CN108736990A - A method of detection multi-source passive microwave data radio frequency interference - Google Patents
A method of detection multi-source passive microwave data radio frequency interference Download PDFInfo
- Publication number
- CN108736990A CN108736990A CN201810231081.4A CN201810231081A CN108736990A CN 108736990 A CN108736990 A CN 108736990A CN 201810231081 A CN201810231081 A CN 201810231081A CN 108736990 A CN108736990 A CN 108736990A
- Authority
- CN
- China
- Prior art keywords
- radio frequency
- frequency interference
- data
- convergence
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/345—Interference values
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/22—Scatter propagation systems, e.g. ionospheric, tropospheric or meteor scatter
Abstract
The invention discloses a kind of methods of detection multi-source passive microwave data radio frequency interference, including detection part and reciprocal portions, detection part is responsible for One-Dimensional Variational iterative calculation convergence and is measured, and after iteration, distribution and the intensity of radio frequency interference signal are detected according to convergence metric；Reciprocal portions are responsible for calculating required geophysical parameters.The method of the present invention can detect different surfaces types, the spatial distribution of radio frequency interference signal in different microwave remote sensor observational datas, can quantify the intensity of radio frequency interference signal in multi-source Microwave Data；Combining wireless electric frequency interference signal of the present invention corrects algorithm, can reuse the data being removed in satellite microwave Data Assimilation because error is larger, improves utilization rate of the satellite-borne microwave data in Data Assimilation and numerical weather forecast；The present invention releases radio frequency interference signal present in microwave bright temperature data with One-Dimensional Variational inverting geophysical parameters are counter, and method is simple, and calculation amount is small.
Description
Technical field
The invention belongs to Atmospheric Microwave remote sensing fields, are related to a kind of detection multi-source passive microwave data radio frequency interference
Method.
Background technology
Microwave observation data can provide land table and atmospheric information under various weather conditions, microwave radiance transfer sensor
Occupy more and more consequence in numerical weather prediction model and Data Assimilation.However, due to radio-frequency spectrum in recent years
Science and commercial user between conflict increasingly sharpen, radio frequency interference is to passive and active microwave remote sensing influence day
It is beneficial serious.Active microwave sensor emission signal or surface reflection radiation signal are easy to the relatively weak of covering earth's surface generation
Heat emission radiation signal so that the information that microwave remote sensor receives has mixed the radiation information other than real surface situation.
Therefore, it gives passive microwave sensor settings special protection frequency range, allows their priority tasks in these frequency ranges, and international regulations
Then active microwave transmitter can be forbidden to be sent out in these wave bands more than defined energy.Nevertheless, these are in microwave band
Shielded frequency range it is seldom, some of frequency ranges are very narrow.However, increasingly due to application of the microwave frequency band in business activity
Extensively, microwave observation data are just influenced by radio frequency interference more and more.Currently, in routine work just extensively
The satellite-borne microwave sensor used, the AMSR-E on such as satellite EOS/Aqua, the WindSat on satellite Coriolis, satellite
The AMSR-2 etc. on MWRI and satellite GCOM-W1 on FY-3, is interfered by radio frequency, especially to some extent
Microwave low frequency observation channel is interfered more serious.Moreover, the accuracy of low frequency microwave data largely influences to land
The precision of table parameter (such as surface temperature, soil moisture, vegetation, Xue Gai, sea ice) inverting.If accurately cannot detect and pick
It removes, this interference often leads to larger inversion error, to significantly reduce existing and passive microwave data in future use
Efficiency.
Previous has researched and developed radio frequency interference signal in various detection satellite-borne microwave radiometer data
Method.Li et al. (2004) initially proposes and detects radio frequency interference with spectral difference method, it is determined that AMSR-E is in C and X
The intensity and distribution of radio frequency interference signal, then further provide and use principal component analysis in the observation of wave band
Method analyzes the radio frequency interference distribution characteristics of land area；Njoku etc. (2005) proposes assembly average and standard
Poor analytic approach, the result of study obtained, which is AMSR-E, to be influenced in the channels 6.925GHz and 10.67GHz by radio frequency interference
Region is respectively at different geographical locations；Li et al. (2006), which then has also been proposed, to be identified with multichannel regression algorithm
The radio frequency interference signal being distributed in WindSat data on ocean surface；Lacava etc. (2016) is analyzed with multidate method
The land face radio frequency interference of AMSR-E C-bands；Zou etc. (2012) proposition Principal Component Analysis can detect the lands MWRI
The radio frequency interference of ground surface is distributed；Zhao etc. (2013) improves Principal Component Analysis, proposes with double principal component analysis
Method is analyzed radio frequency interference of the WindSat data in snow cover area and is distributed；Official jasmine etc. (2014), which proposes to use, to be simplified
Principal Component Analysis the AMSR-E radio frequency interferences of European land area are detected.And various earth's surface is covered
The effect of lid condition, different detection methods has limitation, some to be even completely unsuitable for another kind different surface conditions
Surface condition.Therefore, a kind of method of detection multi-source passive microwave data radio frequency interference provided by the invention can be very
Well under clear sky/have cloud atmospheric condition, for a variety of ground mulching situations, to the nothing in a variety of satellite-borne microwave sensor datas
The interference of line electric frequency is detected.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of detection multi-source passive microwave data radio
A kind of method of frequency interferences, with using general detection method i.e. detectable different surfaces type (bare area surface, different vegetation
Cover top, snow cover surface, ice sheet surface, ocean surface) difference microwave remote sensor observational data (AMSR-E, MWRI,
Windsat, AMSR-2) in radio frequency interference signal.
The above-mentioned purpose of the present invention is achieved by following technical solution：
A method of detection multi-source passive microwave data radio frequency interference, including detection part and reciprocal portions,
Detection part is responsible for One-Dimensional Variational iterative calculation convergence and is measured, and after iteration, radio frequency line is detected according to convergence metric
The distribution of rate interference signal and intensity；Reciprocal portions are responsible for calculating required geophysical parameters.
Using General radiation transmission mode CRTM as the forward mode in One-Dimensional Variational；During Variational Calculation, inverting
The vertical distribution for the atmospheric parameter that required initial fields state vector is provided from NCEP/FNL world business analysiss of data or surface
Parameter acquiring；The core of Variational Calculation is by acquiring the solution for making cost function reach minimum value, obtaining optimum analysis field；To institute
The cost function derivation of definition, and derivative is made to be zero, the minimum of cost function is calculated by progressive alternate；Each time
In iterative process, a new increment is calculated, is then added with primary quantity, the new amount obtained after the addition is used as should
The solution that secondary iteration obtains.
With each iteration obtain atmospheric condition vector analog satellite radiation brightness, then compare the bright temperature analogue value of satellite with
The bright temperature actual observed value of satellite, judges whether cost function reaches convergence；The convergence criterion of use is to examine and simulate bright Wen Yuguan
Whether the variance surveyed between bright temperature is less than 1, and the value of the variance is also as the foundation for judging degree of convergence；In iterative process,
Meet the first iteration stopping of following two conditions：One, certain iteration meets the above-mentioned condition of convergence, then it is assumed that has acquired optimal
Solution；Two, iterations reach preset value.After iteration stopping, if the variance between the bright temperature of simulation and the bright temperature of observation is less than 1, say
Ming Dynasty's valence function convergence, inversion result is reliably effective, exports inversion result；If the variance is more than 1, can be according to actual conditions handle
This criteria relaxation is to 10, and illustrating inversion result, there are certain errors, and can be according to the spatial distribution state of the variance yields
It detects the distribution of radio frequency interference, while judging the intensity of radio frequency interference according to the size of the variance yields, it should
Value is bigger, then shows that the radio frequency interference intensity at this is bigger；If the variance is more than 10, illustrate that inversion result is unreliable,
There are stronger radio frequency interferences, do not export inversion result.
Further, atmospheric parameter vertical distribution includes temperature, humidity and cloud liquid water content etc..
Further, surface parameter includes the surfaces such as surface type, including land, water body, snow lid and ice sheet.Wherein, land
Ground surface parameter includes ground surface type, surface temperature, soil moisture content, canopy water content, vegetation coverage, the soil moisture, leaf
Area index, soil types, vegetation pattern etc.；Water surface parameter include water meter type, water surface temperature, surface wind speed, wind direction,
Salinity etc.；It includes accumulated snow type, accumulated snow temperature, snow depth, density of snow, snow crystal particle size etc. to avenge cap surface parameter；Ice sheet
Surface parameter includes ice sheet type, ice sheet temperature, ice sheet thickness, ice sheet density, ice face roughness etc..
The method of detection multi-source passive microwave data radio frequency interference provided by the invention has the following advantages：
(1) one general detection method detection different surfaces type, different microwave remote sensor observational data of the present invention
In radio frequency interference signal spatial distribution.
(2) present invention can quantify the intensity of radio frequency interference signal in multi-source Microwave Data.
(3) combining wireless electric frequency interference signal of the present invention corrects algorithm, can reuse satellite microwave Data Assimilation
The middle data being removed because error is larger improve utilization of the satellite-borne microwave data in Data Assimilation and numerical weather forecast
Rate.
(4) present invention releases radio frequency line present in microwave bright temperature data with One-Dimensional Variational inverting geophysical parameters are counter
Rate interference signal, method is simple, and calculation amount is small.
Description of the drawings
Fig. 1 is the flow chart of present invention detection multi-source passive microwave data radio frequency interference method.
Fig. 2 is CRTM pattern framework structural schematic diagrams used when being used for variation Inversion Calculation in the present invention.CRTM patterns
Simulation to atmospheric radiative transfer process includes four major parts：Surface emitting and scattering radiate；The absorption of aerosol and dissipate
Penetrate radiation；The absorption and scattering radiation of cloud；The absorption radiation of gas componant in air.
Specific implementation mode
It is specific with reference to the accompanying drawings and examples to introduce essentiality content of the present invention, but the guarantor of the present invention is not limited with this
Protect range.
The method of detection multi-source passive microwave data radio frequency interference provided by the invention, is broadly divided into two portions
Point：Detection part and reciprocal portions.Wherein, detection part is responsible for One-Dimensional Variational iterative calculation convergence measurement, after iteration, root
Distribution and the intensity of radio frequency interference signal are detected according to convergence metric；Reciprocal portions are responsible for calculating the required earth
Physical parameter.
Fig. 1 and Fig. 2 is the tool of detection multi-source passive microwave data radio frequency interference method provided by the invention respectively
Body flow and CRTM pattern framework structural schematic diagrams as forward mode in variation Inversion Calculation.Not only using One-Dimensional Variational method
Various atmospheric parameters (such as temperature, wet Vertical Profile), cloud parameter (cloud amount, cloud can be obtained from satellite microwave, infrared observation data
Heights of roofs), it can be with inverting Land Surface Parameters (such as surface temperature and earth's surface emissivity).The premise of variation inverting be observation field with
Background field error is unbiased, incoherent, and obeys Gaussian error and be distributed these hypothesis conditions, by asking cost function
Minimum obtains the analysis field of minimal error.This cost function J (x) can generally be write as
Wherein, J (x) is cost function；X indicates air (or earth's surface) state variable being inverted；X0Expression air (or ground
Table) initial state vector (or make ambient field vector)；B is background error covariance matrix；YmIt is acquired observation
Data；E is observation field error co-variance matrix；H is preceding to operator, for satellite microwave data inversion geophysical parameters
Speech, H is exactly radiative transmission mode.
The optimum analysis field X generated by One-Dimensional VariationalaIt is the solution for making cost function formula (1) reach minimum value, that is,
Can be by formula (3) to cost function derivation defined in formula (1), and derivative is made to be zero, that is,
It can obtain,
(X-X0)=Δ X={ (B-1+HTE-1H)-1HTE-1}×[Ym-H(X0)] (4)
Then, formula (4) is calculated for loop iteration.In iterative process each time, a new increment is calculated,
The iterative increment is added with initial state vector again, you can obtain the solution of the secondary iterative calculation.Until cost function J (X) reaches
When to minimum or reaching regulation iterations, end loop.The atmospheric condition vector analog satellite radiation obtained with each iteration
Then bright temperature compares the bright temperature analogue value of satellite and the bright temperature actual observed value of satellite, judges whether cost function reaches convergence.Using
Convergence judgment criteria be, after iteration, examine by all residual between the obtained bright temperature analogue value of forward mode and measured value
The variance χ of difference2Value whether be less than 1.χ2Calculation formula is as follows
χ2=[Ym-H(X)]T×E-1×[Ym-H(X)]。 (5)
After iteration stopping, work as χ2When≤1, it is believed that cost function reaches convergence, and inversion result is reliably effective, and output is anti-
Drill result；If the χ2> 1 usually can illustrate Microwave Data by difference according to actual conditions this criteria relaxation to 10
The radio frequency interference of degree, therefore the error of observational data is directly delivered in inversion result, directly results in inversion result
There is also different degrees of errors, and can be according to χ2Distribution characteristics detect the spatial distribution shape of radio frequency interference
Condition, while χ2The size of value can be worth bigger, it is meant that as the measurement for judging radio frequency interference signal strength at this
Radio frequency interference signal is stronger；If χ2When > 10, illustrate that inversion result is unreliable, it is dry that there are stronger radio frequencies
It disturbs, does not export inversion result.
Using General radiation transmission mode (CRTM) as the forward mode in One-Dimensional Variational in the present invention, for simulate to
In the case of determining air and earth's surface state parameter, the bright temperature measurements of certain satellite-borne microwave sensor.CRTM is provided by the satellite in the U.S.
The exploitation of material assimilation United Center, is applicable not only to clear sky, has been also applied for the various weather conditions such as cloud and precipitation.Institute can be simulated
Have by the scattering of the generations such as ice crystal, snow crystal, raindrop, graupel grain and cloud liquid water under microwave frequency, and generates all air, earth's surface
The corresponding radiation value of parameter and radiation gradiant.Many physical processes during CRTM mode computations atmospheric radiative transfer are (such as
Shown in Fig. 2), include mainly four parts：Surface emitting and scattering Radiation Module；The absorption of aerosol and scattering Radiation Module；Cloud
Absorption and scattering Radiation Module；The absorption Radiation Module of gas componant in air.
During Variational Calculation, the ambient field needed for inverting can be objective point from NCEP/FNL world business analysiss of data
Analyse initial fields of the extraction relevant parameter (atmospheric parameter Vertical Profile and earth's surface state parameter) as cost function iteration in field.This
The NCEP/FNL analyses of a little business and forecast data are every six hours 0.25 ° × 0.25 ° data, and the time is respectively 00：
00UTC,06：00UTC,12：00UTC and 18：00UTC.NCEP/FNL data pass through global metadata assimilation system (Global
Data Assimilation System, i.e. GDAS) generate, the system can persistent collection come from Global Remote telecommunication system
(Global Telecommunications System, i.e. GTS) and some other observation, assimilation ground observation, radio
Sounding, sounding balloon, aircraft and moonscope data are used it for analysing scientifically and be studied.Generate FNL data pattern with
The pattern that NCEP is used in GFS (Global Forecast System) is identical, the difference is that FNL data products typically exist
What GFS was generated after about 1 hour of initialization, enable to FNL data collections to more observation data in this way.FNL numbers
According to comprising 27 Standard Gases laminate layers (including earth's surface, 26 defined height layers from 1000mb to 10mb), boundary layer and some
Parameter of other levels, including surface pressure, sea-level pressure, geopotential unit, temperature, Sea Level Temperature, relative humidity etc..
Need to input ice water content, rainwater content etc. in the temperature of NCEP/FNL, humidity, cloud liquid water content, cloud in Variational Calculation
Ambient field of the parameters such as Vertical Profile and surface temperature as inverting.After cost function iteration is restrained, inverting knot is exported
Fruit can be divided into Land Surface Parameters (surface temperature and emissivity spectrum) and atmospheric parameter (temperature, humidity, cloud liquid water content, Yun Zhong
The Vertical Profiles such as ice water content, rainwater content) two major classes.On this basis, then by vertically integrating, the post-processings such as multiple regression
Algorithm can obtain the other retrieval products of such as total moisture content, cloud liquid water total content, surface precipitation rate.
The effect of above-described embodiment is specifically to introduce the essentiality content of the present invention, but those skilled in the art should know
Protection scope of the present invention should not be confined to the specific embodiment by road.
Claims (3)
1. a kind of method of detection multi-source passive microwave data radio frequency interference, it is characterised in that：Including detection part with
Reciprocal portions, detection part are responsible for One-Dimensional Variational iterative calculation convergence and are measured, after iteration, detected according to convergence metric
The distribution of radio frequency interference signal and intensity；Reciprocal portions are responsible for calculating required geophysical parameters；
Using General radiation transmission mode CRTM as the forward mode in One-Dimensional Variational；During Variational Calculation, needed for inverting
Initial fields the state vector vertical distribution or surface parameter of the atmospheric parameter that are provided from NCEP/FNL world business analysiss of data
It obtains；The core of Variational Calculation is by acquiring the solution for making cost function reach minimum value, obtaining optimum analysis field；To being defined
Cost function derivation, and derivative is made to be zero, the minimum of cost function is calculated by progressive alternate；In iteration each time
In the process, a new increment is calculated, is then added with primary quantity, the new amount obtained after the addition is used as this time repeatedly
The solution that generation obtains；
The atmospheric condition vector analog satellite radiation brightness obtained with each iteration, then compares the bright temperature analogue value of satellite and satellite
Bright temperature actual observed value, judges whether cost function reaches convergence；The convergence criterion of use is to examine the bright temperature of simulation and observe bright
Whether the variance between temperature is less than 1, and the value of the variance is also as the foundation for judging degree of convergence；In iterative process, meet
The first iteration stopping of following two conditions：One, certain iteration meets the above-mentioned condition of convergence, then it is assumed that has acquired optimal solution；Two,
Iterations reach preset value；After iteration stopping, if the variance between the bright temperature of simulation and the bright temperature of observation is less than 1, illustrate cost
Function convergence, inversion result is reliably effective, exports inversion result；If the variance is more than 1, this can be marked according to actual conditions
Standard is loosened to 10, and illustrating inversion result, there are certain errors, and can be detected according to the spatial distribution state of the variance yields
The distribution of radio frequency interference, while judging according to the size of the variance yields intensity of radio frequency interference, the value is bigger,
Then show that the radio frequency interference intensity at this is bigger；If the variance is more than 10, illustrate that inversion result is unreliable, exists relatively strong
Radio frequency interference, do not export inversion result.
2. method according to claim 1, it is characterised in that：The vertical distribution of atmospheric parameter includes in temperature, humidity and cloud
Liquid water content.
3. method according to claim 1, it is characterised in that：Surface parameter includes surface type, including land, water body, snow
Lid and ice sheet surface；Wherein, top parameter includes ground surface type, surface temperature, soil moisture content, canopy water content, plants
Coating cover degree, the soil moisture, leaf area index, soil types, vegetation pattern；Water surface parameter includes water meter type, the water surface
Temperature, surface wind speed, wind direction, salinity；Snow cap surface parameter include accumulated snow type, accumulated snow temperature, snow depth, density of snow,
Snow crystal particle size；Ice sheet surface parameter includes ice sheet type, ice sheet temperature, ice sheet thickness, ice sheet density, ice face roughness.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810231081.4A CN108736990A (en) | 2018-03-20 | 2018-03-20 | A method of detection multi-source passive microwave data radio frequency interference |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810231081.4A CN108736990A (en) | 2018-03-20 | 2018-03-20 | A method of detection multi-source passive microwave data radio frequency interference |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108736990A true CN108736990A (en) | 2018-11-02 |
Family
ID=63940840
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810231081.4A Pending CN108736990A (en) | 2018-03-20 | 2018-03-20 | A method of detection multi-source passive microwave data radio frequency interference |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108736990A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109406911A (en) * | 2018-12-12 | 2019-03-01 | 中国气象科学研究院 | A kind of detection of satellite-borne microwave sensor low channel radio frequency interference and bearing calibration |
| CN111505625A (en) * | 2020-04-01 | 2020-08-07 | 中国科学院国家空间科学中心 | Active and passive combined microwave remote sensing detection method for ice and snow internal state distribution |
| CN112730313A (en) * | 2020-12-21 | 2021-04-30 | 国家卫星气象中心(国家空间天气监测预警中心) | Multi-frequency terahertz detector channel selection method and device for ice cloud detection |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105372720A (en) * | 2015-10-27 | 2016-03-02 | 合肥工业大学 | SPSA meteorological parameter solution method based on assimilation of navigation satellite occultation data |
| US20170343485A1 (en) * | 2016-05-10 | 2017-11-30 | Purdue Research Foundation | Retrieval of p-band soil reflectivity from signals of opportunity |
-
2018
- 2018-03-20 CN CN201810231081.4A patent/CN108736990A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105372720A (en) * | 2015-10-27 | 2016-03-02 | 合肥工业大学 | SPSA meteorological parameter solution method based on assimilation of navigation satellite occultation data |
| US20170343485A1 (en) * | 2016-05-10 | 2017-11-30 | Purdue Research Foundation | Retrieval of p-band soil reflectivity from signals of opportunity |
Non-Patent Citations (2)
| Title |
|---|
| 吴莹等: "被动微波遥感观测资料干扰对地表参数反演的影响分析", 《国土资源遥感》 * |
| 施建成等: "微波遥感地表参数反演进展", 《中国科学:地球科学》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109406911A (en) * | 2018-12-12 | 2019-03-01 | 中国气象科学研究院 | A kind of detection of satellite-borne microwave sensor low channel radio frequency interference and bearing calibration |
| CN109406911B (en) * | 2018-12-12 | 2020-02-11 | 中国气象科学研究院 | Low-frequency channel radio frequency interference detection and correction method for satellite-borne microwave sensor |
| CN111505625A (en) * | 2020-04-01 | 2020-08-07 | 中国科学院国家空间科学中心 | Active and passive combined microwave remote sensing detection method for ice and snow internal state distribution |
| CN112730313A (en) * | 2020-12-21 | 2021-04-30 | 国家卫星气象中心(国家空间天气监测预警中心) | Multi-frequency terahertz detector channel selection method and device for ice cloud detection |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Los et al. | Vegetation height products between 60° S and 60° N from ICESat GLAS data. | |
| CN108736990A (en) | A method of detection multi-source passive microwave data radio frequency interference | |
| CN110376138A (en) | Land quality monitoring method based on Airborne Hyperspectral | |
| CN104483271A (en) | Forest biomass amount retrieval method based on collaboration of optical reflection model and microwave scattering model | |
| CN108959705B (en) | Method for predicting subtropical forest biomass | |
| CN102252973A (en) | Method for performing remote-sensing monitoring on soil moisture content | |
| CN106372434A (en) | Passive microwave remote sensing instant ground surface emitting rate estimation method and device | |
| Ruiz-Pérez et al. | Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI | |
| CN109709558A (en) | A kind of physics inversion algorithm of satellite-borne microwave remote sensing Over-land PWV | |
| CN110645961A (en) | Forest resource dynamic change detection method based on remote sensing and NDVI | |
| Cheng et al. | Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China | |
| CN108399393A (en) | A kind of Vegetation canopy negative and positive leaf detection method based on fluorescence remote sensing | |
| CN111366617A (en) | Semi-supervised fuzzy recognition model and soil humidity measurement method based on model | |
| CN105954207A (en) | Method for detecting content of chlorophyll b in canopy of jujube tree | |
| CN113534083B (en) | SAR-based corn stubble mode identification method, device and medium | |
| Ragab et al. | Towards accurate estimation of crop water requirement without the crop coefficient Kc: New approach using modern technologies | |
| CN108416297A (en) | A kind of vegetation information method for quickly identifying based on chlorophyll fluorescence | |
| Zhang et al. | Spectral reflectance properties of major objects in desert oasis: a case study of the Weigan–Kuqa river delta oasis in Xinjiang, China | |
| Kida et al. | Improvement of rain/no-rain classification methods for microwave radiometer observations over the ocean using a 37 GHz emission signature | |
| Bouin et al. | Using scintillometry to estimate sensible heat fluxes over water: first insights | |
| CN111597692A (en) | Surface net radiation estimation method, system, electronic equipment and storage medium | |
| Li et al. | Spectral characteristics of reclaimed vegetation in a rare earth mine and analysis of its correlation with the chlorophyll content | |
| CN110321402A (en) | A kind of prediction technique of mountain area high forest potential distribution | |
| Zhang et al. | Effect of soil moisture and particle size on soil total phosphorus estimation by near-infrared spectroscopy. | |
| CN106092912A (en) | A kind of detection method of jujube tree canopy moisture |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181102 |