. Therefore, this invention provides a remote sensing inversion method for snow black carbon concentration. This method is based on the analytical asymptotic radiative transfer theory, introduces the black carbon extinction coefficient into the calculation of the extinction characteristics of ice-black carbon composite particles, and uses the extinction bands of ice particles and black carbon to solve for the snow black carbon concentration. This invention can effectively improve the quantitative application level of snow cover remote sensing, quantify the spatial distribution pattern of snow cover black carbon, and has practical significance for accurately grasping the snow cover generation and dissipation patterns and protecting regional agricultural and pastoral development and the ecological environment. Specifically, it includes:

[0066] Remote sensing topographic data of the target area is acquired and topographic correction is performed; the reflectance of the snow-covered area in the topographically corrected remote sensing topographic data is acquired; an optical model of pure snow is established based on the reflectance of the snow-covered area using an analytical asymptotic radiative transfer model; the absorption characteristics of ice-black carbon composite particles are introduced into the pure snow optical model to construct a dirty snow optical model, and the dirty snow optical model is optimized and solved using the fitting results of a rigorous radiative transfer model; the black carbon concentration of the snow-covered area is inverted based on the optimized optical model.

[0067] In a specific embodiment of the present invention, a schematic flowchart of a remote sensing inversion method for snow black carbon concentration is shown below. Figure 1 As shown, it includes:

[0068] Acquire remote sensing topographic data of the target area and perform topographic correction;

[0069] This invention combines digital terrain elevation and snow cover area ratio data, and employs an improved SCS+C correction model to perform terrain correction on L2-level satellite remote sensing data, primarily based on MOD09GA. The relationship between the corrected reflectance and the original reflectance is expressed as follows:

[0070]

[0071] c snow,i =b snow,i / a snow,i

[0072] In the formula, ρ COR ρ represents the surface reflectance after SCS+C topographic correction. L2 The surface reflectance is L2 level satellite, cosi is the cosine of the local solar incidence angle, and θ is the surface reflectance. s Where S is the solar zenith angle and S is the surface slope. The azimuth of the sun.

[0073] Due to the slope of the terrain, The solar azimuth angle is denoted as c. Due to the extremely high reflectivity of snow, directly applying full-band correction to ground features would lead to a significant overestimation of the reflectivity of snow-covered areas. This invention aims to remotely invert the black carbon concentration in snow; therefore, accurately obtaining the true surface reflectivity of snow-covered areas is crucial. To this end, this invention introduces the snow area ratio data MOD10A1 to identify snow-covered ground features, where c... snow,i That is, the empirical parameter a in the linear regression equation between the reflectance of the i-th band pixel in the snow-covered area and the cosine of the solar zenith angle. snow,i and b snow,i These are the intercept and slope of the regression equation for the i-th band in the snow-covered area. Considering that the diurnal variation of snow cover has a significant impact on reflectivity, in order to ensure the stability of the correction results, this invention further adopts the ridge regression algorithm, which enhances the robustness of the regression by introducing a regularization term.

[0074] Obtain the reflectance of snow-covered areas in the terrain-corrected remote sensing terrain data.

[0075] This invention further employs the Normalized Difference Snow Index (NDSI) of the MOD10A1 product to determine snow-covered areas in remote sensing topographic data. The NDSI calculation method is as follows:

[0076] NDSI=(ρ COR,GREEN -ρ COR,NIR ) / (ρ COR,GREEN +ρ COR,NIR )

[0077] Where, ρ COR,GREENand ρ COR,NIR These are the surface reflectances of MOD10A1 in the green band and near-infrared band after terrain correction, respectively. When NDSI > 0.4, this invention considers the pixel to be a snow-covered pixel, which can be used for remote sensing inversion of snow cover black carbon.

[0078] Based on the reflectivity of the snow-covered area, an analytical asymptotic radiative transfer model is used to establish a pure snow optical model for the snow-covered area.

[0079] This invention models the optical properties of pure snow based on the Analytical Asymptotic radiative transfer theory (ART). The basic assumptions of ART include the following: the optical thickness of the snow is so large that the influence of the underlying surface can be ignored; for the visible light band, a snow thickness of >10-30 cm is required to satisfy this assumption; for the near-infrared band, the penetration depth is <2-5 cm; the heterogeneity of the snow layer in the horizontal and vertical directions is ignored, assuming it is a planar parallel medium; the influence of close packing on the scattering characteristics of snow is ignored, and all inversion parameters are snow surface parameters; it is assumed that the snow particle size is much larger than the incident light in the visible and near-infrared ranges; when the snow contains black carbon, it is assumed that the black carbon is mixed with the ice particles.

[0080] The ART model presents the relationship between snow reflectance, spherical albedo, and Lambertian reflectance:

[0081]

[0082] Where R is the snow reflectance, r s R0 is the spherical albedo, μ is the Lambert reflectance, and μ0 and μ1 are the cosines of the solar zenith angle and the observed zenith angle, respectively. u(μ0) and u(μ) are escape functions, which characterize the angular distribution of photons escaping when the light source is located at an infinite depth inside the medium. Together, they form the angular function f.

[0083] Spherical albedo r s The calculation involves the single-scattering albedo ω0 and the asymmetry factor g, and its expression is as follows:

[0084] r s =e -y ,

[0085] β, also known as the probability of photon absorption, sums with the single-scattering albedo ω0 to 1. y is an intermediate parameter, influenced by the optical properties of ice particles and black carbon. When snow contains no black carbon, β is the ice particle absorption coefficient κ. abs and extinction coefficient κ ext The ratio:

[0086]

[0087] α is the absorption coefficient of ice, λ is the wavelength, and c is the snow density (parameterized to 1 / 3 g / cm³). 3 ), where χ is the imaginary part of the complex refractive index of ice particles at wavelength λ. Therefore, when the wavelength is determined, the absorption coefficient α of ice can be considered as a known value. In traditional snow radiation transmission models, snow is usually regarded as a medium layer composed of large spherical particles, and most of them use MIE scattering to calculate its extinction efficiency, scattering efficiency and absorption efficiency. In nature, snow particles are affected by a variety of factors such as temperature, humidity and pressure, and their geometric shape is complex and varied, which is far from the assumption of spherical particles. In ART, the irregular shape of snow particles is simulated by controlling the value of the shape factor B.

[0088] This invention introduces an effective absorption length l, which can be expressed as the product of the morphology-dependent parameter ξ and the snow particle size d:

[0089] l=ξd,

[0090] Combining the above equations, we can obtain the spherical albedo r. s The simplified result is:

[0091]

[0092] Where α is the absorption coefficient of ice, l is the effective absorption length, μ and μ0 are the cosines of the solar zenith angle and the observed zenith angle, respectively; u(μ0) and u(μ) are escape functions; it can be found that the above formula contains two unknowns: pure Schrambert reflectance R0 and absorption length l, which can be solved by using reflectance of two bands. These two bands need to satisfy two points: sensitive to inversion parameters; and rarely affected by atmospheric scattering and absorption.

[0093]

[0094] Where λ is the wavelength that affects the reflectivity of snow, and it should generally be within the visible light range. Due to satellite band limitations, 865nm and 1020nm can usually be used, that is, λ1 and λ2 are wavelengths of 865nm and 1020nm respectively. R(λ2) is the satellite reflectivity at the corresponding 1020nm wavelength, u(μ0) and u(μ) are escape functions, R is the snow reflectivity, and R0 is the Lambertian reflectivity.

[0095] By obtaining the pure snow Lambert reflectance R0 and absorption length l, basic modeling of snow properties such as snow reflectance, spherical albedo, and snow grain size can be achieved.

[0096] Ice-black carbon composite particles were introduced into the optical model.

[0097] Assuming that black carbon-induced extinction mainly occurs in the visible light range, while the counterparts of ice particles mainly occur in the near-infrared range, when the particle size distribution follows a Jungian power law distribution, the following applies: Using an index to simplify the absorption coefficient of pure black carbon has proven effective:

[0098]

[0099] k0≡k bc (λ0), λ0=1μm

[0100]

[0101] Where m represents BC absorption The coefficient, μ, represents the cosine of the solar zenith angle. c represents the absorption coefficient of snow containing black carbon. ice c represents the volume concentration of ice particles. bc k represents the volume concentration of black carbon. bc k represents the volume absorption coefficient of pure black carbon. ice α represents the volume absorption coefficient of pure ice. ice λ represents the absorption coefficient of pure ice, k0 represents the volume absorption coefficient of pure black carbon at 1 μm, λ is the wavelength, and λ0 is 1 μm. and F bc n is an intermediate variable. pol =1.75, χ pol =0.47;

[0102] Based on the fundamental theory of ART, the probability β of a photon being struck by an ice-black carbon composite particle is expressed as:

[0103]

[0104] Thus, the spherical albedo of snow is simplified to:

[0105] r s =e -y ,

[0106] Given parameters z = y 2 :

[0107]

[0108] Where f represents an angle function, This represents the volume-normalized absorption coefficient;

[0109] The effective absorption length l is:

[0110] l=ξd,

[0111] The asymmetry factor g of ice crystal clouds in the visible light ranges from 0.74 to 0.76. Given the lack of measured data on the asymmetry factor of snow accumulation in previous studies, the algorithm of this invention uses the median data g = 0.75 for ice crystal clouds. The average value of the absorption enhancement coefficient B in existing studies is 1.6; substituting this value, we get ξ ≈ 11.38.

[0112] Simplified snow reflectance R and spherical albedo r s Represented as:

[0113]

[0114] Where R is the snow reflectance, r s R0 is the snow albedo, R0 is the snow Lambertian reflectance, x is the angle function, α is the pure ice absorptivity, and m represents the BC absorptivity. coefficients, l is the effective suction length, f and As an intermediate variable,

[0115] The above formula contains four unknown parameters: the effective absorption length l, the Lambertian reflectance R0 of a semi-infinite non-absorbing snow layer, the parameter f, and the absorption. The coefficient m; these four parameters can be solved using any four bands of visible light and near-infrared light. This invention takes the snow reflectivity R as an example:

[0116]

[0117] In the above formula, numbers 1 to 4 are spectral channels. The selection principle is that the absorption effect of ice can be ignored for the first two channels (λ1, λ2), and the light absorption effect of pollutants can be ignored for the last two channels (λ3, λ4). This assumption applies to snow with mild pollution.

[0118] The optical model was optimized and solved using the fitting results of a rigorous radiative transfer model.

[0119] When satellite imaging quality is poor, the chevron reflectivity R0 will be distorted, directly leading to a significant decrease in the accuracy of the above inversion formula. Therefore, this invention simplifies the expression for the chevron reflectivity R0 by fitting the results of a rigorous radiative transfer model:

[0120]

[0121] A = 1.247, B = 1.186, C = 5.157

[0122] p(θ)=11.1exp(-0.087θ)+1.1exp(-0.014θ)

[0123] cosθ=-cosθ0cosθv +sinθ0sinθ v cosφ r

[0124] Where R0 is the Lambertian reflectance, μ and μ0 are the cosines of the solar zenith angle and the observed zenith angle, respectively, p(θ) is the phase function, cosθ is an intermediate variable, θ0 is the solar zenith angle, and θ v φ represents the observed zenith angle. r This represents the relative azimuth angle. Therefore, only three unknowns remain: the effective absorption length *l*, the absorption... The coefficient m and the spectral absorption coefficient k of black carbon at 1 μm abs (λ), this invention uses two wavelengths within the visible light range to calculate the effective absorption length l and absorption. Coefficient m.

[0125]

[0126] The concentration of black carbon in snow can be expressed as:

[0127]

[0128] K(λ)=Fα pol (λ),

[0129]

[0130] Where B is the absorption enhancement parameter, R0 is the Lambertian reflectance of snow, and R1 can be taken as the reflectance of snow at 400 nm. Let f be an exponential function of wavelength, f be an angular function, and l be the absorption length. For black carbon, F is taken as 0.95, and α... pol (λ0) is the absorption coefficient of black carbon; for black carbon, the real part of the complex refractive index n pol and the imaginary part χ pol The variation with wavelength is very small and is usually considered a constant, n pol =1.75, χ pol =0.47.

[0131] In one experimental embodiment of the present invention:

[0132] This invention is based on multiple sets of snow black carbon and organic carbon samples collected in northern Xinjiang between 2016 and 2019. A comprehensive investigation was conducted on the changes in snow black carbon concentration and its radiative forcing effect. These data form the key support for verifying the accuracy of different snow black carbon remote sensing inversion algorithms. Sample collection covered different areas in northern Xinjiang, including various land cover types such as meadows, urban areas, highways, and industrial zones. The time span included both instantaneous sampling and long-term sequential observations within meteorological fields. Typically, contaminated snow samples were sealed in Whirl-Pak bags (approximately 2 liters in capacity, unmelted) and frozen before being transported to the laboratory for filtration and analysis. Over 300 snow samples were collected, melted, and filtered, and the black carbon content in each sample was determined using the DRI2015A method.

[0133] Based on measured data (127 sets) from northern Xinjiang, the accuracy of the snow cover black carbon inversion algorithm was verified using three evaluation indicators: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). Figure 2 As shown, at a spatial resolution of 500 meters for MODIS satellite data, the MAE, RMSE, and R² values ​​obtained by the inversion algorithm proposed in this invention are 0.55 μg / g, 0.9 μg / g, and 0.88, respectively, demonstrating high inversion accuracy. The above results indicate that the remote sensing estimation method for snow black carbon concentration based on the radiative transfer model proposed in this invention has good applicability in the northern Xinjiang region.

[0134] Meanwhile, the proposed snow cover black carbon inversion algorithm is highly flexible and universal, and does not depend on a specific satellite platform or computing environment. The algorithm can be deployed using Python, Google Earth Engine (GEE), or JAVA. The algorithm can adapt to satellite data of different scales and provide key data support for local and even global climate change research, water resource management, and ecological protection.

[0135] This invention is independent of specific computing environments and can seamlessly integrate with cloud computing platforms such as Google Earth Engine, Microsoft Planetary Compute, and PIE-Engine. It is also compatible with local development environments such as Visual Studio Code, PyCharm, and R-Studio. Furthermore, this invention is not limited to specific programming languages, supporting multiple languages ​​including JavaScript, Python, Matlab, R, and IDL, further enhancing the applicability and flexibility of the algorithm. Specifically, because it is not limited to specific satellites, this invention can be deployed on various platforms, including ground-based, space-based, and spaceborne platforms. At the ground-based level, this invention can perform inversion using spectral / image data acquired by spectrometers or imaging spectrometers, achieving accurate inversion at a single-point scale. At the space-based level, this invention can be integrated into airborne platforms of UAVs or manned aircraft to obtain regional-scale spatial distribution maps of snow cover and black carbon based on aerial photography data. Moreover, due to the algorithm's... With low computing power requirements, some airborne platforms can even achieve real-time computing. At the spaceborne level, the algorithm proposed in this invention can be deployed on hyperspectral or multispectral satellite platforms, including but not limited to hyperspectral platforms such as ZY-1 02D (30-meter resolution), Hyperion (30-meter resolution), EnMAP, PRISMA (30-meter resolution), and CHIME-X (30-meter resolution); as well as multispectral platforms such as Sentinel-2 (10-meter resolution), Landsat series (30-meter resolution), Sentinel-3 (300-meter resolution), and MODIS (500-meter resolution).

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote sensing inversion method for snow black carbon concentration, characterized in that, include: Acquire remote sensing topographic data of the target area and perform topographic correction; Obtain the reflectance of snow-covered areas in the terrain-corrected remote sensing topographic data; Based on the reflectivity of the snow-covered area, an analytical asymptotic radiative transfer model is used to establish a pure snow optical model, including: Based on the analytical asymptotic radiative transfer model, the relationship between snow reflectivity, spherical albedo, and Lambertian reflectivity in snow-covered areas is established as follows: ； in, For snow reflectivity, For spherical albedo, For Lambertian reflectance, and These are the cosines of the solar zenith angle and the observed zenith angle, respectively. and Let be the escape function, characterizing the angular distribution of photons escaping when the light source is located at an infinite depth inside the medium; together they form an angular function. ; The pure snow optical model also includes: The pure snow optical model is simplified based on the spherical albedo in the pure snow optical model: The method for obtaining the spherical albedo is as follows: ； in, Albedo is the single-scattering albedo. It is an asymmetric factor. This represents the probability that a photon is absorbed. As an intermediate variable, when the snow in the snow-covered area does not contain black carbon, its calculation method is as follows: ； in, The snow mass absorption coefficient is... Extinction coefficient, The absorption coefficient of ice. For wavelength, snow density, For ice particles at wavelength of The imaginary part of the complex refractive index at time , The shape factor is used to simulate the morphology of snow particles; the effective absorption length is... Represented as: ； in, For morphology-dependent parameters, Snow particle size; In summary, the simplified Lambert reflectance is obtained. and effective absorption length for: ； in, and The wavelengths are 865 nm and 1020 nm, respectively. Satellite reflectivity at a wavelength of 1020 nm; Ice-black carbon composite particles were introduced into the pure snow optical model to construct a dirty snow optical model, and the dirty snow optical model was optimized and solved by fitting the results of a rigorous radiative transfer model. The black carbon concentration in the snow-covered area was inverted based on the optimized optical model.

2. The remote sensing inversion method for snow black carbon concentration according to claim 1, characterized in that: Acquire remote sensing topographic data of the target area and perform topographic correction, including: The remote sensing topographic data is corrected using an improved SCS+C correction model. The corrected reflectance is expressed as follows: ； in, The surface reflectance after SCS+C topographic correction. For L2 level satellite surface reflectivity, Let be the cosine of the local solar incidence angle. The zenith angle of the sun. The slope of the ground. The azimuth of the sun. The slope aspect of the terrain; Let be the empirical parameters of the linear regression equation between the reflectance of the i-th band pixel in the snow-covered area and the cosine of the solar zenith angle. and These are the intercept and slope of the regression equation for the i-th band in the snow-covered area, respectively.

3. The remote sensing inversion method for snow cover black carbon concentration according to claim 1, characterized in that: Obtain the reflectance of snow-covered areas in the terrain-corrected remote sensing terrain data, including: Obtain the normalized snow cover index of each pixel in the remote sensing terrain data, and determine the snow cover area in the remote sensing terrain data based on the normalized snow cover index of each pixel. When the normalized snow index is greater than a set threshold, the pixel is determined to be a snow-covered pixel. All pixels are determined in turn to obtain the snow-covered area in the remote sensing terrain data.

4. The method for remote sensing inversion of snow black carbon concentration according to claim 3, characterized in that: The normalized snow cover index for each pixel in the remote sensing terrain data is obtained and represented as: ； in, and These represent the surface reflectance in the green band and near-infrared band of the remote sensing topographic data after topographic correction.

5. The remote sensing inversion method for snow black carbon concentration according to claim 1, characterized in that: The pure snow optical model is introduced with ice-black carbon composite particles, specifically: When the particle size distribution follows a Jung power law distribution, the Ångström exponent is used to simplify the absorption coefficient of pure black carbon, expressed as: ； in, This represents the Ångström absorption coefficient of black carbon. This indicates the absorption coefficient of snow containing black carbon. Indicates the volume concentration of ice particles. Indicates the volume concentration of black carbon. This represents the volume absorption coefficient of pure black carbon. This represents the volume absorption coefficient of pure ice. This represents the absorption coefficient of pure ice. This represents the volume absorption coefficient of pure black carbon at 1 μm. For wavelength, It is 1μm. and As an intermediate variable, , ; Based on the analytical asymptotic radiative transport model, the probability of a photon being absorbed by an ice-black carbon composite particle is expressed as: ； in, Characterizes the probability of a photon being absorbed by an ice particle. Characterizes the probability of photons being absorbed by black carbon; The reflectance and spherical albedo of snow containing black carbon are expressed as follows: ； in, For snow reflectivity, For spherical albedo, For Lambertian reflectance, It is an angle function. The absorption coefficient of ice. For effective suction length, and It is an intermediate variable.

6. The remote sensing inversion method for snow black carbon concentration according to claim 1, characterized in that: The dirty snow optical model is optimized and solved using the fitting results of a rigorous radiative transfer model. The expression is as follows: ； in, For Lambertian reflectance, and These are the cosines of the solar zenith angle and the observed zenith angle, respectively. Let be the phase function. As an intermediate variable, The zenith angle of the sun. Indicates the observed zenith angle. Indicates the relative azimuth angle.

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