CN109283144A - The strong long remote sensing calculation method for lasting variation of tidal height muddiness river mouth suspension bed sediment - Google Patents
The strong long remote sensing calculation method for lasting variation of tidal height muddiness river mouth suspension bed sediment Download PDFInfo
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Abstract
The invention discloses a kind of long remote sensing calculation methods for lasting variation of strong tidal height muddiness river mouth suspension bed sediment, include the following steps: step S1: receiving the Landsat satellite remote sensing date under the identical hydrologic condition of different year；Step S2: Remote Sensing Data Processing；Step S3: the above spectral measurement in size tidewater face simultaneously carries out the sampling of synchronous meter layer suspension bed sediment, acquires Remote Sensing Reflectance；Wave band where analyzing curve of spectrum peak value, the corresponding wave band of peak value is silt sensitive band, the wave band data for selecting different sensors on Landsat corresponding with reflectance peak, establishes the statistical regression mode between surface layer Suspended Sedimentation Concentration and the corresponding Remote Sensing Reflectance of sensitive band；It is calculated again by precision test, obtains quantitative remote sensing optimal computed mode；Step S4: the quantitative remote sensing optimal computed mode that step S3 is obtained is applied to step S2 treated in remotely-sensed data, obtains the remotely-sensed data under the identical hydrologic condition of each region suspension bed sediment different year, carrying out suspension bed sediment length lasts mutation analysis.
Description
Technical field
The present invention relates to a kind of calculation method of estuary sediment variation, specifically strong tidal height muddiness river mouth suspension bed sediment length is gone through
The remote sensing calculation method of Shi Bianhua.
Background technique
Critical environments element of the suspension bed sediment as water body, be river mouth bay landforms mould, the main influence of beach evolution
The factor.In recent decades, due to the influence of basin natural trend and mankind's activity, at various pressures, river mouth suspension bed sediment is
Deep variation occurs, river mouth will be protected and bring far-reaching influence with improvement.
Under many factors collective effects such as strong tidal height muddiness river mouth crosses in Jiang Hai, salt fresh water converges, tide stormy waves, river mouth
Silt luffing is big, and change in time and space is violent.The calculating analysis for lasting variation long for strong tidal height muddiness river mouth suspension bed sediment, actual measurement are gone through
History data are few, and measurement data, there are certain contingency, traditional large area samples and observes costly, obtained number in real time
According to that can only be in discrete website, it be difficult effectively to analyze the long spatial-temporal characteristics lasted of river mouth suspension bed sediment.The advantage of remote sensing is
Frequently with the planar information of lasting offer topographical features, this is for traditional earth observation hand based on sparse discrete point
Section is a revolutionary variation.Measured data big compared with cost, data volume is rare is more advantageous to using remotely-sensed data from face
The visual angle in domain and a long period scale come calculate analysis strong tidal height muddiness river mouth mankind's activity with change naturally it is multiple
Suspension bed sediment spatial-temporal characteristics under pressure, to make up the deficiency of prototype measurement.
Summary of the invention
The technical problem to be solved by the present invention is being provided a kind of for strong tidal height using the longer historical archives data of remote sensing
The long calculation method for lasting variation of muddy river mouth suspension bed sediment.
The technical proposal for solving the technical problem of the invention is as follows: a kind of strong tidal height muddiness river mouth suspension bed sediment is long to be gone through
The remote sensing calculation method of Shi Bianhua, includes the following steps:
Step S1: the Landsat satellite remote sensing date under the identical hydrologic condition of different year is received；
Step S2: Remote Sensing Data Processing；
Step S3: the above spectral measurement in size tidewater face is carried out using field spectroradiometer and carries out synchronous meter layer suspension bed sediment
Sampling, acquires Remote Sensing Reflectance；Wave band where analyzing curve of spectrum peak value, the corresponding wave band of peak value are silt sensitive band, choosing
The wave band data for selecting different sensors on Landsat corresponding with reflectance peak, establishes surface layer Suspended Sedimentation Concentration and sensitivity
Statistical regression mode between the corresponding Remote Sensing Reflectance of wave band；It is calculated again by precision test, obtains quantitative remote sensing and most preferably count
Calculation mode；
Step S4: the quantitative remote sensing optimal computed mode that step S3 is obtained is applied to step S2 treated remotely-sensed data
On, obtain the long variation remotely-sensed data lasted of each region suspension bed sediment.
Further, the step S1 specifically:
Choose Landsat satellite remotely-sensed data, meanwhile, require to look up using some tidal station as the identical season of reference,
The remotely-sensed data of similar tide feelings.
Further, the Remote Sensing Data Processing of the step S2 includes geometric correction, radiation calibration and atmospheric correction.
Further, the step S2 is specific as follows:
Firstly the need of geometric correction is carried out, geometric correction is completed in ENVI software platform；
Equally distributed ground control point is chosen, the resampling of 30m resolution ratio is carried out to image, corrects root-mean-square error
(RMSE) control is within 0.5 pixel；On the basis of geometric correction, radiation calibration is carried out to remote sensing image, i.e., by sensor
Digital output value (DN value) converted with the radiance of corresponding atural object by quantitative relation formula；Then to radiation calibration
As a result atmospheric correction is carried out；Model (ICM) is corrected using sunshine difference, radiation calibration and atmospheric correction are carried out to image, calculated
Formula is as follows:
Lλ=GainDN+Bias (1)
Formula (1) is that initial DN value is converted to radiance, in formula, LλFor the radiance of λ wave band；DN is image
Pixel gray value, value range be 0~255；λ is band value；Gain is gain；Bias is deviation；Formula (2) is will to radiate
Brightness value is converted to atmosphere apparent reflectance, in formula, ρλFor reflectivity；D is solar distance parameter；ESUNλFor solar spectrum radiation
Amount, θ is solar zenith angle, mutually remaining with solar elevation.
Further, in the geometric correction, registration image uses 1:10000 topographic map.
Further, the step S3 specifically:
The above spectral measurement in size tidewater face is carried out using field spectroradiometer and carries out synchronous meter layer suspension bed sediment sampling, water
Body spectral reflectivity is calculated by formula (3):
Rrs(λ)=(Lw-rLsky(λ))/(πLp(λ)/Rp(λ)) (3)
In formula: RrsFor Reservoir water surface spectral reflectance；λ is wavelength；LwFor from water spoke brightness；R is the Fresnel reflection on extra large surface
Rate；LskyFor the diffusing reflection spoke brightness of skylight；LpFor the spoke brightness of the standard hawk of diffusing reflection rate 20%；RpFor known reference plate
Reflectivity；
Wave band where analyzing curve of spectrum peak value again, the corresponding wave band of peak value are silt sensitive band, selection and peak value pair
The wave band data of different sensors in the Landsat answered is established between sediment concentration and the corresponding Remote Sensing Reflectance of sensitive band
Statistical regression mode, carry out the building of outstanding husky Inversion Calculation algorithm；Meanwhile choosing with research area that have the outstanding sand data of actual measurement same
The statistical regression mode of building is applied to have and surveys outstanding husky data synchronization or quasi synchronous by step or quasi synchronous remote sensing image
Remote sensing image carries out precision test calculating, chooses the best statistical model of correlation highest, precision test as quantitative remote sensing most
Good calculating mode.
Further, the field spectroradiometer uses the Field Spec Pro portable field spectroradiometer of ASD company.
Further, the wavelength band of the field spectroradiometer is 350-2500nm, is acquired 10 days, measurement period 9:
00-15:00, every integral point measurement.
Further, the step S4 specifically:
In ENVI software platform, quantitative remote sensing in step S3 is calculated mode and is applied to step S2 treated Landsat-
In TM remotely-sensed data, the quantitative remote sensing silt field of strong tidal height turbid water body under the identical hydrologic condition of different times is calculated, and
It is combined by river mouth point-line-surface and counts the long delta data lasted of each region suspension bed sediment.
Compared with the existing technology, beneficial effects of the present invention are as follows: strong tidal height muddiness river mouth suspension bed sediment length is lasted
Variation is researched and analysed, and is based primarily upon the actual measurement hydrographic data statistical analysis of different periods of history at present, but surveys historical data
Less, cost of observation is big, measurement erect-position is sparse and observation data are there are certain contingency, is difficult effectively to analyze river mouth suspension bed sediment
The long spatial-temporal characteristics lasted.Using the earth observation file data of remote sensing longer cycle, be conducive to analyze long period variation,
Representation of the historical data, to make up the deficiency of prototype measurement, by horn of plenty and deepen understanding strong tidal height muddiness river mouth suspension bed sediment compared with
The general morphologictrend of long time scale provides new visual angle.
Detailed description of the invention
Fig. 1 is the long remote sensing calculation method route map for lasting variation of strong tidal height muddiness river mouth suspension bed sediment；
Fig. 2 is Hangzhou Wan water spectral curve graph；
Fig. 3 is Suspended Sediment In Hangzhou Bay retrieving concentration ideograph；
Fig. 4 a- Fig. 4 c is various years Hangzhou Wan remote sensing silt field pattern；
Fig. 5 is the suspension bed sediment data comparison figure of the different times of the mouth of Hangzhou estuary profile extraction.
Specific embodiment
Below in conjunction with specification route map, the invention will be further described, but the invention is not limited to following implementations
Example.
1, strong tidal height muddiness estuary region: strong tidal height muddiness river mouth of the invention is Qiantang Estuary Hangzhou Wan.
Qiantang Estuary Hangzhou Wan is a typical trumpet type Macro-tidal estuary gulf, the gulf mouthful reed wide 98.5km of tidal harbour section, gulf
Push up the wide 16.5km of Ganpu section, area about 4800km2.Gulf mouthful mean range is 2-3m, and Xiang Wanding is gradually increased to 5-6m, Ganpu
Many years mean range 5.66m, extreme tide range reach 9.0m, occupy first of China tidal estuary.Silt inside and outside Hangzhou Wan under natural conditions
Exchange very active, actual measurement spring season maximum sediment concentration 5-6kg/m3。
2, the selection of remotely-sensed data
Macro-tidal estuary long period scale suspension bed sediment change procedure is analyzed to calculate, it is desirable that remote sensing satellite data pick-up
With good hand down, achieves and data time span is big, the high feature of data precision.Based on this, Landsat series is chosen
Satellite data, the satellite are the responsible road resource satellites of U.S. NASA, and 8 have been emitted since 1972 and (has still been transported at present
Behavior Landsat 7, Landsat 8), have accumulated the earth observation file data more than 40 years.The satellite spatial is differentiated
Rate 30m, revisiting period 16 days, spatial and temporal resolution was moderate, quality is stable, has multiple wave bands again, was conducive to analyze long period change
Change.Landsat data Global Subscriber on (http://glovis.usgs.gov) can freely download use.Landsat system
Column satellite is shown in Table 1.Meanwhile under natural conditions, strong tidal height muddiness estuary sediment exchange is active, under the conditions of flood withered season, different tidal stencils
Suspension bed sediment variation acutely, carry out it is long last variation and calculate, require to look up using some tidal station as identical season of reference, similar
The remotely-sensed data of damp feelings, so just with the comparativity on space-time.
1 Landsat series of satellites of table
The remotely-sensed data source (table 2) of 3 phases of Landsat-TM, time span 1988-2009, to make are selected herein
Different times image is comparable, and the tidal stencils at satellite imagery moment is small falling using Hangzhou Wan Zhapu tidal station as reference
The damp phase.
2 Landsat remotely-sensed data source of table
| Serial number | Sensor type | Time | The lunar calendar | Imaging time | Zhapu station tide feelings | The hydrology phase |
| 1 | LANDSAT-5TM | 1988/7/7 | May 24 | 9:56:04 | The neap ebb tide phase | Hong Ji |
| 2 | LANDSAT-5TM | 1998/7/3 | Intercalation early May ten | 10:03:31 | The neap ebb tide phase | Hong Ji |
| 3 | LANDSAT-5TM | 2009/7/17 | Intercalation May 25 | 10:14:16 | The neap ebb tide phase | Hong Ji |
3, the processing of remotely-sensed data
Geometric correction and radiant correction are passed through to the image data of downloading, to eliminate atmosphere, illumination, landform and sensor
Influence of the factors such as itself to clutter reflections guarantees the authenticity on data space position and radiation.It is specific as follows:
For the consistency for guaranteeing remotely-sensed data spatial position, it is necessary first to carry out geometric correction, geometric correction is soft in ENVI
It is completed in part platform, registration image uses 1:10000 topographic map, for the geometric accurate correction for guaranteeing image, needs to select more as far as possible
Control point.The resampling that equally distributed ground control point carries out 30m resolution ratio to image is chosen, root-mean-square error is corrected
(RMSE) control is within 0.5 pixel.On the basis of geometric correction, in order to eliminate the error that sensor itself generates, need
Radiation calibration is carried out to remote sensing image, i.e., is passed through the Digital output value of sensor (DN value) with the radiance of corresponding atural object
Quantitative relation formula is converted.Then atmospheric correction is carried out to radiation calibration result, the purpose of atmospheric correction is that elimination is big
Gas is to the influence in sun optical transmission process, to obtain true clutter reflections rate.Model (ICM) is corrected using sunshine difference
Radiation calibration is carried out to image and atmospheric correction, calculation formula are as follows:
Lλ=GainDN+Bias (1)
Formula (1) is that initial DN value is converted to radiance, in formula, LλFor the radiance of λ wave band；DN is image
Pixel gray value, value range be 0~255；λ is band value；Gain is gain；Bias is deviation.Formula (2) is will to radiate
Brightness value is converted to atmosphere apparent reflectance, in formula, ρλFor reflectivity；D is solar distance parameter；ESUNλFor solar spectrum radiation
Amount, θ is solar zenith angle, mutually remaining with solar elevation.These parameters can be obtained in remotely-sensed data header file.
4, quantitative remote sensing calculates the building of mode
Strong tidal height muddiness river mouth Suspended Sedimentation Concentration is high, and silt signal is strong in spectrum, extracts remote sensing information in the science of realization
Before, by monitoring the relationship of water spectral feature and surface layer Suspended Sedimentation Concentration, it is crucial for establishing quantitative remote sensing and calculating mode
Step.
In May, 2016 carries out Hangzhou Wan waters spectrum in-site measurement, and website is located at Hangzhou Wan northeast waters, utilizes ASD
The Field Spec Pro portable field spectroradiometer of company carries out the above spectral measurement in size tidewater face and suspends with synchronous surface layer
Sediment load sampling.The wavelength band of spectrometer is 350-2500nm, is acquired 10 days, and measurement period 9:00-15:00, every integral point is surveyed
Amount.Reservoir water surface spectral reflectance is calculated by formula (3):
Rrs(λ)=(Lw-rLsky(λ))/(πLp(λ)/Rp(λ)) (3)
In formula: RrsFor Reservoir water surface spectral reflectance；λ is wavelength；LwFor from water spoke brightness；R is the Fresnel reflection on extra large surface
Rate takes 0.03 under this paper measuring condition；LskyFor the diffusing reflection spoke brightness of skylight；LpFor the standard hawk of diffusing reflection rate 20%
Spoke brightness；RpFor the reflectivity of known reference plate.
This investigates the effective sample for obtaining 60 groups of spectrum altogether.Fig. 2 is Hangzhou Wan water spectral curve, it is seen that silt-including water
The curve of spectrum there are two reflection peaks, the principal reflection peak positioned at red spectral band and the secondary peak positioned near infrared band.
Turbid water body spectral reflectivity has differences the response of Suspended Sedimentation Concentration in different wave length position, passes through analysis
Relationship where two peak values between the Remote Sensing Reflectance of wave band and actual measurement surface layer Suspended Sedimentation Concentration (SSC), with different warps
The mode of testing carries out outstanding husky Inversion Calculation.The suspension concentration (SSC) and near infrared band (Landsat- established based on least square method
TM4,0.63 μm -0.69 μm) Remote Sensing Reflectance and red wave band (Landsat-TM3,0.76 μm -0.90 μm) Remote Sensing Reflectance ratio
Fitting regression equation correlation height (R2=0.775, n=60), see Fig. 3.Meanwhile precision test in terms of, in July, 2014 13-
(lunar calendar June 17, spring tide) on the 14th 6 fixed point hydrologic and sediment survey data carried out in the mouth of Hangzhou estuary and June 13 in 2014
Day landsat-8 image (lunar calendar May 16, spring tide) is plesiochronous, by mode apply with plesiochronous image, each positional accuracy can
Up to 80%, which can be used as Qiantang Estuary Hangzhou Wan Characteristics of Surface Suspended Sediment remote sensing and quantitatively calculates mode:
SSC=0.12e2.1X
In formula: X is the ratio of near infrared band and red wave band Remote Sensing Reflectance；SSC is surface layer Suspended Sedimentation Concentration.
5, quantitative remote sensing calculates mode applied to multi-temporal remote sensing image, carries out Inversion Calculation
Under ENVI software platform, utilize " Band Math (band math) " tool that quantitative remote sensing in step 4 is calculated mould
Formula SSC=0.12e2.1XIt is applied to step 3 treated in Landsat-TM remotely-sensed data, the identical water of different times is calculated
The suspension bed sediment field pattern (Fig. 4 a- Fig. 4 c) in the river mouth under the conditions of text.Fig. 5 is the different times of the mouth of Hangzhou estuary profile extraction
Suspension bed sediment data comparison figure, it is seen then that the long river mouth suspension bed sediment number lasted can effectively be calculated based on Landsat remote sensing image
According to.
Claims (9)
1. a kind of long remote sensing calculation method for lasting variation of strong tidal height muddiness river mouth suspension bed sediment, which is characterized in that including as follows
Step:
Step S1: the Landsat satellite remote sensing date under the identical hydrologic condition of different year is received；
Step S2: Remote Sensing Data Processing；
Step S3: it carries out the above spectral measurement in size tidewater face using field spectroradiometer and carries out synchronous meter layer suspension bed sediment to take
Sample acquires Remote Sensing Reflectance；Wave band where analyzing curve of spectrum peak value, the corresponding wave band of peak value are silt sensitive band, selection
The wave band data of different sensors on Landsat corresponding with reflectance peak establishes surface layer Suspended Sedimentation Concentration and sensitive wave
Statistical regression mode between the corresponding Remote Sensing Reflectance of section；It is calculated again by precision test, obtains quantitative remote sensing optimal computed
Mode；
Step S4: being applied to the quantitative remote sensing optimal computed mode that step S3 is obtained step S2 treated in remotely-sensed data,
Obtain the long variation remotely-sensed data lasted of each region suspension bed sediment.
2. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 1,
It is characterized in that, the step S1 specifically:
The remotely-sensed data of Landsat satellite is chosen, meanwhile, it requires to look up using some tidal station as identical season of reference, similar
The remotely-sensed data of damp feelings.
3. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 1,
It is characterized in that, the Remote Sensing Data Processing of the step S2 includes geometric correction, radiation calibration and atmospheric correction.
4. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 1,
It is characterized in that, the step S2 is specific as follows:
Firstly the need of geometric correction is carried out, geometric correction is completed in ENVI software platform；
Equally distributed ground control point is chosen, the resampling of 30m resolution ratio is carried out to image, is corrected root-mean-square error (RMSE)
Control is within 0.5 pixel；On the basis of geometric correction, radiation calibration is carried out to remote sensing image, i.e., by the number of sensor
Change output valve (DN value) to be converted with the radiance of corresponding atural object by quantitative relation formula；Then to radiation calibration result into
Row atmospheric correction；Model (ICM) is corrected using sunshine difference, radiation calibration and atmospheric correction are carried out to image, calculation formula is such as
Under:
Lλ=GainDN+Bias (1)
Formula (1) is that initial DN value is converted to radiance, in formula, LλFor the radiance of λ wave band；DN is the picture of image
First gray value, value range are 0~255；λ is band value；Gain is gain；Bias is deviation；Formula (2) is by radiance
Value is converted to atmosphere apparent reflectance, in formula, ρλFor reflectivity；D is solar distance parameter；ESUNλFor solar spectrum amount of radiation, θ
It is mutually remaining with solar elevation for solar zenith angle.
5. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 4,
It is characterized in that, registration image uses 1:10000 topographic map in the geometric correction.
6. the long remote sensing calculating side for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 1 or 4
Method, which is characterized in that the step S3 specifically:
The above spectral measurement in size tidewater face is carried out using field spectroradiometer and carries out synchronous meter layer suspension bed sediment sampling, water body light
Reflectivity is composed to calculate by formula (3):
Rrs(λ)=(Lw-rLsky(λ))/(πLp(λ)/Rp(λ)) (3)
In formula: RrsFor Reservoir water surface spectral reflectance；λ is wavelength；LwFor from water spoke brightness；R is the Fresnel reflection rate on extra large surface；
LskyFor the diffusing reflection spoke brightness of skylight；LpFor the spoke brightness of the standard hawk of diffusing reflection rate 20%；RpFor known reference plate
Reflectivity；
Wave band where analyzing curve of spectrum peak value again, the corresponding wave band of peak value are silt sensitive band, are selected corresponding with peak value
The wave band data of different sensors in Landsat establishes the system between sediment concentration and the corresponding Remote Sensing Reflectance of sensitive band
Regression Model is counted, the building of outstanding husky Inversion Calculation algorithm is carried out；Meanwhile choose with research area have the outstanding sand data of actual measurement it is synchronous or
The statistical regression mode of building is applied to have the outstanding husky data synchronization of actual measurement or quasi synchronous remote sensing by quasi synchronous remote sensing image
Image carries out precision test calculating, chooses the best statistical model of correlation highest, precision test and most preferably counts as quantitative remote sensing
Calculation mode.
7. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 6,
It is characterized in that, the field spectroradiometer uses the Field Spec Pro portable field spectroradiometer of ASD company.
8. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 6,
It is characterized in that, the wavelength band of the field spectroradiometer is 350-2500nm, acquire 10 days, measurement period 9:00-15:
00, every integral point measurement.
9. the long remote sensing calculation method for lasting variation of a kind of strong tidal height muddiness river mouth suspension bed sediment according to claim 6,
It is characterized in that, the step S4 specifically:
In ENVI software platform, quantitative remote sensing in step S3 is calculated mode and is applied to step S2 treated that Landsat-TM is distant
Feel in data, the quantitative remote sensing silt field of strong tidal height turbid water body under the identical hydrologic condition of different times is calculated, and pass through
River mouth point-line-surface, which combines, counts the long delta data lasted of each region suspension bed sediment.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893540A (en) * | 2010-07-09 | 2010-11-24 | 青岛海洋地质研究所 | Eight neighborhood algorithm-based method for estimating scale error of suspended sediment inverse model |
| CN102081033A (en) * | 2009-11-26 | 2011-06-01 | 上海海洋大学 | Suspended sand concentration calculating method based on coastal zone imager |
| CN104764716A (en) * | 2014-10-21 | 2015-07-08 | 青岛海洋地质研究所 | Inversion method and device for suspended sediment concentration in water body |
| CN105004846A (en) * | 2015-07-02 | 2015-10-28 | 南京信息工程大学 | Satellite remote sensing method used for monitoring turbidity of high-turbidity seawater |
| CN105300864A (en) * | 2015-12-07 | 2016-02-03 | 广州地理研究所 | Quantitative remote sensing method of suspended sediment |
| CN106126826A (en) * | 2016-06-27 | 2016-11-16 | 中国科学院南京地理与湖泊研究所 | A kind of muddy water body in lake concentration of suspension evaluation method based on VIIRS sensor |
-
2018
- 2018-10-26 CN CN201811257737.6A patent/CN109283144A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081033A (en) * | 2009-11-26 | 2011-06-01 | 上海海洋大学 | Suspended sand concentration calculating method based on coastal zone imager |
| CN101893540A (en) * | 2010-07-09 | 2010-11-24 | 青岛海洋地质研究所 | Eight neighborhood algorithm-based method for estimating scale error of suspended sediment inverse model |
| CN104764716A (en) * | 2014-10-21 | 2015-07-08 | 青岛海洋地质研究所 | Inversion method and device for suspended sediment concentration in water body |
| CN105004846A (en) * | 2015-07-02 | 2015-10-28 | 南京信息工程大学 | Satellite remote sensing method used for monitoring turbidity of high-turbidity seawater |
| CN105300864A (en) * | 2015-12-07 | 2016-02-03 | 广州地理研究所 | Quantitative remote sensing method of suspended sediment |
| CN106126826A (en) * | 2016-06-27 | 2016-11-16 | 中国科学院南京地理与湖泊研究所 | A kind of muddy water body in lake concentration of suspension evaluation method based on VIIRS sensor |
Non-Patent Citations (2)
| Title |
|---|
| 李婧等: "长江口水域悬沙含量时空变化卫星遥感定量研究方法探讨", 《海洋学报》 * |
| 陈燕: "渤海湾近岸海域悬浮泥沙浓度遥感反演模型研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110823190A (en) * | 2019-09-30 | 2020-02-21 | 广州地理研究所 | Island reef shallow sea water depth prediction method based on random forest |
| CN110851789A (en) * | 2019-09-30 | 2020-02-28 | 广州地理研究所 | Island reef shallow sea water depth prediction method based on extreme gradient lifting |
| CN110849334A (en) * | 2019-09-30 | 2020-02-28 | 广州地理研究所 | Island reef shallow sea water depth prediction method based on classification and regression tree |
| CN110823839A (en) * | 2019-11-25 | 2020-02-21 | 东南大学 | Asphalt pavement reflectivity testing device and method considering solar altitude angle difference |
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| CN116879237B (en) * | 2023-09-04 | 2023-12-12 | 自然资源部第二海洋研究所 | Atmospheric correction method for offshore turbid water body |
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