CN112749494B - Method for acquiring dynamic accumulated snow depth - Google Patents
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Abstract
The invention discloses a method for acquiring dynamic accumulated snow depth, which comprises the following steps: obtaining an effective correlation length Pex of inversion of a meteorological station by inversion of an optimal iterative method of correlation length of a snow layer of the station, and obtaining the effective correlation length Pex of other dry snow pixels on the snow surface according to a Kring interpolation method; and for each snow pixel, searching a snow depth inversion model in a corresponding range according to the size of the relevant length, and calculating the snow depth to obtain a spatial distribution result of the snow depth. By dynamically inverting the correlation length and combining the brightness and temperature gradient of the passive microwave radiometer, a new inversion algorithm is established, so that the degree of the evolution of the snow particle size can be dynamically determined, an inversion model of a corresponding stage is searched, the accuracy of snow depth inversion is improved, and the snow depth inversion can be realized in a large area.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a method for acquiring dynamic accumulated snow depth.
Background
The inversion of snow depth/water equivalent has been in progress for over thirty years, but there are still many challenges. The most important challenge is how to separate the snow accumulation (depth) from the influence of other snow accumulation characteristic parameters (density, particle size and other structural characteristic parameters). In an inversion algorithm, the prior information of the snow structure parameters is important for snow depth inversion, and the snow microstructure parameters are gradually changed along with the evolution of time. However, in various regional linear statistical empirical methods widely used at present, the parameters are set as fixed values, for example, the classic Chang algorithm (WESTDC product in snow depth in China with refitting coefficients by train researchers in the northwest province), the wind cloud algorithm (wind cloud snow depth product in the national weather bureau), the NCIDC product and the product of the American snow ice data center do not consider the change of the snow microstructure, and the influence of structural parameter evolution on snow depth inversion cannot be solved.
Disclosure of Invention
According to the method, the relevant length of the accumulated snow can be inverted by simulating and matching the field accumulated snow profile, and the influence caused by accumulated snow deterioration can be dynamically calculated. By dynamically inverting the correlation length and combining the brightness and temperature gradient of the passive microwave radiometer, a new inversion algorithm is established, so that the degree of the evolution of the snow particle size can be dynamically determined, an inversion model of a corresponding stage is searched, the accuracy of snow depth inversion is improved, and the snow depth inversion can be realized in a large area.
The invention provides a method for acquiring the dynamic snow depth, which improves the snow depth inversion accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for acquiring dynamic accumulated snow depth comprises the following steps: obtaining an effective correlation length Pex of inversion of a meteorological station by inversion of an optimal iterative method of correlation length of a snow layer of the station, and obtaining the effective correlation length Pex of other dry snow pixels on the snow surface according to a Kring interpolation method; and for each snow pixel, searching a snow depth inversion model in a corresponding range according to the size of the relevant length, and calculating the snow depth to obtain a spatial distribution result of the snow depth. The lookup table is:
when Pex is less than or equal to 0.2,
SD＝1.174*(Tb19V-Tb37V)/(1-0.7*f)+9.765 R2＝0.5614；
2.0.2 < Pex is less than or equal to 0.25,
SD＝0.8209*(Tb19V-Tb37V)/(1-0.7*f)+2.646 R2＝0.878；
3.0.25 Pex is less than or equal to 0.3,
SD＝0.5572*(Tb19V-Tb37V)/(1-0.7*f)+0.6282 R2＝0.9249；
when Pex >0.3,
SD＝0.3292*(Tb19V-Tb37V)/(1-0.7*f)-0.6341 R2＝0.584。
the optimal iterative inversion method comprises the following steps: inputting the snow depth of the meteorological station into an MEMLS model, traversing the value of the correlation length within a certain range by a certain step length, taking the vertical polarization difference (18.7-36.5) GHz as an inversion operator, and outputting the result of the correlation length at the moment as the inversion value of the station when the error between the model simulation and satellite data is minimum.
A certain range of the correlation length is specifically chosen to be (0mm, 0.6mm) and the step size is chosen to be 0.005 mm.
Before the inversion step by the optimal iterative method of the station snow-layer correlation length, the method also comprises data acquisition and preprocessing of the acquired data.
The data acquisition includes: acquiring AMSR2 radiometer data, ground meteorological station snow depth, longitude and latitude data and MODIS surface coverage data MOD12Q 1.
The preprocessing the acquired data comprises: preprocessing acquired AMSR2 data, the preprocessing of acquired AMSR2 data being divided into radiation correction and terrestrial mask, the radiation correction comprising: using a radiation correction formula L ═ Gain × DN + Bias, where L is a brightness temperature value after radiation correction, Gain and Bias are radiation correction parameters, DN is a pixel value in data provided by AMSR2, and according to a scientific data set provided by AMSR2, Gain ═ 0.01 and Bias ═ 0 are known; substituting the acquired AMSR2 data into a radiation correction formula to acquire corrected brightness temperature data; the land mask includes: processing the land mask through the land identification code; preprocessing the acquired meteorological station data, and removing meteorological station data with accumulated snow depth less than 3 cm; the acquired MODIS surface coverage data MOD12Q1 are preprocessed, the land classification data with the resolution of 500 meters are converted into grid data with the resolution of 0.25 degrees according to the conversion mode of geographic coordinates, the MOD12Q1 provides five forest coverage types of evergreen coniferous forests, evergreen broadleaf forests, deciduous coniferous forests, deciduous broadleaf forests, mixed forests and the like, and forest coverage rate data with the resolution of 0.25 degrees are calculated.
According to snow depth data actually obtained by a snow station or a field experiment, optimal fitting is carried out by using a cost function constructed by a snow microwave radiation MEMLS model, and optimal snow related length (CL) data on an observation point are obtained; performing Kriging interpolation on the limited CL data on the observation points, and assisting with a Kelly snow judgment algorithm to enable each snow pixel in a space range to obtain the CL data, so that errors caused by the assumption of the fixed value of the snow structure parameter in an snow depth inversion algorithm are overcome; and finally, for each snow pixel, searching a snow depth inversion model in a corresponding range according to the size of the relevant length, calculating the snow depth, and effectively improving the snow depth inversion accuracy.
Drawings
FIG. 1 is a schematic diagram of an EFAST method global sensitivity analysis process;
FIG. 2 is a schematic diagram of a microwave radiation transmission process in a forest area;
FIG. 3(a) shows the result of sensitivity to characteristic parameters of V-polarized snow, and FIG. 3(b) shows the result of sensitivity to characteristic parameters of H-polarized snow;
FIG. 4(a) is a time-domain sensitivity result of a V-polarized snow characteristic parameter snow super station and a field snow season observation, and FIG. 4(b) is a time-domain sensitivity result of an H-polarized snow characteristic parameter snow super station and a field snow season observation;
FIG. 5 is a result of validation of the inversion accuracy of the effective correlation length;
fig. 6(a) -6(e) show the accuracy verification result of the snow depth inversion algorithm, which is the verification result of the 2018 website, where fig. 6(a) shows the verification result of the new algorithm, fig. 6(b) - (e) show the verification results of other algorithms, fig. 6(b) shows the verification result of the Chang algorithm, fig. 6(c) shows the verification result of the Foster algorithm, fig. 6(d) shows the verification result of the nsadc algorithm, and fig. 6(e) shows the verification result of the WESTDC algorithm.
Detailed Description
The technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of the present invention.
Brightness temperature of passive microwave radiometer: the high-performance Microwave Radiometer-2 (The Advanced Microwave Scanning Radiometer 2, AMSR2) is a passive Microwave Radiometer which is carried on a Global environment Change Observation satellite (Global Change Observation Session-Water 1, GCOM-W1), and GCOM-W1 is used for observing sea surface temperature and chlorophyll concentration, reducing Water and Water vapor, such as vegetation and soil moisture content, and better understanding The phenomenon through comprehensive Observation of land, sea and air. GCOM-W1 is mainly composed of solar panels and AMSR 2. And the solar panel converts sunlight on the orbit into battery energy and provides necessary electric power required by the satellite. The high performance microwave radiometer 2(AMSR2) is a microwave observation sensor that observes natural radiation from the surface, sea surface, atmosphere, etc. The AMSR2 receives an antenna portion from a ground microwave, scans an arc of the ground surface at a frequency of 1 rotation 1 time in 1.5 seconds, and can observe a range of about 1450 km wide. With this scanning method, AMSR2 can observe over 99% of the sites on the earth once in two short nights. The AMSR2 has the largest antenna diameter (about 2 m) of the observation sensor for satellite mounting, and the rotating part is about 2.7 m high and weighs about 250 kg. Other sensing parameters are shown in table 1:
TABLE 1 AMSR2 radiometer parameter settings
Landcover Modis surface coating data: according to the IGBP 0.05deg ground surface classification data, the coverage of the underlying surfaces of forests, farmlands, bare soil, grasslands, water bodies and the like in the microwave light temperature grid is calculated so as to develop an algorithm (eliminating the influence of a large area of water, eliminating the influence of the forests and comparing the accuracy with other algorithms).
Snow station data and field observation data: 2953 snow weather station data, two snow super stations (including time-series snow section information), 2017 and 2018 and 2019 field line observation data (including snow section information) in two snow seasons.
Analyzing global sensitivity of snow microstructure parameters based on an EFAST method: the method is a precondition for inversion by analyzing the sensitivity of the parameters of the remote sensing physical model. The EFAST (extended Fourier amplifier Sensitivity test) global Sensitivity analysis method not only can analyze the Sensitivity of a single parameter, but also considers the coupling influence among the parameters, and is suitable for analyzing the parameter Sensitivity of a high-dimensional nonlinear model. The remote sensing physical model is often a high-dimensional nonlinear model, and the model parameters are possibly dependent, so that the parameter sensitivity analysis is relatively complex.
At present, a plurality of methods for carrying out sensitivity analysis on model parameters are available, and no unified standard exists. At present, the research of the accumulated snow adopts a local sensitivity analysis method, namely a control variable method to know the action influence of each parameter, and the method only considers the influence of a single parameter on a model result. However, the sensitivity of the model parameters is also influenced by other parameters in the model, namely, the indirect influence of the mutual coupling effect among the parameters on the model result. The global sensitivity analysis method developed in recent years can simultaneously analyze the influence of the change of a plurality of parameters on the model result and can also analyze the direct and indirect influence of each parameter on the model result. The method is introduced into an accumulated snow microwave emission model for the first time, the influence of quantification of each accumulated snow structure parameter is obtained, and a more definite guiding significance is provided for an inversion algorithm.
The EFAST method comprises the following steps:
the model to be analyzed:
y＝f(x)，x＝(x1，x2，...，xn)
frequency domain transformation:
fourier expansion:
wherein the Fourier coefficients are:
model total variance:
variance of each parameter:
results for each parameter sensitivity:
EFAST sensitivity results output: the Total Sensitivity Index (TSI) reflects the sum of the direct contribution rate of the parameters and the contribution of the model output Total variance indirectly through the interactive coupling effect with other parameters; the Main Sensitivity Index (MSI) reflects the Sensitivity without taking into account the cross-coupling between parameters. FIG. 1 shows a schematic diagram of the overall sensitivity analysis process of the EFAST method, and Table 2 shows the range and distribution of initial input parameters.
TABLE 2 initial input parameter Range and distribution
Since passive microwave radiometric data can only identify dry Snow, we only discuss the parameter sensitivity under dry Snow condition (Snow weather ═ 0). The frequency and the satellite incidence angle are set to be the same as AMSR2, f is 18.7GHz, f is 36.5GHz, and the incidence angle θ is 55 °.
The basis for setting the parameter ranges is derived from literature, product data, field investigation, previous research and the like, and is not developed here.
Effective correlation length Pex inversion: the microwave radiation contribution of the snow structure parameters, which determine the microwave scattering efficiency in the snow, is crucial for the inversion of the snow depth/snow water equivalent SWE. According to the result of global sensitivity analysis, the particle size of the snow is the most critical parameter influencing scattering in the process of transmitting the snow microwave radiation, and various algorithms in the current region or the world are set as constants by experience and are applied to a snow microwave emission model in turn.
Therefore, the method provided by the invention utilizes meteorological station data matching optimization iteration to solve the effective correlation length Pex by combining the snow microwave emission model MEMLS and the brightness temperature of the passive microwave radiometer.
Optimal iterative cost function:
psi is the brightness temperature of model simulation, Pex and eff are target output effective correlation lengths, SD is the snow depth of an observation point, lambda i is other model input parameters, and psi is the satellite brightness temperature. The radiation brightness temperature of the accumulated snow can be regarded as a high-order function of each parameter, so that when other parameters are fixed, the snow depth is input, and an effective correlation length inversion result can be obtained through calculation by changing Pex optimal iteration.
Fig. 2 shows a schematic diagram of a microwave radiation transmission process in a forest area, and when forest coverage exists in a pixel, the radiation transmission process is as shown in fig. 2. Wherein, the surface of the accumulated snow is subjected to microwave irradiation to obtain the brightness temperature; the brightness temperature of the forest radiation after the atmospheric transmission; sixthly, the atmospheric downward radiation is reflected by forest and snow, the brightness temperature after the atmospheric transmission and the brightness temperature of the atmospheric direct upward emission.
Tb_sat＝Ψ*ta+r(ta*Tbatm↓)+Tb_atm↑
Tb＝(1-f)*Tb_snow+f*Tb_forest
Tb_forest＝tf*Tb_snow+tf*(1-esnow)(1-w)(1-tf)*Tforest+(1-w)(1-tf)*Tforest
Wherein Tb _ sat is a satellite radiometer value obtained by theoretical calculation; ta is the atmospheric transmittance (the influence of the atmosphere on the microwave radiation frequency of 18.7 and 36.5GHz is not considered, so that ta ═ 1 is calculated, and (r (ta ×) Tb _ atm ↓) + Tb _ atm ℃ ×) is a sky background radiation value, including the atmospheric uplink radiation and the radiation of the atmospheric downlink radiation reflected by the ground and then uplink to the radiometer, and can be calculated according to a semi-empirical formula; f is the forest coverage, and is obtained by calculation according to the Modis landcover ground surface coverage data; tb _ snow is the simulated brightness temperature of the pure pixels in the non-forest region; tb _ forest is the simulated bright temperature of the pure pixels in the forest region, tf is the forest transmittance (the value adopts the published calculation result of (Cheet. Remote sens. environ.2017), the forest transmittance at 18.7GHz is 0.895, and the forest transmittance at 36.5GHz is 0.656); w is the single scattering albedo of the forest and is set as an empirical value of 0.05; esnow is the snow cover emissivity and is obtained through simulation calculation of the MEMLS model. Table 3 gives the auxiliary input parameters and the MEMLS model parameter configuration.
TABLE 3 auxiliary input parameters and MEMLS model parameter configuration
Wherein, the background radiation of sky and the reflectivity of frozen earth in different channels are calculated according to a semi-empirical formula (Palliainen et. IEEE Trans. Geosci. remote Sens.1993, Wegm muller et. IEEE Trans. Geosci. remote Sens.1999) assuming that the surface roughness is 1 cm.
Snow depth/snow water equivalent inversion
The dynamic inversion algorithm considering the dynamic change of the snow particle size comprises the following steps:
firstly, calculating to obtain an effective correlation length result of the meteorological station of the past year through an effective correlation length inversion algorithm, and performing segmented snow depth and brightness temperature gradient fitting on the result to obtain a new inversion model. Regression was performed as follows:
SD＝slope*(Tb19V-Tb37V)/(1-0.7*f)+intercept
when Pex is less than or equal to 0.2, Number is 14832, and linear regression fitting is carried out at a confidence interval of 0.95:
SD＝1.174*(Tb19V-Tb37V)/(1-0.7*f)+9.765 R2＝0.5614
6.0.2 < Pex ≦ 0.25, Number 41043, linear regression fit at 0.95 confidence interval:
SD＝0.8209*(Tb19V-Tb37V)/(1-0.7*f)+2.646 R2＝0.878
7.0.25 < Pex ≦ 0.3, Number 41248, 0.95 confidence interval, linear regression fit:
SD＝0.5572*(Tb19V-Tb37V)/(1-0.7*f)+0.6282 R2＝0.9249
when Pex >0.3, Number 35561, 0.95 confidence interval for linear regression fitting:
SD＝0.3292*(Tb19V-Tb37V)/(1-0.7*f)-0.6341 R2＝0.584
and (3) an inversion process:
1. judging the accumulated snow area (dry snow) by a microwave radiometer;
2. the effective correlation length Pex is inverted by the meteorological station, and the effective correlation length Pex of other dry snow pixels on the snow surface is obtained according to a Kring interpolation method;
3. according to the result of the effective correlation length Pex, searching a snow depth inversion model in a corresponding range, and calculating the snow depth;
4. and time-domain filtering, which is optional, can mainly remove abnormal points in the time domain.
And (3) verifying the result and the precision: the global sensitivity analysis result is shown in fig. 3(a) as the sensitivity result of the characteristic parameter of V-polarized snow, and fig. 3(b) as the sensitivity result of the characteristic parameter of H-polarized snow; FIG. 4(a) is a time-domain sensitivity result of a V-polarized snow characteristic parameter snow super station and a field snow season observation, and FIG. 4(b) is a time-domain sensitivity result of an H-polarized snow characteristic parameter snow super station and a field snow season observation; sensitivity order of parameters affecting snow depth inversion: TSI (CL) > TSI (ST) > TSI (SD) > TSI (TS) > TSI (TG), and the result of V polarization is similar to that of H polarization. The correlation length is the main factor (according with the cognition of the mainstream in the academic world); however, some new findings exist, in the late stage of snow accumulation, along with the increase of the density of snow accumulation, the influence of the density of the snow accumulation on medium and small-sized snow accumulation is increased, and the H polarization result is more obvious; other influencing factors are low in sensitivity and can be ignored; the snow depth inversion body scattering factor suggests to select a V polarization result, so that the snow depth sensitivity is increased, and the influence of other parameters is inhibited. The effect of the relevant length is always high throughout the snow season, so the effect of its dynamic changes must be considered.
Effective correlation length Pex inversion: and (3) algorithm precision verification: FIG. 5 shows the result of validation of the inversion accuracy of the effective correlation length; the root mean square error RMSE of the correlation length inversion result and the accuracy result of the Ice cube calculation value observed in the field is shown in the table 4.
TABLE 4 root mean square error RMSE of correlation length inversion result and accuracy result of Ice cube calculation value observed in field
Snow depth inversion results and accuracy verification: fig. 6(a) -6(e) show the accuracy verification results of the snow depth inversion algorithm, using the 2018 website verification results, where fig. 6(a) is the new algorithm verification result, fig. 6(b) - (e) are the other algorithm verification results, where fig. 6(b) is the Chang algorithm verification result, fig. 6(c) is the Foster algorithm verification result, fig. 6(d) is the NSIDC algorithm verification result, and fig. 6(e) is the WESTDC algorithm verification result.
In conclusion, the optimal snow related length (CL) data on the observation point is obtained by performing optimal fitting on snow depth data actually obtained by a snow station or a field experiment by using a cost function constructed by a snow microwave radiation MEMLS model; performing Kriging interpolation on the limited CL data on the observation points, and assisting with a Kelly snow judgment algorithm to enable each snow pixel in a space range to obtain the CL data, so that errors caused by the assumption of the fixed value of the snow structure parameter in an snow depth inversion algorithm are overcome; and finally, for each snow pixel, searching a snow depth inversion model in a corresponding range according to the size of the relevant length, calculating the snow depth, and effectively improving the snow depth inversion accuracy.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, the method and the core concept thereof according to the present invention may be modified in the specific embodiments and the application scope thereof, and in summary, the present specification should not be construed as limiting the present invention.
Claims (5)
1. A method for acquiring dynamic accumulated snow depth comprises the following steps: obtaining inversion effective correlation length of a meteorological station by inversion of an optimal iterative method of correlation length of a snow accumulation layer of the station, wherein snow is dry snow, and obtaining effective correlation length of other dry snow pixels besides the dry snow pixels of the meteorological station according to a Kring interpolation method; for each dry snow pixel, searching a snow depth inversion model in a corresponding range according to the relevant length Pex of the dry snow pixel, and calculating the snow depth to obtain an inverted snow depth spatial distribution result; the lookup table is:
1) when Pex is less than or equal to 0.2,
SD＝1.174*(Tb19V-Tb37V)/(1-0.7*f)+9.765R2＝0.5614；
2) when Pex is more than 0.2 and less than or equal to 0.25,
SD＝0.8209*(Tb19V-Tb37V)/(1-0.7*f)+2.646R2＝0.878；
3) when Pex is more than 0.25 and less than or equal to 0.3,
SD＝0.5572*(Tb19V-Tb37V)/(1-0.7*f)+0.6282R2＝0.9249；
4) when Pex is greater than 0.3,
SD＝0.3292*(Tb19V-Tb37V)/(1-0.7*f)-0.6341R2＝0.584；
wherein SD is the inverted accumulated snow depth, and f is the forest coverage;
the optimal iterative inversion method comprises the following steps: the snow depth observed by a ground meteorological station is input into an MEMLS model, then the value of the correlation length is traversed within a certain range by a certain step length, the vertical polarization difference (18.7-36.5) GHz is used as an inversion operator, and when the error between the simulation of the MEMLS model and the radiation count data of the satellite AMSR2 is minimum, the result of the correlation length at the moment is output as the inversion value of the station.
2. The method for obtaining the dynamic accumulated snow depth as claimed in claim 1, wherein the range of the correlation length Pex is selected as (0mm, 0.6mm), and the step size is selected as 0.005 mm.
3. The method for acquiring the dynamic snow depth as claimed in claim 1, wherein before the step of inversion by the iterative method of the station snow layer correlation length optimization, the method further comprises data acquisition and preprocessing of the acquired data.
4. The method for acquiring dynamic snow depth according to claim 3, wherein the data acquisition comprises: acquiring AMSR2 radiometer data, snow depth observed by a ground meteorological site, longitude and latitude data and MODIS land classification data MOD12Q 1.
5. The method for acquiring dynamic snow depth as claimed in claim 4, wherein the preprocessing the acquired data comprises: preprocessing acquired AMSR2 radiation count data, the preprocessing of acquired AMSR2 radiation count data being divided into radiation corrections and a terrestrial mask, the radiation corrections comprising: using a radiation correction formula L ═ Gain DN + Bias, wherein L is a brightness temperature value after radiation correction, Gain and Bias are radiation correction parameters, DN is a pixel value in data provided by AMSR2, and according to a scientific data set provided by AMSR2 radiometer data, Gain ═ 0.01 and Bias ═ 0 can be known; substituting the acquired AMSR2 radiation count data into a radiation correction formula to obtain corrected brightness temperature data; the land mask includes: processing the land mask through the land identification code; preprocessing the acquired snow depth data observed by the ground meteorological station, and removing the snow depth data observed by the ground meteorological station, wherein the observed snow depth is less than 3 cm; preprocessing the acquired MODIS land classification data MOD12Q1, converting original land classification data MOD12Q1 with the resolution of 500 meters into grid data with the resolution of 0.25 degrees according to the conversion mode of geographic coordinates, providing five forest coverage types of evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest and mixed forest by MOD12Q1, and calculating forest coverage rate data with the resolution of 0.25 degrees.
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