CN107014763A - Chlorophyll remote-sensing inversion device and method - Google Patents
Chlorophyll remote-sensing inversion device and method Download PDFInfo
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Abstract
The embodiments of the invention provide a kind of chlorophyll remote-sensing inversion device and method.The embodiment of the present invention forms improved Retrieving Chlorophyll-a Concentration model, has considered influence of the coastal waters complex optical characteristics to chlorophyll a on the basis of the sensitivity analysis of chlorophyll a spectral characteristic；HICO Hyperspectral imagings are pre-processed simultaneously, the image average wave spectrum in experiment sample area is obtained, the Retrieving Chlorophyll-a Concentration model of structure is applied into pretreated image progress chlorophyll a realizes inverting.The embodiment of the present invention greatly improves the precision of Retrieving Chlorophyll-a Concentration.
Description
Technical field
The present invention relates to Ocean Color Remote Sensing inverting field, more particularly to the chlorophyll remote sensing to offshore river mouth complex spectrum condition
Inverting device and method.
Background technology
Chlorophyll a is the important indicator for evaluating water nutrition state and barren degree, passes through the measure to chlorophyll-a concentration
Research may determine that the Eutrophic Extent of water body.But offshore sea waters Retrieving Chlorophyll-a Concentration is by water body optics complex characteristics
Influence, inversion accuracy is relatively low.
The content of the invention
In view of the foregoing, the embodiment of the present invention provides a kind of chlorophyll remote-sensing inversion device, anti-applied to water colour parameter
Drill equipment.The inverting device includes：
Remote sensing images acquisition module, the high-spectrum remote sensing for obtaining predeterminable area from satellite earth receiving station；
Water colour parameter acquisition module, Remote Sensing Reflectance and water colour parameter for obtaining the predeterminable area；
POP response simulation module, for carrying out the response simulation with reference to POP to the high-spectrum remote sensing；And
Inverse model builds module, is analyzed for the sensitive spectral characteristic to chlorophyll a and builds the anti-of chlorophyll a
Drill model；
POP acquisition module, for by being pre-processed to the high-spectrum remote sensing, obtaining the predeterminable area
Image average wave spectrum；
Mask process module, the mask for carrying out water body to the high-spectrum remote sensing using water body index algorithm is carried
Take；
Chlorophyll inversion module, the EO-1 hyperion for the inverse model of the chlorophyll a of structure to be applied to pre-process is distant
Feel image, determine the concentration value of each pixel chlorophyll a in target in hyperspectral remotely sensed image, obtain the inversion result of chlorophyll a；And
As a result output module, the inversion result for exporting the chlorophyll a.
The embodiment of the present invention also provides a kind of chlorophyll remote sensing inversion method, applied to water colour parametric inversion equipment, wherein,
Methods described includes：
The high-spectrum remote sensing of predeterminable area is obtained from satellite earth receiving station；
Obtain the Remote Sensing Reflectance and water colour parameter of the predeterminable area；
Response simulation with reference to POP is carried out to the high-spectrum remote sensing；And
The sensitive spectral characteristic of chlorophyll a is analyzed and the inverse model of chlorophyll a is built；
By being pre-processed to the high-spectrum remote sensing, the image average wave spectrum of the predeterminable area is obtained；
The mask that water body is carried out to the high-spectrum remote sensing using water body index algorithm is extracted；
The high-spectrum remote sensing that the inverse model of the chlorophyll a of structure is applied to pre-process, determines that EO-1 hyperion is distant
Feel the concentration value of each pixel chlorophyll a in image, obtain the inversion result of chlorophyll a；And
Export the inversion result of the chlorophyll a.
Preferably, the satellite ground that the water colour parametric inversion equipment is set respectively by network with multiple predeterminable areas connects
Station and the connection of Underway measurements equipment communication are received, the remote sensing images acquisition module is obtained default from satellite earth receiving station
Described in the high-spectrum remote sensing in region, and the remote sensing images acquisition module obtains institute by the Underway measurements equipment
State Remote Sensing Reflectance and water colour parameter.
Preferably, the above-mentioned response simulation carried out to the high-spectrum remote sensing with reference to POP, including：
The Remote Sensing Reflectance of above-mentioned acquisition is collected by HICO band sensors by convolution algorithm and realizes simulation, simulation makes
Calculation formula is as follows：
Wherein, λ is wavelength, and λ min are the start wavelength of passage, and λ max are the termination wavelength of passage, and r (λ) is correspondence λ ripples
Long Reflectivity for Growing Season, f (λ) is spectral response functions.
Preferably, the above-mentioned sensitive spectral characteristic to chlorophyll a is analyzed and builds the inverse model of chlorophyll a,
Step includes：
The reference wave spectrum of Hyperspectral imaging based on simulation, according to two wave band algorithms, calculate actual measurement chlorophyll-a concentration and
The root-mean-square error (RMSE) of the chlorophyll a of Rrs (λ 2)/Rrs (λ 1) estimations, by the minimum corresponding λ 1 of RMSE as optimal
Wavelength；
Wherein, λ 2 initial value is set to 600~700nm, and λ 1 selection range is between 600~800nm, and calculating obtains λ 1
For 673nm, λ 2 is 700nm, and optimal two band combination is obtained for [Rrs (700)/Rrs (673)], optimal for two wave band algorithms
Four band combinations are [Rrs (674)-1-Rrs(687)-1]*[Rrs(723)-1-Rrs(673)-1]-1。
Preferably, above-mentioned use water body index algorithm carries out the mask extraction of water body to the high-spectrum remote sensing,
Step includes：
The mask for realizing water body using following formula is extracted：
NDWI=(NIR-IR)/(IR+NIR)
Wherein, NIR is that the 62nd wave band, the IR of high-spectrum remote sensing are the 44th wave band, NDWI<- 0.05 image section
For water body.
Compared with prior art, chlorophyll remote-sensing inversion device and method provided in an embodiment of the present invention, passes through chlorophyll a
The sensitivity analysis of spectral characteristic, forms improved Retrieving Chlorophyll-a Concentration model, removes or weakens coastal waters complex optical characteristics
Influence to chlorophyll a, can effectively improve the inversion accuracy of chlorophyll a.
To enable the above objects, features and advantages of the present invention to become apparent, preferred embodiment cited below particularly, and coordinate
Appended accompanying drawing, is described in detail below.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be attached to what is used required in embodiment
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore is not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 is the block diagram for the water colour parametric inversion equipment that present pre-ferred embodiments are provided.
Fig. 2 is that the chlorophyll for being applied to the water colour parametric inversion equipment shown in Fig. 1 that present pre-ferred embodiments are provided is distant
Feel the flow chart of inversion method.
Fig. 3 is the signal that the water colour parametric inversion equipment shown in Fig. 1 is communicated by network with multiple satellite earth receiving stations
Figure.
Fig. 4 is signal of the water colour parametric inversion equipment shown in Fig. 1 by network and multiple Underway measurements equipment communications
Figure.
Fig. 5 is the schematic diagram of the chlorophyll remote sensing estimation model of the embodiment of the present invention.
Fig. 6 is example Determination of Chlorophyll remote-sensing inversion the result schematic diagram of the invention.
Main element symbol description
| Water colour parametric inversion equipment | 100 |
| Satellite earth receiving station | 200 |
| Underway measurements equipment | 300 |
| Chlorophyll remote-sensing inversion device | 10 |
| Input unit | 11 |
| Memory | 12 |
| Processor | 13 |
| Remote sensing images acquisition module | 101 |
| Water colour parameter acquisition module | 102 |
| POP response simulation module | 103 |
| Inverse model builds module | 104 |
| POP acquisition module | 105 |
| Mask process module | 106 |
| Chlorophyll inversion module | 107 |
| As a result output module | 108 |
Embodiment
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Generally exist
The component of the embodiment of the present invention described and illustrated in accompanying drawing can be arranged and designed with a variety of configurations herein.Cause
This, the detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit claimed invention below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, belongs to the scope of protection of the invention.
As shown in figure 1, be present pre-ferred embodiments provide based on water constituent spectrum nonlinear effect correction with
In the block diagram for the water colour parametric inversion equipment 100 that inverting is carried out to offshore river mouth water colour parameter.The water colour parameter is anti-
Drill equipment 100 may be, but not limited to, PC (personal computer, PC), tablet personal computer, server etc. possess
Data analysis and the computing device of disposal ability.
The water colour parametric inversion equipment 100 also includes a chlorophyll remote-sensing inversion device 10, input unit 11, memory
12 and processor 13.In present pre-ferred embodiments, chlorophyll remote-sensing inversion device 10 include at least one can with software or
The form of firmware (firmware) is stored in the memory 12 or is solidificated in the operation of the water colour parametric inversion equipment 100
Software function module in system (operating system, OS).The processor 13 is used to perform in the memory 12
The executable software module of storage, such as software function module and computer included by described chlorophyll remote-sensing inversion device 10
Program etc..In the present embodiment, the chlorophyll remote-sensing inversion device 10 can also be integrated in the operating system, as described
A part for operating system.Specifically, the chlorophyll remote-sensing inversion device 10 includes remote sensing images acquisition module 101, water colour
Parameter acquisition module 102, POP response simulation module 103, inverse model build module 104, POP acquisition module 105, mask
Processing module 106, Chlorophyll inversion module 107 and result output module 108.It should be noted that, in other embodiments,
A portion in the above-mentioned functions module that the chlorophyll remote-sensing inversion device 10 includes can also be omitted, or it can be with
Including other more functional modules.
Above-mentioned each functional module is described in detail below in conjunction with Fig. 2.
Referring to Fig. 2, being the water colour parametric inversion equipment 100 being applied to shown in Fig. 1 that present pre-ferred embodiments are provided
Water colour parameter inversion method flow chart.The idiographic flow and step shown in Fig. 2 will be described in detail below.
Step S01, the remote sensing images acquisition module 101 from satellite earth receiving station obtain predeterminable area (or " estimation
Area ") high-spectrum remote sensing.
In detail, as shown in figure 3, satellite earth receiving station 200 can be set in different estimation area, with from satellite reception not
With the high-spectrum remote sensing in estimation area.In this way, water colour parametric inversion equipment 100 can be connect by network with the satellite ground
Receive station and set up communication connection so that the remote sensing images acquisition module 101 can obtain the bloom from satellite earth receiving station 200
Spectrum remote-sensing image.In a kind of embodiment, the high-spectrum remote sensing can be directed to sea using what is set on satellite
The hyperspectral imager (Hyperspectral Imager for Coastal Ocean, HICO) of bank is obtained.Therefore, the height
Spectral remote sensing image is a kind of HICO images, can be described as HICO high-spectrum remote sensings.
Step S02, the water colour parameter acquisition module 102 obtains the Remote Sensing Reflectance and water colour ginseng of the predeterminable area
Number.The water colour parameter includes CDOM (chromophoric dissolved organic matter, Colored dissolved organic matter), hanged
Float, concentration of chlorophyll a etc..
In this implementation, the Remote Sensing Reflectance can be obtained by the field survey to each detection sampling point (predeterminable area), institute
Stating water colour parameter can be obtained by experimental assays.Underway measurements can be carried out in the waters of the test block of setting, take each sampling point
Remote Sensing Reflectance and synchronization water colour parameter, then joined by the input unit 11 of the water colour parametric inversion equipment 100
The mode that number (such as mouse, keyboard) is manually entered obtains the Remote Sensing Reflectance and water colour parameter.Wherein, it can be adopted by spectrum
Diversity method is measured more than the water surface obtains the Remote Sensing Reflectance.When measuring water spectral, in order to avoid shade and too
The influence that positive direct light shines, using following observation geometric angles.Observed azimuth is 135 ° or so and (sets the incident orientation of the sun
Angle is 0 °), view zenith angle θ is 40 ° or so.The data of measurement include：The brightness of on-gauge plate reflection spoke, the mark for blocking direct sunlight
Quasi- plate reflection spoke brightness, the brightness of water surface spoke, the brightness of skylight spoke and on-gauge plate reflection spoke brightness.While wave spectrum is measured, note
Record the gps coordinate of each measuring point.When experimental assays obtain water colour parameter, the water body sample gathered in each observation station can be filled
In brown bottle interior sealing freezen protective, laboratory measurement is sent to.The measure of chlorophyll a uses metric measurement, suspension
Using weighting method after dried, CDOM spectral absorptance uses spectrophotometry.
In addition, in the present embodiment, as shown in figure 4, also can be in the test block of setting or estimation area (predeterminable area), such as Xu
Three trial zones such as coral reef conservation (25 points), the mouth of the Zhujiang River (18 points), Hanjiang estuary (22 points) are heard to set respectively
Underway measurements equipment 300 is put, the Remote Sensing Reflectance is tested by Underway measurements equipment 300 and collection water sample chemical examination is obtained
The water colour parameter.Further, as shown in figure 4, the water colour parametric inversion equipment 100 can pass through network and the multiple reality
Test the Underway measurements equipment 200 that area sets respectively to communicate, and then obtain described automatically by the Underway measurements equipment 200
Remote Sensing Reflectance and water colour parameter.
Step S03,103 pairs of the POP response simulation module high-spectrum remote sensing carries out the response with reference to POP
Simulation.
In the present embodiment, in step S03, in order to keep ground wave spectrum consistent with remote sensing image spectral resolution
Property, and the inverse model based on ground structure is to the applicability of falling sensor, before model is corrected and is verified, using described
103 pairs of the POP response simulation module high-spectrum remote sensing carries out the response simulation with reference to POP.The mode of simulation can be with
The Remote Sensing Reflectance (EO-1 hyperion of fieldwork) of above-mentioned acquisition is collected into HICO band sensors by convolution algorithm to realize
Simulation.Specific formula for calculation is as follows：
In above formula, λ is wavelength, and λ min are the start wavelength of passage, and λ max are the termination wavelength of passage, and r (λ) is correspondence λ
The Reflectivity for Growing Season of wavelength, f (λ) is spectral response functions.
Step S04, the inverse model builds module 104 and the sensitive spectral characteristic of chlorophyll a is analyzed and built
The inverse model of chlorophyll a.
In detail, the reference wave spectrum of the HICO Hyperspectral imagings based on simulation, can calculate actual measurement according to two wave band algorithms
The root-mean-square error (RMSE) of chlorophyll-a concentration and the chlorophyll a of Rrs (λ 2)/Rrs (λ 1) estimations, by minimum RMSE correspondences
λ 1 as optimal wavelength.In one example, the initial value that λ 2 can be set is set to 600~700nm, and λ 1 selection range exists
Between 600~800nm.In this way, the λ 1 that an experiment is obtained is 673nm, λ 2 is 700nm, i.e., for its optimal ripple of two wave band algorithms
Section is combined as [Rrs (700)/Rrs (673)], and it is [Rrs (674) that same algorithm, which obtains optimal four band combination,-1-Rrs(687
)-1]*[Rrs(723)-1-Rrs(673)-1]-1, so realize, the analysis based on the sensitive spectral characteristic of chlorophyll a builds chlorophyll a
Inverse model.
Step S05, the POP acquisition module 105 obtains institute by being pre-processed to the high-spectrum remote sensing
State the image average wave spectrum of predeterminable area, i.e. water body Remote Sensing Reflectance Rrs.Wherein, the pretreatment includes conventional radiation, air
Correction and geometric manipulations etc..Wherein, Atmospheric models parameter is configured according to actual information：Air type is set to the middle latitude winter
Season, aerosol type is set to marine aerosol type, and due to lacking synchronous atmospheric information, experiment uses iterative algorithm, made
Obtain clear water region near infrared band is equal to 0, the optical thickness of iterative atmospheric aerosol from water radiance.
Step S06, the mask process module 106 enters water-filling using water body index algorithm to the high-spectrum remote sensing
The mask of body is extracted.In one example, above-mentioned mask extraction process process can be realized using following formula：
NDWI=(NIR-IR)/(IR+NIR) (2)
In formula (2), NIR is that the 62nd wave band, the IR of HICO high-spectrum remote sensings are the 44th wave band.By experimental analysis,
NDWI<- 0.05 is water body.
The inverse model of the chlorophyll a of structure is applied to pre-process by step S07, the Chlorophyll inversion module 107
High-spectrum remote sensing, determine the concentration value of each pixel chlorophyll a in target in hyperspectral remotely sensed image, obtain the anti-of chlorophyll a
Drill result.
Step S08, the result output module 108 exports the inversion result of the chlorophyll a.Specifically, this implementation
In example, the inversion result, Jin Erfang can be exported by the output device of the water colour parametric inversion equipment 100, such as display
Just related personnel observes.
To sum up, it can summarize that the invention mainly comprises the following aspects：
A. the image average wave spectrum for testing sample area (predeterminable area), i.e. water body remote sensing is obtained by accurate atmospheric correction to reflect
Rate Rrs.Atmospheric models parameter setting is very crucial, and air type is set to middle latitude winter in the present invention, and aerosol type is set
Marine aerosol type is set to, due to lacking synchronous atmospheric information, experiment can use iterative algorithm so that clear water region is closely red
Wave section is equal to 0 from water radiance, and iterative Determination of Aerosol Optical is so as to realizing atmospheric correction；
B. the sensitivity spectrum specificity analysis of chlorophyll-a concentration is surveyed, optimal two band combination can be obtained by iterating to calculate
It is [Rrs (674) for [Rrs (700)/Rrs (673)], and optimal four band combination-1-Rrs(687)-1]*[Rrs(723)-1-
Rrs(673)-1]-1, two optimal band combinations, four band combination algorithms and chlorophyll-a concentration are fitted, improved
Coastal waters chlorophyll inverse model, such as following formula
Chla=-8.654+18.6557x1+58.4024x2 (3)
In above formula, Chla is the concentration of chlorophyll a, x1For [Rrs (700)/Rrs (673)], x2For [Rrs (674)-1-Rrs
(687)-1]*[Rrs(723)-1-Rrs(673)-1]-1
The numerical results of the present invention show, by the sensitivity analysis of chlorophyll a spectral characteristic, are selected using iterative algorithm
Both are carried out coupling and form improved Retrieving Chlorophyll-a Concentration model by two optimal wave bands and the merging of four band groups, are considered
Influence of the coastal waters complex optical characteristics to chlorophyll a, greatly improves the inversion accuracy of chlorophyll a.
The embodiment of the present invention is illustrated below by way of actual tests case.
1. test place：
In an experimentation, the Xuwen Coral Reef nature reserve area, the mouth of the Zhujiang River, three experiments of Hanjiang estuary are have selected
Area, has synchronously carried out water body sampling and the measurement (totally 137 sampling points) of water surface reflectivity, wherein 85 sampling points are green for leaf
The structure of plain a inverse models, 52 sampling points are used for the evaluation (such as table 1 below) of model inversion precision.
Position, time and the measure the item of 1 2005 years~2013 field test sampled points of table
2. the acquisition of water body Remote Sensing Reflectance
Spectra collection method uses water surface above mensuration.When measuring water spectral, in order to avoid shade and the sun are straight
The influence of illumination is penetrated, using following observation geometric angles.Observed azimuth be 135 ° or so (set the incident azimuth of the sun as
0 °), view zenith angle θ is 40 ° or so.The data of measurement include：The brightness of on-gauge plate reflection spoke, the on-gauge plate for blocking direct sunlight
Reflect spoke brightness, the brightness of water surface spoke, the brightness of skylight spoke and on-gauge plate reflection spoke brightness.While wave spectrum is measured, record is each
The gps coordinate of measuring point.
3. the chemical examination of water quality parameter
Water body sample is mounted in brown bottle interior sealing freezen protective, is sent to laboratory measurement.The measure of chlorophyll a is used
Metric measurement, suspension uses weighting method after dried, and CDOM spectral absorptance uses spectrophotometry.
4.HICO images refer to the simulation of spectral response
In order to keep the uniformity of ground wave spectrum and remote sensing image spectral resolution, and the inverting mould built based on ground
Type to the applicability of falling sensor, model correct with verify before, by convolution algorithm by the high spectrum reflection of fieldwork
Rate collects HICO band sensors.
5. chlorophyll a sensitivity spectral characteristic analysis
Based on the sensitivity spectrum specificity analysis of actual measurement chlorophyll-a concentration, optimal two band combination is obtained by iterating to calculate
It is [Rrs (674) for [Rrs (700)/Rrs (673)], and optimal four band combination-1-Rrs(687)-1]*[Rrs(723)-1-
Rrs(673)-1]-1。
6. improved chlorophyll a remote sensing estimation model is built
Improved model is, by two wave bands and four band combinations, to reach and remove chromophoric dissolved organic matter (CDOM) influence
Purpose.By 137 chlorophyll-a concentration data by order arrangement from small to large, every 3 data pick-ups, 5 data, extract altogether
2/3 data are used to model.1/3 independent data is used to verify.Inverse model after obtained improvement is more suitable for complicated water
There is more convictive phase between the inverting work of the chlorophyll a in domain, actual measurement two groups of quantity of chlorophyll-a concentration and band index
Guan Xing.Numerical fitting (R2=0.8874) has more preferable applicability than other algorithms.Improved model is as shown in Figure 5.
The pretreatment of 7.HICO target in hyperspectral remotely sensed image
By being pre-processed to HICO high-spectrum remote sensings, including conventional radiation, atmospheric correction and geometric manipulations, obtain
The image average wave spectrum in sample area, i.e. water body Remote Sensing Reflectance must be tested.Wherein Atmospheric models parameter is set according to actual information
Put：Air type is set to middle latitude winter, and aerosol type is set to marine aerosol type, due to lacking synchronous air
Information, experiment use iterative algorithm so that clear water region near infrared band from water radiance be equal to 0, iterative air gas
The optical thickness of colloidal sol.The mask for carrying out water body using water body index algorithm is extracted, and passes through experimental analysis, NDWI<- 0.05 is water
Body.
8. inversion result is verified
The result is shown in Fig. 6.It can be seen that improve after model modeling data (N=85) the result RMSE=4.0035
μ g/L, MAE=2.9782 μ g/L, the result of its individual authentication data (N=52) is RMSE=4.3555 μ g/L, MAE=
3.5534μg/L.Improved model Retrieving Chlorophyll-a Concentration model has higher inversion accuracy.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.
Claims (10)
1. a kind of chlorophyll remote-sensing inversion device, applied to water colour parametric inversion equipment, it is characterised in that the chlorophyll remote sensing
Inverting device includes：
Remote sensing images acquisition module, the high-spectrum remote sensing for obtaining predeterminable area from satellite earth receiving station；
Water colour parameter acquisition module, Remote Sensing Reflectance and water colour parameter for obtaining the predeterminable area；
POP response simulation module, for carrying out the response simulation with reference to POP to the high-spectrum remote sensing；And
Inverse model builds module, is analyzed for the sensitive spectral characteristic to chlorophyll a and builds the inverting mould of chlorophyll a
Type；
POP acquisition module, for by being pre-processed to the high-spectrum remote sensing, obtaining the shadow of the predeterminable area
As average wave spectrum；
Mask process module, the mask for being carried out water body to the high-spectrum remote sensing using water body index algorithm is extracted；
Chlorophyll inversion module, for the high-spectrum remote-sensing figure for being applied to pre-process by the inverse model of the chlorophyll a of structure
Picture, determines the concentration value of each pixel chlorophyll a in target in hyperspectral remotely sensed image, obtains the inversion result of chlorophyll a；And
As a result output module, the inversion result for exporting the chlorophyll a.
2. chlorophyll remote-sensing inversion device as claimed in claim 1, it is characterised in that the water colour parametric inversion equipment passes through
The satellite earth receiving station and Underway measurements equipment communication that network is set respectively with multiple predeterminable areas are connected, and make described distant
Feel image collection module to obtain described in the high-spectrum remote sensing of predeterminable area from satellite earth receiving station, and the remote sensing figure
As acquisition module passes through the Underway measurements equipment acquisition Remote Sensing Reflectance and water colour parameter.
3. chlorophyll remote-sensing inversion device as claimed in claim 1, it is characterised in that the POP response simulation module is to institute
Stating the mode for the response simulation that high-spectrum remote sensing carries out reference POP includes：
The Remote Sensing Reflectance of above-mentioned acquisition is collected by HICO band sensors by convolution algorithm and realizes simulation, what simulation was used
Calculation formula is as follows：
Wherein, λ is wavelength, and λ min are the start wavelength of passage, and λ max are the termination wavelength of passage, and r (λ) is correspondence λ wavelength
Reflectivity for Growing Season, f (λ) is spectral response functions.
4. chlorophyll remote-sensing inversion device as claimed in claim 1, it is characterised in that the inverse model builds module and passed through
In the following manner is analyzed the sensitive spectral characteristic of chlorophyll a and builds the inverse model of chlorophyll a：
The reference wave spectrum of Hyperspectral imaging based on simulation, according to two wave band algorithms, calculates the chlorophyll-a concentration and Rrs of actual measurement
The root-mean-square error (RMSE) of the chlorophyll a of (λ 2)/Rrs (λ 1) estimations, by the minimum corresponding λ 1 of RMSE as optimal ripple
It is long；
Wherein, λ 2 initial value is set to 600~700nm, and λ 1 selection range is between 600~800nm, and calculating obtains λ 1 and is
673nm, λ 2 is 700nm, and optimal two band combination is obtained for [Rrs (700)/Rrs (673)], optimal four for two wave band algorithms
Band combination is [Rrs (674)-1-Rrs(687)-1]*[Rrs(723)-1-Rrs(673)-1]-1。
5. chlorophyll remote-sensing inversion device as claimed in claim 1, it is characterised in that the mask process module uses water body
The mode that the mask that exponentiation algorithm carries out water body to the high-spectrum remote sensing is extracted includes：
The mask for realizing water body using following formula is extracted：
NDWI=(NIR-IR)/(IR+NIR)
Wherein, NIR is that the 62nd wave band, the IR of high-spectrum remote sensing are the 44th wave band, NDWI<- 0.05 image section is water
Body.
6. a kind of chlorophyll remote sensing inversion method, applied to water colour parametric inversion equipment, it is characterised in that methods described includes：
The high-spectrum remote sensing of predeterminable area is obtained from satellite earth receiving station；
Obtain the Remote Sensing Reflectance and water colour parameter of the predeterminable area；
Response simulation with reference to POP is carried out to the high-spectrum remote sensing；And
The sensitive spectral characteristic of chlorophyll a is analyzed and the inverse model of chlorophyll a is built；
By being pre-processed to the high-spectrum remote sensing, the image average wave spectrum of the predeterminable area is obtained；
The mask that water body is carried out to the high-spectrum remote sensing using water body index algorithm is extracted；
The high-spectrum remote sensing that the inverse model of the chlorophyll a of structure is applied to pre-process, determines high-spectrum remote-sensing shadow
The concentration value of each pixel chlorophyll a, obtains the inversion result of chlorophyll a as in；And
Export the inversion result of the chlorophyll a.
7. chlorophyll remote sensing inversion method as claimed in claim 6, it is characterised in that the water colour parametric inversion equipment passes through
The satellite earth receiving station and Underway measurements equipment communication that network is set respectively with multiple predeterminable areas are connected, to pass through net
Network is obtained from satellite earth receiving station described in the high-spectrum remote sensing of predeterminable area, and passes through the Underway measurements equipment
Obtain the Remote Sensing Reflectance and water colour parameter.
8. chlorophyll remote sensing inversion method as claimed in claim 6, it is characterised in that carried out to the high-spectrum remote sensing
With reference to the response simulation of POP, the step of include：
The Remote Sensing Reflectance of above-mentioned acquisition is collected by HICO band sensors by convolution algorithm and realizes simulation, what simulation was used
Calculation formula is as follows：
Wherein, λ is wavelength, and λ min are the start wavelength of passage, and λ max are the termination wavelength of passage, and r (λ) is correspondence λ wavelength
Reflectivity for Growing Season, f (λ) is spectral response functions.
9. chlorophyll remote sensing inversion method as claimed in claim 6, it is characterised in that to the sensitive spectral characteristic of chlorophyll a
Analyzed and built the inverse model of chlorophyll a, the step of include：
The reference wave spectrum of Hyperspectral imaging based on simulation, according to two wave band algorithms, calculates the chlorophyll-a concentration and Rrs of actual measurement
The root-mean-square error (RMSE) of the chlorophyll a of (λ 2)/Rrs (λ 1) estimations, by the minimum corresponding λ 1 of RMSE as optimal ripple
It is long；
Wherein, λ 2 initial value is set to 600~700nm, and λ 1 selection range is between 600~800nm, and calculating obtains λ 1 and is
673nm, λ 2 is 700nm, and optimal two band combination is obtained for [Rrs (700)/Rrs (673)], optimal four for two wave band algorithms
Band combination is [Rrs (674)-1-Rrs(687)-1]*[Rrs(723)-1-Rrs(673)-1]-1。
10. chlorophyll remote sensing inversion method as claimed in claim 6, it is characterised in that using water body index algorithm to described
High-spectrum remote sensing carry out water body mask extract, the step of include：
The mask for realizing water body using following formula is extracted：
NDWI=(NIR-IR)/(IR+NIR)
Wherein, NIR is that the 62nd wave band, the IR of high-spectrum remote sensing are the 44th wave band, NDWI<- 0.05 image section is water
Body.
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|---|---|---|---|---|
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100111369A1 (en) * | 2008-09-26 | 2010-05-06 | Robert Lussier | Portable Intelligent Fluorescence and Transmittance Imaging Spectroscopy System |
| CN101852722A (en) * | 2010-05-20 | 2010-10-06 | 北京航空航天大学 | Method for evaluating remote sensing inversion accuracy of chlorophyll a in water body |
| US20110047867A1 (en) * | 2003-11-07 | 2011-03-03 | Holland Kyle H | Plant treatment based on a water invariant chlorophyll index |
| CN102313526A (en) * | 2010-07-07 | 2012-01-11 | 中国科学院地理科学与资源研究所 | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data |
| CN102508959A (en) * | 2011-10-31 | 2012-06-20 | 南京师范大学 | Four-band semi-analysis model for inverting chlorophyll a concentration in high-turbidity water body |
| 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 |
| CN104820224A (en) * | 2015-05-08 | 2015-08-05 | 中国科学院南京地理与湖泊研究所 | MODIS satellite high-precision monitoring method for chlorophyll-a in eutrophic lake water body |
| CN105987879A (en) * | 2016-04-12 | 2016-10-05 | 山东理工大学 | Coastal river mouth water color parameter inversion apparatus |
| CN106053370A (en) * | 2016-08-02 | 2016-10-26 | 山东省科学院海洋仪器仪表研究所 | Inversion method for offshore secchi disk depth based on HICO simulation |
-
2017
- 2017-04-07 CN CN201710223339.1A patent/CN107014763B/en not_active Expired - Fee Related
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110047867A1 (en) * | 2003-11-07 | 2011-03-03 | Holland Kyle H | Plant treatment based on a water invariant chlorophyll index |
| US20100111369A1 (en) * | 2008-09-26 | 2010-05-06 | Robert Lussier | Portable Intelligent Fluorescence and Transmittance Imaging Spectroscopy System |
| CN101852722A (en) * | 2010-05-20 | 2010-10-06 | 北京航空航天大学 | Method for evaluating remote sensing inversion accuracy of chlorophyll a in water body |
| CN102313526A (en) * | 2010-07-07 | 2012-01-11 | 中国科学院地理科学与资源研究所 | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data |
| CN102508959A (en) * | 2011-10-31 | 2012-06-20 | 南京师范大学 | Four-band semi-analysis model for inverting chlorophyll a concentration in high-turbidity water body |
| 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 |
| CN104820224A (en) * | 2015-05-08 | 2015-08-05 | 中国科学院南京地理与湖泊研究所 | MODIS satellite high-precision monitoring method for chlorophyll-a in eutrophic lake water body |
| CN105987879A (en) * | 2016-04-12 | 2016-10-05 | 山东理工大学 | Coastal river mouth water color parameter inversion apparatus |
| CN106053370A (en) * | 2016-08-02 | 2016-10-26 | 山东省科学院海洋仪器仪表研究所 | Inversion method for offshore secchi disk depth based on HICO simulation |
Non-Patent Citations (2)
| Title |
|---|
| 从丽侠: "闽江福州段水质遥感监测及其与土地利用联动分析", 《中国优秀硕士学位论文全文数据库 工程科技I辑》 * |
| 张方方: "基于软分类的太湖水体叶绿素a浓度遥感反演与长时间序列分析", 《中国博士学位论文全文数据库 工程科技I辑》 * |
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