CN108761034B - A kind of multi-source satellite soil moisture retrieval method - Google Patents
A kind of multi-source satellite soil moisture retrieval method Download PDFInfo
- Publication number
- CN108761034B CN108761034B CN201810473645.5A CN201810473645A CN108761034B CN 108761034 B CN108761034 B CN 108761034B CN 201810473645 A CN201810473645 A CN 201810473645A CN 108761034 B CN108761034 B CN 108761034B
- Authority
- CN
- China
- Prior art keywords
- soil
- emissivity
- soil moisture
- temperature
- dielectric constant
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
- G01N33/246—Earth materials for water content
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Food Science & Technology (AREA)
- Analytical Chemistry (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Medicinal Chemistry (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Radiation Pyrometers (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a kind of multi-source satellite soil moisture retrieval methods, which comprises the following steps: collects the bright temperature data of multi-source satellite, and obtains the same spatial resolution brightness temperature product serial of unification；Soil moisture microwave remote sensing inverse model is constructed, bright temperature is carried out and is converted with emissivity, extracts the grid emissivity one by one of microwave radiometer scanning track；Influence to vegetation and roughness of ground surface is rejected and is corrected；Soil dielectric constant is converted with emissivity, be associated with using the dielectric constant after conversion with soil moisture it is counter push away, realize the remote-sensing inversion of soil moisture.The present invention is main input data using the quasi real time brightness temperature data of multiple satellite platforms observation, the bright temperature data for extracting different satellite sources carries out cutting splicing, the inverting and space-time for realizing surface soil water are rebuild, and the soil moisture product of continuous high-spatial and temporal resolution is obtained.
Description
Technical field
The invention belongs to hydrological remote sensing technical fields, and in particular to a kind of multi-source satellite soil moisture retrieval method.
Background technique
Soil moisture is weather, numerical weather forecast, hydrologic(al) prognosis, agricultural arid monitoring and prediction and Land data assimilation etc.
The important land surface parameters of research field.The measurement method for obtaining soil moisture classics at present mainly has: weight method, tensometer method,
NEUTRON METHOD, electric-resistivity method etc., although the soil moisture precision of website artificial observation is high, the sparse or with a varied topography ground of website
Area can not lay website, and classical measurement method has the disadvantages of blocky scarce survey, data is discontinuous, it is difficult to show soil moisture
Spatial distribution.
The development of satellite remote sensing technology provides effective means for the acquisition of the soil humidity information of regional scale, but defends
The spatial resolution of star remote-sensing inversion surface soil humidity is low, and there are very large space heterogeneity, the soil of different spatial resolutions
Earth humidity characteristic is inconsistent；Due to the scanning track of satellite and coverage area, the shadow of the factors such as the veils such as vegetation, massif
It rings, there is part bands, partial region data deficient phenomena, and shortage of data region varies over, can not
For the soil moisture signature analysis of specific region and continuous time, need to rebuild missing data；Therefore, continuous high
The acquisition of spatial and temporal resolution soil moisture has become a hot topic of research and difficult point, while being also agricultural arid monitoring, Forecasting Flood etc.
Business department very relationship the problem of.
A series of domestic and international business satellites for carrying main passive microwave sensor succeed in sending up, such as: SMOS (Soil
Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), FY3B (MWRI:
Microwave Radiation Imager), FY3C (MWRI:Microwave Radiation Imager), AMSR
(Advanced Microwave Scanning Radiometer 2) etc., utilizes the microwave remote sensor energy being mounted on satellite
A large amount of thin solum humidity informations are enough obtained, the joint inversion of multi-source satellite soil moisture is in basin-wide or regional soil moisture
There is irreplaceable advantage in monitoring.
Summary of the invention
In order to obtain the soil moisture product of continuous high-spatial and temporal resolution, the present invention provides a kind of multi-source satellite soil
The method that the timely bare weight of humidity retrieval is built cuts, splicing by data, obtains using the satellite bright temperature data of domestic and international mainstream
Format is consistent, the consistent bright temperature data of resolution ratio, constructs soil moisture retrieval model, obtains soil moisture product；It will utilize
The information such as the soil water-containing information obtained for three days, antecedent precipitation are gone to carry out statistical regression interpolation, to obtain the land of space and time continuous
Table soil moisture product.
In order to obtain the soil moisture of continuous high-spatial and temporal resolution, the present invention specifically uses following technical scheme:
A kind of multi-source satellite soil moisture retrieval method, which comprises the following steps:
Step 1, the bright temperature data of multi-source satellite is collected, and obtains same spatial resolution (0.25 °) brightness of unification
Temperature product serial；
Step 2, soil moisture microwave remote sensing inverse model is constructed, bright temperature is carried out and is converted with emissivity, extract microwave radiation
The grid emissivity one by one of meter scanning track；
Include:
Step 21, soil moisture microwave remote sensing inverse model is constructed:
TBp=Tu+exp(-τa)×[{TdrSpexp(-2τc)}+{eSpTsexp(-τc)+Tc(1-ωp)[1-exp(-τc)][1
+rSpexp(-τc)] (1)
Wherein: TBpFor brightness temperature, TuFor the uplink radiation of atmosphere, TdThe downlink radiation for being atmosphere at the top of canopy, τaFor
The optical thickness of atmosphere, that subscript p is indicated is polarization mode, TcFor the temperature of vegetation, τcFor the optical thickness factor of vegetation, rSp
=1-eSpFor the reflectivity of soil surface；TsIt is effective physical temperature of earth's surface, ωpFor the single scattering albedo of vegetation；
Step 22, the quasi real time surface temperature and microwave radiometer of satellite remote sensing and the acquisition of real-time weather forecasting model are utilized
The bright temperature of observation extracts the grid emissivity one by one of microwave radiometer scanning track:
γp=e(-τsecθ) (3)
In formula: TBpFor brightness temperature；T is physical temperature；E is emissivity；Subscript p represents polarization direction, including horizontal pole
Change (H) and vertical polarization (V), τ vegetation opacity, ω single scattering albedo, γ canopy transmissivity,The ground of polarization direction p
Table emissivity, θ are radiometer view angle or incident；
Step 3, the influence to vegetation and roughness of ground surface is rejected and is corrected；
Step 4, soil dielectric constant is converted with emissivity, is closed using the dielectric constant after conversion with soil moisture
Connection is counter to be pushed away, and realizes the remote-sensing inversion of soil moisture.
The bright temperature data of domestic and international high-spatial and temporal resolution is collected in the step 1, and according to the feature of the bright temperature of different satellites
Carry out format conversion, resampling, the space splicing, same spatial resolution (0.25 °) the brightness temperature for generating unification of data
Product serial is spent, specifically includes the following steps:
Step 11, domestic and international bright temperature data is collected；
Step 12, the format of different satellite bright temperature datas is different, carries out Data Format Transform, is uniformly converted to HDF5；
Step 13, the spatial resolution of different satellite bright temperature datas is different, resampling is carried out, so that spatial resolution one
It causes；
Step 14, the track of the inswept earth of different satellites is different, needs to carry out space splicing to bright temperature data；
Step 15, generate that format is consistent, the consistent bright temperature temperature product of spatial resolution；
Soil moisture microwave remote sensing inverse model is constructed in the step 2, is carried out bright temperature and is converted with emissivity, extracts microwave
Radiometer scans the grid emissivity one by one of track, and specific steps include:
Step 21, on the basis of classical τ-ω model, the brightness temperature observed using microwave radiometer is wet with soil
The relationship of degree constructs soil moisture microwave remote sensing inverse model；
TBp=Tu+exp(-τa)×[{TdrSpexp(-2τc)}+{eSpTsexp(-τc)+Tc(1-ωp)[1-exp(-τc)][1
+rSpexp(-τc)] (1)
Wherein: TBpFor brightness temperature, TuFor the uplink radiation of atmosphere, TdThe downlink radiation for being atmosphere at the top of canopy, τaFor
The optical thickness of atmosphere, that subscript p is indicated is polarization mode, TcFor the temperature of vegetation, τcFor the optical thickness factor of vegetation, rSp
=1-eSpFor the reflectivity of soil surface；TsIt is effective physical temperature of earth's surface, ωpFor the single scattering albedo of vegetation.
Step 22, the quasi real time surface temperature and microwave radiometer of satellite remote sensing and the acquisition of real-time weather forecasting model are utilized
The bright temperature of (such as SMAP, SMOS and AMSR2) observation extracts the grid emissivity one by one of microwave radiometer scanning track；It is bright
Spend temperature (TB), physical temperature (T), emissivity (e) by following physical equation establish contact:
γp=e(-τsecθ) (3)
In formula: TBpFor brightness temperature；T is physical temperature；E is emissivity；Subscript p represents polarization direction, including horizontal pole
Change (H) and vertical polarization (V), τ vegetation opacity, ω single scattering albedo, γ canopy transmissivity,The ground of polarization direction p
Table emissivity, θ are radiometer view angle or incident.
The extraction to soil moisture is improved in the step 3, the influence to vegetation and roughness of ground surface is kicked out of
And correction, comprising:
τ=bVWC (4)
In formula: τ vegetation opacity, VWC vegetation water content,The soil emissivity of polarization direction p, b depend on planting
By type, the parameter of microwave frequency, θ is radiometer view angle or incident.
Soil dielectric constant is converted with emissivity in the step 4, utilizes the dielectric constant and soil after conversion
Humidity association is counter to be pushed away, and realizes that the remote-sensing inversion step of soil moisture includes:
Step 41, soil dielectric constant be associated with emissivity it is counter push away, soil dielectric constant and emissivity relationship pass through famous
Fresnel formula state, dielectric constant same polarization emissivity can be converted.
In formula, εrUpper soll layer dielectric constant,Horizontal polarization emissivity,Vertical polarization emissivity, θ are radiation
Count view angle or incident.
Step 42, right using the dielectric constant and existing soil water-containing and soil dielectric constant relational model after conversion
Soil water-containing is counter to be pushed away, to realize that the inverting of soil moisture is extracted.
In formula: ρbSoil block volume density；ρsSoil solid particle specific gravity；mvSoil volumetric water content；εfwFree water dielectric
Constant；εsSoil particle dielectric constant, α, β are empirical index number.
A kind of beneficial effects of the present invention: side that the timely bare weight of multi-source satellite soil moisture retrieval is built provided by the invention
Method downloads the brightness temperature data of mainstream satellite, carries out uniform format processing, Design Based on Spatial Resampling, data splicing to bright temperature data
Etc. obtaining, format is consistent, the bright temperature product of the consistent Time Continuous of spatial resolution；On the basis of classical τ-ω model, soil is established
Earth humidity passive microwave remote sensing inversion algorithm, it may be assumed that the surface temperature that is obtained using satellite remote sensing and real-time weather forecasting model and
The bright temperature of microwave radiometer observation, extracts grid emissivity one by one；Emissivity is established by fresnel formula and soil is situated between
The relationship of electric constant, emissivity and dielectric constant are converted；The relational model of soil moisture and dielectric constant is established, is realized
The remote-sensing inversion of soil moisture.This method fully considers the advantages of each satellite, is basic data, data source with brightness temperature
It is reliable and stable；Soil moisture passive microwave remote sensing inversion algorithm is established, based on classical Remote Sensing Model, ensure that the objective of result
Reasonability realizes the soil moisture retrieval of multi-source satellite, obtains the soil of the consistent high-spatial and temporal resolution of Time Continuous spatial distribution
Earth humidity product.
Detailed description of the invention
Fig. 1 is bright temperature data processing schematic of the present invention.
Fig. 2 is inversion algorithm schematic diagram in the present invention.
Fig. 3 is bright temperature data amount schematic diagram in the present invention.
Fig. 4 is original bright temperature data schematic diagram in the present invention.
Fig. 5 is to splice bright temperature data schematic diagram in the present invention.
Fig. 6 is soil moisture product schematic diagram in the present invention.
Specific embodiment
The invention will be further described in the following with reference to the drawings and specific embodiments.
As illustrated in fig. 1 and 2, a kind of method that the timely bare weight of multi-source satellite soil moisture retrieval is built provided by the invention, packet
It includes following steps: step 1, collecting the bright temperature data of domestic and international high-spatial and temporal resolution, and according to the feature of the bright temperature of different satellites, into
The format conversion of row data, resampling, space splicing, same spatial resolution (0.25 °) brightness temperature for generating unification
Product serial, specifically:
Step 11, domestic and international bright temperature data is collected；
Step 12, the format of different satellite bright temperature datas is different, carries out Data Format Transform, is uniformly converted to HDF5；
Step 13, the spatial resolution of different satellite bright temperature datas is different, resampling is carried out, so that spatial resolution one
It causes；
Step 14, the track of the inswept earth of different satellites is different, needs to carry out space splicing to bright temperature data；
Step 15, generate that format is consistent, the consistent bright temperature temperature product of spatial resolution；
Step 2, soil moisture microwave remote sensing inverse model is constructed, bright temperature is carried out and is converted with emissivity, extract microwave radiation
The grid emissivity one by one of meter scanning track, specifically:
Step 21, soil moisture microwave is constructed with the relationship of soil moisture using the brightness temperature of microwave radiometer observation
Remote sensing estimation model；TBp=Tu+exp(-τa)×[{TdrSpexp(-2τc)}+{eSpTsexp(-τc)+Tc(1-ωp)[1-exp(-
τc)][1+rSpexp(-τc)] (1)
Wherein: TBpFor brightness temperature, TuFor the uplink radiation of atmosphere, TdThe downlink radiation for being atmosphere at the top of canopy, τaFor
The optical thickness of atmosphere, that subscript p is indicated is polarization mode, TcFor the temperature of vegetation, τcFor the optical thickness factor of vegetation, rSp
=1-eSpFor the reflectivity of soil surface；TsIt is effective physical temperature of earth's surface, ωpFor the single scattering albedo of vegetation.
Step 22, the quasi real time surface temperature and microwave radiometer of satellite remote sensing and the acquisition of real-time weather forecasting model are utilized
The bright temperature of (such as SMAP, SMOS and AMSR2) observation is extracted the grid emissivity one by one of microwave radiometer scanning track, is built
Relational expression under Liru:
γp=e(-τsecθ) (3)
Wherein, TBpFor brightness temperature；T is physical temperature；E is emissivity；Subscript p represents polarization direction, including horizontal pole
Change (H) and vertical polarization (V), τ vegetation opacity, ω single scattering albedo, γ canopy transmissivity,The ground of polarization direction p
Table emissivity, θ are radiometer view angle or incident.
Step 3, in order to improve the extraction to soil moisture, the influence to vegetation and roughness of ground surface is kicked out of and school
Just, specifically:
τ=bVWC (4)
In formula: τ is vegetation opacity, and VWC is vegetation water content,For the soil emissivity of polarization direction p, b is
Depending on vegetation pattern, the parameter of microwave frequency, θ is radiometer view angle or incident.
Step 4, using the dielectric constant and existing soil water-containing and soil dielectric constant relational model after conversion, to soil
Earth it is aqueous it is counter pushed away, thus realize soil moisture inverting extract, specifically:
Step 41, soil dielectric constant be associated with emissivity it is counter push away, dielectric constant same polarization emissivity is converted:
In formula, εrUpper soll layer dielectric constant；Horizontal polarization emissivity；Vertical polarization emissivity, θ are radiation
Count view angle or incident.
Step 42, right using the dielectric constant and existing soil water-containing and soil dielectric constant relational model after conversion
Soil water-containing is counter to be pushed away, and realizes that the inverting of soil moisture is extracted:
In formula: ρbSoil block volume density；ρsSoil solid particle specific gravity；mvSoil volumetric water content；εfwFree water dielectric
Constant；εsSoil particle dielectric constant, α, β are empirical index number.
By taking regional as an example, study the bright temperature data of the bright temperature data in area from the whole world, under be downloaded from the official of each satellite
Square website, specifically:
Step 1, the bright temperature data of domestic and international high-spatial and temporal resolution is downloaded, and according to the feature of the bright temperature of different satellites, is carried out
The format conversion of data, resampling, space splicing, same spatial resolution (0.25 °) the brightness temperature production for generating unification
Product sequence, specifically:
Step 11, domestic and international bright temperature data is collected, as shown in Figure 3；
Step 12, the format of different satellite bright temperature datas is different, carries out Data Format Transform, is uniformly converted to HDF5, such as
Shown in Fig. 4；
Step 13, the spatial resolution of different satellite bright temperature datas is different, resampling is carried out, so that spatial resolution one
It causes；
Step 14, the track of the inswept earth of different satellites is different, needs to carry out space splicing to bright temperature data；
Step 15, generate that format is consistent, the consistent bright temperature temperature product of spatial resolution, as shown in Figure 5；
Step 2, soil moisture microwave remote sensing inverse model is constructed, bright temperature is carried out and is converted with emissivity, extract microwave radiation
The grid emissivity one by one of meter scanning track, specifically:
Step 21, on the basis of classical τ-ω model, the brightness temperature observed using microwave radiometer is wet with soil
The relationship of degree constructs soil moisture microwave remote sensing inverse model；
TBp=Tu+exp(-τa)×[{TdrSpexp(-2τc)}+{eSpTsexp(-τc)+Tc(1-ωp)[1-exp(-τc)][1
+rspexp(-τc)] (1)
Wherein: TBpFor brightness temperature, TuFor the uplink radiation of atmosphere, TdThe downlink radiation for being atmosphere at the top of canopy, τaFor
The optical thickness of atmosphere, that subscript p is indicated is polarization mode, TcFor the temperature of vegetation, τcFor the optical thickness factor of vegetation, rSp
=1-eSpFor the reflectivity of soil surface；TsIt is effective physical temperature of earth's surface, ωpFor the single scattering albedo of vegetation.
Step 22, the quasi real time surface temperature and microwave radiometer of satellite remote sensing and the acquisition of real-time weather forecasting model are utilized
The bright temperature of (such as SMAP, SMOS and AMSR2) observation extracts the grid emissivity one by one of microwave radiometer scanning track；It is bright
Spend temperature (TB), physical temperature (T), emissivity (e) by following physical equation establish contact:
γp=e(-τsecθ) (3)
In formula: TBpFor brightness temperature；T is physical temperature；E is emissivity；Subscript p represents polarization direction, including horizontal pole
Change (H) and vertical polarization (V), τ vegetation opacity, ω single scattering albedo, γ canopy transmissivity,The ground of polarization direction p
Table emissivity, θ are radiometer view angle or incident.
Step 3, in order to improve the extraction to soil moisture, the influence to vegetation and roughness of ground surface is kicked out of and school
Just, specifically:
τ=bVWC (4)
In formula: τ vegetation opacity, VWC vegetation water content,The soil emissivity of polarization direction p, b depend on planting
By type, the parameter of microwave frequency, the view angle of θ radiometer or incident.
Step 4, soil dielectric constant be associated with emissivity it is counter push away, soil dielectric constant and emissivity relationship pass through famous
Fresnel formula is stated, and can be converted to dielectric constant same polarization emissivity, is realized inverting (such as Fig. 6 of soil moisture
It is shown), specifically:
Step 41, soil dielectric constant be associated with emissivity it is counter push away, soil dielectric constant and emissivity relationship pass through famous
Fresnel formula state, dielectric constant same polarization emissivity can be converted.
In formula, εrUpper soll layer dielectric constant,Horizontal polarization emissivity,Vertical polarization emissivity, θ radiometer
View angle or incident.
Step 42, right using the dielectric constant and existing soil water-containing and soil dielectric constant relational model after conversion
Soil water-containing is counter to be pushed away, to realize that the inverting of soil moisture is extracted.
In formula: ρbSoil block volume density；ρsSoil solid particle specific gravity；mvSoil volumetric water content；εfwFree water dielectric
Constant；εsSoil particle dielectric constant, α, β are empirical index number.
Claims (4)
1. a kind of multi-source satellite soil moisture retrieval method, which comprises the following steps:
Step 1, the bright temperature data of multi-source satellite is collected, and obtains the same spatial resolution brightness temperature product sequence of unification
Column；
Step 2, soil moisture microwave remote sensing inverse model is constructed, bright temperature is carried out and is converted with emissivity, microwave radiometer is extracted and sweeps
Retouch the grid emissivity one by one of track；
Include:
Step 21, soil moisture microwave remote sensing inverse model is constructed:
TBp=Tu+exp(-τa)×[{TdrSpexp(-2τc)}+{eSpTsexp(-τc)+Tc(1-ωp)[1-exp(-τc)][1+
rSpexp(-τc)](1)
Wherein: TBpFor brightness temperature, TuFor the uplink radiation of atmosphere, TdThe downlink radiation for being atmosphere at the top of canopy, τaFor atmosphere
Optical thickness, subscript p indicate be polarization mode, TcFor the temperature of vegetation, τcFor the optical thickness factor of vegetation, rSp=1-
eSpFor the reflectivity of soil surface；TsIt is effective physical temperature of earth's surface, ωpFor the single scattering albedo of vegetation；
Step 22, the quasi real time surface temperature and microwave radiometer obtained using satellite remote sensing and real-time weather forecasting model is observed
Bright temperature, extract microwave radiometer scanning track grid emissivity one by one:
γp=e(-τsecθ) (3)
In formula: TBpFor brightness temperature；T is physical temperature；E is emissivity；Subscript p represents polarization direction, including horizontal polarization (H)
With vertical polarization (V), τ vegetation opacity, ω single scattering albedo, γ canopy transmissivity,The earth's surface of polarization direction p emits
Rate, θ are radiometer view angle or incident；
Step 3, the influence to vegetation and roughness of ground surface is rejected and is corrected；
Step 4, soil dielectric constant is converted with emissivity, is associated with instead using the dielectric constant after conversion with soil moisture
It pushes away, realizes the remote-sensing inversion of soil moisture.
2. multi-source satellite soil moisture retrieval method according to claim 1, which is characterized in that the step 3 includes:
τ=bVWC (4)
In formula: τ vegetation opacity, VWC vegetation water content,The soil emissivity of polarization direction p, b depend on vegetation class
Type, the parameter of microwave frequency, θ are radiometer view angle or incident.
3. multi-source satellite soil moisture retrieval method according to claim 2, which is characterized in that the step 4 includes:
Step 41, soil dielectric constant is converted with emissivity:
In formula, εrUpper soll layer dielectric constant,Horizontal polarization emissivity,Vertical polarization emissivity, θ are radiometer sight
Angle measurement or incident；
Step 42, using the dielectric constant and existing soil water-containing and soil dielectric constant relational model after conversion, to soil
It is aqueous it is counter pushed away, thus realize soil moisture inverting extract:
In formula: ρbSoil block volume density；ρsSoil solid particle specific gravity；mvSoil volumetric water content；εfwFree water dielectric is normal
Number；εsSoil particle dielectric constant, α, β are empirical index number.
4. multi-source satellite soil moisture retrieval method according to claim 3, which is characterized in that the step 1 include with
Lower step:
Step 11, domestic and international bright temperature data is collected；
Step 12, the format of different satellite bright temperature datas is different, carries out Data Format Transform, is uniformly converted to HDF5；
Step 13, the spatial resolution of different satellite bright temperature datas is different, resampling is carried out, so that spatial resolution is consistent；
Step 14, the track of the inswept earth of different satellites is different, needs to carry out space splicing to bright temperature data；
Step 15, generate that format is consistent, the consistent bright temperature temperature product of spatial resolution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810473645.5A CN108761034B (en) | 2018-05-16 | 2018-05-16 | A kind of multi-source satellite soil moisture retrieval method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810473645.5A CN108761034B (en) | 2018-05-16 | 2018-05-16 | A kind of multi-source satellite soil moisture retrieval method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108761034A CN108761034A (en) | 2018-11-06 |
| CN108761034B true CN108761034B (en) | 2019-04-30 |
Family
ID=64008468
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810473645.5A Active CN108761034B (en) | 2018-05-16 | 2018-05-16 | A kind of multi-source satellite soil moisture retrieval method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108761034B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109992863B (en) * | 2019-03-22 | 2021-04-02 | 北京师范大学 | LAI inversion method and device |
| CN110610054B (en) * | 2019-09-23 | 2021-03-23 | 北京师范大学 | Method and system for constructing cuboid inversion model of soil humidity |
| CN110849816A (en) * | 2019-12-16 | 2020-02-28 | 西南交通大学 | Ground sampling grid device applied to low-altitude unmanned remote sensing inversion of soil humidity |
| CN111239209B (en) * | 2020-02-17 | 2022-07-19 | 中国科学院上海天文台 | Full-polarization single reflection simulation method and system for opportunistic signal reflection remote sensing |
| CN111337551A (en) * | 2020-03-13 | 2020-06-26 | 山东航向电子科技有限公司 | Soil humidity measuring method of double-linear polarization antenna GNSS |
| CN111965330B (en) * | 2020-08-18 | 2021-05-25 | 自然资源部国土卫星遥感应用中心 | Bare soil water content inversion method based on dual-polarization emissivity |
| CN111983333B (en) * | 2020-08-20 | 2021-05-25 | 自然资源部国土卫星遥感应用中心 | Method for separating soil radiation signals of passive microwave remote sensing mixed pixel components |
| CN112505068B (en) * | 2020-11-03 | 2023-08-11 | 桂林理工大学 | GNSS-IR-based earth surface soil humidity multi-star combination inversion method |
| EP4050334A1 (en) * | 2021-02-26 | 2022-08-31 | Tata Consultancy Services Limited | System and method for root zone soil moisture estimation for vegetation cover using remote sensing |
| CN113158139B (en) * | 2021-02-26 | 2021-10-08 | 河海大学 | Downscale product error calculation method for satellite observation rainfall data |
| CN113552080B (en) * | 2021-06-17 | 2022-08-02 | 中国科学技术大学 | Real-time inversion algorithm for ultra-spectrum remote sensing earth surface atmosphere relative humidity |
| CN113533379B (en) * | 2021-07-19 | 2022-04-29 | 自然资源部国土卫星遥感应用中心 | Method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data |
| CN114264672A (en) * | 2021-12-22 | 2022-04-01 | 南京晓庄学院 | Deep soil humidity inversion method based on microwave remote sensing |
| CN113985489B (en) * | 2021-12-29 | 2022-04-12 | 中南大学 | Method and device for obtaining earth surface microwave dielectric constant field |
| CN114814173B (en) * | 2022-06-27 | 2022-10-04 | 中国科学院地理科学与资源研究所 | Dielectric constant-based satellite-borne GNSS-R soil humidity inversion method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7135871B1 (en) * | 2004-12-30 | 2006-11-14 | The United State Of America As Represented By The Secretary Of Agriculture | Soil moisture sensor |
| CN101435873A (en) * | 2008-12-24 | 2009-05-20 | 中国中医科学院中药研究所 | Remote sense monitoring method of medicinal plant resource based on concomitant species and community classification |
| CN101614651A (en) * | 2009-07-29 | 2009-12-30 | 北京大学 | A kind of data assimilation method for monitoring soil moisture |
| CN101865909A (en) * | 2010-06-07 | 2010-10-20 | 大连海事大学 | Microwave remote sensing soil moisture monitoring system and method thereof |
| CN101936921A (en) * | 2010-03-05 | 2011-01-05 | 中国农业科学院农业资源与农业区划研究所 | Method for inversing soil moisture content from AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data |
| CN105606631A (en) * | 2016-02-01 | 2016-05-25 | 中国科学院遥感与数字地球研究所 | Method for jointly inversing soil moisture through salinity satellite dual-waveband brightness temperature data |
| CN106226260A (en) * | 2016-08-10 | 2016-12-14 | 武汉大学 | A kind of combination microwave and the Soil Moisture Inversion method of infrared remote sensing image |
-
2018
- 2018-05-16 CN CN201810473645.5A patent/CN108761034B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7135871B1 (en) * | 2004-12-30 | 2006-11-14 | The United State Of America As Represented By The Secretary Of Agriculture | Soil moisture sensor |
| CN101435873A (en) * | 2008-12-24 | 2009-05-20 | 中国中医科学院中药研究所 | Remote sense monitoring method of medicinal plant resource based on concomitant species and community classification |
| CN101614651A (en) * | 2009-07-29 | 2009-12-30 | 北京大学 | A kind of data assimilation method for monitoring soil moisture |
| CN101936921A (en) * | 2010-03-05 | 2011-01-05 | 中国农业科学院农业资源与农业区划研究所 | Method for inversing soil moisture content from AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data |
| CN101865909A (en) * | 2010-06-07 | 2010-10-20 | 大连海事大学 | Microwave remote sensing soil moisture monitoring system and method thereof |
| CN105606631A (en) * | 2016-02-01 | 2016-05-25 | 中国科学院遥感与数字地球研究所 | Method for jointly inversing soil moisture through salinity satellite dual-waveband brightness temperature data |
| CN106226260A (en) * | 2016-08-10 | 2016-12-14 | 武汉大学 | A kind of combination microwave and the Soil Moisture Inversion method of infrared remote sensing image |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108761034A (en) | 2018-11-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108761034B (en) | A kind of multi-source satellite soil moisture retrieval method | |
| Liang et al. | The global land surface satellite (GLASS) product suite | |
| Jia et al. | Long-term global land surface satellite (GLASS) fractional vegetation cover product derived from MODIS and AVHRR data | |
| CN101738620B (en) | Method by utilizing passive microwave remote sensing data AMSR-E (Advanced Microwave Scanning Radiometer-EOS ) to invert surface temperature | |
| Zheng et al. | Assessment of roughness length schemes implemented within the Noah land surface model for high-altitude regions | |
| Yuan et al. | WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982–2008 | |
| CN105550423B (en) | The method of CMORPH satellite Rainfall Products NO emissions reductions based on Fuzzy-OLS and Multi-environment factor variable | |
| Lendzioch et al. | Estimating snow depth and leaf area index based on UAV digital photogrammetry | |
| Livneh et al. | Multi-criteria parameter estimation for the Unified Land Model | |
| CN101936921A (en) | Method for inversing soil moisture content from AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data | |
| Chen et al. | Downscaling of satellite remote sensing soil moisture products over the Tibetan Plateau based on the random forest algorithm: Preliminary results | |
| CN108982548A (en) | A kind of soil moisture inversion method based on passive microwave remote sensing data | |
| Ray et al. | Evaluation and inter-comparison of satellite soil moisture products using in situ observations over Texas, US | |
| Du et al. | Impact of the Zhalong Wetland on neighboring land surface temperature based on remote sensing and GIS | |
| Sun et al. | Microwave and meteorological fusion: A method of spatial downscaling of remotely sensed soil moisture | |
| Ruiz-Barradas et al. | Great Plains hydroclimate variability: The view from North American regional reanalysis | |
| Rahmati et al. | Assessment of Gini-, entropy-and ratio-based classification trees for groundwater potential modelling and prediction | |
| Shwetha et al. | Prediction of land surface temperature under cloudy conditions using microwave remote sensing and ANN | |
| Tedesco et al. | Dynamic approaches for snow depth retrieval from spaceborne microwave brightness temperature | |
| CN108681639A (en) | A kind of dynamic precipitation NO emissions reduction method considering local variable and global variable | |
| CN112014542A (en) | Method and device for making soil moisture map of vegetation coverage area, storage medium and equipment | |
| Ács et al. | Sensitivity of WRF-simulated planetary boundary layer height to land cover and soil changes | |
| Liu et al. | A dataset of energy, water vapor and carbon exchange observations in oasis-desert areas from 2012 to 2021 in a typical endorheic basin | |
| Dörwald et al. | Dune movement under climatic changes on the north‐eastern Tibetan Plateau as recorded by long‐term satellite observation versus ERA‐5 reanalysis | |
| Tao et al. | Multi-year surface radiative properties and vegetation parameters for hydrologic modeling in regions of complex terrain—Methodology and evaluation over the Integrated Precipitation and Hydrology Experiment 2014 domain |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |