CN108982548B - Surface soil moisture retrieval method based on passive microwave remote sensing data - Google Patents
Surface soil moisture retrieval method based on passive microwave remote sensing data Download PDFInfo
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Abstract
The invention discloses a surface soil moisture inversion method based on passive microwave remote sensing data, which comprises the following steps: obtaining a soil component spatial distribution image and microwave brightness temperature spatial distribution images of all wave bands; calculating the average effective temperature of the earth surface by using the vertical and horizontal polarization brightness temperature data of the K wave band, and constructing a microwave brightness temperature polarization difference index of the X wave band by using the vertical and horizontal polarization brightness temperature data of the X wave band; substituting the average effective temperature of the earth surface and the microwave brightness temperature polarization difference index of the X wave band into a mathematical model of the soil dielectric constant and a microwave radiation transmission model, and solving the vegetation optical thickness and the soil dielectric constant; and substituting the soil dielectric constant into the soil moisture content inversion model, and calculating to obtain the soil moisture content on the earth surface. Compared with the data value obtained by the original algorithm, the data value of the water content of the soil calculated by the improved inversion algorithm has higher inversion accuracy under the condition of a multi-water surface.
Description
Technical Field
The invention relates to the technical field of remote sensing surface soil moisture content inversion, in particular to a surface soil moisture inversion method based on passive microwave remote sensing data.
Background
The soil water is an unsaturated water body in the soil layer, is widely distributed on the surface layer of the land and is a necessary water source for plant growth, so that the soil water resource is an important resource related to the agricultural production quality; meanwhile, the soil water resource is a link for interconversion of surface water and underground water, can enter the atmosphere in a evapotranspiration mode, and is supplemented in the soil surface layer in a precipitation mode, so that the soil water resource is a core factor in surface energy exchange and is also an important carrier composition for surface material energy circulation in the world. The global-scale soil water resource distribution state is an important basis for balancing and evaluating the earth surface evapotranspiration mode (vegetation growth) and the precipitation mode (ecosystem quality), and is also basic data for researching the hydrological characteristics and climate change in a region. Under the large background of global climate warming, global-scale soil moisture distribution data has become one of the indispensable guarantees of scientists to study global climate and hydrological environmental changes and to build a reasonable global climate response model. On the basis, the establishment of the global sustainable development policy is supported by more scientific basis. The global soil moisture data set is the data set that is currently the most spatial scale macroscopic in studies on soil moisture. The traditional soil moisture monitoring method mainly comprises the step of acquiring a real-time surface soil moisture content value through a manual observation or automatic station observation means at a meteorological station. However, such observation means is not only time-consuming and labor-consuming, but also suffers from the disadvantage that the measured soil moisture content value is not spatially representative enough. Firstly, the measurement data only represents the average level of the soil moisture content in the range of less than 10cm in the horizontal direction pointed by a single measurement point probe; secondly, due to the huge station building observation cost and the complexity of the geographical environment which is difficult to control on a global scale, soil moisture observation stations which have consistent observation specifications and sufficient quantity and are uniformly distributed in all regions of seven continents cannot be obtained almost certainly in the global scope to meet the requirements of observation cartography. With the progress of science and technology, the acquisition of satellite remote sensing data and the research of a satellite remote sensing soil moisture inversion method effectively make up for the disadvantages of space continuity and observation cost of soil moisture data observed by using sites, and make it possible to acquire near-real-time earth surface soil moisture content distribution images with high space-time resolution by using the satellite remote sensing data.
Under the background, the large-space-scale soil moisture inversion research based on satellite remote sensing is developed from the seventies of the twentieth century by the countries in the united states and the european union successively to meet the requirements of monitoring and applying the soil moisture distribution on the national scale, the continent scale and the global scale. The microwave remote sensing data has good penetrability to cloud layers and vegetation canopies, and is an ideal data source for monitoring soil moisture change information. Common microwave remote sensing data includes microwave radiometer data sets and microwave scatterometer (radar) data sets. The mechanism of inverting the earth surface soil moisture by receiving data through the microwave scatterometer is complex, the data resolution ratio is too high, so that the occupied space is large, the cost is high, the processing steps are complex, and the inversion process has strict and accurate requirements on the earth surface auxiliary parameters, so that the application cost of the data is integrally high. The microwave radiometer belongs to a passive microwave technology, has high global coverage of data, is easy to obtain, and is suitable for monitoring the soil moisture on the earth surface of a large area. The microwave radiometer comprises data sets of two modes of ascending and descending, wherein the revisit period of each mode of data set in the middle and low latitude areas is 1-3 days (generally 1-2 days in most areas in China), and the high time resolution ensures that the near real-time monitoring of the space change of the soil moisture on the earth surface can be achieved for 1-2 times per day. The spatial resolution of the data set is generally in the range of 10-60km, and generally speaking, the spatial resolution range reaches a perfect balance between the spatial mapping precision and the data complexity degree in the relatively macroscopic global scale research.
The principle of inverting the earth surface soil moisture by using the microwave remote sensing technology is mainly developed by taking a radiation transmission model of passive microwaves as a main mathematical and physical basis by means of microwave signals of lower frequency bands, such as X-band, C-band and L-band, extremely strong penetration capacity to the atmosphere and vegetation layer and characterization capacity to the soil-water mixture dielectric constant in soil. Based on the above principle, many different passive microwave soil moisture inversion algorithms can be developed and widely applied in different fields and scales worldwide. The Land Parameter Recovery Model (LPRM), proposed by Amsterdam, the Netherlands, is one of the classical algorithms in this field, which was first proposed for the X-and C-bands (M.Owe, R.deJeu, and T.Holmes, "Multisensor horizontal restriction of local-vertical floor movement," Journal of geographic Research-Earth Surface, vol.113, pp.196-199, Jan 182008), and was promoted in recent studies to the L-band (R.van der Schalie, R.M.Parinsause, L.J.Renllo, A.I.J.M.van. j.j.van. j.j.j.v., C.H.Aurea, R.M.M.M.P.and R.Aurea-Earth Surface penetration, "calcium dioxide Model, M.32, calcium dioxide, calcium oxide. Compared with other similar products in different research areas or watershed ranges all over the world, the soil moisture product inverted by the LPRM algorithm has quite a lot of research, and most of the skills consider that the soil moisture product inverted by the LPRM algorithm has higher overall precision and wider application value.
At present, soil moisture inversion products with the resolution of 25km of an X-wave band and a C-wave band based on an LPRM algorithm and an AMSR-E/AMSR2 passive microwave radiometer are released to the world for free by NASA (American space and space administration), and a standard LPRM algorithm used by the set of products strictly screens the earth surface environment when inverting the soil moisture. Wherein, the pixels of the 25km pixels containing more than a certain area (more than or equal to 5 percent of the pixel area) of the river and lake underlay surface are not inverted. Thus, the overall accuracy of the global inversion is improved, but the global effective land coverage ratio of the set of data products is reduced to a certain extent, and the application of the products in the global range is limited, particularly in tropical and subtropical areas with wet earth surfaces and more rivers and lakes.
Disclosure of Invention
The invention provides a surface soil moisture inversion method based on passive microwave remote sensing data on the basis of an original LPRM algorithm, the algorithm improves the inversion accuracy of a plateau algorithm in a multi-water surface pixel to a certain extent, and the soil moisture product inverted by the algorithm has application value in a multi-water surface area.
A surface soil moisture inversion method based on passive microwave remote sensing data comprises the following steps:
step 1): collecting and arranging a passive microwave bright temperature data set (passive microwave bright temperature data set) and a ground soil component distribution data set (soil texture data set); obtaining a spatial distribution image (spatial distribution image of soil texture) of soil components and a spatial distribution image (spatial distribution image of microwave brightness temperature) of each wave band through pretreatment;
the microwave brightness temperature distribution diagram of each wave band displays microwave brightness temperature data, and the microwave brightness temperature data comprises: vertical and horizontal polarized light temperature data of an X band (10.7GHz), and vertical and horizontal polarized light temperature data of a K band (18GHz and 23.8 GHz);
step 2): calculating the average effective temperature of the earth surface by using the vertical and horizontal polarization brightness temperature data of the K wave band in the step 1), and constructing a microwave brightness temperature polarization difference index (MPDI) of the X wave band by using the vertical and horizontal polarization brightness temperature data of the X wave band;
step 3): substituting the average effective temperature of the earth surface and the microwave bright temperature polarization difference index (MPDI) of the X wave band obtained in the step 2) into a mathematical model of the soil dielectric constant and a microwave radiation transmission model, and solving the vegetation optical thickness and the soil dielectric constant by a least square method;
step 4): substituting the soil dielectric constant obtained by the calculation in the step 3) into a soil moisture content inversion model, and calculating to obtain the soil moisture content on the earth surface.
In the step 1), the pretreatment of the passive microwave brightness temperature data set comprises the following steps:
A) extracting brightness temperature data of corresponding wave bands from an HDF format file storing the brightness temperature data, and performing spatial resampling processing on the brightness temperature data according to the spatial resolution of 25km to obtain a two-dimensional matrix for recording the spatial distribution condition of the brightness temperature data;
B) adding a geographical projection to the two-dimensional matrix obtained in step a) (the projection type is: and (3) performing equal-integral-cutting cylindrical projection by taking 30-degree latitude lines of the south latitude and the north latitude as cutting lines) to generate microwave brightness-temperature spatial distribution images of all wave bands.
The preprocessing of the surface soil composition distribution data set comprises:
a) performing spatial resampling processing on the originally stored earth surface soil component distribution image with higher spatial resolution (generally, the spatial resolution of the image is less than 25km) to obtain an earth surface soil component distribution image with the spatial resolution of 25 km;
b) and performing projection transformation on the images to ensure that the geographical projection type of the transformed images is the same as the projection type of the microwave brightness temperature spatial distribution images.
The prior art is adopted for preprocessing the passive microwave brightness temperature data set and preprocessing the earth surface soil component distribution data set.
Obtaining a soil component distribution map (spatial distribution image of soil texture) with a resolution of 25km and a microwave brightness temperature distribution map (spatial distribution of microwave brightness texture) of each waveband with the resolution of 25km through pretreatment;
the soil component distribution map displays soil component data, and the soil component data comprises the content of surface clay grains, the content of surface sand grains and soil volume weight parameters.
The X wave band is 10.7 GHz.
The K wave band is two wave bands of 18GHz and 23.8 GHz.
In the step 2), the average effective temperature of the earth surface is calculated by using the vertical and horizontal polarization brightness temperature data of the K wave band in the step 1), and the method specifically comprises the following steps:
calculating the intermediate value tau by equation (1)a(f)Theta is the satellite observation angle of incidence, AoAs a parameter of the oxygen content of the atmosphere, AwvThe content of atmospheric water vapor to be solved;
τa(f)＝sec(θ)[Ao+Awv](1)
calculating the intermediate value t by the formula (2)a(f)；
ta(f)＝exp(-τa(f))(2)
Calculating the intermediate value e by equation (3)bl(p,f)，tcFor the unknowns to be solved, eos(p,f)The emissivity of the dry bare land is shown, and omega represents the vegetation scattering rate;
ebl(p,f)＝tceos(p,f)+(1-tc)(1-ω)(3)；
calculating the intermediate value e by equation (4)(p,f)，ffwsFor the unknowns to be solved, ew(p,f)The emissivity of the water surface is represented, and omega represents vegetation scattering rate;
e(p,f)＝ffwsew(p,f)+(1-ffws)ebl(p,f)(4)
calculating the average effective temperature of the earth surface by taking a K wave band as an inversion wave band and calculating a middle value t by a formula (2)a(f)And the intermediate value e calculated by equation (4)(p,f)Substituting into the inverse equation (5):
Tb(p,f)＝Ts[ta(f)e(p,f)+(1-ta(f))δ](5)
wherein, Tb(p,f)Represents the brightness temperature of a certain channel, p represents vertical (V) polarization or horizontal (H) polarization, and f represents 18.7GHz or 23.8 GHz; t issRepresenting the average effective surface temperature to be solved, and delta represents the ratio of the atmospheric air/surface temperature;
the K wave band is 18GHz and 23.8GHz, the 18GHz wave band comprises two channels of vertical (V) polarization and horizontal (H) polarization, and each channel corresponds to brightness temperature data; the 18GHz wave band comprises two channels of vertical (V) polarization and horizontal (H) polarization, and each channel corresponds to brightness temperature data; respectively substituting brightness temperature data of four channels into an inversion formula (5) to obtain 4 equations, and solving 4 unknowns Ts、Awv、tc、ffws。
The microwave bright temperature polarization difference index (MPDI) of the X wave band is constructed by utilizing the vertical and horizontal polarization bright temperature data of the X wave band, and the prior art can be specifically adopted.
In the step 3), the average effective temperature of the earth surface and the microwave bright temperature polarization difference index (MPDI) of the X wave band are substituted into a mathematical model of the soil dielectric constant and a microwave radiation transmission model, and the vegetation optical thickness and the soil dielectric constant are solved by a least square method, wherein the prior art can be specifically adopted.
The mathematical model of MPDI regarding the dielectric constant of the soil in the LPRM method (A.G.C.A.Meestizers, R.A.M.De Jeu, and M.Owe, "analytical engineering of the transformation optical depth from the microwaveable polarization orientation index," ie Geoscience and removal Sensing Letters, vol.2, pp.121-123, Apr 2005.), and the microwave radiation transmission model of the X-band horizontally polarized bright temperature (E.G.Njoku. 1999).
And 4) substituting the soil dielectric constant obtained by calculation in the step 3) into a soil moisture content inversion model to calculate the earth surface soil moisture content, wherein the prior art can also be adopted. The final result of the surface soil moisture content can be further solved using the soil composition information and the soil dielectric constant values obtained by inversion according to a soil moisture content inversion model (m.c. dobson, f.t.ula by, m.t.hallikainen, and m.a.electrodes, "Microwave dielectric floor of soil oil.2.dielectric mixing models," IEEE Transactions on Geoscience and remove Sensing, vol.23, pp.35-46,1985.).
The method of the invention follows most procedures of the open-ended LPRM algorithm, and the LRPM algorithm uses Ka (37GHz) wave band to invert the average effective temperature of the earth surface, and the method is from Holmes et al (T.R.H.Holmes, R.A.M.De Jeu, M.Owe, and A.J.Dolman, "Land surface temperature from Ka band (37GHz) passive microwave environments," Journal of geographic Research-Atmospheres, vol.114, Feb 252009.). The difference lies in that in the step 2, two wave bands of 18GHz and 23.8GHz are adopted as K wave bands, and the calculation is combined with a specific formula, and verification shows that the error of the prior LRPM algorithm under the multi-water-surface subsurface is larger, but the inversion method of the K wave band used in the improved algorithm greatly weakens the error, and further, the soil moisture inversion result with higher precision can be obtained.
Compared with the prior art, the invention has the following advantages:
aiming at the multi-water surface environment, the LPRM surface soil moisture retrieval method based on passive microwave remote sensing satellite data is improved, the improved core lies in replacing the surface average effective temperature retrieval method which is not suitable for the condition into a more suitable algorithm, and the method has the advantages that:
the improved surface effective average temperature algorithm overcomes the inversion error caused by using an empirical formula when the parameter is inverted by the original method. The original inversion method (Ka-band inversion method) [14 and 15] utilizes the high numerical correlation between the surface effective average temperature of the pure land surface and the Ka-band vertical polarization brightness temperature to construct an empirical formula so as to complete the inversion of the surface effective average temperature, but in fact, the empirical formula is not suitable for the multi-water surface. In the improved algorithm, the K wave band algorithm of the replaced Ka wave band method can be well suitable for the multi-water surface. Because the effective average temperature of the earth surface is a fixed input parameter for inverting the soil moisture in the LPRM algorithm, the inversion accuracy of the LPRM algorithm on the moisture content of the earth surface under the multi-water earth surface environment can be well improved, the inversion accuracy of large-scale (such as global scale) passive microwave soil moisture data under the multi-water earth surface environment of a high-humidity region can be further improved, and the application feasibility of the large-scale passive microwave soil moisture data is improved.
Drawings
FIG. 1 is a schematic flow chart of a surface soil moisture inversion method based on passive microwave remote sensing data according to the present invention;
FIG. 2 is a diagram of a study area of an example of algorithm accuracy verification and a distribution diagram of 54 soil moisture observation automation stations used in the accuracy verification process;
fig. 3 is a scatter-to-point ratio diagram of "microwave inversion soil moisture VS site observed soil moisture" obtained by using the original algorithm and the improved algorithm respectively in the precision verification process, where fig. 3(a) is a scatter-to-point ratio diagram with FWS <0.05, fig. 3(b) is a scatter-to-point ratio diagram with FWS <0.15, fig. 3(c) is a scatter-to-point ratio diagram with FWS <0.25, and fig. 3(d) is a scatter-to-point ratio diagram with FWS <0.5.
Detailed Description
The soil composition data includes surface clay and sand content, and soil volume weight parameters. The invention collects soil component data provided by a 'Soilgrid 1 km' database (website: https:// Soilgrids. org /). The invention uses the soil average index data map layer of the surface soil (within the range of 0-5 cm) in the database. And adopting an average value sampling method to resample the soil component data with the resolution of 1km to 25 km.
And 2, calculating the average effective temperature of the earth surface by using the K-waveband brightness temperature data in the step 1, wherein the brightness temperature values of four waveband channels (18GHz-V,18GHz-H,23GHz-V,23GHz-H, -V and-H respectively represent a vertical polarization mode and a horizontal polarization mode) are used in the step. And each channel obtains an equation, and the final four-formula equation set is solved through iteration to obtain parameters to be solved, namely the average effective temperature of the earth surface, and other three unknown parameters including the effective moisture content (unit: millimeter) of the atmosphere, the vegetation transmittance and the pure water area ratio (FWS) of the earth surface pixels. The specific inversion formula is:
Tb(p,f)＝Ts[ta(f)e(p,f)+(1-ta(f))δ](5)
wherein T isb(p,f)Represents the light temperature, T, of a certain channel (p represents V polarization or H polarization, and f represents 18.7GHz or 23.8GHz)sRepresenting the average surface effective temperature to be determined, and δ represents the ratio of atmospheric/land temperature.
Calculating the intermediate value tau by equation (1)a(f)Theta is the satellite observation angle of incidence, AoAs a parameter of the oxygen content of the atmosphere, AwvThe content of atmospheric water vapor to be solved;
τa(f)＝sec(θ)[Ao+Awv](1)
calculating the intermediate value t by the formula (2)a(f)；
ta(f)＝exp(-τa(f))(2)
Calculating the intermediate value e by equation (3)bl(p,f)，tcIs on demandUnknown quantity, eos(p,f)The emissivity of the dry bare land is shown, and omega represents the vegetation scattering rate;
ebl(p,f)＝tceos(p,f)+(1-tc)(1-ω)(3)；
calculating the intermediate value e by equation (4)(p,f)，ffwsFor the unknowns to be solved, ew(p,f)The emissivity of the water surface is represented, and omega represents vegetation scattering rate;
e(p,f)＝ffwsew(p,f)+(1-ffws)ebl(p,f)(4)
calculating the average effective temperature of the earth surface by taking a K wave band as an inversion wave band and calculating a middle value t by a formula (2)a(f)And the intermediate value e calculated by equation (4)(p,f)Substituting into inversion equation (5):
the K wave band is 18GHz and 23.8GHz, the 18GHz wave band comprises two channels of vertical (V) polarization and horizontal (H) polarization, and each channel corresponds to brightness temperature data; the 18GHz wave band comprises two channels of vertical (V) polarization and horizontal (H) polarization, and each channel corresponds to brightness temperature data; respectively substituting brightness temperature data of four channels into an inversion formula (5) to obtain 4 equations, and solving 4 unknowns Ts、Awv、tc、ffws. The construction equation forms a nonlinear equation system consisting of four equations and four unknowns to be solved. Solving the equation set can obtain the average effective temperature T of the earth surfaces。
Then, the microwave brightness temperature polarization difference index (MPDI) of the X wave band is constructed by utilizing the horizontal and vertical polarization data of the X wave band,
wherein T isH-XAnd TV-XRespectively representing the horizontal and vertical polarization brightness temperature of the X band.
Step 3, using a relation model of the lpdi and the soil dielectric constant in the LPRM method, an lpdi Function1(k, τ) can be obtained according to the relation model. Where Function1 represents a generalized nonlinear Function for this relationship model, k and τ represent the soil dielectric constant and vegetation optical thickness, respectively.
An equation Function2 of vegetation optical thickness tau and soil dielectric constant k with respect to X-wave band on-satellite observation horizontal polarization brightness temperature, namely T, is established by utilizing a microwave radiation transmission model of X-wave band horizontal polarization brightness temperatureH-XFunction2(k, τ). And constructing a binary nonlinear equation system by using Function1 and Function2, and solving k and tau by using a Jacobian iteration method or a Newton steepest descent method.
And 4, expressing the soil dielectric constant as a nonlinear function of the soil water content and the soil components according to a soil water content inversion model (referred to as a Dobson model), namely tau is a Dobson (SSM, clay, sand, bulk density). SSM represents surface soil moisture content, clay, sand, bulk density represent percent soil aggregate, percent sand, and soil bulk weight, respectively, these three parameters being provided by the "Soilgrid 1 km" database. Therefore, a unitary nonlinear function of tau relative to the water content of the surface soil can be obtained, and the function is solved in a Jacobian iteration mode, so that the final target parameter, namely the water content of the surface soil can be obtained.
And 5, verifying the precision. In the embodiment of the invention, a river-Huai region with a multi-water surface is used as an experimental research area, soil moisture observation values of time points which are closest to the satellite transit time and are observed by 54 automatic soil moisture observation stations in the experimental research area are subjected to precision verification and evaluation, and the date range of the used data is 3/1/2010 to 9/2010 and 30/2010.
The basic basis of the precision evaluation is the RMSE between the microwave soil moisture and the observed value of the soil moisture of the station and the magnitude of a correlation coefficient. Because the station and the microwave data have the difference in dimension originally, the dimension conversion needs to be carried out on the microwave soil water content data before comparison, namely the microwave soil water content data is subjected to dimension conversion
θ'AMSR＝(θAMSR-m(θAMSR))×(s(θr)/s(θAMSR))+m(θr)
θAMSRAnd thetarRespectively representing the AMSR-E microwave inversion soil moisture content and the reference ground station observation soil moisture content value. Theta'AMSRFor AMSR-E microwave inversion after scale conversionSoil moisture content, s and m represent the standard deviation and mean of each data set, respectively.
According to the inverted surface pixel pure water surface area ratio index FWS in the third step, pixels to be subjected to precision evaluation are divided into four types, namely FWS <0.05,0.05< ═ FWS <0.15,0.15< ═ FWS <0.25 and 0.25< - > FWS <0.5, in each type of pixels, soil moisture content obtained by the improved LPRM algorithm and the inversion of the original LPRM algorithm is subjected to precision evaluation by using RMSE and R respectively, and the results are shown in Table 1. Meanwhile, fig. 2 shows a scatter diagram of soil moisture inversion values obtained by the two algorithms with respect to the earth surface station observed values.
TABLE 1
The results shown in table 1 and fig. 2 show that the improved soil moisture inversion method compared to the original LPRM algorithm is comparable in accuracy to the original algorithm in drier surface environments (FWS < 0.05). However, as the pure water surface area ratio of the earth surface increases, i.e., when FWS >0.05, the RMSE value of the soil moisture data inverted with respect to the modified algorithm is significantly less than that of the data inverted with respect to the original algorithm, while the corresponding correlation coefficient value R of the former is also significantly higher than that of the latter. The precision evaluation result also shows that the inversion precision of the improved algorithm is higher in the interval of FWS <0.25 than in the interval of FWS > 0.25. In conclusion, the inversion accuracy of soil moisture under the condition of the surface of the watery ground by passive microwave inversion is effectively improved.
Claims (4)
1. A surface soil moisture inversion method based on passive microwave remote sensing data is characterized by comprising the following steps:
step 1): collecting and arranging a passive microwave brightness temperature data set and an earth surface soil component distribution data set; obtaining a soil component spatial distribution image and microwave brightness temperature spatial distribution images of all wave bands through pretreatment;
the microwave brightness temperature distribution diagram of each wave band displays microwave brightness temperature data, and the microwave brightness temperature data comprises: vertical and horizontal polarization brightness temperature data of the X wave band and vertical and horizontal polarization brightness temperature data of the K wave band;
step 2): calculating the average effective temperature of the earth surface by using the vertical and horizontal polarization brightness temperature data of the K wave band in the step 1), and constructing a microwave brightness temperature polarization difference index of the X wave band by using the vertical and horizontal polarization brightness temperature data of the X wave band;
calculating the average effective temperature of the earth surface by using the vertical and horizontal polarization brightness temperature data of the K wave band in the step 1), which specifically comprises the following steps:
calculating the intermediate value tau by equation (1)a(f)Theta is the satellite observation angle of incidence, AoAs a parameter of the oxygen content of the atmosphere, AwvThe content of atmospheric water vapor to be solved;
τa(f)＝sec(θ)[Ao+Awv](1)
calculating the intermediate value t by the formula (2)a(f)；
ta(f)＝exp(-τa(f)) (2)
Calculating the intermediate value e by equation (3)bl(p,f)，tcFor the unknowns to be solved, eos(p,f)The emissivity of the dry bare land is shown, and omega represents the vegetation scattering rate;
ebl(p,f)＝tceos(p,f)+(1-tc)(1-ω) (3)；
calculating the intermediate value e by equation (4)(p,f)，ffwsFor the unknowns to be solved, ew(p,f)The emissivity of the water surface is represented, and omega represents vegetation scattering rate;
e(p,f)＝ffwsew(p,f)+(1-ffws)ebl(p,f)(4)
calculating the average effective temperature of the earth surface by taking a K wave band as an inversion wave band and calculating a middle value t by a formula (2)a(f)And the intermediate value e calculated by equation (4)(p,f)Substituting into the inversion formula (5),
Tb(p,f)＝Ts[ta(f)e(p,f)+(1-ta(f))δ](5)
wherein, Tb(p,f)Represents the light temperature of a certain channel, p represents the verticalPolarization or horizontal polarization, f represents 18.7GHz or 23.8 GHz; t issRepresenting the average effective surface temperature to be solved, and delta represents the ratio of the atmospheric air/surface temperature;
the K wave band is 18.7GHz and 23.8GHz, the 18.7GHz wave band comprises two channels of vertical polarization and horizontal polarization, and each channel corresponds to brightness temperature data; the 23.8GHz band comprises two channels of vertical polarization and horizontal polarization, and each channel corresponds to brightness temperature data; respectively substituting brightness temperature data of four channels into an inversion formula (5) to obtain 4 equations, and solving 4 unknowns Ts、Awv、tc、ffws；
Step 3): substituting the average effective temperature of the earth surface and the microwave bright temperature polarization difference index of the X wave band obtained in the step 2) into a mathematical model of the soil dielectric constant and a microwave radiation transmission model, and solving the vegetation optical thickness and the soil dielectric constant by a least square method;
step 4): substituting the soil dielectric constant obtained by the calculation in the step 3) into a soil moisture content inversion model, and calculating to obtain the soil moisture content on the earth surface.
2. The earth surface soil moisture inversion method based on the passive microwave remote sensing data as claimed in claim 1, characterized in that in step 1), a soil component distribution map with a resolution of 25km and a microwave brightness and temperature distribution map with each wave band with a resolution of 25km are obtained through preprocessing.
3. The earth surface soil moisture inversion method based on the passive microwave remote sensing data as claimed in claim 1), wherein in the step 1), the soil composition distribution map displays soil composition data, and the soil composition data comprises earth surface clay content, earth surface sand content and soil volume weight parameters.
4. The earth surface soil moisture inversion method based on passive microwave remote sensing data according to claim 1, characterized in that in step 1), the X wave band is 10.7 GHz.
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| CN110221301A (en) * | 2019-07-24 | 2019-09-10 | 中国科学院遥感与数字地球研究所 | Soil Moisture Inversion method and device |
| CN110852473B (en) * | 2019-09-24 | 2022-06-03 | 广东省科学院广州地理研究所 | Land water reserve prediction method and equipment based on neural network algorithm |
| CN111610201B (en) * | 2020-04-30 | 2021-08-10 | 中国科学院空天信息创新研究院 | Passive microwave multi-channel synergistic soil moisture inversion method and device |
| CN111811660B (en) * | 2020-07-14 | 2021-06-04 | 中国科学院空天信息创新研究院 | Optimization processing method for brightness temperature data of two-dimensional synthetic aperture microwave radiometer |
| CN112161998B (en) * | 2020-09-02 | 2023-12-05 | 国家气象信息中心 | Soil water content measuring method and device, electronic equipment and storage medium |
| CN112462369A (en) * | 2020-10-30 | 2021-03-09 | 国家卫星气象中心(国家空间天气监测预警中心) | Method and device for microwave imager to invert sea atmosphere rainfall |
| CN114111553B (en) * | 2021-11-25 | 2023-12-08 | 安徽理工大学 | Method for rapidly obtaining thickness of newly-increased farmland reconstructed soil body filling layer |
| CN114926509B (en) * | 2022-07-20 | 2022-10-25 | 中南大学 | Ground stress blocking display area remote sensing tracking method based on satellite microwave brightness temperature |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
| CN105930664A (en) * | 2016-04-26 | 2016-09-07 | 中国科学院地理科学与资源研究所 | Method for estimating instantaneous surface emissivity from passive microwave data |
| CN107085712A (en) * | 2017-04-28 | 2017-08-22 | 山东省农业可持续发展研究所 | A kind of agricultural arid monitoring method based on MODIS data |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE50206554D1 (en) * | 2002-01-29 | 2006-06-01 | Ams Advanced Microwave Systems | Microwave arrangement for product moisture measurement, with temperature compensation |
| CN103969268B (en) * | 2014-04-04 | 2016-06-29 | 中国科学院华南植物园 | The passive microwave remote sensing inversion method of surface soil physical parameter |
| CN105466957B (en) * | 2016-01-07 | 2018-05-25 | 中国农业科学院农业资源与农业区划研究所 | A kind of method of FY-3C passive microwaves data estimation soil moisture |
| CN105606631B (en) * | 2016-02-01 | 2019-07-26 | 中国科学院遥感与数字地球研究所 | A kind of method of salinity satellite two waveband bright temperature data joint inversion soil moisture |
| US20170343485A1 (en) * | 2016-05-10 | 2017-11-30 | Purdue Research Foundation | Retrieval of p-band soil reflectivity from signals of opportunity |
| CN106372434B (en) * | 2016-08-31 | 2020-03-06 | 中国科学院遥感与数字地球研究所 | Method and device for estimating instantaneous surface emissivity by passive microwave remote sensing |
-
2018
- 2018-07-20 CN CN201810801987.5A patent/CN108982548B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
| CN105930664A (en) * | 2016-04-26 | 2016-09-07 | 中国科学院地理科学与资源研究所 | Method for estimating instantaneous surface emissivity from passive microwave data |
| CN107085712A (en) * | 2017-04-28 | 2017-08-22 | 山东省农业可持续发展研究所 | A kind of agricultural arid monitoring method based on MODIS data |
Non-Patent Citations (2)
| Title |
|---|
| Present status of soil moisture estimation by microwave remote sensing;Kousik Das 等;《cogent geoscience》;20151008(第1期);第1-21页 * |
| 针对被动微波AMSR-E数据的土壤水分反演算法;毛克彪 等;《高技术通讯》;20100625;第20卷(第6期);第651-659页 * |
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