CN114778476A - Alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing - Google Patents
Alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing Download PDFInfo
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Abstract
The invention belongs to the technical field of data analysis, and particularly relates to an unmanned aerial vehicle remote sensing-based alfalfa cotton field soil water content monitoring model. Therefore, the growth state and the water shortage condition of the vegetation in the cotton field can be rapidly mastered, scientific guidance is provided for scientific water utilization and accurate irrigation, and the method has important significance for water conservation and stable yield of cotton planting.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing.
Background
The method has the advantages that the traditional cultivated land soil water content is mostly realized through the traditional soil water content monitoring water section, the method can only acquire the soil water content information of a limited area, the cost is high, time and labor are wasted, and the data is only effective to the sampling time; accurate soil water content information cannot be acquired in real time, and agricultural production management is not facilitated.
With the development of satellite technology, inversion monitoring of soil water content by using visible light, thermal infrared and radar satellites is continuously promoted, more basic research and application achievements are obtained, but the influences of incapability of penetrating cloud layers through optical images, limited ground resolution of thermal infrared data, long reentry period, complex radar satellite monitoring technology and the like are limited, and technical guidance of agricultural production is limited.
In recent years, with the rapid breakthrough of the internet of things technology, the monitoring index and performance of the internet of things sensor are greatly improved, the internet of things sensor or satellite image data is adopted in partial areas for field water content acquisition, information such as farmland soil water content and temperature can be continuously acquired, but the use cost of the sensor is still too high, the damage rate is high, the sensor is not beneficial to mechanized operation after being arranged, the arrangement space is limited, and the like, so that the use and popularization of the sensor are limited.
Disclosure of Invention
The invention discloses an unmanned aerial vehicle remote sensing-based alfalfa cotton field soil water content monitoring model, which aims to solve the technical problems that the existing monitoring mode in the background technology is high in cost and damage rate, is not beneficial to mechanized operation after being laid and is limited in laying space.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing includes following steps:
step 1: acquiring unmanned aerial vehicle remote sensing data and ground survey data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground survey data to obtain preprocessed image data;
step 2: separating vegetation and bare soil boundaries in a farmland range based on the image data obtained in the step 1 to obtain an alfalfa cotton field farmland range and image data;
and step 3: carrying out vegetation index calculation and vegetation coverage calculation based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
and 4, step 4: calculating a water stress index, a canopy-bare soil temperature difference and a vegetation coverage index based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
and 5: establishing an alfalfa cotton field soil water content monitoring model based on the calculation results in the step 3 and the step 4;
and 6: and verifying the alfalfa cotton field soil water content monitoring model based on ground survey data, and selecting a model meeting the conditions.
The invention processes the remote sensing data through the remote sensing data of the unmanned aerial vehicle, calculates the vegetation index, the vegetation coverage, the water stress index, the layer-bare soil temperature difference and the vegetation coverage index based on the processed data, establishes the alfalfa cotton field soil water content monitoring model based on the calculation result, verifies the precision of the alfalfa cotton field soil water content monitoring model through the data actually measured on the ground, selects the optimal alfalfa cotton field soil water content monitoring model, thereby realizing the establishment of the alfalfa cotton field soil water content monitoring model, subsequently only needs to preprocess the remote sensing data actually monitored by the unmanned aerial vehicle, then puts the remote sensing data into the alfalfa cotton field soil water content monitoring model for calculation to obtain the water content, thereby realizing the rapid grasp of the growth state and the water shortage condition of the cotton field vegetation and further providing scientific guidance for scientific water and accurate irrigation, has important significance for water saving and stable yield of cotton planting. And this application is based on unmanned aerial vehicle remote sensing technology data collection, calculates and obtains the cotton field water content based on alfalfa cotton field soil water content monitoring model to need not to install monitoring facilities at the planting ground, thereby effectual solved among the prior art technical problem, this application can not hinder mechanized operation promptly, can not receive the space restriction, and because ground need not install monitoring facilities, thereby can not cause the damage of device because of artificial factor.
Preferably, the processing of the remote sensing data of the unmanned aerial vehicle comprises:
processing the remote sensing data of the unmanned aerial vehicle to obtain an original multispectral image, an original visible light image and an original thermal infrared image; cutting the original multispectral image, the original RGB visible light image and the original thermal infrared image to obtain the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel;
respectively registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel by using a known high-precision reference image of the land parcel to obtain the registered multispectral image, RGB visible light image and thermal infrared image;
based on the RGB visible light images after registration, generating a panchromatic image grayscale by adopting a panchromatic image synthesis formula:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
in the formula: rredThe red waveband data in the RGB visible light image after registration; r isgreenGreen wave band data in the RGB visible light image after registration; rblueThe blue waveband data in the RGB visible light image after registration;
and fusing the registered excessive spectral images and the panchromatic image gradycale to obtain a fused multispectral image.
The invention ensures the precision of subsequent processing by preprocessing the data.
Preferably, the step 2 comprises the following steps:
step 2.1: distinguishing soil properties by using the registered RGB visible light images, segmenting different land properties, and extracting a cultivated land range;
step 2.2: based on the RGB visible light images after registration, preliminarily obtaining a classified image of a vegetation growing area and bare soil by adopting a gray segmentation method;
step 2.3: determining a green-blue vegetation index GBRI and a simple vegetation index SRI based on the following formulas:
in the formula: r isGreenGreen wave band data in the multispectral image; r isBlueBlue waveband data in the multispectral image;
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; r isredRed wave band data in the multispectral image;
the numerical range of the vegetation index is [ -1,1], and a negative value indicates that the ground is covered by cloud, water, snow and the like and has high reflection to visible light; 0 represents rock or bare earth, etc., and NIR and R are approximately equal; positive values indicate vegetation coverage and increase with increasing coverage. Because the growing periods of the alfalfa and the cotton are different, the ground coverage degree is also different, and therefore the planting ranges of the cotton and the alfalfa in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (4) superposing the results output in the step (2.2) and the step (2.3), confirming to obtain the final distribution area vector files of the alfalfa, the cotton and the bare soil, and completing the drawing output result.
The invention fully considers the condition of the water content of the bare soil, realizes the monitoring of the water content of the farmland soil, and not only can reflect the water content of the soil in a vegetation area, but also can fully reflect the water content of the soil in the bare soil area.
Preferably, the step 3 comprises the following steps:
step 3.1: step 3.1: calculating a vegetation index map NDVI in the multispectral image by adopting the following formula:
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; rredRed wave band data in the multispectral image;
step 3.2: calculating the vegetation coverage of alfalfa and cotton VFC by adopting the following formula:
in the formula: NDVIvegIs the value of 95% confidence in the vegetation index rating chart NDVI, NDVIsoilIs the value of 2% disposability in the vegetation index rating chart NDVI.
Preferably, the step 4 comprises the following steps:
step 4.1: superposing the vector files of the distribution areas of the alfalfa and the cotton to the registered thermal infrared image, and performing mask processing by utilizing ENVI software to obtain alfalfa and cotton canopy mask files; masking the registered thermal infrared image and performing data statistics to respectively obtain canopy temperature T corresponding to each pixel of the alfalfa distribution area and the cotton distribution arealeafAnd the maximum value T of the canopy temperature of eliminating 1% of data at two ends in the thermal infrared imagel_maxMinimum value Tl_minAnd mean value Tl_c(ii) a Mean value of canopy temperature Tl_cThe average value of the temperatures of the canopies with bare soil removed in the corresponding areas is shown;
step 4.2: superposing the vector file of the distribution area of the bare soil to the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; masking the infrared image, and performing data statistics to obtain bare soil temperature T corresponding to each pixel in the bare soil distribution areasoil(ii) a And eliminating normal distribution in thermal infrared image dataObtaining the maximum value, the minimum value and the average value in the remaining 98% of data of the data of 1% at the two ends;
step 4.3: by canopy temperature TleafMinus the average temperature T of the bare soils_cTo obtain the data value T of the temperature difference between the canopy and the bare soills；
Step 4.4: calculating the water stress index of the canopy and the bare soil based on the following formula:
in the formula: CWSIleafIs the water stress index of the canopy; t is a unit ofleafIs the canopy temperature; t is a unit ofl_macIs the maximum value of the canopy temperature; t is a unit ofl_minIs the minimum value of canopy temperature;
in the formula: CWSIsoilThe water stress index of the bare soil; t is a unit ofsoilBare soil temperature; t is a unit ofs_maxThe maximum value of the bare soil temperature; t iss_minIs the bare soil temperature minimum;
step 4.5: and (3) calculating a CSTI index of the canopy-soil temperature difference and vegetation coverage:
in the formula: VFC is the vegetation coverage of alfalfa and cotton; t is a unit oflsCanopy-bare soil temperature difference data values.
Preferably, the step 5 comprises the following steps:
the step 5 comprises the following steps:
step 5.1: subjecting the canopy to CWSIleafAnd water stress index CWSI of bare soilsoilMaking independent variable, taking the water content of the canopy soil as dependent variable, and establishing a unary linear regression model;
cotton field moisture content model:
ycotton＝k1*CWSIleaf+β1；
in the formula, k1、β1Slope and constant, ycottonCWSI (CWSI) which is the water content of soil in cotton fieldleaf(ii) the water stress index of the canopy;
alfalfa field soil moisture content model:
yalfalfa＝k2*CWSIleaf+β2；
in the formula, k2、β2Slope and constant, y, respectivelyalfalfaCWSI which is the water content of the soil in the alfalfa fieldleafWater stress index of canopy;
establishing a unitary linear regression model by taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable;
bare soil water content model:
ysoil＝k3*CWSIsoil+β3；
in the formula, k3、β3Slope and constant, y, respectivelysoilThe water content of the bare soil is;
two-factor soil water content model:
constructing a linear regression model by taking the canopy-bare soil temperature difference and the vegetation coverage index CSTI as independent variables and the soil water content as dependent variables:
y4＝k4*CSTI+β4；
in the formula, k4、β4Slope and constant, y4The water content of the soil of the cotton field;
and step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of alfalfa cotton field soil,
is the weight of the single-factor model, and eta is the weight of the double-factor model; phi is a constant.
Preferably, in the step 6, the soil water content is predicted through the alfalfa cotton field soil water content monitoring model to obtain a predicted value, the predicted value and an actual value actually measured in ground survey data are subjected to error analysis and correlation analysis, and a decision coefficient R of two groups of variables is compared2And the root mean square error RMSE is used for verifying the soil moisture content accuracy inverted by the comprehensive soil moisture content model.
Preferably, the acquisition of the remote sensing data of the unmanned aerial vehicle in the step 1 is as follows:
and acquiring remote sensing images of the irrigation operation land parcel by adopting an unmanned aerial vehicle remote sensing platform in the first three days, the first day, the last day and the last three days of cotton field irrigation from late 4 month to late 8 month, and acquiring data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours every day.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the invention processes the remote sensing data through the remote sensing data of the unmanned aerial vehicle, calculates the vegetation index, the vegetation coverage, the water stress index, the layer-bare soil temperature difference and the vegetation coverage index based on the processed data, establishes the alfalfa cotton field soil water content monitoring model based on the calculation result, verifies the precision of the alfalfa cotton field soil water content monitoring model through the data actually measured on the ground, selects the optimal alfalfa cotton field soil water content monitoring model, thereby realizing the establishment of the alfalfa cotton field soil water content monitoring model, subsequently only needs to preprocess the remote sensing data actually monitored by the unmanned aerial vehicle, then puts the remote sensing data into the alfalfa cotton field soil water content monitoring model for calculation to obtain the water content, thereby realizing the rapid grasp of the growth state and the water shortage condition of the cotton field vegetation and further providing scientific guidance for scientific water and accurate irrigation, has important significance for water saving and stable yield of cotton planting. And this application is based on unmanned aerial vehicle remote sensing technology data collection, calculates and obtains the cotton field water content based on alfalfa cotton field soil water content monitoring model to need not to install monitoring facilities at the planting ground, thereby effectual solved among the prior art technical problem, this application can not hinder mechanized operation promptly, can not receive the space restriction, and because ground need not install monitoring facilities, thereby can not cause the damage of device because of artificial factor.
According to the method, the remote sensing data of the unmanned aerial vehicle of the cotton field is extracted, the distribution areas of the alfalfa, the cotton and the bare soil are identified and mapped, the vegetation canopy temperature and the bare soil temperature of the cotton field are analyzed, the alfalfa cotton field soil water content monitoring model is comprehensively constructed, the precision of the alfalfa cotton field soil water content monitoring model is verified through the data actually monitored on the ground, and the accurate judgment of the field soil water content remotely sensed by the unmanned aerial vehicle is realized. The method is beneficial to mastering the growth state and the water shortage condition of cotton field vegetation, particularly cotton, so as to guide scientific irrigation, accurate irrigation and water conservation, and has important significance for implementing water-saving and yield-stabilizing engineering for cotton planting.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is an overall flow chart of the present invention.
FIG. 2 is a flow chart of the present invention for boundary separation of vegetation from bare soil in a cultivated land area.
FIG. 3 is a flow chart of the alfalfa cotton field soil water content monitoring model construction.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of embodiments of the present application, generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Referring to the attached drawings 1 to 3, the alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing comprises the following steps:
step 1: the method comprises the steps of collecting unmanned aerial vehicle remote sensing data and ground survey data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground survey data to obtain preprocessed image data;
the acquisition of the remote sensing data of the unmanned aerial vehicle in the step 1 is as follows:
and acquiring remote sensing images of the irrigation operation land parcel by adopting an unmanned aerial vehicle remote sensing platform in the first three days, the first day, the last day and the last three days of cotton field irrigation from late 4 month to late 8 month, and acquiring data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours every day.
The processing of unmanned aerial vehicle remote sensing data includes:
processing the remote sensing data of the unmanned aerial vehicle to obtain an original multispectral image, an original visible light image and an original thermal infrared image; cutting the original multispectral image, the original RGB visible light image and the original thermal infrared image to obtain the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel;
respectively registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel by using a known high-precision reference image of the land parcel to obtain the registered multispectral image, RGB visible light image and thermal infrared image;
based on the RGB visible light image after registration, generating a panchromatic image grayscale by adopting a panchromatic image synthesis formula:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
in the formula: rredThe red wave band data in the RGB visible light image after registration; r isGreenGreen wave band data in the RGB visible light image after registration; rblueThe blue waveband data in the RGB visible light image after registration;
and fusing the registered excessive spectral image and the panchromatic image gradycale to obtain a fused multispectral image.
The invention ensures the precision of subsequent processing by preprocessing the data.
And 2, step: separating vegetation and bare soil boundaries in a farmland range based on the image data obtained in the step 1 to obtain an alfalfa cotton field farmland range and image data;
the step 2 comprises the following steps:
step 2.1: distinguishing soil properties by using the registered RGB visible light images, segmenting different land properties, and extracting a cultivated land range;
step 2.2: based on the RGB visible light images after registration, preliminarily obtaining classified images of a vegetation growing area and bare soil by adopting a gray segmentation method;
step 2.3: determining a green-blue vegetation index GBRI and a simple vegetation index SRI based on the following formulas:
in the formula: r isGreenGreen wave band data in the multispectral image; rBlueBlue waveband data in the multispectral image;
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; r isredRed wave band data in the multispectral image;
the numerical range of the vegetation index is [ -1,1], and a negative value indicates that the ground is covered by cloud, water, snow and the like and has high reflection to visible light; 0 represents rock or bare earth, etc., and NIR and R are approximately equal; positive values indicate vegetation coverage and increase with increasing coverage. Because the growing periods of the alfalfa and the cotton are different, the ground coverage degree is also different, and therefore the planting ranges of the cotton and the alfalfa in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (4) superposing the results output in the step (2.2) and the step (2.3), confirming to obtain the final distribution area vector files of the alfalfa, the cotton and the bare soil, and completing the drawing output result.
The invention fully considers the condition of the water content of the bare soil, realizes the monitoring of the water content of the farmland soil, and not only can reflect the water content of the soil in a vegetation area, but also can fully reflect the water content of the soil in the bare soil area.
And step 3: calculating vegetation index and vegetation coverage based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
the step 3 comprises the following steps:
step 3.1: step 3.1: calculating a vegetation index map NDVI in the multispectral image by adopting the following formula:
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; r isredRed waveband data in the multispectral image;
step 3.2: calculating the vegetation coverage of alfalfa and cotton VFC by adopting the following formula:
in the formula: NDVIvegIs the value of 95% confidence in the vegetation index rating chart NDVI, NDVIsoilIs the value of 2% disposability in the vegetation index rating chart NDVI.
And 4, step 4: calculating a water stress index, a canopy-bare soil temperature difference and a vegetation coverage index based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
the step 4 comprises the following steps:
step 4.1: vector file of distribution areas of alfalfa and cottonSuperposing the image to the registered thermal infrared image, and performing mask processing by utilizing ENVI software to obtain alfalfa and cotton canopy mask files; masking the registered thermal infrared image and performing data statistics to respectively obtain the canopy temperature T corresponding to each pixel of the alfalfa distribution area and the cotton distribution arealeafAnd the maximum value T of the canopy temperature of the thermal infrared image with 1% of data at two ends removedl_maxMinimum value Tl_minAnd mean value Tl_c(ii) a Mean value of canopy temperature Tl_cThe average value of the temperatures of the canopies with bare soil removed in the corresponding areas is shown;
step 4.2: superposing the bare soil distribution area vector file to the registered infrared image, and performing mask processing by utilizing ENVI software to obtain a bare soil mask file; masking the infrared image, and performing data statistics to obtain bare soil temperature T corresponding to each pixel in the bare soil distribution areasoil(ii) a Removing data of 1% of two ends of normal distribution in the thermal infrared image data to obtain the maximum value, the minimum value and the average value in the remaining 98% of data;
step 4.3: by canopy temperature TleafMinus the average temperature T of the bare soils_cTo obtain the data value T of the temperature difference between the canopy and the bare soills；
Step 4.4: calculating the water stress index of the canopy and the bare soil based on the following formula:
in the formula: CWSIleafIs the water stress index of the canopy; t isleafIs the canopy temperature; t is a unit ofl_maxIs the maximum canopy temperature; t is a unit ofl_minIs the minimum value of canopy temperature;
in the formula: CWSIsoilThe water stress index of the bare soil; t is a unit ofsoilThe temperature of the bare soil; t iss_maxThe maximum value of the bare soil temperature;Ts_minis the bare soil temperature minimum;
step 4.5: and (3) calculating a CSTI index of the canopy-soil temperature difference and vegetation coverage:
in the formula: VFC is the vegetation coverage of alfalfa and cotton; t islsCanopy-bare soil temperature difference data value.
And 5: establishing an alfalfa cotton field soil water content monitoring model based on the calculation results in the step 3 and the step 4;
the step 5 comprises the following steps:
step 5.1: water stress index CWSI of canopyleafAnd water stress index CWSI of bare soilsoilMaking independent variable, taking the water content of the canopy soil as dependent variable, and establishing a unary linear regression model;
cotton field water content model:
ycotton＝k1*CWSIleaf+β1；
in the formula, k1、β1Slope and constant, y, respectivelycottonCWSI is the soil moisture content of cotton fieldleaf(ii) the water stress index of the canopy;
alfalfa field soil moisture content model:
yalfalfa＝k2·CWSIleaf+β2；
in the formula, k2、β2Slope and constant, yalfalfaCWSI, which is the water content of alfalfa soilleaf(ii) the water stress index of the canopy;
establishing a unitary linear regression model by taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable; bare soil water content model:
ysoil＝k3*CWSIsoil+β3；
in the formula, k3、β3Slope and constant, y, respectivelysoilThe water content of the bare soil is;
two-factor soil water content model:
constructing a linear regression model by taking the canopy-bare soil temperature difference and the vegetation coverage index CSTI as independent variables and the soil water content as dependent variables:
y4＝k4*CSTI+β4；
in the formula, k4、β4Slope and constant, y, respectively4The soil moisture content of the cotton field;
step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of alfalfa cotton field soil,
is the weight of the single-factor model, and eta is the weight of the double-factor model; phi is a constant.
And 6: and verifying the alfalfa cotton field soil water content monitoring model based on ground survey data, and selecting an optimal model.
In the step 6, the soil water content is predicted through the alfalfa cotton field soil water content monitoring model to obtain a predicted value, the predicted value and an actual value actually measured in ground survey data are subjected to error analysis and correlation analysis, and a decision coefficient R of two groups of variables is compared2And the root mean square error RMSE is used for verifying the soil moisture content accuracy inverted by the comprehensive soil moisture content model.
The invention processes the remote sensing data through the remote sensing data of the unmanned aerial vehicle, calculates the vegetation index, the vegetation coverage, the water stress index, the layer-bare soil temperature difference and the vegetation coverage index based on the processed data, establishes the alfalfa cotton field soil water content monitoring model based on the calculation result, verifies the precision of the alfalfa cotton field soil water content monitoring model through the data actually measured on the ground, selects the optimal alfalfa cotton field soil water content monitoring model, realizes the establishment of the alfalfa cotton field soil water content monitoring model, subsequently only needs to preprocess the remote sensing data actually monitored by the unmanned aerial vehicle, then puts the remote sensing data into the alfalfa cotton field soil water content monitoring model for calculation to obtain the water content, thereby realizing the rapid grasp of the growth state and the water shortage condition of the cotton field vegetation and further providing scientific guidance for scientific water use and accurate irrigation, has important significance for water saving and stable yield of cotton planting. And this application is based on unmanned aerial vehicle remote sensing technology data collection, calculates based on alfalfa cotton field soil water content monitoring model and obtains the cotton field water content to need not installing monitoring facilities at the planting field, thereby effectual solved among the prior art technical problem, this application can not hinder mechanized operation promptly, can not receive space limitation, and because ground need not install monitoring facilities, thereby can not cause the damage of device because of artificial factor.
The invention is further described with reference to the following implementation:
an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing includes following steps:
step 1: acquiring unmanned aerial vehicle remote sensing data and ground survey data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground survey data to obtain preprocessed image data;
the acquisition of the remote sensing data of the unmanned aerial vehicle in the step 1 is as follows:
in the late 4 th to late 8 th, the cotton seedling stage to the boll stage is positive, which is the key period of cotton growth, ten times of irrigation operation are required according to the requirements of water and fertilizer management operation of the traditional cotton field, and the first irrigation operation starts at the beginning of 6 months. The remote sensing image of the irrigation operation land parcel is collected by adopting an unmanned aerial vehicle remote sensing platform in the first three days, the first day, the last day and the last three days of cotton field irrigation each time, and data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours are collected every day.
This embodiment unmanned aerial vehicle remote sensing platform includes: the system comprises a four-rotor unmanned aerial vehicle, a flight control system, a Zenmuse H20T imaging system (comprising an RGB visible light camera and a thermal infrared camera), a RedEdge-MX airborne multispectral imager, a ground control system, a data processing system and a micro portable computer.
Wherein the four-rotor unmanned aerial vehicle is a Xinjiang longitude and latitude M300, and the endurance time is 30 minutes; the Zenmuse H20T imaging system is a product of Dajiang innovation corporation, the RGB visible light camera viewing angle is 82.9 degrees, and the effective pixels are 1200 ten thousand; the thermal infrared camera sensor is an uncooled vanadium oxide (VOx) microbolometer, the wavelength range is 8-14 mu m, the field angle is 40.6 degrees, and the temperature measurement range in a high-gain mode is-40 ℃ to 150 ℃. The RedEdge-MX airborne multi-spectral imager is a product of MicaSense company, adopts a hovering scanning imaging mode, and has a spectral range of 400-900nm (blue band central wavelength of 475nm and wave width of 40nm, green band central wavelength of 560nm and wave width of 20nm, red band central wavelength of 668nm and wave width of 10nm, red band central wavelength of 717nm and wave width of 10nm, near infrared band central wavelength of 840nm and wave width of 40nm), a horizontal field angle of 47.2 degrees and a spatial resolution of 0.02m @60 m height.
The unmanned aerial vehicle remote sensing operation is clear and cloudless on the day, the wind speed is less than 3 grades, the navigation speed is 1m/s, the navigation height is 50m, the course overlapping degree is 75%, and the side direction overlapping degree is 75%. After data are collected according to the specification, the PIX4D software is used for data preprocessing.
The processing of the unmanned aerial vehicle remote sensing data comprises the following steps:
processing the remote sensing data of the unmanned aerial vehicle to obtain an original multispectral image, an original visible light image and an original thermal infrared image; cutting the original multispectral image, the original RGB visible light image and the original thermal infrared image to obtain the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel;
the original Thermal infrared images were analyzed and corrected by the DJI Thermal Analysis Tool supplied by Innovation of Xinjiang.
The method comprises the steps of processing an originally collected cotton field unmanned aerial vehicle remote sensing data set through Pix4D software to obtain a multispectral image with ground resolution of 3.6cm, an original RGB visible light image with ground resolution of 1.8cm and an original thermal infrared image, obtaining standard preprocessing data, and cutting the multispectral image and the RGB visible light image of a target land block according to a 500-meter buffer area range through ENVI software.
Respectively registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel by using a known high-precision reference image of the land parcel to obtain the registered multispectral image, RGB visible light image and thermal infrared image;
based on the RGB visible light images after registration, generating a panchromatic image grayscale by adopting a panchromatic image synthesis formula:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
in the formula: r isredThe red wave band data in the RGB visible light image after registration; rgreenGreen wave band data in the RGB visible light image after registration; r isblueThe blue waveband data in the RGB visible light image after registration;
and fusing the registered excessive spectral image and the panchromatic image gradycale by adopting an NNDiffuse Pan sharpening module of ENVI software to obtain a fused multispectral image.
Collecting and processing the ground survey data:
after unmanned aerial vehicle image acquisition accomplished, ground data acquisition was carried out in step, mainly soil moisture content. And (3) measuring the soil moisture content by adopting a traditional soil sampling drying method, carrying out soil drilling and soil sampling at the center of each square sampling area, wherein the soil sampling depth is 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100cm, taking out the soil sample, quickly filling the soil sample into an aluminum box for weighing, putting the soil sample into a drying box for drying at 105 ℃ for 8 hours, then weighing, calculating the soil mass moisture content, and multiplying the soil mass moisture content by the soil volume weight to obtain the Soil Moisture Content (SMC).
Meanwhile, a cell is defined in a test area according to a 9-grid, distribution areas of the alfalfa, the cotton and the bare soil are fully considered, moisture sensors are distributed at 0-15cm, 0-30cm and 0-60cm, and real-time soil moisture information under three depths of the alfalfa, the cotton and the bare soil is obtained.
The invention ensures the precision of subsequent processing by preprocessing the data.
And 2, step: separating vegetation and bare soil boundaries in a farmland range based on the image data obtained in the step 1 to obtain an alfalfa cotton field farmland range and image data;
referring to fig. 2, the step 2 includes the steps of:
step 2.1: distinguishing soil properties by using the registered RGB visible light images, segmenting different land properties, and extracting a cultivated land range; the soil body properties include: ploughing, ploughing roads, buildings, canals, shelters, etc.
Step 2.2: based on the RGB visible light images after registration, preliminarily obtaining a classified image of a vegetation growing area and bare soil by adopting a gray segmentation method;
specifically, the method comprises the following steps:
the method selects 50 typical representative areas of cotton plants, alfalfa plants and bare soil from RGB visible light images acquired in each test, respectively counts the gray scales of the cotton plants, the alfalfa plants and the bare soil in green bands, and analyzes whether a vegetation and the bare soil have crossed areas through histogram comparison to obtain classified images of the alfalfa, the cotton and the bare soil.
Step 2.3: in the visible light wave band range, due to the influence of chlorophyll, the chlorophyll has strong absorption effect on blue light (B) and red light (R) and strong reflection effect on green light (G); influenced by the structure of vegetation leaf cells, the Near Infrared (NIR) reflection effect is strong. The green-blue vegetation index GBRI and the simple vegetation index SRI are determined based on the following formulas:
in the formula: r isGreenGreen wave band data in the multispectral image; r isBlueBlue waveband data in the multispectral image are obtained;
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; r isredRed wave band data in the multispectral image;
through the analysis and calculation, the green-blue vegetation index GBRI is 1.38; the simple vegetation index SRI is 1.45.
The numerical range of the vegetation index is [ -1,1], and a negative value indicates that the ground is covered by cloud, water, snow and the like, and has high reflection to visible light; 0 represents rock or bare earth, etc., and NIR and R are approximately equal; positive values indicate vegetation coverage and increase with increasing coverage. Because the growing periods of the alfalfa and the cotton are different, the ground coverage degree is also different, and therefore the planting ranges of the cotton and the alfalfa in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (4) superposing the results output in the step (2.2) and the step (2.3), confirming to obtain the final distribution area vector files of the alfalfa, the cotton and the bare soil, and completing the drawing output result.
The invention fully considers the condition of the water content of the bare soil, realizes the monitoring of the water content of the farmland soil, and not only can reflect the water content of the soil in a vegetation area, but also can fully reflect the water content of the soil in the bare soil area.
And step 3: calculating vegetation index and vegetation coverage based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
the step 3 comprises the following steps:
step 3.1: step 3.1: calculating a vegetation index map NDVI in the multispectral image by adopting the following formula:
in the formula: r isNIRThe data are near infrared waveband data in the multispectral image; rredRed wave band data in the multispectral image;
step 3.2: calculating the vegetation coverage of alfalfa and cotton VFC using the following formula:
in the formula: NDVIvegIs the value of 95% confidence in the vegetation index grading chart NDVI, NDVIsoilIs the value of 2% disposability in the vegetation index rating chart NDVI.
And 4, step 4: calculating a water stress index, a canopy-bare soil temperature difference and a vegetation coverage index based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
the step 4 comprises the following steps:
step 4.1: superposing the vector files of the distribution areas of the alfalfa and the cotton to the registered thermal infrared image, and performing mask processing by utilizing ENVI software to obtain alfalfa and cotton canopy mask files; masking the registered thermal infrared image and performing data statistics to respectively obtain canopy temperature T corresponding to each pixel of the alfalfa distribution area and the cotton distribution arealeafAnd the maximum value T of the canopy temperature of the thermal infrared image with 1% of data at two ends removedl_maxMinimum value Tl_minAnd mean value Tl_c(ii) a Mean value of canopy temperature Tl_cThe average value of the temperatures of the canopies in which the bare soil is removed in the corresponding areas is referred to;
and 4.2: superposing the vector file of the distribution area of the bare soil to the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; masking the infrared image, and performing data statistics to obtain bare soil temperature T corresponding to each pixel in the bare soil distribution areasoil(ii) a Removing data of 1% of two ends of normal distribution in the thermal infrared image data to obtain the maximum value, the minimum value and the average value in the rest 98% of data;
step 4.3: by canopy temperature TleafMinus the average temperature T of the bare soils_cTo obtain the data value T of the temperature difference between the canopy and the bare soills；
Step 4.4: calculating the water stress index of the canopy and the bare soil based on the following formula:
in the formula: CWSIleafIs a crownWater stress index of the layer; t is a unit ofleafIs the canopy temperature; t is a unit ofl_macIs the maximum value of the canopy temperature; t is a unit ofl_minIs the minimum value of canopy temperature;
in the formula: CWSIsoilThe water stress index of the bare soil; t is a unit ofsoilThe temperature of the bare soil; t iss_maxThe maximum value of the bare soil temperature; t is a unit ofs_minIs the bare soil temperature minimum;
by the calculation, canopy and bare soil water stress indexes of different time every day are obtained, and water stress index change information of different time at 11-21 time every day is analyzed.
Step 4.5: and (3) calculating a CSTI index of the canopy-soil temperature difference and vegetation coverage:
in the formula: VFC is the vegetation coverage of alfalfa and cotton; t is a unit oflsCanopy-bare soil temperature difference data values.
And 5: establishing an alfalfa cotton field soil water content monitoring model based on the calculation results in the step 3 and the step 4;
the step 5 comprises the following steps:
step 5.1: water stress index CWSI of canopyleafAnd water stress index of bare soil CWSIsoilMaking independent variables, taking the water content of the canopy soil as dependent variables, and establishing a unitary linear regression model;
cotton field water content model:
ycotton＝k1*CWSIleaf+β1；
in the formula, k1、β1Slope and constant, y, respectivelycottonCWSI (CWSI) which is the water content of soil in cotton fieldleafWater stress index of canopy;
alfalfa field soil moisture content model:
yalfalfa＝k2*CWSIleaf+β2；
in the formula, k2、β2Slope and constant, y, respectivelyalfalfaCWSI which is the water content of the soil in the alfalfa fieldleaf(ii) the water stress index of the canopy;
establishing a unitary linear regression model by taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable;
bare soil water content model:
ysoil＝k3*CWSIsoil+β3；
in the formula, k3、β3Slope and constant, y, respectivelysoilThe water content of the bare soil is;
two-factor soil water content model:
constructing a linear regression model by taking the canopy-bare soil temperature difference and the vegetation coverage index CSTI as independent variables and the soil water content as dependent variables:
y4＝k4*CSTI+β4；
in the formula, k4、β4Slope and constant, y4The water content of the soil of the cotton field;
step 5.2: comprehensively constructing a soil water content monitoring model of the alfalfa cotton field:
wherein y is the water content of the alfalfa cotton field soil,
is the weight of the single-factor model, and eta is the weight of the double-factor model; phi is a constant.
And 6: and verifying the alfalfa cotton field soil water content monitoring model based on ground survey data, and selecting a model meeting a preset condition.
According to the method, the alfalfa cotton field soil water content monitoring model is established through the analysis, and in order to verify the accuracy of the alfalfa cotton field soil water content monitoring model, the accuracy of the alfalfa cotton field soil water content monitoring model is evaluated by simultaneously acquiring ground data.
In the step 6, the soil water content is predicted through the alfalfa cotton field soil water content monitoring model to obtain a predicted value, the predicted value and an actual value actually measured in ground survey data are subjected to error analysis and correlation analysis, and the decision coefficients R of the two groups of variables are compared2And the root mean square error RMSE is used for verifying the soil moisture content accuracy inverted by the comprehensive soil moisture content model.
The invention processes the remote sensing data through the remote sensing data of the unmanned aerial vehicle, calculates the vegetation index, the vegetation coverage, the water stress index, the layer-bare soil temperature difference and the vegetation coverage index based on the processed data, establishes the alfalfa cotton field soil water content monitoring model based on the calculation result, verifies the precision of the alfalfa cotton field soil water content monitoring model through the data actually measured on the ground, selects the optimal alfalfa cotton field soil water content monitoring model, realizes the establishment of the alfalfa cotton field soil water content monitoring model, subsequently only needs to preprocess the remote sensing data actually monitored by the unmanned aerial vehicle, then puts the remote sensing data into the alfalfa cotton field soil water content monitoring model for calculation to obtain the water content, thereby realizing the rapid grasp of the growth state and the water shortage condition of the cotton field vegetation and further providing scientific guidance for scientific water use and accurate irrigation, has important significance for water saving and stable yield of cotton planting. And this application is based on unmanned aerial vehicle remote sensing technology data collection, calculates and obtains the cotton field water content based on alfalfa cotton field soil water content monitoring model to need not to install monitoring facilities at the planting ground, thereby effectual solved among the prior art technical problem, this application can not hinder mechanized operation promptly, can not receive the space restriction, and because ground need not install monitoring facilities, thereby can not cause the damage of device because of artificial factor.
The above-mentioned embodiments only express the specific embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, without departing from the technical idea of the present application, several changes and modifications can be made, which are all within the protection scope of the present application.
Claims (8)
1. The utility model provides an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing which characterized in that includes following step:
step 1: acquiring unmanned aerial vehicle remote sensing data and ground survey data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground survey data to obtain preprocessed image data;
and 2, step: based on the image data obtained in the step 1, vegetation and bare soil boundaries in a farmland range are separated to obtain an alfalfa cotton field farmland range and image data;
and 3, step 3: carrying out vegetation index calculation and vegetation coverage calculation based on the alfalfa cotton field cultivated land range and the image data obtained in the step (2);
and 4, step 4: calculating a water stress index, a temperature difference between a canopy and bare soil and a vegetation coverage index based on the farmland range of the alfalfa cotton field obtained in the step (2) and the image data;
and 5: establishing an alfalfa cotton field soil water content monitoring model based on the calculation results in the step 3 and the step 4;
and 6: and verifying the alfalfa cotton field soil water content monitoring model based on ground survey data, and selecting a model meeting the conditions.
2. The remote sensing alfalfa cotton field soil water content monitoring model by unmanned aerial vehicle of claim 1,
the processing of the unmanned aerial vehicle remote sensing data comprises the following steps:
processing the remote sensing data of the unmanned aerial vehicle to obtain an original multispectral image, an original visible light image and an original thermal infrared image; cutting the original multispectral image, the original RGB visible light image and the original thermal infrared image to obtain the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel;
respectively registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land parcel by using a known high-precision reference image of the land parcel to obtain the registered multispectral image, RGB visible light image and thermal infrared image;
based on the RGB visible light images after registration, generating a panchromatic image grayscale by adopting a panchromatic image synthesis formula:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rbiue；
in the formula: r isredThe red waveband data in the RGB visible light image after registration; rgreenGreen wave band data in the RGB visible light image after registration; rblueThe blue waveband data in the RGB visible light image after registration;
and fusing the registered excessive spectral images and the panchromatic image gradycale to obtain a fused multispectral image.
3. The remote sensing alfalfa cotton field soil water content monitoring model by unmanned aerial vehicle of claim 2, wherein the step 2 comprises the following steps:
step 2.1: distinguishing soil properties by using the registered RGB visible light images, segmenting different land properties, and extracting a cultivated land range;
step 2.2: based on the RGB visible light images after registration, preliminarily obtaining classified images of a vegetation growing area and bare soil by adopting a gray segmentation method;
step 2.3: determining a green-blue vegetation index GBRI and a simple vegetation index SRI based on the following formulas:
in the formula: r isGreenGreen wave band data in the multispectral image; rBlueBlue waveband data in the multispectral image;
in the formula: rNIRThe data of near infrared wave band in the multispectral image is obtained; rredRed wave band data in the multispectral image;
step 2.4: and (4) superposing the results output in the step 2.2 and the step 2.3, confirming to obtain the final distribution area vector file of the alfalfa, the cotton and the bare soil, and completing the drawing output result.
4. The remote sensing alfalfa cotton field soil water content monitoring model by unmanned aerial vehicle of claim 3, wherein the step 3 comprises the following steps:
step 3.1: calculating a vegetation index map NDVI in the multispectral image by adopting the following formula:
in the formula: r isNIRThe data of near infrared wave band in the multispectral image is obtained; rredRed wave band data in the multispectral image;
step 3.2: calculating the vegetation coverage of Melissitus ruthenicus and cotton by adopting the following formula:
in the formula: NDVIvegIs a value of 95% confidence in the vegetation index plot NDVI, NDVIsoilIs the value of 2% disposability in the vegetation index rating chart NDVI.
5. The remote sensing alfalfa cotton field soil water content monitoring model by unmanned aerial vehicle of claim 4, wherein the step 4 comprises the following steps:
step 4.1: superimposing the distribution area vector file of the alfalfa and the cotton to the registered thermal infrared image,performing mask treatment by using ENVI software to obtain alfalfa and cotton canopy mask files; masking the registered thermal infrared image and performing data statistics to obtain canopy temperature T corresponding to each pixel of the alfalfa distribution area and the cotton distribution area respectivelyleafAnd the maximum value T of the canopy temperature of the thermal infrared image with 1% of data at two ends removedl_maxMinimum value Tl_minAnd mean value Tl_c(ii) a Mean value of canopy temperature Tl_cThe average value of the temperatures of the canopies in which the bare soil is removed in the corresponding areas is referred to;
and 4.2: superposing the vector file of the distribution area of the bare soil to the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; masking the infrared image, and performing data statistics to obtain bare soil temperature T corresponding to each pixel in the bare soil distribution areasoil(ii) a Removing data of 1% of two ends of normal distribution in the thermal infrared image data to obtain the maximum value, the minimum value and the average value in the rest 98% of data;
step 4.3: by canopy temperature TleafMinus the average temperature T of the bare soils_cTo obtain the data value T of the temperature difference between the canopy and the bare soills；
Step 4.4: calculating the water stress index of the canopy and the bare soil based on the following formula:
in the formula: CWSIleafIs the water stress index of the canopy; t isleafIs the canopy temperature; t isl_maxIs the maximum value of the canopy temperature; t isl_minIs the minimum value of canopy temperature;
in the formula: CWSIsoilThe water stress index of the bare soil; t is a unit ofsoilBare soil temperature; t iss_maxThe maximum value of the bare soil temperature; t iss_minIs the bare soil temperature minimum;
step 4.5: calculating the canopy-soil temperature difference and vegetation coverage CSTI index:
in the formula: VFC is vegetation coverage of Melissitus ruthenicus and cotton; t islsCanopy-bare soil temperature difference data values.
6. The unmanned aerial vehicle remotely sensed alfalfa cotton field soil water content monitoring model as claimed in claim 5, wherein said step 5 comprises the steps of:
step 5.1: water stress index CWSI of canopyleafAnd water stress index of bare soil CWSIsoilMaking independent variable, taking the water content of the canopy soil as dependent variable, and establishing a unary linear regression model;
cotton field water content model:
ycotton＝k1*CWSIleaf+β1；
in the formula, k1、β1Slope and constant, ycottonCWSI (CWSI) which is the water content of soil in cotton fieldleafWater stress index of canopy;
soil water content model of Melissitus ruthenicus field:
yalfalfa＝k2*CWSIleaf+β2；
in the formula, k2、β2Slope and constant, yalfalfaCWSI (CWSI) is the soil water content of alfalfa soilleafWater stress index of canopy;
establishing a unitary linear regression model by taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable;
bare soil water content model:
ysoil＝k3*CWSIsoil+β3；
in the formula, k3、β3Are respectively provided withIs a slope and a constant, ysoilThe water content of the bare soil is;
two-factor soil water content model:
constructing a linear regression model by taking the canopy-bare soil temperature difference and the vegetation coverage index CSTI as independent variables and the soil water content as dependent variables:
y4＝k4*CSTI+β4；
in the formula, k4、β4Slope and constant, y, respectively4The soil moisture content of the cotton field;
and step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of alfalfa cotton field soil,
is the weight of the single-factor model, and eta is the weight of the double-factor model; phi is a constant.
7. The remote sensing unmanned aerial vehicle alfalfa cotton field soil water content monitoring model as claimed in claim 1, wherein in step 6, the alfalfa cotton field soil water content monitoring model is used for soil water content prediction to obtain a predicted value, the predicted value and an actual value actually measured in ground survey data are used for error analysis and correlation analysis, and a decision coefficient R of two groups of variables is compared2And the root mean square error RMSE is used for verifying the soil moisture content accuracy inverted by the comprehensive soil moisture content model.
8. The remote sensing unmanned aerial vehicle alfalfa cotton field soil water content monitoring model as claimed in claim 1, wherein the remote sensing unmanned aerial vehicle data acquisition in step 1 is as follows:
and acquiring remote sensing images of the irrigation operation land parcel by adopting an unmanned aerial vehicle remote sensing platform in the first three days, the first day, the last day and the last three days of cotton field irrigation from late 4 month to late 8 month, and acquiring data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours every day.
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| CN115791866A (en) * | 2023-01-13 | 2023-03-14 | 太原理工大学 | Soil moisture content real-time monitoring system and method based on thermal infrared remote sensing data |
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