CN107131977A - A kind of land face latent heat flux remote sensing estimation method - Google Patents
A kind of land face latent heat flux remote sensing estimation method Download PDFInfo
- Publication number
- CN107131977A CN107131977A CN201710409478.3A CN201710409478A CN107131977A CN 107131977 A CN107131977 A CN 107131977A CN 201710409478 A CN201710409478 A CN 201710409478A CN 107131977 A CN107131977 A CN 107131977A
- Authority
- CN
- China
- Prior art keywords
- heat flux
- latent heat
- remote sensing
- land face
- estimation method
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K17/00—Measuring quantity of heat
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Combustion & Propulsion (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
The invention discloses a kind of land face latent heat flux remote sensing estimation method, chosen and the closely related procedure parameter of land face latent heat flux by collecting the existing remote sensing features parameter product in test block and meteorological factor data, and according to existing remote sensing features parameter product and meteorological data；Simplify tradition Priestley Taylor latent heat flux remote sensing equations, including simplify soil heat flux and soil vegetative cover mixed model, introduce empirical coefficient, set up the mixed type latent heat flux Remote Sensing Model based on exponential equation：LE=Rnexp(a0+a1Ta+a2NDVI), in formula, LE is land face latent heat flux, RnFor net radiation, TaFor air themperature, NDVI is normalized differential vegetation index, a0,a1And a2For empirical coefficient；According to the existing actual ground flux observation data in test block and remote sensing features parameter product, the empirical coefficient of model is obtained by regression analysis；Obtain estimating area land face latent heat flux value using research on utilization area meteorological data, remote sensing features parameter product and calculating.The evaluation method of the present invention is based on the mixed type latent heat flux Remote Sensing Model of exponential equation, has the advantages that explicit physical meaning, input parameter are few, simple, workable.
Description
Technical field
The present invention relates to land face latent heat flux fields of measurement, more particularly to a kind of land face latent heat flux remote sensing estimation method.
Background technology
Land face latent heat flux is entered greatly from soil and vegetation by steam during the soil evaporation of land face and transpiration
Energy during gas, including transpiration, soil evaporation, Water Evaporation and the corresponding energy of Vegetation canopy interception water process
Amount, plays vital effect in terms of analyzed area climatic model and support resource management and environmental planning.It is latent
Heat flux process is complicated, with underlying surface surface temperature, underlying surface saturation vapour pressure, reference altitude atmospheric vapour pressure, air force
Learn impedance, underlying surface surface impedance etc. relevant.Remote sensing technology provides the abundant characteristic parameter for characterizing the change of land face latent heat, high
Degree has merged spatial surface heterogeneity, is that the latent heat flux estimation of land face opens new way.
The method of remote sensing appraising land face latent heat flux is a kind of indirect measuring method, is by the parameter of remotely-sensed data inverting
It is coupled together with atmospheric parameter, calculating obtains actual land face latent heat flux.The theoretical foundation of remote sensing appraising latent heat flux is ground
Water heat transport rule in table energy-balance equation, atmosphere boundary theory and soil-vegetation-atmosphere Continuum (SPAC)
Deng.Satellite remote sensing estimates land face latent heat flux since 1970s, pin landside draught monitor and latent heat flux
Estimation, by the use of remote sensing as driving data, has generated a series of land face latent heat flux Remote Sensing Model and algorithm, has such as been based on
Physical model (Shuttleworth＆Wallace, 1985 of Land surface energy budget；Xin Xiaozhou etc., 2007), empirical statistics algorithm
(Jackson, et al., 1983), Penman-Monteith algorithms (Peman, 1948；Monteith,1965；Mu et al.,
2011), remote sensing deltic method (Jiang＆Islam, 2001；Wang, et al., 2006) and data assimilation method
(Caparrini, et al., 2003) etc..These methods are required to consider the excessive non-remote sensing such as aerodynamic resistance and wind speed
Parameter, and the determination subjectivity of remote sensing images " dry and wet side " is too big, greatly reduces these models and algorithm in actual applications
Operability.
The content of the invention
It is an object of the invention to provide a kind of few, the easily operated land face latent heat flux remote sensing estimation method of input parameter.
The purpose of the present invention is achieved through the following technical solutions：
A kind of land face latent heat flux remote sensing estimation method, comprises the following steps：
1) the existing remote sensing features parameter product in test block and meteorological factor data are collected, and according to existing remote sensing features parameter
Product and meteorological data are chosen and the closely related procedure parameter of land face latent heat flux；
2) tradition Priestley-Taylor latent heat flux remote sensing equations are simplified, including simplification soil heat flux and soil-
Vegetation mixed model, introduces empirical coefficient, sets up the mixed type latent heat flux Remote Sensing Model based on exponential equation：
LE=Rn exp(a0+a1Ta+a2NDVI),
In formula：
LE is land face latent heat flux,
RnFor net radiation,
TaFor air themperature,
NDVI is normalized differential vegetation index,
a0,a1And a2For empirical coefficient；
3) data and remote sensing features parameter product are observed according to the existing actual ground flux in test block, passes through regression analysis
Obtain step 2) in model empirical coefficient；
4) meteorological data in research on utilization area, remote sensing features parameter product and step 3) obtained empirical coefficient, calculate
To estimation area land face latent heat flux value.
Above-mentioned steps 1) in, include air with the closely related procedure parameter of land face latent heat flux and ecological physics is joined
Number.
Above-mentioned steps 2) in, select RnDirect proportion function calculate soil heat flux G：
G=k0Rn,
Wherein, k0For empirical coefficient.
Above-mentioned steps 2) in, selection humidity index expression formula replaces Δ/(Δ+γ) to simplify land face latent heat flux algorithm：
Δ/(Δ+γ)=exp (k1+k2Ta),
Wherein, Δ is that saturation vapour is pressed in temperature TaThe slope at place, γ is psychrometer constant, k1And k2For empirical coefficient.
The step 2) in, select air themperature TaWith normalized differential vegetation index NDVI exponential function, passed through while increasing
Test coefficient h01、h1And h2To parameterize Priestley-Taylor coefficients：
F=exp (h0+h1Ta+h2NDVI),
Wherein, f is Priestley-Taylor coefficients.
Above-mentioned steps 3), existing actual measurement experimental data includes：Land face latent heat flux LE, surface net radiation Rn, Air Temperature
Spend TaWith normalized differential vegetation index NDVI.
Above-mentioned earth's surface is meadow and/or farmland.
Compared with the conventional method, the beneficial effects of the invention are as follows：
The present invention makes full use of meteorological data, remotely-sensed data and ground by theory anatomy, experimental verification and concrete application
Flux observation data are coupled, basic by theory of algorithm of Priestley-Taylor, absorb and combine the excellent of semiempirical model
Point, the empirical coefficient obtained using the latent heat flux of actual measurement by homing method builds a kind of mixed type based on exponential equation
Latent heat flux Remote Sensing Model, compared to traditional model based on energy balance, input parameter is few, workable.
Mixed type latent heat flux Remote Sensing Model based on exponential equation, existing clear and definite theoretical foundation has input ginseng again
Number less, flexibly, it is simple, easily operated the advantages of.Therefore, the applicable surface than traditional remote sensing latent heat flux appraising model is wider,
There is good application prospect.
Brief description of the drawings
Fig. 1 a and Fig. 1 b are respectively the Miyun station and shop obtained using the latent heat flux Remote Sensing Model evaluation method of the present invention
The latent heat flux at pottery station and the scatter diagram of field observation data.
Embodiment
The method of the present invention is described in detail with reference to specific embodiment.
First, the mixed type latent heat flux remote sensing algorithm based on exponential equation that the present invention is used is built.
1. being evapotranspired theoretical and algorithm frame based on Priestley-Taylor, simplify soil heat flux and Δ/(Δ+γ),
Build the mixed type latent heat flux Remote Sensing Model based on exponential equation.Wherein, soil heat flux G is considered RnDirect proportion letter
Number, Δ/(Δ+γ) can be replaced with humidity index expression formula, and expression is as follows：
LE=(Rn-G)fΔ/(Δ+γ) (1)
G=k0Rn (2)
Δ/(Δ+γ)=exp (k1+k2Ta) (3)
In formula, LE is land face latent heat flux, RnFor net radiation, TaFor air themperature.Δ is that saturation vapour is pressed in temperature TaPlace
Slope, γ be psychrometer constant, k0、k1And k2It is empirical coefficient, f is Priestley-Taylor coefficients.
F has close contact with atmospheric parameter and Eco-environment Factors.Wherein, temperature, wind speed, steam pressure difference, soil
Water content, vegetation water content, Atmospheric CO2Concentration and vegetation root soil water content are all f major influence factors；Surface vegetation
Covering situation is to influence the important factor of transpiration, and vegetation absorbs CO by photosynthesis2To carry out transpiration, leaf area index
It is the leading indicator of vegetation state with normalized differential vegetation index (NDVI), the present invention selects NDVI as the main ginseng of transpiration
Number；Surface temperature (LST) is the factor for controlling Remote sensing, often has the influence of cloud, LST can not be obtained daily, this
Invention utilizes air themperature TaTo replace surface temperature.Present invention selection air themperature TaWith normalized differential vegetation index NDVI finger
Number function, while the coefficient h that enlarges one's experience01、h1And h2To parameterize Priestley-Taylor coefficients：
F=exp (h0+h1Ta+h2NDVI) (4)
In order to calculate land face latent heat flux, consolidated equation (1), (2), (3) and (4), land face latent heat flux equation can be with table
Up to for：
LE=Rn(1-k0)exp(k1+k2Ta)exp(h0+h1Ta+h2NDVI) (5)
In view of 1-k0It can be expressed as exp (g0), wherein g0It is constant, such formula (5) is reduced to：
LE=Rn exp(g0)exp(k1+k2Ta)exp(h0+h1Ta+h2NDVI)
I.e.：
LE=Rn exp[g0+h0+k1+(h1+k2)Ta+h2NDVI]
If：a0=g0+h0+k1, a1=h1+k2, a2=h2, above-mentioned formula is further simplified, mixed type latent heat flux is obtained
Remote Sensing Model：
LE=Rn exp(a0+a1Ta+a2NDVI) (6)
2. meadow and agrotype for different websites, utilize the earth's surface net spoke closely related with land face latent heat flux
Penetrate, air themperature and remote sensing normalized differential vegetation index, corresponding empirical coefficient a in formula (6) is obtained based on statistical regression0、a1With
a2, using 7 U.S. atmospheric radiations observation websites observations collected in table 1 day surface net radiation, day latent heat flux, day soil
Heat flux and day sensible heat flux data regression, obtain final equation：
LE=Rn exp(-1.536+0.013Ta+0.878NDVI) (7)
The data that 7 U.S. atmospheric radiation observation station point is collected in table 1 include：
1) flux observation data：2003-2005 day surface net radiations, day latent heat flux, day soil heat flux and day are aobvious
Heat flux data；
2) meteorological data：2003-2005 day air themperature data；
3) remotely-sensed data：The MODIS synchronous with flux observation NDVI data.
1. 2003-2005 of table, 7 U.S.'s atmospheric radiation observation website overview brief introductions
Haihe basin latent heat flux is estimated using the mixed type latent heat flux remote sensing algorithm based on exponential equation of structure,
Comprise the following steps：
1. collecting Haihe basin meteorological data space interpolation into 0.05 degree of Grid data, calculating obtains surface net radiation number
According to；
2. the latent heat flux of single lattice point is obtained using equation (7)；
3. the result obtained according to step 2, expands the land face latent heat flux for obtaining whole Haihe basin；
4. the relation for the latent heat flux that the land face latent heat flux of the Haihe basin of statistical analysis estimation is observed with website, it is determined that
The simulation precision of algorithm；
5. assessment models are in the analog capability of Haihe basin.
In the forest land of Haihe basin Mountainous Area of North, (Beijing was close successively from 2006 for Chinese Haihe basin flux observation station
Cloud) established with farmland (Hebei Huailai), middle part outskirts of a town farmland (Beijing Daxing), southern Plains farmland (Hebei Guantao) it is multiple dimensioned
The observational network of surface flux and meteorological element, covers the main underlying surface type of Haihe basin.Present invention employs Miyun and
The website of Guantao two is used as checking and application.Wherein, Miyun station (40 ° of 37'50.82 " N, 117 ° 19 ' 23.83 " E) is located at Beijing
The city sub- town of Miyun County new city, underlying surface is fruit tree, corn/bare area, height above sea level 350m.Guantao erect-position is in Hebei province Guanyao County He Zhai
Village, underlying surface is Wheat/Maize, cotton.The longitude and latitude of observation station is 115.1274 ° of E, 36.5150 ° of N, height above sea level 30m.Miyun and
The station data of Guantao two was concentrated in 2008, data by Cold and drought Region scientific data center website (http:// westdc.westgis.ac.cn/haihe) download acquisition.
In order to verify simulation precision of the model in Haihe basin, with Chinese Haihe basin Radiation Observation data and MODIS
NDVI data calculate land face latent heat flux, and are compared with latent heat flux observed result.
Land face latent heat flux evaluation method is as follows：
LE=Rn exp(-1.536+0.013Ta+0.878NDVI) (I)
Wherein, LE is land face latent heat flux, RnFor net radiation, TaFor air themperature, NDVI is normalized differential vegetation index.
Based on data processing software Grapher, the model estimated value of flux observation website and dissipating for ground observation value are set up
Point diagram, as shown in Figure 1a, the latent heat flux simulation error at Miyun station is 17.5W/m2, coefficient correlation square is 0.86, root mean square
Error is 34.2W/m2；As shown in Figure 1 b, the latent heat flux simulation error at Guantao station is -2.1W/m2, coefficient correlation square is
0.51, root-mean-square error is 56.9W/m2.As a result show, algorithm simulation value has preferable uniformity with ground observation value.
Learnt by analysis, the mixed type latent heat flux remote sensing algorithm landside latent heat flux simulation effect based on exponential equation
Fruit is preferable, and simply, it is flexible, workable.
A kind of meadow provided by the present invention and farmland latent heat flux remote sensing appraising are described above by specific embodiment
Algorithm, it will be understood by those of skill in the art that in the scope for not departing from essence of the invention, can do certain to the present invention
Deformation is changed, and is not limited to content disclosed in this invention.
Claims (7)
1. a kind of land face latent heat flux remote sensing estimation method, comprises the following steps：
1) the existing remote sensing features parameter product in test block and meteorological factor data are collected, and according to existing remote sensing features parameter product
Chosen and the closely related procedure parameter of land face latent heat flux with meteorological data；
2) simplify tradition Priestley-Taylor latent heat flux remote sensing equations, including simplify soil heat flux and soil-vegetation
Mixed model, introduces empirical coefficient, sets up the mixed type latent heat flux Remote Sensing Model based on exponential equation：
LE=Rn exp(a0+a1Ta+a2NDVI),
In formula：
LE is land face latent heat flux,
RnFor net radiation,
TaFor air themperature,
NDVI is normalized differential vegetation index,
a0,a1And a2For empirical coefficient；
3) data and remote sensing features parameter product are observed according to the existing actual ground flux in test block, obtained by regression analysis
Step 2) in model empirical coefficient；
4) meteorological data in research on utilization area, remote sensing features parameter product and step 3) obtained empirical coefficient, calculating estimated
Calculate area land face latent heat flux value.
2. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that：The step 1) in, with land
Latent heat flux closely related procedure parameter in face includes air and ecological physical parameter.
3. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that：The step 2) in, select Rn
Direct proportion function calculate soil heat flux G：
G=k0Rn,
Wherein, k0For empirical coefficient.
4. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that：The step 2) in, selection
Humidity index expression formula replaces Δ/(Δ+γ) to simplify land face latent heat flux algorithm：
Δ/(Δ+γ)=exp (k1+k2Ta),
Wherein, Δ is that saturation vapour is pressed in temperature TaThe slope at place, γ is psychrometer constant, k1And k2For empirical coefficient.
5. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that the step 2) in, selection
Air themperature TaWith normalized differential vegetation index NDVI exponential function, while the coefficient h that enlarges one's experience01、h1And h2To parameterize
Priestley-Taylor coefficients：
F=exp (h0+h1Ta+h2NDVI),
Wherein, f is Priestley-Taylor coefficients.
6. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that the step 3) in, it is existing
Actual measurement experimental data include：Land face latent heat flux LE, surface net radiation Rn, air themperature TaWith normalized differential vegetation index NDVI.
7. land face latent heat flux remote sensing estimation method as claimed in claim 1, it is characterised in that：The earth's surface be meadow and/
Or farmland.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710409478.3A CN107131977A (en) | 2017-06-02 | 2017-06-02 | A kind of land face latent heat flux remote sensing estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710409478.3A CN107131977A (en) | 2017-06-02 | 2017-06-02 | A kind of land face latent heat flux remote sensing estimation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107131977A true CN107131977A (en) | 2017-09-05 |
Family
ID=59734254
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710409478.3A Pending CN107131977A (en) | 2017-06-02 | 2017-06-02 | A kind of land face latent heat flux remote sensing estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107131977A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957360A (en) * | 2010-09-10 | 2011-01-26 | 北京大学 | Method and system for measuring surface evapotranspiration quantity |
| CN104077475A (en) * | 2014-06-24 | 2014-10-01 | 北京师范大学 | Global integrated land surface evapotranspiration and estimation system and method based on multiple algorithms |
-
2017
- 2017-06-02 CN CN201710409478.3A patent/CN107131977A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957360A (en) * | 2010-09-10 | 2011-01-26 | 北京大学 | Method and system for measuring surface evapotranspiration quantity |
| CN104077475A (en) * | 2014-06-24 | 2014-10-01 | 北京师范大学 | Global integrated land surface evapotranspiration and estimation system and method based on multiple algorithms |
Non-Patent Citations (1)
| Title |
|---|
| KAICUN WANG 等: "A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature", 《JOURNAL OF GEOPHYSICAL RESEARCH ATMOSPHERES》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Decadal trends in evaporation from global energy and water balances | |
| Romps | Clausius–Clapeyron scaling of CAPE from analytical solutions to RCE | |
| Ni et al. | Synthesis and analysis of biomass and net primary productivity in Chinese forests | |
| Xiao et al. | Data-driven diagnostics of terrestrial carbon dynamics over North America | |
| Meijide et al. | Controls of water and energy fluxes in oil palm plantations: Environmental variables and oil palm age | |
| Noureldin et al. | Rice yield forecasting models using satellite imagery in Egypt | |
| Crueger et al. | The Madden–Julian oscillation in ECHAM6 and the introduction of an objective MJO metric | |
| CN104077475B (en) | A kind of global land table based on many algorithm integrations evapotranspires estimating system and method | |
| Lafont et al. | Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE | |
| Kim et al. | Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery | |
| Chen et al. | Assessment of foliage clumping effects on evapotranspiration estimates in forested ecosystems | |
| Chang et al. | A modified MOD16 algorithm to estimate evapotranspiration over alpine meadow on the Tibetan Plateau, China | |
| Calera et al. | Monitoring barley and corn growth from remote sensing data at field scale | |
| Ballesteros et al. | Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain | |
| El-Shirbeny et al. | Crop water requirements in Egypt using remote sensing techniques | |
| Béziat et al. | Evaluation of a simple approach for crop evapotranspiration partitioning and analysis of the water budget distribution for several crop species | |
| CN105841847B (en) | A kind of method for estimating Surface latent heat fluxes | |
| CN106845428A (en) | A kind of crop yield remote sensing estimation method and system | |
| Koster et al. | Hydroclimatic controls on the means and variability of vegetation phenology and carbon uptake | |
| Li et al. | Assessing MODIS carbon and water fluxes in grasslands and shrublands in semiarid regions using eddy covariance tower data | |
| Lim et al. | A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia | |
| Castellví et al. | A new procedure based on surface renewal analysis to estimate sensible heat flux: A case study over grapevines | |
| De Melo et al. | An agrometeorological–spectral model to estimate soybean yield, applied to southern Brazil | |
| Wang et al. | Improving the simulation of tropical convective cloud-top heights in CAM5 with CloudSat observations | |
| Alves et al. | On the performance of the Canadian Land Surface Scheme driven by the ERA5 reanalysis over the Canadian boreal forest |
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: 20170905 |