CN109406405A - A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity - Google Patents
A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity Download PDFInfo
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- CN109406405A CN109406405A CN201811184489.7A CN201811184489A CN109406405A CN 109406405 A CN109406405 A CN 109406405A CN 201811184489 A CN201811184489 A CN 201811184489A CN 109406405 A CN109406405 A CN 109406405A
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 148
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000003643 water by type Substances 0.000 claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 238000000605 extraction Methods 0.000 claims abstract description 11
- 230000003595 spectral effect Effects 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 238000010276 construction Methods 0.000 claims abstract description 4
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 238000001228 spectrum Methods 0.000 claims description 19
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 16
- 239000011707 mineral Substances 0.000 claims description 16
- 239000000126 substance Substances 0.000 claims description 11
- 150000003839 salts Chemical class 0.000 claims description 7
- 230000015572 biosynthetic process Effects 0.000 claims description 6
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 claims description 5
- 230000005855 radiation Effects 0.000 claims description 5
- 238000003786 synthesis reaction Methods 0.000 claims description 5
- 238000000513 principal component analysis Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 239000011780 sodium chloride Substances 0.000 claims description 4
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 238000001308 synthesis method Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 2
- 230000000149 penetrating effect Effects 0.000 claims 1
- 238000012284 sample analysis method Methods 0.000 claims 1
- 238000011002 quantification Methods 0.000 abstract description 3
- 238000011156 evaluation Methods 0.000 abstract description 2
- XZPVPNZTYPUODG-UHFFFAOYSA-M sodium;chloride;dihydrate Chemical compound O.O.[Na+].[Cl-] XZPVPNZTYPUODG-UHFFFAOYSA-M 0.000 abstract 1
- 239000012267 brine Substances 0.000 description 5
- HPALAKNZSZLMCH-UHFFFAOYSA-M sodium;chloride;hydrate Chemical compound O.[Na+].[Cl-] HPALAKNZSZLMCH-UHFFFAOYSA-M 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- ZOXJGFHDIHLPTG-UHFFFAOYSA-N Boron Chemical compound [B] ZOXJGFHDIHLPTG-UHFFFAOYSA-N 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 2
- 229910052796 boron Inorganic materials 0.000 description 2
- 229910052736 halogen Inorganic materials 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 239000011777 magnesium Substances 0.000 description 2
- 229910052749 magnesium Inorganic materials 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000011591 potassium Substances 0.000 description 2
- 229910052700 potassium Inorganic materials 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- PMZURENOXWZQFD-UHFFFAOYSA-L Sodium Sulfate Chemical compound [Na+].[Na+].[O-]S([O-])(=O)=O PMZURENOXWZQFD-UHFFFAOYSA-L 0.000 description 1
- 229910052770 Uranium Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052810 boron oxide Inorganic materials 0.000 description 1
- -1 brine ion Chemical class 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- JKWMSGQKBLHBQQ-UHFFFAOYSA-N diboron trioxide Chemical compound O=BOB=O JKWMSGQKBLHBQQ-UHFFFAOYSA-N 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 229910052602 gypsum Inorganic materials 0.000 description 1
- 239000010440 gypsum Substances 0.000 description 1
- 150000002367 halogens Chemical class 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000011734 sodium Substances 0.000 description 1
- 229910052708 sodium Inorganic materials 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 150000003467 sulfuric acid derivatives Chemical class 0.000 description 1
- JFALSRSLKYAFGM-UHFFFAOYSA-N uranium(0) Chemical compound [U] JFALSRSLKYAFGM-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/178—Methods for obtaining spatial resolution of the property being measured
- G01N2021/1785—Three dimensional
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
Abstract
The invention belongs to salt lake water body information extraction technology fields, and in particular to a kind of remote sensing quantitative estimation method suitable for salt lake water body salinity.The present invention includes the following steps: that one, satellite remote sensing date source obtains；Two, satellite remote sensing date pre-processes；Three, salt lake water body edge of a body of water line drawing；Four, salt lake waters satellite remote-sensing image difference brightness value point is chosen；Five, salt lake waters satellite remote-sensing image difference point withdrawing spectral information；Six, salt lake water body salinity Indices are constructed；Seven, salt lake water body salinity three-dimensional index is constructed；Eight, salt lake water body salinity information extraction；Nine, the qualitative estimation of salt lake water body salinity；Ten, salt lake water sampling and salinity measurement；11, salt lake water body salinity quantitative inversion model construction；12, inverse model precision evaluation.By this method, can fast quantification estimation of brine water body salinity information, analysis salt lake water body salinity evolution trend is had a very important significance.
Description
Technical field
The invention belongs to salt lake water body information extraction technology fields, and in particular to a kind of suitable for salt lake water body salinity
High-definition remote sensing quantitative estimation method.
Background technique
China is that salt lake is distributed most one of countries in the world, and it is more with quantity, type is complete, it is resourceful, be rich in
Rare element and be world-famous for.The west areas such as Qinghai, Tibet, the Inner Mongol and Xinjiang are dispersed with extensive salt lake, salt lake halogen
Not only salinity is high for water, but also the salts resources such as the potassium that is richly stored with, magnesium, lithium, boron and uranium, potential value are very big.It is general next
It says, fresh water lake and salt water lake be not at mine, and also and not all salt lake all contains mine, the only higher salt lake of maturity, i.e. salinity
It high salt lake could be at mine.Zhang Pengxi (1987) et al. grinds Salt Lake Brines In Qaidam Basin salinity and brine ion concentration
Study carefully, has shown that lithium in brine, sodium, magnesium, potassium, boron and brine salinity are linearly related, the rare and scatter element especially in brine
Generally there is increase and increased trend with salinity.Therefore, water body salinity in quantitative estimation salt lake is for analyzing salt lake
Supply-runoff-excretion environment, identification salt lake mineral resources enrichment positions have great importance.
Traditional salt Lake Water Body salinity information acquiring pattern is to carry out artificial area water sample to the salt lake domain Quan Hu water body to adopt
Collection then send to analysis test laboratory progress chemical analysis and measures mineralising angle value.Since China salt lake is numerous, most of salt lakes
Area's nature transportation condition is severe, is difficult to complete intensive artificial face formula water sampling.Therefore, traditional salt Lake Water Body salinity is quantitative
The analysis method period is longer, difficulty is larger, is unfavorable for the Fast Evaluation of numerous salt lake differences waters ore-bearing potential.Remote sensing technology conduct
New investigative technique has the characteristics that macroscopic view, comprehensive, dynamic and quick, is earth resources survey and exploitation, land management, ring
Border monitoring and global Journal of Sex Research, open a kind of new detection means and method (plum Anxin etc., 2001).With remotely-sensed data
The raising of spatial resolution and technological means it is improved day by day, received more and more attention using remote sensing technology research salt lake
(Zhang Bo, 2007；Zhang great Lin etc., 2007).Bhargana etc. tests low concentration single component in laboratory conditions and (contains
NaCl) the wave spectrum of salt water, Crowley etc. have carried out the mineral spectras such as saltcake and gypsum in sulphate salt lake water and have measured, summarized
The Huanghe River Estuary of this kind of mineral is gone out.Tian Shufang (2005), Zhang great Lin (2007) etc. are based on the TM of intermediate resolution (30m)
Data carry out quantitative estimation research to the total salt and boron oxide content of Zabuye Salt Lake In Tibet, and estimation result has centainly macro
It sees indicative.But the spatial resolution for being limited to remotely-sensed data is lower, and the mineralising angle value of a pixel represents 900m in image2Halogen
The average value of water salinity, and known brine deposit degree sample point only has 1m2Waters, estimation result have very in practical applications
Big limitation.Therefore, a kind of salt lake water body salinity quantitative estimation method based on High Resolution Remote Sensing Data is developed
Very necessary, this method reduces the investigation of salt lake salinity and is parsed into for quick, quantitative judge salt lake waters salinity height
This, prediction rich ore waters has a very important significance.
Summary of the invention
Present invention solves the technical problem that: the present invention provides a kind of high-definition remote sensing suitable for salt lake water body salinity
Quantitative estimation method can effectively reduce reconnoitring and chemical analysis cost for salt lake water body salinity quantitative judge.
The technical solution adopted by the present invention:
A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity, includes the following steps:
Step 1: the acquisition in High Resolution Remote Sensing Data source: choosing and cover a certain salt lake saline numeric field data acquisition quality
High II High Resolution Remote Sensing Data of WV；
Step 2: II satellite remote sensing date of WV pre-processes: being defended to II high-resolution of salt lake waters WV obtained in step 1
Star remotely-sensed data carries out image mosaic and colored synthesis, obtains pretreated remote sensing image；
Step 3: realizing salt lake waters edge of a body of water line drawing based on II High Resolution Remote Sensing Data of WV: in step 2
Pretreated II High Resolution Remote Sensing Data of WV carries out the line information extraction of salt lake waters edge of a body of water, obtains accurate salt lake saline
Domain contour line；
Step 4: WV II high-resolution remote sensing image difference brightness value point in salt lake waters is chosen: being obtained according to step 3
II high-resolution remote sensing image of salt lake waters WV radiance value size, choose the section of a brightness value from high to low,
The multiple points for representing II high-resolution remote sensing image difference brightness value of WV are uniformly chosen on section；
Step 5: II high-resolution remote sensing image difference point withdrawing spectral information of salt lake waters WV: to being selected in step 4
The curve of spectrum of multiple points carries out signature analysis in II high-resolution remote sensing image of WV taken, in high-resolution data visible light wave
Selection brightness value of image in section domain is maximum, the spectrum maximum wave band x of deformationization, near infrared band domain selection brightness value of image it is minimum,
Compose the smallest wave band y of deformationization；
Step 6: salt lake water body salinity Indices SIWV II of the building based on II high-resolution remote sensing image of WV: will
The II high-resolution remote sensing image brightness value of WV obtained in step 5 is maximum, composes the maximum wave band x of deformationization, most with brightness value
Index of small, the spectrum the smallest wave band y of deformationization ratio as inverting mineral concentration of lake water information, expression formula are as follows:
II=WV of SIWV II (band x)/WV II (band y) (formula 1)
Salinity index SIWV II is defined with WV II (band x) and WV II (band y) band ratio, value
Range is greater than zero；
Step 7: salt lake water body salinity three-dimensional index of the building based on II high-resolution remote sensing image of WV: in order to abundant
Reflect II remote sensing image different-waveband of WV to the spectral signature of the mineral concentration of lake water, according to determining for the salinity index in step 6
Justice promotes salinity index dimension, and the II high-resolution remote sensing image three-dimensional salinity of WV that salt lake water body can be obtained refers to
Number, i.e. WV II (band x1)/WV II (band y1), WV II (band x2)/WV II (band y2), WV II (band x3)/WV
Ⅱ(band y3)；The three-dimensional salinity index provides the information richer compared with the one-dimensional salinity index in step 6, is salt
Good basis has been established in the qualitative estimation of lake salinity content；
Step 8: the salt lake water body salinity information extraction based on II high-resolution remote sensing image of WV: being based on salt lake water body
Remote sensing information extracting method carries out information separation to the II remote sensing three-dimensional index of mineral concentration of lake water WV constructed in step 7, obtains WV
The remote sensing image (PC1, PC2, PC3) of II 3 components of remotely-sensed data；In conjunction with salt lake water isopleth of water depth figure, verifying first is main
The qualitative height information for reflecting mineral concentration of lake water content of the height of ingredient image (PC1) brightness value, rather than the depth of lake water is believed
Breath；
Step 9: the qualitative estimation of salt lake water body salinity based on II high-resolution remote sensing image of WV: to being obtained in step 8
The first principal component image (PC1) of the reflection salt lake water body salinity information taken carries out density slice according to certain magnitude interval,
The each magnitude ascending to brightness value after segmentation is assigned to color from shallow to deep respectively, II remote sensing image color of salt lake WV
The depth represents the height of salt lake water body salinity, as the qualitative estimation figure of the salt lake water body salinity；
Step 10: salt lake water sampling and salinity measurement: uniformly acquire the salt lake different direction waters it is several each and every one
Lake water sample, and the latitude and longitude coordinates of each sample point are recorded, it is measured through indoor chemical analysis and obtains water sample mineralising angle value；
Step 11: the salt lake water body salinity quantitative inversion model construction based on II high-resolution remote sensing image of WV: choosing
Take 2/3rds water sample mineralising angle value and respective coordinates point II-PC1 brightness value of image of WV as statistical sample, based on system
Analysis method is counted, is constructed using II-PC1 brightness value of image of WV as independent variable, water body salinity true value is the quantitative anti-of dependent variable
Model is drilled, model simulation results coefficient of determination value (R) is higher, and absolute value evaluated error is smaller；
Step 12: the salt lake water body salinity quantitative inversion model accuracy based on II high-resolution remote sensing image of WV is commented
Valence: using the corresponding II-PC1 brightness value of image of WV of remaining one third water sample sample collection point as independent variable, step 10 is substituted into
One resulting quantitative inversion model obtains the salinity quantitative inversion value of each water sample sample, by each sample
Salinity quantitative inversion value is obtained with the analysis of chemical analysis Data-Statistics: the salt lake water body mine based on II high-resolution remote sensing image of WV
Change degree quantitative inversion model is averaged inversion accuracy higher than 95%, is effective inverse model.
In the step 1, acquisition quality height refers to that the acquisition time of High Resolution Remote Sensing Data is noon,
And cloudless sky；High Resolution Remote Sensing Data refers to that DigitalGlobe company, the U.S. emits, multi light spectrum hands space point
Resolution be 1.8 meters, via radiation with II satellite remote sensing date of the WorldView- of geometric correction.
In the step 2, preprocessing process includes the image edge that II remotely-sensed data of WV is completed based on histogram matching
It is embedding, the RGB color synthesis of II remotely-sensed data of WV, 8,4,1 wave band is realized based on three wave band colored synthesis methods.
In the step 3, edge of a body of water line refers to that salt lake waters boundary contour, edge of a body of water line information extracting method are using non-
Supervised classification method carries out information extraction to the 8th wave band image of II remotely-sensed data of WV.
In the step 4, multiple quantity for choosing II remote sensing image difference brightness value of WV need to be greater than 5 points.
In the step 5, the II remote sensing image visible light wave range domain WV totally 6 wave bands, wave-length coverage be 450nm~
745nm；Totally two wave bands, wave-length coverage are 770nm~1040nm in the II remote sensing image near infrared band domain WV；The wave band x of selection
Refer to that II third wave band data of WV, the wave band y of selection refer to the 8th wave band data of WV II.
In the step 7, WV II (band x1)/WV II (band y1) refers to that the radiation of the 3rd wave band of II remote sensing image of WV is bright
Angle value and the radiance value of the 8th wave band are divided by；WV II (band x2)/WV II (band y2) refers to II remote sensing image of WV
The radiance value of 3 wave bands and the radiance value of the 7th wave band are divided by；WV II (band x3)/WV II (band y3) refers to
The radiance value of the radiance value and the 8th wave band of the 4th wave band of II remote sensing image of WV is divided by.
In the step 8, remote sensing information extracting method is Principal Component Analysis (referred to as " PCA " method)；3 components it is distant
Sense image (PC1, PC2, PC3) refers to the first principal component after II remote sensing three-dimensional index of WV progress principal component transform, obtained
(PC1), Second principal component, (PC2) and third principal component (PC3).
In the step 9, density slice is carried out according to certain magnitude interval and is referred to according to II first principal component image brilliance of WV
It is worth and uniform in size is divided into 7 grades.
In the step 10, water sample salinity chemical analysis measuring method uses 105 DEG C of drying measuring methods.
In the step 11, salt lake water body quantitative inversion model is Ym=0.185Xm+ 208.659, YmIt is m's for number
The mineralising angle value of water body sample quantitative inversion, XmII-PC1 the brightness value of image of water sample point WV for being m for number, quantitative inversion mould
Type analog result coefficient of determination value R=0.650, absolute value evaluated error are 10.9g/l.
In the step 12, the average inversion accuracy of salt lake water body quantitative inversion model is 96.90%.
Beneficial effects of the present invention:
Method of the invention can fast quantification identify salt lake waters salinity content, substantially reduce salt lake water salinity
Investigation and analysis cost have great importance to analysis salt lake formation and identification rich ore waters, are also distant based on aviation/space flight
Sense technology quickly identifies that salt lake rich ore waters provides important Technical Reference.
Detailed description of the invention
Fig. 1 is II high-resolution remote sensing image grayscale image of certain salt lake waters WV；
Fig. 2 is II high-resolution remote sensing image difference point spectral profile figure of certain salt lake WV；
Fig. 3 is II remote sensing image three-dimensional salinity index principal component analysis the first factor figure of certain salt lake WV；
Fig. 4 is II remote sensing quantitative estimation figure of certain salt lake water body salinity WV.
Specific embodiment
Invention is further described in detail in the following with reference to the drawings and specific embodiments.
A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity of the present invention, including walk as follows
It is rapid:
Step 1: the acquisition in High Resolution Remote Sensing Data source.The high-resolution for choosing a certain salt lake waters of covering is distant
Feel data, data be U.S. DigitalGlobe company emit, multi light spectrum hands spatial resolution be 1.8 meters, via radiation with it is several
WorldView- II (referred to as " WV II ") satellite remote sensing date of what correction, data acquisition time noon, and cloudless sky；
Step 2: II satellite remote sensing date of WV pre-processes.To the II satellite remote sensing number of salt lake waters WV obtained in step 1
According to, the image mosaic of II remotely-sensed data of WV is completed based on histogram matching, it is distant based on three wave band colored synthesis methods realization WV II
The RGB color synthesis for feeling 8,4,1 wave band of data, obtains pretreated remote sensing image, as shown in Figure 1；
Step 3: realizing salt lake waters edge of a body of water line drawing based on II satellite remote sensing date of WV.To the WV obtained in step 2
The 8th wave band image of II satellite remote sensing date carries out salt lake waters boundary contour using non-supervised classification (ISODATA method)
Information extraction obtains accurate salt lake waters contour line；
Step 4: WV II high-resolution remote sensing image difference brightness value point in salt lake waters is chosen.It is obtained according to step 3
II high-resolution remote sensing image of salt lake waters edge of a body of water line WV radiance value size, choose an Optical Spectrum Brightness by height to
Low section, uniformly chooses the multiple points for representing II high-resolution remote sensing image difference brightness value of WV on section, and points need big
In 5 points；
Step 5: II high-resolution remote sensing image difference point withdrawing spectral information of salt lake waters WV.To being selected in step 4
The curve of spectrum of the II high-resolution remote sensing image difference brightness value point of salt lake waters WV taken carries out signature analysis, in visible light
Wave band domain (450nm~745nm) selects brightness value of image maximum, the spectrum maximum II third wave band data (WV II-of WV of deformationization
Band3), near infrared band domain (770nm~1040nm) selection brightness value of image minimum, the spectrum the smallest wave band WV II of deformationization
8th wave band data (II-band8 of WV), as shown in Figure 2；
Step 6: salt lake water body salinity Indices SIWV II of the building based on II high-resolution remote sensing image of WV.It will
The II high-resolution remote sensing image difference point Optical Spectrum Brightness of salt lake waters WV obtained in step 5 is maximum, spectrum deformationization is maximum
Wave band x, and the index that brightness value is minimum, ratio of the spectrum the smallest wave band y of deformationization is as inverting mineral concentration of lake water information,
Expression formula are as follows:
II=WV of SIWV II (band x)/WV II (band y) (formula 1)
Salinity index SIWV II is defined with WV II (band x) and WV II (band y) band ratio, value
Range is greater than zero；
Step 7: salt lake water body salinity three-dimensional index of the building based on II high-resolution remote sensing image of WV.In order to abundant
Reflect II remote sensing image different-waveband of WV to the spectral signature of the mineral concentration of lake water, according to determining for the salinity index in step 6
Justice promotes salinity index dimension, and the II high-resolution remote sensing image three-dimensional salinity of WV that salt lake water body can be obtained refers to
Number, that is, the radiance value of the radiance value and the 8th wave band that refer to the 3rd wave band of II remote sensing image of WV are divided by (II (band of WV
3)/WV II (band 8)), the radiance value of the radiance value and the 7th wave band of the 3rd wave band of II remote sensing image of WV is divided by
The radiation of the radiance value and the 8th wave band of (WV II (band 3)/WV II (band 7)), the 4th wave band of II remote sensing image of WV is bright
Angle value is divided by (WV II (band 4)/WV II (band 8)).The three-dimensional salinity index is provided compared with one in step 6
The richer information of salinity index is tieed up, has established good basis for the qualitative estimation of salt lake salinity content；
Step 8: the salt lake water body salinity information extraction based on II remote sensing image of WV.(referred to as based on Principal Component Analysis
" PCA " method) information separation is carried out to the II remote sensing three-dimensional index of mineral concentration of lake water WV constructed in step 7, obtain II remote sensing number of WV
According to first principal component (PC1), the remote sensing image of Second principal component, (PC2) and third principal component (PC3) 3 components；In conjunction with salt lake
Lake water isopleth of water depth figure, the height of verifying first principal component image (PC1) brightness value is qualitative to reflect mineral concentration of lake water content
Height information, rather than the depth information of lake water, as shown in Figure 3；
Step 9: the qualitative estimation of salt lake water body salinity based on II high-resolution remote sensing image of WV.To being obtained in step 8
The first principal component image (PC1) of the reflection salt lake water body salinity information taken carries out density slice according to certain magnitude interval,
8 grades are uniformly divided into, each magnitude ascending to brightness value after segmentation is assigned to color from shallow to deep, salt lake WV respectively
The depth of II remote sensing image color represents the height of salt lake water body salinity, as the qualitative estimation of salt lake water body salinity
Figure；
Step 10: salt lake water sampling and salinity measurement.Uniformly acquire the salt lake different direction waters it is several each and every one
Lake water sample, and the latitude and longitude coordinates of each sample point are recorded, indoor chemical analysis measures water sample using 105 DEG C of drying measuring methods
Mineralising angle value；
Step 11: the salt lake water body salinity quantitative inversion model construction based on II high-resolution remote sensing image of WV.Choosing
Take 2/3rds water sample mineralising angle value and respective coordinates point II-PC1 brightness value of image of WV as statistical sample, based on system
Analysis method is counted, is constructed using II-PC1 brightness value of image of WV as independent variable, water body salinity true value is the quantitative anti-of dependent variable
Drill model Ym=0.185Xm+ 208.659, YmFor the mineralising angle value for the water body sample quantitative inversion that number is m, XmIt is m for number
II-PC1 brightness value of image of water sample point WV.Quantitative inversion model simulation results coefficient of determination value R=0.650, absolute value are estimated
Meter error is 10.9g/l, as shown in Figure 4；
Step 12: the salt lake water body salinity quantitative inversion model accuracy based on II high-resolution remote sensing image of WV is commented
Valence.Using the corresponding II-PC1 brightness value of image of WV of remaining one third water sample sample collection point as independent variable, step 10 is substituted into
One resulting quantitative inversion model obtains the salinity quantitative inversion value of each water sample sample.By to each sample
Salinity quantitative inversion value is obtained with the analysis of chemical analysis Data-Statistics: the salt lake water body mine based on II high-resolution remote sensing image of WV
The change degree quantitative inversion model inversion accuracy that is averaged is 96.9%, is effective inverse model such as table 1.
Certain the salt lake water sample salinity chemical analysis value of table 1 and model inversion accuracy comparison table
The above analysis, salt lake water body salinity can carry out fast quantification estimation by this method, substantially reduce
The investigation of salt lake water body salinity and analysis cost, improve the recognition efficiency in salt lake rich ore waters, for analyzing the shape in salt lake
Have great importance at evolution, mineral resources enrichment positions.
The embodiment of the present invention is explained in detail above, above embodiment is only most highly preferred embodiment of the invention,
But the present invention is not limited to above-described embodiments, it within the knowledge of a person skilled in the art, can also be
It is made a variety of changes under the premise of not departing from present inventive concept.
Claims (12)
1. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity, it is characterised in that: including as follows
Step:
Step 1: the acquisition in High Resolution Remote Sensing Data source: it is high to choose a certain salt lake saline numeric field data acquisition quality of covering
II High Resolution Remote Sensing Data of WV；
Step 2: II satellite remote sensing date of WV pre-processes: distant to salt lake waters II high-resolution satellite of WV obtained in step 1
Feel data and carry out image mosaic and colored synthesis, obtains pretreated remote sensing image；
Step 3: realizing salt lake waters edge of a body of water line drawing based on II High Resolution Remote Sensing Data of WV: locating in advance in step 2
II High Resolution Remote Sensing Data of WV after reason carries out the line information extraction of salt lake waters edge of a body of water, obtains accurate salt lake waters wheel
Profile；
Step 4: WV II high-resolution remote sensing image difference brightness value point in salt lake waters is chosen: the salt obtained according to step 3
The radiance value size of II high-resolution remote sensing image of lake water domain WV is chosen the section of a brightness value from high to low, is being cutd open
The multiple points for representing II high-resolution remote sensing image difference brightness value of WV are uniformly chosen on face；
Step 5: II high-resolution remote sensing image difference point withdrawing spectral information of salt lake waters WV: to what is chosen in step 4
The curve of spectrum of multiple points carries out signature analysis in II high-resolution remote sensing image of WV, in high-resolution data visible light wave range domain
Brightness value of image maximum, the spectrum maximum wave band x of deformationization are selected, near infrared band domain selection brightness value of image minimum, spectrum shape
Change the smallest wave band y；
Step 6: salt lake water body salinity Indices SIWV II of the building based on II high-resolution remote sensing image of WV: by step
The II high-resolution remote sensing image brightness value of WV obtained in five is maximum, composes the maximum wave band x of deformationization, with brightness value minimum, spectrum
Index of the ratio of the smallest wave band y of deformationization as inverting mineral concentration of lake water information, expression formula are as follows:
II=WV of SIWV II (band x)/WV II (band y) (formula 1)
Salinity index SIWV II is defined with WV II (band x) and WV II (band y) band ratio, value range
Greater than zero；
Step 7: salt lake water body salinity three-dimensional index of the building based on II high-resolution remote sensing image of WV: in order to sufficiently reflect
II remote sensing image different-waveband of WV will according to the definition of the salinity index in step 6 to the spectral signature of the mineral concentration of lake water
Salinity index dimension is promoted, and the II high-resolution remote sensing image three-dimensional salinity index of WV of salt lake water body can be obtained, i.e.,
WVⅡ(band x1)/WVⅡ(band y1)、WVⅡ(band x2)/WVⅡ(band y2)、WVⅡ(band x3)/WVⅡ
(band y3)；The three-dimensional salinity index provides the information richer compared with the one-dimensional salinity index in step 6, is salt lake
Good basis has been established in the qualitative estimation of salinity content；
Step 8: the salt lake water body salinity information extraction based on II high-resolution remote sensing image of WV: being based on the remote sensing of salt lake water body
Information extracting method carries out information separation to the II remote sensing three-dimensional index of mineral concentration of lake water WV constructed in step 7, and it is distant to obtain WV II
Feel the remote sensing image (PC1, PC2, PC3) of 3 components of data；In conjunction with salt lake water isopleth of water depth figure, first principal component is verified
The qualitative height information for reflecting mineral concentration of lake water content of the height of image (PC1) brightness value, rather than the depth information of lake water；
Step 9: the qualitative estimation of salt lake water body salinity based on II high-resolution remote sensing image of WV: to what is obtained in step 8
Reflect salt lake water body salinity information first principal component image (PC1), according to certain magnitude interval carry out density slice, to point
It cuts the ascending each magnitude of rear brightness value and is assigned to color from shallow to deep, the depth of II remote sensing image color of salt lake WV respectively
The height of salt lake water body salinity is represented, as the qualitative estimation figure of the salt lake water body salinity；
Step 10: salt lake water sampling and salinity measurement: uniformly acquiring each and every one several lake water in the salt lake different direction waters
Sample, and the latitude and longitude coordinates of each sample point are recorded, it is measured through indoor chemical analysis and obtains water sample mineralising angle value；
Step 11: the salt lake water body salinity quantitative inversion model construction based on II high-resolution remote sensing image of WV: choosing three
II-PC1 the brightness value of image of WV of/bis- water sample mineralising angle value and respective coordinates point is based on statistical as statistical sample
Analysis method is constructed using II-PC1 brightness value of image of WV as independent variable, and water body salinity true value is the quantitative inversion mould of dependent variable
Type, model simulation results coefficient of determination value (R) is higher, and absolute value evaluated error is smaller；
Step 12: the salt lake water body salinity quantitative inversion model accuracy based on II high-resolution remote sensing image of WV is evaluated: with
Corresponding II-PC1 the brightness value of image of WV of remaining one third water sample sample collection point is independent variable, is substituted into obtained by step 11
Quantitative inversion model, obtain the salinity quantitative inversion value of each water sample sample, pass through the salinity to each sample
Quantitative inversion value is obtained with the analysis of chemical analysis Data-Statistics: the salt lake water body salinity based on II high-resolution remote sensing image of WV is fixed
Amount inverse model is averaged inversion accuracy higher than 95%, is effective inverse model.
2. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: acquisition quality height refers to that the acquisition time of High Resolution Remote Sensing Data is positive the period of the day from 11 a.m. to 1 p.m in the step 1
Between, and cloudless sky；High Resolution Remote Sensing Data refers to that DigitalGlobe company, the U.S. emits, multi light spectrum hands space
Resolution ratio be 1.8 meters, via radiation with II satellite remote sensing date of the WorldView- of geometric correction.
3. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: preprocessing process includes the image for completing II remotely-sensed data of WV based on histogram matching in the step 2
It inlays, the RGB color synthesis of II remotely-sensed data of WV, 8,4,1 wave band is realized based on three wave band colored synthesis methods.
4. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: edge of a body of water line refers to that salt lake waters boundary contour, edge of a body of water line information extracting method are to use in the step 3
Non-supervised classification carries out information extraction to the 8th wave band image of II remotely-sensed data of WV.
5. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: multiple quantity for choosing II remote sensing image difference brightness value of WV need to be greater than 5 points in the step 4.
6. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: in the step 5, the II remote sensing image visible light wave range domain WV totally 6 wave bands, wave-length coverage be 450nm~
745nm；Totally two wave bands, wave-length coverage are 770nm~1040nm in the II remote sensing image near infrared band domain WV；The wave band x of selection
Refer to that II third wave band data of WV, the wave band y of selection refer to the 8th wave band data of WV II.
7. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: WV II (band x1)/WV II (band y1) refers to the spoke of the 3rd wave band of II remote sensing image of WV in the step 7
The radiance value for penetrating brightness value and the 8th wave band is divided by；WV II (band x2)/WV II (band y2) refers to II remote sensing shadow of WV
As the radiance value of the radiance value and the 7th wave band of the 3rd wave band is divided by；WVⅡ(band x3)/WVⅡ(band
Y3) refer to that the radiance value of the radiance value and the 8th wave band of the 4th wave band of II remote sensing image of WV is divided by.
8. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: remote sensing information extracting method is Principal Component Analysis (referred to as " PCA " method) in the step 8；3 components
Remote sensing image (PC1, PC2, PC3) refers to the first principal component after II remote sensing three-dimensional index of WV progress principal component transform, obtained
(PC1), Second principal component, (PC2) and third principal component (PC3).
9. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: carrying out density slice in the step 9 according to certain magnitude interval and referring to according to II first principal component image of WV
Brightness value is uniform in size to be divided into 7 grades.
10. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: water sample salinity chemical analysis measuring method uses 105 DEG C of drying measuring methods in the step 10.
11. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: salt lake water body quantitative inversion model is Y in the step 11m=0.185Xm+ 208.659, YmIt is for number
The mineralising angle value of the water body sample quantitative inversion of m, XmII-PC1 the brightness value of image of water sample point WV for being m for number, it is quantitative anti-
Model simulation results coefficient of determination value R=0.650 is drilled, absolute value evaluated error is 10.9g/l.
12. a kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity according to claim 1,
It is characterized by: the average inversion accuracy of salt lake water body quantitative inversion model is 96.90% in the step 12.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111060456A (en) * | 2019-12-27 | 2020-04-24 | 核工业北京地质研究院 | Method for identifying sandstone uranium ore structure excretion zone by using remote sensing image |
| CN111816263A (en) * | 2020-06-30 | 2020-10-23 | 核工业北京地质研究院 | Method for qualitatively identifying salinity water area mineralization quantity based on ETM + remote sensing data |
| CN113140000A (en) * | 2021-03-26 | 2021-07-20 | 中国科学院东北地理与农业生态研究所 | Water body information estimation method based on satellite spectrum |
| CN113673737A (en) * | 2020-05-14 | 2021-11-19 | 中国科学院南京地理与湖泊研究所 | Estimation method for dissolved carbon dioxide in algae-type lake water body based on satellite remote sensing image |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2036430C1 (en) * | 1991-02-20 | 1995-05-27 | Общество с ограниченной ответственностью "ДВС ЛТД" | Method of determination of parameters of currents on sea surface |
| CN1595203A (en) * | 2004-06-29 | 2005-03-16 | 中国国土资源航空物探遥感中心 | Layered lineage identification method for high spectrum mineral |
| CN101504353A (en) * | 2008-02-06 | 2009-08-12 | 香港科技大学 | Method and system for providing near ground suspending particulate distribution |
| WO2010052530A1 (en) * | 2008-11-05 | 2010-05-14 | Ecoserv Remote Observation Centre Co. Ltd. | Multi-polarization combined radar-radiometer system |
| US8032314B2 (en) * | 2008-09-29 | 2011-10-04 | The United States Of America As Represented By The Secretary Of The Navy | MLD-modified synthetic ocean profiles |
| CN102721644A (en) * | 2012-06-21 | 2012-10-10 | 中国科学院对地观测与数字地球科学中心 | Method and device for processing remote sensing data of water environment |
| CN103207415A (en) * | 2012-12-30 | 2013-07-17 | 中南大学 | Method for extracting mineralization-alteration information |
| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| KR101621222B1 (en) * | 2015-10-30 | 2016-05-16 | 길주형 | Smart Water quality measurement sensor |
| KR101745578B1 (en) * | 2017-02-28 | 2017-06-09 | 주식회사 지오스토리 | Tidal channel extraction system using remote sensing data |
| WO2017098223A1 (en) * | 2015-12-09 | 2017-06-15 | Bae Systems Plc | Improvements in and relating to remote sensing |
| KR20170138297A (en) * | 2016-06-07 | 2017-12-15 | (주)링크옵틱스 | Water quality environmental monitoring system |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| CN107991244A (en) * | 2016-10-27 | 2018-05-04 | 核工业北京地质研究院 | A kind of quartz content quantitative calculation method for being suitable for thermal infrared high-spectrum remote-sensing |
| CN108038351A (en) * | 2017-12-06 | 2018-05-15 | 武汉大学 | A kind of chlorophyll computational methods based on remote sensing image and water ecological model |
-
2018
- 2018-10-11 CN CN201811184489.7A patent/CN109406405A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2036430C1 (en) * | 1991-02-20 | 1995-05-27 | Общество с ограниченной ответственностью "ДВС ЛТД" | Method of determination of parameters of currents on sea surface |
| CN1595203A (en) * | 2004-06-29 | 2005-03-16 | 中国国土资源航空物探遥感中心 | Layered lineage identification method for high spectrum mineral |
| CN101504353A (en) * | 2008-02-06 | 2009-08-12 | 香港科技大学 | Method and system for providing near ground suspending particulate distribution |
| US8032314B2 (en) * | 2008-09-29 | 2011-10-04 | The United States Of America As Represented By The Secretary Of The Navy | MLD-modified synthetic ocean profiles |
| WO2010052530A1 (en) * | 2008-11-05 | 2010-05-14 | Ecoserv Remote Observation Centre Co. Ltd. | Multi-polarization combined radar-radiometer system |
| CN102721644A (en) * | 2012-06-21 | 2012-10-10 | 中国科学院对地观测与数字地球科学中心 | Method and device for processing remote sensing data of water environment |
| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| CN103207415A (en) * | 2012-12-30 | 2013-07-17 | 中南大学 | Method for extracting mineralization-alteration information |
| KR101621222B1 (en) * | 2015-10-30 | 2016-05-16 | 길주형 | Smart Water quality measurement sensor |
| WO2017098223A1 (en) * | 2015-12-09 | 2017-06-15 | Bae Systems Plc | Improvements in and relating to remote sensing |
| KR20170138297A (en) * | 2016-06-07 | 2017-12-15 | (주)링크옵틱스 | Water quality environmental monitoring system |
| CN107991244A (en) * | 2016-10-27 | 2018-05-04 | 核工业北京地质研究院 | A kind of quartz content quantitative calculation method for being suitable for thermal infrared high-spectrum remote-sensing |
| KR101745578B1 (en) * | 2017-02-28 | 2017-06-09 | 주식회사 지오스토리 | Tidal channel extraction system using remote sensing data |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| CN108038351A (en) * | 2017-12-06 | 2018-05-15 | 武汉大学 | A kind of chlorophyll computational methods based on remote sensing image and water ecological model |
Non-Patent Citations (5)
| Title |
|---|
| JUNHU WANG: "High Resolution Remote Sensing Information Indentification for the Salinity in GASIKULE Salt Lake", 《2012 2ND INTERNATIONALCONFORENCE ON REMOTE SENSING ENVIRONMENT AND TRANSPORTATION ENGINEERING》 * |
| 姜红: "博斯腾湖矿化度遥感反演及空间分布特征研究", 《环境监测管理与技术》 * |
| 王俊虎: "含铀盐湖矿化度高分辨率遥感估测", 《地球科学 中国地质大学学报》 * |
| 王俊虎: "基于高光谱烟感技术峰盐湖富铀水体识别模型构建", 《国土与自然资源研究》 * |
| 赫晓慧: "《遥感基础导论》", 31 May 2016, 黄河水利出版社 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111060456A (en) * | 2019-12-27 | 2020-04-24 | 核工业北京地质研究院 | Method for identifying sandstone uranium ore structure excretion zone by using remote sensing image |
| CN111060456B (en) * | 2019-12-27 | 2022-09-20 | 核工业北京地质研究院 | Method for identifying sandstone uranium ore structure excretion zone by using remote sensing image |
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| CN113673737B (en) * | 2020-05-14 | 2023-07-28 | 中国科学院南京地理与湖泊研究所 | Algae type lake water body dissolved carbon dioxide estimation method based on satellite remote sensing image |
| CN111816263A (en) * | 2020-06-30 | 2020-10-23 | 核工业北京地质研究院 | Method for qualitatively identifying salinity water area mineralization quantity based on ETM + remote sensing data |
| CN111816263B (en) * | 2020-06-30 | 2024-01-16 | 核工业北京地质研究院 | Method for qualitatively identifying mining amount of water area of salt lake based on ETM+remote sensing data |
| CN113140000A (en) * | 2021-03-26 | 2021-07-20 | 中国科学院东北地理与农业生态研究所 | Water body information estimation method based on satellite spectrum |
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