CN107389895B - Soil moisture mixed type remote sensing inversion method and system - Google Patents
Soil moisture mixed type remote sensing inversion method and system Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of soil moisture mixed type remote sensing inversion method and system, which comprises the first remote sensing image data and the first meteorological data of earth's surface type area needed for obtaining estimation area；Wherein, the first remote sensing image data includes the first visible light wave range reflectivity, the first near infrared band reflectivity and the first short infrared wave band reflectivity, and the first meteorological data includes the first relative humidity and the first air themperature；According to the first remote sensing image data, first ratio vegetation index and the first surface water index in estimation area are calculated；According to the first ratio vegetation index, the first surface water index, the first meteorological data and soil moisture mixed type remote sensing estimation model, first soil moisture in estimation area is calculated.Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention and system, required input parameter is few, simple, flexible, easily operated, has wide applicable surface and application prospect.
Description
Technical field
The present embodiments relate to microwave remote sensing technique field more particularly to a kind of soil moisture mixed type remote-sensing inversion sides
Method and system.
Background technique
Soil moisture is moisture due to being drawn in the soil by gravity, hollow billet gravitation, hydrone gravitation, grogs surface molecular
The effect of the various power such as power, forms different types of moisture.Soil moisture is the most important characterization parameter of earth's surface arid information, is
The important evidence of agricultural production and water resources management.Remote sensing technology is capable of providing the multi-source multidimensional multidate information of earth's surface, for ground
Table Soil Moisture Inversion opens new approach.
Traditional soil moisture, which obtains, to be carried out with the data in observation point, wherein applying at most is earth boring auger
Take soil weighing and Neutron mensuration.Realize the soil moisture estimation of extensive area, remote sensing technology is feasible approach
One of.Satellite remote sensing evapotranspires estimation since the 1970s, for Soil Moisture Inversion, using remote sensing as therein defeated
Enter parameter, produced numerous soil moisture model and method, is such as based on thermal inertia model, vegetation index and surface temperature
The temperature vegetation drought index method and vertical drought index (Perpendicular Drought in triangle character space
Index, hereinafter referred to as PDI) etc..
In existing soil moisture remote sensing inversion method, it is thus necessary to determine that surface temperature triangle it is dry while, it is wet while and soil
Line parameter, still, these parameters be difficult accurately to determine, parametrization is difficult, reliability is low, vulnerable to interference, and then limits this
The a little operability of method in practical applications.
Summary of the invention
Aiming at the problems existing in the prior art, the embodiment of the present invention provides a kind of soil moisture mixed type remote-sensing inversion side
Method and system.
In a first aspect, the embodiment of the present invention provides a kind of soil moisture mixed type remote sensing inversion method, which comprises
The first remote sensing image data and the first meteorological data of earth's surface type area needed for obtaining estimation area；Wherein, described
First remote sensing image data includes the first visible light wave range reflectivity, the first near infrared band reflectivity and the first short-wave infrared wave
Section reflectivity, first meteorological data include the first relative humidity and the first air themperature；
According to first remote sensing image data, first ratio vegetation index and the first surface water in the estimation area are calculated
Separate index number；
According to first ratio vegetation index, the first surface water index, first meteorological data and soil
Moisture mixed type remote sensing estimation model calculates first soil moisture in the estimation area.
Second aspect, the embodiment of the present invention provide a kind of soil moisture mixed type remote-sensing inversion system, the system comprises:
First obtains module, the first remote sensing image data and the first gas for earth's surface type area needed for obtaining estimation area
Image data；Wherein, first remote sensing image data includes the first visible light wave range reflectivity, the first near infrared band reflectivity
With the first short infrared wave band reflectivity, first meteorological data includes the first relative humidity and the first air themperature；
First computing module, for according to first remote sensing image data, the first ratio for calculating the estimation area to be planted
By index and the first surface water index；
Second computing module, for according to first ratio vegetation index, the first surface water index, described the
One meteorological data and soil moisture mixed type remote sensing estimation model calculate first soil moisture in the estimation area.
The third aspect, the embodiment of the present invention provide a kind of soil moisture mixed type remote-sensing inversion equipment, and the equipment includes
Memory and processor, the processor and the memory complete mutual communication by bus；The memory storage
There is the program instruction that can be executed by the processor, the processor calls described program instruction to be able to carry out above-mentioned soil moisture
Mixed type remote sensing inversion method.
Fourth aspect, the embodiment of the present invention provide a kind of computer readable storage medium, are stored thereon with computer program,
The computer program realizes above-mentioned soil moisture mixed type remote sensing inversion method when being executed by processor.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention and system will survey meteorological data, reality
It surveys soil moisture and remote sensing image data combines, by calculating ratio vegetation index and earth's surface using remote sensing image data
Moisture index is to rely on the quantitative relationship between soil moisture and ratio vegetation index and surface water index, is absorbing
On the basis of experience Soil Moisture Inversion algorithm advantage, the soil moisture by surveying is introduced by returning the experience system being calculated
Number constructs soil moisture mixed type remote sensing estimation model, using the soil moisture mixed type remote sensing estimation model, calculates estimation
The soil moisture in area.Described method and system, existing specific physical basis, and have input parameter few, simple, flexible, easy
In operation the advantages that, therefore, have broader applicable surface and application prospect.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is soil moisture mixed type remote sensing inversion method flow chart provided in an embodiment of the present invention；
Fig. 2 is the structural schematic diagram of soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention；
Fig. 3 is the structural schematic diagram of soil moisture mixed type remote-sensing inversion equipment provided in an embodiment of the present invention；
Fig. 4 is the scatterplot that the soil moisture provided in an embodiment of the present invention surveyed by estimation soil moisture and ground is established
Figure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical solution in the embodiment of the present invention is explicitly described, it is clear that described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Fig. 1 is soil moisture mixed type remote sensing inversion method flow chart provided in an embodiment of the present invention, as shown in Figure 1, institute
The method of stating includes:
Step 11, the first remote sensing image data and the first meteorological data for obtaining earth's surface type area needed for estimating area；Its
In, first remote sensing image data includes that the first visible light wave range reflectivity, the first near infrared band reflectivity and first are short
Wave infrared band reflectivity, first meteorological data include the first relative humidity and the first air themperature；
Step 12, according to first remote sensing image data, calculate first ratio vegetation index and the in the estimation area
One surface water index；
Step 13, according to first ratio vegetation index, the first surface water index, first meteorological data
With soil moisture mixed type remote sensing estimation model, first soil moisture in the estimation area is calculated.
Specifically, from the remote sensing images in estimation area, the first remote sensing image data of earth's surface type area needed for obtaining, than
Such as, estimation area is located at Beijing, and required earth's surface type area is farmland region, then obtains from the remote sensing images in estimation area first
First remote sensing image data in farmland region is got, wherein first remote sensing image data is reflected including the first visible light wave range
Rate, the first near infrared band reflectivity and the first short infrared wave band reflectivity；From Professional Meteorological office or other meteorological observations
In device, get estimation area, region, farmland the first meteorological data, first meteorological data include the first relative humidity and
First air themperature.
Then, according to the first visible light wave range reflectivity and the first near infrared band reflectivity, is calculated
One ratio vegetation index, specific formula for calculation are as follows: RI=ρNIR/ρVIS, wherein ρVISFor the first visible light wave range reflectivity, ρNIR
For the first near infrared band reflectivity, RI is the first ratio vegetation index；According to the first near infrared band reflectivity and institute
The first short infrared wave band reflectivity is stated, calculates the first surface water index, specific formula for calculation are as follows: WI=ρNIR/ρSWIR,
Wherein, WI is the first surface water index, ρSWIRFor the first short infrared wave band reflectivity.
Finally, resulting first ratio vegetation index, the first surface water index and actual measurement will be calculated
First meteorological data, i.e. the first relative humidity and the first air themperature substitute into the meter of soil moisture mixed type remote sensing estimation model
It calculates in formula, the soil moisture in estimation area can be calculated.Wherein, the calculating of the soil moisture mixed type remote sensing estimation model
Formula, specifically: SM=a0+a1RH+a2Ta+a3RI+a4WI, wherein SM is Soil Moisture Retrieval, and RH is relative humidity, TaFor
Air themperature, a0、a1、a2、a3And a4, it is referred to as regression coefficient.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention passes through the remote sensing images number from estimation area
In, the remote sensing image data of earth's surface type area needed for obtaining calculates the ratio in estimation area using the remote sensing image data
Then value vegetation index and surface water index will calculate the resulting ratio vegetation index and the surface water index,
And the meteorological data of actual measurement, including relative humidity and air themperature, soil moisture mixed type remote sensing estimation model is substituted into, thus
Calculate the soil moisture in estimation area.The existing specific physical basis of this method, and have input parameter few, simple, flexible, easy
In operation the advantages that, therefore, have wide applicable surface and application prospect.
Optionally, on the basis of the above embodiments, the method also includes constructing soil moisture mixed type remote-sensing inversion
The step of model, specifically includes:
Obtain the second soil moisture, the second remote sensing image data and second meteorology in ground surface type region described in test block
Data；Wherein, second remote sensing image data include the second visible light wave range reflectivity, the second near infrared band reflectivity and
Second short infrared wave band reflectivity, second meteorological data include the second relative humidity and the second air themperature；
According to second remote sensing image data, the second ratio vegetation index and the second surface water of the test block are calculated
Separate index number；
According to second ratio vegetation index, the second surface water index, second meteorological data and described
Second soil moisture constructs soil moisture mixed type remote sensing estimation model.
Specifically, the specific establishment process of the soil moisture mixed type remote sensing estimation model referred in above-described embodiment are as follows:
Firstly, the second remote sensing image data of the earth surface area type is obtained, for example, test block from the remote sensing images of test block
Positioned at Beijing, the earth surface area type is that farmland region gets farmland region then from the remote sensing images of test block
Second remote sensing image data, first remote sensing image data include the second visible light wave range reflectivity, the second near infrared band
Reflectivity and the second short infrared wave band reflectivity；From Professional Meteorological office or other meteorological observation devices, experiment is got
Second meteorological data in area, region, farmland, second meteorological data includes the second relative humidity and the second air themperature, from reality
In the monitoring soil moisture device of border, the soil moisture in test block farmland region is got.
Then, according to the second visible light wave range reflectivity and the second near infrared band reflectivity, is calculated
Two ratio vegetation indexs, specific formula for calculation are as follows: RI=ρNIR/ρVIS, wherein ρVISFor the second visible light wave range reflectivity, ρNIR
For the second near infrared band reflectivity, RI is the second ratio vegetation index；According to the second near infrared band reflectivity and institute
The second short infrared wave band reflectivity is stated, calculates the second surface water index, specific formula for calculation are as follows: WI=ρNIR/ρSWIR,
Wherein, WI is the second surface water index, ρSWIRFor the second short infrared wave band reflectivity.
Finally, resulting second ratio vegetation index, the second surface water index will be calculated and pass through reality
Second relative humidity, second air themperature and the soil moisture of the test block obtained is surveyed, the soil water is substituted into
Divide the formula of mixed type remote sensing estimation model, i.e. SM=a0+a1RH+a2Ta+a3RI+a4In WI, is calculated, calculated by returning
State the regression coefficient a in formula0、a1、a2、a3And a4, to construct soil moisture mixed type remote sensing estimation model.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention passes through the remote sensing images number from test block
In, the remote sensing image data of earth's surface type area needed for obtaining calculates ratio vegetation and refers to using the remote sensing image data
Several and surface water index；The resulting ratio vegetation index and the surface water index will be calculated, and passes through actual measurement
Relative humidity, air themperature and the soil moisture of the test block of acquisition are calculated by returning, calculate soil moisture mixed type
Regression coefficient in remote sensing estimation model, to construct the mixed type remote-sensing inversion mould for calculating estimation area's soil moisture
Type.The existing specific physical basis of this method, and have many advantages, such as that input parameter is few, simple, flexible, easily operated, therefore, tool
There are wide applicable surface and application prospect.
In the following, with a specific embodiment, the soil moisture mixed type remote sensing of the present invention is described in detail embodiment offer
Inversion method.
Fig. 4 is the scatterplot that the soil moisture provided in an embodiment of the present invention surveyed by estimation soil moisture and ground is established
Figure.
Firstly, using supervised classification method, identifying the Farmland of test block from the MODIS remote sensing images of test block
Domain obtains near infrared band reflectivity, visible light wave range reflectivity and short from the MODIS remote sensing images in the farmland region
Wave infrared band reflectivity.
Then, the object spectrum curve for studying vegetation finds, vegetation is near infrared band l300~2500nm after study
Absorptivity increases, reflectivity decline, and three strong absorption paddy, near infrared band are formed at 1.45 μm, 1.95 μm and 2.6-2.7 μm
It is lower in visible light wave range reflectivity caused by the change of the absorptivity mainly variation of vegetation water content, in short-wave infrared wave
Section, i.e. first moisture absorption paddy, the change of reflectivity is mainly due to caused by vegetation moisture absorption.Based on this, ratio is constructed
It is worth vegetation index and surface water formula of index:
Wherein, RI is ratio vegetation index, and WI is surface water index, ρVISFor visible light wave range reflectivity, ρNIRIt is close
Infrared band reflectivity, ρSWIRFor short infrared wave band reflectivity.
The quantitative relationship between soil moisture and ratio vegetation index and surface water index is established, soil moisture is analyzed
Closely related ecological parameter various combination mode introduces empirical coefficient, establishes soil moisture mixed type remote sensing inversion method: SM
=a0+a1RH+a2Ta+a3RI+a4WI；
Wherein, SM is soil moisture, and RH is relative humidity, and Ta is air themperature, a0、a1、a2、a3And a4, it is referred to as returning
Coefficient.
Appropriate and soil is collected for the agrotype of different websites according to the existing true experimental data in test block
The closely related relative air humidity of moisture, air themperature, ratio vegetation index and surface water exponent data are counted by returning
It calculates, obtains the corresponding regression coefficient in the soil moisture mixed type remote sensing inversion method.
By actual measurement relative air humidity, air themperature, ratio vegetation index and the ground of collecting 3 websites of Haihe basin
Table moisture index data calculate by returning, obtain final equation are as follows:
SM=0.1641+0.0471RH+0.0026Ta+0.0035RI+0.0045WI.
Wherein, 3 actual measurement farmland flux of Chinese Haihe basin observe website, the detailed longitude and latitude of website and ground mulching
Information is shown in Table 1, and the data of collection specifically include that meteorological data, specially 2000-2008 daily relative air humidity and sky
Gas temperature data and the synchronous remote sensing image data of MODIS.
Three soil moisture observation websites of Haihe basin of 1. model calibration of table
The embodiment of the present invention is calculated into resulting soil moisture mixed type remote sensing inversion method, is applied to Hebei China Huailai
Farmland region.Huailai observation station is located at Huailai, Hebei county area, and longitude and latitude is 40.35 ° of N, 115.79 ° of E, mainly with summer corn
It is in two seasons of one year for chief crop.Experiment station's affiliated area has the North China Plain and the North China Plain to the double of Mongolian plat transition
Weight Ecological-geographical Characters.In 10 kilometer range of experiment station periphery, ground surface type is abundant, there is farmland, waters, mountainous region, grassland and wet
Ground beach.It is estimation area with Huailai observation station periphery, which is mainly the remote sensing images of 2008-2010 MODIS
Data.In order to verify the soil moisture measurement effect of model, the field inspection data of soil moisture and satellite synchronization are calculated
Carry out correlation analysis.
The soil moisture of the estimation soil moisture and ground actual measurement that are obtained according to the model inversion of the estimation each observation point in area
Scatter plot is established, result is as shown in figure 4, mean error is 0.066m3/m3, related coefficient square (R2) it is 0.28, mean square deviation
For 0.082m3/m3.The result shows that model estimate value and field observation data correlation with higher.By analyzing as it can be seen that building
Vertical soil moisture remote-sensing inversion new method is very effective to estimation soil moisture.
Optionally, on the basis of the various embodiments described above, first remote sensing image data is from MODIS or LandSat
Or got in No. five satellite remote sensing images of high score.
Specifically, in above-described embodiment, first remote sensing image data in the estimation area referred to is from the distant of the estimation area
It is got in sense image, the remote sensing images in the estimation area, which can be, to be acquired from MODIS remote sensing images, can also be with
It is to be got from LandSat satellite remote sensing images, is also possible to get from No. five satellite remote sensing images of high score.
Correspondingly, the second remote sensing image data of the test block being mentioned in above-described embodiment, is the remote sensing from test block
It is got in image, the remote sensing images of the test block are also can be from MODIS remote sensing images or LandSat satellite remote sensing
It is obtained in No. five satellite remote sensing images of image or high score.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention, by from MODIS remote sensing images or
In No. five satellite remote sensing images of LandSat satellite remote sensing images or high score, the remote sensing images number of estimation area or test block is obtained
According to so that the soil moisture mixed type remote sensing inversion method is more scientific.
Optionally, on the basis of the various embodiments described above, the first visible light wave range reflectivity is from the remote sensing figure
What the 1st wave band of picture was got；The first near infrared band reflectivity is got from the 2nd wave band of the remote sensing images
's；The first short infrared wave band reflectivity is obtained from the 6th wave band of the remote sensing images or the 5th wave band or the 7th wave band
It arrives.
Specifically, first remote sensing image data in the estimation area being mentioned in above-described embodiment, including the first visible light wave
Section reflectivity, the first near infrared band reflectivity and the first short infrared wave band reflectivity, wherein first visible light wave
Section reflectivity can be obtained from the 1st wave band of estimation area's remote sensing images；The first near infrared band reflectivity can be from estimating
It calculates and is obtained in the 2nd wave band of area's remote sensing images；The first short infrared wave band reflectivity can be from estimation area's remote sensing images
It obtains, can also be obtained from the 5th wave band of estimation area's remote sensing images in 6th wave band, it can also be from estimation area's remote sensing images
It is obtained in 7th wave band.
Correspondingly, the second remote sensing image data of the test block being mentioned in above-described embodiment, including the second visible light wave
Section reflectivity, the second near infrared band reflectivity and the second short infrared wave band reflectivity, wherein second visible light wave
Section reflectivity can be obtained from the 1st wave band of test block remote sensing images；The second near infrared band reflectivity can be from reality
It tests in the 2nd wave band of area's remote sensing images and obtains；The second short infrared wave band reflectivity can be from test block remote sensing images
It obtains, can also be obtained from the 5th wave band of test block remote sensing images in 6th wave band, it can also be from test block remote sensing images
It is obtained in 7th wave band.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention passes through the 1st wave band from remote sensing images
Middle acquisition visible light wave range reflectivity obtains near infrared band reflective rate, from remote sensing from the 2nd wave band of remote sensing images
Visible light wave range reflectivity is obtained in the 6th wave band or the 5th wave band or the 7th wave band of image, so that the soil moisture mixed type
Remote sensing inversion method is more scientific.
Optionally, on the basis of the various embodiments described above, the ground surface type region includes but is not limited to farmland region.
Specifically, in above-described embodiment, the earth surface area type being mentioned to can be farmland region, be also possible to meadow
Region, short vegetation area or other ground surface types region, the embodiment of the present invention are not defined earth's surface type area.
Soil moisture mixed type remote sensing inversion method provided in an embodiment of the present invention passes through the ground surface type region that will be applicable in
From farmland region, other ground surface type regions such as meadow, short vegetation area are generalized to, so that the soil moisture mixed type is distant
Feel inversion method, there is more wide applicable surface and more wide application prospect.
Fig. 2 is the structural schematic diagram of soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, such as Fig. 2 institute
Show, the system comprises: first obtains module 21, the first computing module 22 and the second computing module 23, in which:
The first remote sensing image data and first of earth's surface type area needed for first acquisition module 21 is used to obtain estimation area
Meteorological data；Wherein, first remote sensing image data includes the first visible light wave range reflectivity, the reflection of the first near infrared band
Rate and the first short infrared wave band reflectivity, first meteorological data include the first relative humidity and the first air themperature；The
One computing module 22 is used to calculate according to first remote sensing image data first ratio vegetation index and the in the estimation area
One surface water index；Second computing module 23 according to first ratio vegetation index, first surface water for referring to
Several, described first meteorological data and soil moisture mixed type remote sensing estimation model calculate first soil moisture in the estimation area.
Specifically, first module 21 is obtained from the remote sensing images in estimation area, the first of earth's surface type area needed for obtaining
Remote sensing image data, for example, estimation area is located at Beijing, required earth's surface type area is farmland region, then first obtains module
21 from the remote sensing images in estimation area, get first remote sensing image data in farmland region, wherein first remote sensing images
Data include the first visible light wave range reflectivity, the first near infrared band reflectivity and the first short infrared wave band reflectivity；From
In Professional Meteorological office or other meteorological observation devices, get estimation area, region, farmland the first meteorological data, described first
Meteorological data includes the first relative humidity and the first air themperature.
First computing module 22 according to the first visible light wave range reflectivity and the first near infrared band reflectivity,
Calculate the first ratio vegetation index, specific formula for calculation are as follows: RI=ρNIR/ρVIS, wherein ρVISIt is anti-for the first visible light wave range
Penetrate rate, ρNIRFor the first near infrared band reflectivity, RI is the first ratio vegetation index, anti-according to first near infrared band
Rate and the first short infrared wave band reflectivity are penetrated, the first surface water index, specific formula for calculation are as follows: WI=are calculated
ρNIR/ρSWIR, wherein WI is the first surface water index, ρSWIRFor the first short infrared wave band reflectivity.
Second computing module 23 will calculate resulting first ratio vegetation index, the first surface water index with
And the first meteorological data of actual measurement, i.e. the first relative humidity and the first air themperature, substitute into soil moisture mixed type remote-sensing inversion
In the calculation formula of model, the soil moisture in estimation area can be calculated.Wherein, the soil moisture mixed type remote-sensing inversion mould
The calculation formula of type, specifically: SM=a0+a1RH+a2Ta+a3RI+a4WI, wherein SM is soil moisture, and RH is relative humidity,
TaFor air themperature, a0、a1、a2、a3And a4, it is referred to as regression coefficient.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, function is referring in particular to above method reality
Example is applied, details are not described herein again.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention passes through the remote sensing images number from estimation area
In, the remote sensing image data of earth's surface type area needed for obtaining calculates ratio vegetation and refers to using the remote sensing image data
Several and surface water index will calculate the resulting ratio vegetation index and the surface water index, and the gas of actual measurement
Image data, including relative humidity and air themperature substitute into soil moisture mixed type remote sensing estimation model, to calculate estimation area
Soil moisture.The existing specific physical basis of system, but it is few, simple, flexible, easily operated etc. excellent with input parameter
Therefore point has wide applicable surface and application prospect.
Optionally, on the basis of the above embodiments, the system comprises: first obtains module, the first computing module, the
Two computing modules, second obtain module, third computing module and the 4th computing module, in which:
Second acquisition module is used to obtain the second soil moisture, the second remote sensing figure of ground surface type region described in test block
As data and the second meteorological data；Wherein, second remote sensing image data include the second visible light wave range reflectivity, it is second close
Infrared band reflectivity and the second short infrared wave band reflectivity, second meteorological data include the second relative humidity and second
Air themperature；The second ratio that third computing module is used to calculate the test block according to second remote sensing image data is planted
By index and the second surface water index；4th computing module is used for according to second ratio vegetation index, second ground
Table moisture index, second meteorological data and second soil moisture, construct the soil moisture mixed type remote-sensing inversion
Model.
Specifically, soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, including the first acquisition module,
First computing module, the second computing module, second obtain module, third computing module and the 4th computing module, wherein described
First obtains module, first computing module and second computing module referring in particular to above-described embodiment, no longer goes to live in the household of one's in-laws on getting married herein
It states.
The second acquisition module from the remote sensing images of test block, can obtain the second distant of the earth surface area type
Feel image data, for example, test block is located at Beijing, the earth surface area type is farmland region, then described second obtains mould
Block from the remote sensing images of test block, can get second remote sensing image data in farmland region, wherein second remote sensing
Image data includes the second visible light wave range reflectivity, the second near infrared band reflectivity and the reflection of the second short infrared wave band
Rate；The second acquisition module can get test block Farmland from Professional Meteorological office or other meteorological observation devices
Second meteorological data in domain, second meteorological data include the second relative humidity and the second air themperature, can also be from reality
Monitoring soil moisture device in, get the practical soil moisture in test block farmland region.
The third computing module can be according to the second visible light wave range reflectivity and second near infrared band
Reflectivity calculates the second ratio vegetation index, specific formula for calculation are as follows: RI=ρNIR/ρVIS, wherein ρVISFor the second visible light
Wave band reflectivity, ρNIRFor the second near infrared band reflectivity, RI is the second ratio vegetation index；According to second near-infrared
Wave band reflectivity and the second short infrared wave band reflectivity, calculate the second surface water index, specific formula for calculation are as follows:
WI=ρNIR/ρSWIR, wherein WI is the second surface water index, ρSWIRFor the second short infrared wave band reflectivity.
4th computing module can will calculate resulting second ratio vegetation index, second surface water
Index, second relative humidity, second air themperature and the soil moisture of the test block, substitute into the soil water
Divide in the calculation formula of mixed type remote sensing estimation model, i.e. substitution SM=a0+a1RH+a2Ta+a3RI+a4In WI, counted by returning
It calculates, calculates the regression coefficient in formula, a0、a1、a2、a3And a4, construct soil moisture mixed type remote sensing estimation model.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, passes through the remote sensing images from test block
In, the remote sensing image data of earth's surface type area needed for obtaining calculates ratio vegetation index using the remote sensing image data
With surface water index, the resulting ratio vegetation index and the surface water index will be calculated, and will be obtained by actual measurement
Relative humidity, air themperature and the soil moisture of the test block obtained are calculated by returning, it is distant to calculate soil moisture mixed type
Feel the regression coefficient in inverse model, constructs the mixed type remote sensing estimation model of the soil moisture for calculating estimation area.Institute
System is stated, not only there is specific physical basis, but also has many advantages, such as that input parameter is few, simple, flexible, easily operated, therefore, tool
There are wide applicable surface and application prospect.
Optionally, on the basis of the various embodiments described above, the first acquisition module is specifically used for: from MODIS or
First remote sensing image data is obtained in No. five satellite remote sensing images of LandSat or high score.
Specifically, refer in above-described embodiment first obtains module, can be got from the remote sensing images in estimation area
First remote sensing image data, wherein the MODIS that the remote sensing images in the estimation area can be the first acquisition module from estimation area is distant
It is acquired in sense image, is also possible to get from LandSat satellite remote sensing images, be also possible to from high score five
It is got in satellite remote sensing images.
Correspondingly, be mentioned in above-described embodiment second obtains module, can be obtained from the remote sensing images of test block
Second remote sensing image data, wherein the remote sensing images of the test block can be the second acquisition module from the test block
Acquired in MODIS remote sensing images, be also possible to get from LandSat satellite remote sensing images, be also possible to from
It is got in No. five satellite remote sensing images of high score.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, by from MODIS remote sensing images or
In No. five satellite remote sensing images of LandSat satellite remote sensing images or high score, the remote sensing images number of estimation area or test block is obtained
According to so that the system is more scientific.
Optionally, on the basis of the various embodiments described above, the first acquisition module is specifically used for:
From the 1st wave band of the remote sensing images, the first visible light wave range reflectivity is obtained；From the remote sensing images
2nd wave band obtains the first near infrared band reflectivity；From the 6th wave band of the remote sensing images or the 5th wave band or the 7th wave
Section obtains the first short infrared wave band reflectivity.
Specifically, it is mentioned to the first acquisition module in above-described embodiment, can be got from the remote sensing images in estimation area
First remote sensing images of the first remote sensing image data, the estimation area include the first visible light wave range reflectivity, the first near-infrared
Wave band reflectivity and the first short infrared wave band reflectivity, wherein the first visible light wave range reflectivity can be by first
Module is obtained to obtain from the 1st wave band of estimation area's remote sensing images；The first near infrared band reflectivity can be obtained by first
Modulus block is obtained from the 2nd wave band of estimation area's remote sensing images；The first short infrared wave band reflectivity can be obtained by first
Modulus block is obtained from the 6th wave band of estimation area's remote sensing images, can also be obtained from the 5th wave band of estimation area's remote sensing images,
It can also be obtained from the 7th wave band of estimation area's remote sensing images.
Correspondingly, the second acquisition module is mentioned in the above-described embodiment being mentioned in above-described embodiment, it can be from experiment
The second remote sensing image data is got in the remote sensing images in area, the second remote sensing images number is reflected including the second visible light wave range
Rate, the second near infrared band reflectivity and the second short infrared wave band reflectivity, wherein the second visible light wave range reflection
Rate can be obtained from the 1st wave band of test block remote sensing images by the second acquisition module；The second near infrared band reflectivity
It can be obtained from the 2nd wave band of test block remote sensing images by the second acquisition module；The second short infrared wave band reflectivity
It can be obtained from the 6th wave band of test block remote sensing images by the second acquisition module, it can also be from the 5th of test block remote sensing images the
It obtains, can also be obtained from the 7th wave band of estimation area's remote sensing images in wave band.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, passes through the 1st wave band from remote sensing images
Middle acquisition visible light wave range reflectivity obtains near infrared band reflective rate, from remote sensing from the 2nd wave band of remote sensing images
Visible light wave range reflectivity is obtained in the 6th wave band or the 5th wave band or the 7th wave band of image, so that the system is more scientific.
Optionally, on the basis of the various embodiments described above, the first acquisition module is specifically used for: obtaining estimation area Zhong Bao
Include but be not limited to first remote sensing image data and the first meteorological data in farmland region.
Specifically, refer in above-described embodiment first obtains module, required earth surface area class in available estimation area
First remote sensing image data of type, wherein the earth surface area can be farmland region, be also possible to meadow region, short plant
By region or other ground surface types region.
Soil moisture mixed type remote-sensing inversion system provided in an embodiment of the present invention, passes through the ground surface type region that will be applicable in
From farmland region, other ground surface type regions such as meadow, short vegetation area are generalized to, so that the soil moisture mixed type is distant
The applicable surface for feeling Inversion System is broader, and application prospect is more wide.
Fig. 3 is the structural schematic diagram of soil moisture mixed type remote-sensing inversion equipment provided in an embodiment of the present invention, such as Fig. 3 institute
Show, the soil moisture mixed type remote-sensing inversion equipment, comprising: processor (processor) 31,32 He of memory (memory)
Bus 33, in which:
The processor 31 and the memory 32 complete mutual communication by the bus 33；The processor 31
For calling the program instruction in the memory 32, to execute method provided by above-mentioned each method embodiment, for example,
The first remote sensing image data and the first meteorological data of earth's surface type area needed for obtaining estimation area；Wherein, first remote sensing
Image data includes the first visible light wave range reflectivity, the first near infrared band reflectivity and the reflection of the first short infrared wave band
Rate, first meteorological data include the first relative humidity and the first air themperature；According to first remote sensing image data, meter
Calculate first ratio vegetation index and the first surface water index in the estimation area；According to first ratio vegetation index, institute
The first surface water index, first meteorological data and soil moisture mixed type remote sensing estimation model are stated, the estimation is calculated
First soil moisture in area.
The embodiment of the present invention discloses a kind of computer program product, and the computer program product is non-transient including being stored in
Computer program on computer readable storage medium, the computer program include program instruction, when described program instructs quilt
When computer executes, computer is able to carry out method provided by above-mentioned each method embodiment, for example, obtains estimation institute, area
Need first remote sensing image data and the first meteorological data in ground surface type region；Wherein, first remote sensing image data includes
First visible light wave range reflectivity, the first near infrared band reflectivity and the first short infrared wave band reflectivity, first gas
Image data includes the first relative humidity and the first air themperature；According to first remote sensing image data, the estimation area is calculated
The first ratio vegetation index and the first surface water index；According to first ratio vegetation index, first surface water
Separate index number, first meteorological data and soil moisture mixed type remote sensing estimation model calculate first soil in the estimation area
Moisture.
The embodiment of the present invention provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage
Medium storing computer instruction, the computer instruction make the computer execute side provided by above-mentioned each method embodiment
Method, for example, the first remote sensing image data and the first meteorological data of earth's surface type area needed for obtaining estimation area；Wherein,
First remote sensing image data includes that the first visible light wave range reflectivity, the first near infrared band reflectivity and the first shortwave are red
Wave section reflectivity, first meteorological data include the first relative humidity and the first air themperature；According to first remote sensing
Image data calculates first ratio vegetation index and the first surface water index in the estimation area；According to first ratio
Vegetation index, the first surface water index, first meteorological data and soil moisture mixed type remote sensing estimation model, meter
Calculate first soil moisture in the estimation area.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in a computer readable storage medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: ROM, RAM, magnetic disk or light
The various media that can store program code such as disk.
The embodiments such as soil moisture mixed type remote-sensing inversion equipment described above are only schematical, wherein described
Unit may or may not be physically separated as illustrated by the separation member, and component shown as a unit can be with
It is or may not be physical unit, it can it is in one place, or may be distributed over multiple network units.It can
It is achieved the purpose of the solution of this embodiment with selecting some or all of the modules therein according to the actual needs.This field is common
Technical staff is without paying creative labor, it can understands and implements.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above various embodiments is only to illustrate the technical solution of the embodiment of the present invention, rather than it is right
It is limited；Although the embodiment of the present invention is described in detail referring to foregoing embodiments, the ordinary skill of this field
Personnel are it is understood that it is still possible to modify the technical solutions described in the foregoing embodiments, or to part
Or all technical features are equivalently replaced；And these are modified or replaceed, it does not separate the essence of the corresponding technical solution
The range of each embodiment technical solution of the embodiment of the present invention.
Claims (8)
1. a kind of soil moisture mixed type remote sensing inversion method characterized by comprising
The first remote sensing image data and the first meteorological data of earth's surface type area needed for obtaining estimation area；Wherein, described first
Remote sensing image data includes that the first visible light wave range reflectivity, the first near infrared band reflectivity and the first short infrared wave band are anti-
Rate is penetrated, first meteorological data includes the first relative humidity and the first air themperature；
According to first remote sensing image data, the first ratio vegetation index and the first surface water for calculating the estimation area refer to
Number；
According to first ratio vegetation index, the first surface water index, first meteorological data and soil moisture
Mixed type remote sensing estimation model calculates first soil moisture in the estimation area；
The step of the method also includes building soil moisture mixed type remote sensing estimation models, specifically includes:
Obtain the second soil moisture, the second remote sensing image data and the second meteorological number in ground surface type region described in test block
According to；Wherein, second remote sensing image data includes the second visible light wave range reflectivity, the second near infrared band reflectivity and the
Two short infrared wave band reflectivity, second meteorological data include the second relative humidity and the second air themperature；
According to second remote sensing image data, the second ratio vegetation index and the second surface water for calculating the test block refer to
Number；
According to second ratio vegetation index, the second surface water index, second meteorological data and described second
Soil moisture constructs soil moisture mixed type remote sensing estimation model.
2. the method according to claim 1, wherein first remote sensing image data be from MODIS or
It is got in No. five satellite remote sensing images of LandSat or high score.
3. according to the method described in claim 2, it is characterized in that, the first visible light wave range reflectivity is from the remote sensing
What the 1st wave band of image was got；The first near infrared band reflectivity is got from the 2nd wave band of the remote sensing images
's；The first short infrared wave band reflectivity is obtained from the 6th wave band of the remote sensing images or the 5th wave band or the 7th wave band
It arrives.
4. method according to any one of claim 1-3, which is characterized in that the ground surface type region includes Farmland
Domain.
5. a kind of soil moisture mixed type remote-sensing inversion system characterized by comprising
First obtains module, the first remote sensing image data and the first meteorological number for earth's surface type area needed for obtaining estimation area
According to；Wherein, first remote sensing image data includes the first visible light wave range reflectivity, the first near infrared band reflectivity and the
One short infrared wave band reflectivity, first meteorological data include the first relative humidity and the first air themperature；
First computing module, for according to first remote sensing image data, the first ratio vegetation for calculating the estimation area to refer to
Several and the first surface water index；
Second computing module, for according to first ratio vegetation index, the first surface water index, first gas
Image data and soil moisture mixed type remote sensing estimation model calculate first soil moisture in the estimation area；
The system also includes:
Second obtains module, for obtaining the second soil moisture, the second remote sensing images of ground surface type region described in test block
Data and the second meteorological data；Wherein, second remote sensing image data include the second visible light wave range reflectivity, it is second close red
Wave section reflectivity and the second short infrared wave band reflectivity, second meteorological data include that the second relative humidity and second are empty
Temperature degree；
Third computing module, for according to second remote sensing image data, the second ratio vegetation for calculating the test block to refer to
Several and the second surface water index；
4th computing module, for according to second ratio vegetation index, the second surface water index, second gas
Image data and second soil moisture, construct the soil moisture mixed type remote sensing estimation model.
6. system according to claim 5, which is characterized in that the first acquisition module is specifically used for: from MODIS or
First remote sensing image data is obtained in No. five satellite remote sensing images of LandSat or high score.
7. a kind of soil moisture mixed type remote-sensing inversion equipment, which is characterized in that including memory and processor, the processor
Mutual communication is completed by bus with the memory；The memory is stored with the program that can be executed by the processor
Instruction, the processor call described program instruction to be able to carry out the method as described in Claims 1-4 is any.
8. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program is located
Manage the method realized as described in Claims 1-4 is any when device executes.
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| CN108717044B (en) * | 2018-05-24 | 2021-07-30 | 青海师范大学 | Surface soil water content satellite remote sensing estimation method for removing vegetation coverage influence |
| CN108956538B (en) * | 2018-06-28 | 2021-03-09 | 中国石油天然气股份有限公司 | Remote sensing detection method and device for river oil spilling |
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| CN111860325B (en) * | 2020-07-20 | 2023-09-15 | 河南大学 | Soil moisture inversion method, soil moisture inversion device, computer readable medium and electronic equipment |
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