CN114778476B - Method for establishing alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing - Google Patents
Method for establishing 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, in particular to an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing, and (3) recognizing and drawing distribution areas of alfalfa, cotton and bare soil, analyzing the temperature of a cotton field vegetation canopy and the temperature of the bare soil, comprehensively constructing an unmanned aerial vehicle remote sensing monitoring model for the water content of the alfalfa cotton field soil, verifying the model precision, and realizing accurate discrimination of the water content of the field soil remotely sensed by the unmanned aerial vehicle. Thereby realizing the rapid grasp of the growing state of cotton field vegetation and the water shortage condition, further providing scientific guidance for scientific water and accurate irrigation, and having 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 a method for establishing an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing.
Background
The traditional farmland soil moisture content is mostly realized by a traditional soil moisture monitoring water section, and the method can only acquire the soil moisture content information of a limited area, so that the cost is high, time and labor are wasted, and the data are only effective for sampling time; accurate soil water content information cannot be obtained in real time, and agricultural production management is not facilitated.
With the development of satellite technology, inversion monitoring of soil water content by utilizing visible light, thermal infrared and radar satellites is continuously advanced, more basic research and application results are obtained, but the method is limited by the influence that an optical image cannot penetrate through a cloud layer, the thermal infrared data ground resolution is limited, the re-returning period is long, the radar satellite monitoring technology is complex and the like, and the method has limitation on technical guidance of agricultural production.
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, and the field water content is acquired by adopting the internet of things sensor or satellite image data in a part of areas, so that the information such as the water content and the temperature of the cultivated soil 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 arrangement, 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 establishment method, aiming at solving the technical problems that the existing monitoring mode is high in cost, high in damage rate and unfavorable for mechanized operation after arrangement and limited in arrangement space.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for establishing an alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing comprises the following steps:
Step 1: collecting unmanned aerial vehicle remote sensing data and ground investigation data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground investigation data to obtain preprocessed image data;
step 2: separating vegetation and bare soil boundaries in the cultivated land range based on the image data obtained in the step 1 to obtain the cultivated land range and the image data of the cotton field of alfalfa;
Step 3: calculating a vegetation index and a vegetation coverage based on the alfalfa cotton fields cultivated land range and the image data obtained in the step 2;
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 obtained in the step 2 and the image data;
Step 5: based on the calculation results in the step 3 and the step 4, establishing a alfalfa cotton field soil water content monitoring model;
Step 6: and verifying the alfalfa cotton field soil water content monitoring model based on the ground survey data, and selecting a model meeting the conditions.
According to the method, remote sensing data are processed through unmanned aerial vehicle remote sensing data, vegetation indexes, vegetation coverage, water stress indexes, layer-bare soil temperature difference and vegetation coverage indexes are calculated based on the processed data, an alfalfa cotton field soil water content monitoring model is built based on the calculation results, accuracy of the alfalfa cotton field soil water content monitoring model is verified through ground actual measurement data, an optimal alfalfa cotton field soil water content monitoring model is selected, accordingly the establishment of the alfalfa cotton field soil water content monitoring model is achieved, the water content can be obtained by means of calculation after the remote sensing data actually monitored by the unmanned aerial vehicle are preprocessed and placed into the alfalfa cotton field soil water content monitoring model, scientific guidance is provided for scientific water and accurate irrigation, and important significance is provided for water conservation and stable production of cotton planting. The application collects data based on unmanned aerial vehicle remote sensing technology, and calculates the cotton field water content based on an alfalfa cotton field soil water content monitoring model, so that monitoring equipment is not required to be installed on a planting field, and the technical problems in the prior art are effectively solved.
Preferably, the processing of the remote sensing data of the unmanned aerial vehicle 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 a multispectral image, an RGB visible light image and a thermal infrared image of a target land block;
Registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land block respectively by using the known high-precision reference image of the land block to obtain registered multispectral image, RGB visible light image and thermal infrared image;
Based on the registered RGB visible light images, a full-color image synthesis formula is adopted to generate full-color image graduate:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
Wherein: r red is red band data in the registered RGB visible light image; * R green is the green band data in the registered RGB visible image; r blue is blue band data in the registered RGB visible light image;
And fusing the registered excessive spectrum images with the full-color image gradeale to obtain fused multispectral images.
The invention ensures the accuracy of the subsequent processing by preprocessing the data.
Preferably, the step 2 includes the steps of:
Step 2.1: distinguishing soil properties by using the registered RGB visible light images, and dividing different soil properties to extract a cultivated land range;
Step 2.2: based on the registered RGB visible light image, a gray segmentation method is adopted to preliminarily obtain a classification image of a vegetation growing area and bare soil;
Step 2.3: the green-blue vegetation index GBRI and the simple vegetation index SRI are determined based on the following formulas:
Wherein: r Green is green band data in the multispectral image; r Blue is blue band data in the multispectral image;
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
The numerical range of the vegetation index is [ -1,1], and negative values indicate that the ground coverage is cloud, water, snow and the like, and the vegetation index has high reflection on visible light; 0 represents rock, bare soil or the like, and NIR and R are approximately equal; positive values indicate vegetation coverage and increase as coverage increases. Because the growing period of alfalfa is different from that of cotton, the ground coverage is different, so that the cotton and the alfalfa planting range in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (3) superposing the results output in the step (2.2) and the step (2.3), confirming that the final distribution area vector file of alfalfa, cotton and bare soil is obtained, and finishing drawing output results.
The invention fully considers the condition of bare soil water content, realizes the monitoring of farmland soil water content, and can not only reflect the soil water content of a vegetation area, but also fully reflect the soil water content of the bare soil area.
Preferably, the step 3 includes the steps of:
step 3.1: step 3.1: calculating a vegetation index map NDVI in the multispectral image by adopting the following formula:
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
step 3.2: the alfalfa and cotton vegetation coverage VFC was calculated using the following formula:
Wherein: NDVI veg is the 95% confidence value in the vegetation index grading NDVI, and NDVI soil is the 2% confidence value in the vegetation index grading NDVI.
Preferably, the step4 includes the steps of:
Step 4.1: superposing the distribution area vector files of the alfalfa and the cotton into the registered thermal infrared image, and performing mask processing by using ENVI software to obtain the alfalfa and cotton canopy mask files; performing mask processing and data statistics on the registered thermal infrared images to respectively obtain a canopy temperature T leaf corresponding to each pixel of the alfalfa distribution area and the cotton distribution area, and a canopy temperature maximum value T l_max, a minimum value T l_min and an average value T l_c of 1% data at two ends of the thermal infrared images; the average value T l_c of the canopy temperature refers to the average value of the canopy temperature of the bare soil removed from the corresponding area;
Step 4.2: superposing the bare soil distribution area vector file into the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; performing mask processing on the infrared image, and performing data statistics to obtain a bare soil temperature T soil corresponding to each pixel of the bare soil distribution area; removing 1% of data at 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: subtracting the bare soil temperature average value from the canopy temperature T leaf to obtain a canopy-bare soil temperature difference data value T ls;
step 4.4: water stress indexes of canopy and bare soil are calculated based on the following formula:
Wherein: CWSI leaf is the water stress index of the canopy; t leaf is the canopy temperature; t l_max is the maximum value of the canopy temperature; t l_min is the minimum value of the canopy temperature;
Wherein: CWSI soil is the water stress index of bare soil; t soil is the bare soil temperature; t s_max is the maximum value of the bare soil temperature; t s_min is the minimum value of bare soil temperature;
step 4.5: and (3) calculating a canopy-soil temperature difference and vegetation coverage CSTI index:
wherein: VFC is alfalfa and cotton vegetation coverage; t ls canopy-bare soil temperature difference data values.
Preferably, the step 5 includes the steps of:
said step 5 comprises the steps of:
Step 5.1: taking the water stress index CWSI leaf of the canopy and the water stress index CWSI soil of bare soil as independent variables, taking the water content of the canopy soil as a dependent variable, and establishing a unitary linear regression model;
Cotton ground moisture content model:
ycotton＝k1*CWSIleaf+β1；
Wherein k 1、β1 is slope and constant, y cotton is cotton soil water content, and the water stress index of CWSI leaf canopy;
alfalfa ground soil moisture content model:
yalfalfa＝k2*CWSIleaf+β2；
Wherein k 2、β2 is slope and constant, y alfalfa is alfalfa ground soil water content, and the water stress index of CWSI leaf canopy;
taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable, and establishing a unitary linear regression model;
Bare soil moisture content model:
ysoil＝k3*CWSIsoil+β3；
Wherein k 3、β3 is the slope and constant, and y soil is the moisture content of bare soil;
Two factor soil moisture content model:
constructing a linear regression model by taking canopy-bare soil temperature difference and vegetation coverage index CSTI as independent variables and soil water content as dependent variables:
y4＝k4*CSTI+β4；
Wherein k 4、β4 is slope and constant, and y 4 is cotton soil moisture content;
Step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of the cotton field soil of alfalfa, The model weight is a single factor model weight, and eta is a double factor model weight; phi is constant.
Preferably, in the step 6, the soil moisture content is predicted by an alfalfa cotton field soil moisture content monitoring model to obtain a predicted value, error analysis and correlation analysis are performed on the predicted value and an actual value actually measured in ground investigation data, and the accuracy of inverting the soil moisture content by the comprehensive soil moisture content model is verified by comparing the determination coefficients R 2 and the root mean square error RMSE of the two groups of variables.
Preferably, the collection of the remote sensing data of the unmanned aerial vehicle in the step 1 is as follows:
The remote sensing image of the irrigation operation land is acquired 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 irrigation of the cotton field in the last ten days of 4 months to 8 months, and the data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours are acquired every day.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
According to the method, remote sensing data are processed through unmanned aerial vehicle remote sensing data, vegetation indexes, vegetation coverage, water stress indexes, layer-bare soil temperature difference and vegetation coverage indexes are calculated based on the processed data, an alfalfa cotton field soil water content monitoring model is built based on the calculation results, accuracy of the alfalfa cotton field soil water content monitoring model is verified through ground actual measurement data, an optimal alfalfa cotton field soil water content monitoring model is selected, accordingly the establishment of the alfalfa cotton field soil water content monitoring model is achieved, the water content can be obtained by means of calculation after the remote sensing data actually monitored by the unmanned aerial vehicle are preprocessed and placed into the alfalfa cotton field soil water content monitoring model, scientific guidance is provided for scientific water and accurate irrigation, and important significance is provided for water conservation and stable production of cotton planting. The application collects data based on unmanned aerial vehicle remote sensing technology, and calculates the cotton field water content based on an alfalfa cotton field soil water content monitoring model, so that monitoring equipment is not required to be installed on a planting field, and the technical problems in the prior art are effectively solved.
According to the method, the unmanned aerial vehicle remote sensing data of the cotton field are extracted, the distribution areas of alfalfa, cotton and bare soil are identified and mapped, the cotton field vegetation canopy temperature and the bare soil temperature are analyzed, an alfalfa cotton field soil water content monitoring model is comprehensively constructed, the accuracy of the alfalfa cotton field soil water content monitoring model is verified through data actually monitored on the ground, and accurate judgment of the field soil water content of unmanned aerial vehicle remote sensing is achieved. Is helpful to grasp the growing state and water shortage condition of cotton field vegetation, especially cotton, and further guides scientific irrigation, accurate irrigation and water saving, thus having important significance for the implementation of cotton planting water saving and stable yield engineering.
Drawings
The invention will now be described by way of example and 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 for separating vegetation from bare soil boundaries in the cultivated land area according to the present invention.
FIG. 3 is a flow chart of the alfalfa cotton fields soil moisture content monitoring model construction of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the 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 application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1 to 3, a method for establishing a alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing comprises the following steps:
Step 1: collecting unmanned aerial vehicle remote sensing data and ground investigation data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground investigation data to obtain preprocessed image data;
the collection of unmanned aerial vehicle remote sensing data in step 1 is as follows:
The remote sensing image of the irrigation operation land is acquired 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 irrigation of the cotton field in the last ten days of 4 months to 8 months, and the data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours are acquired every day.
The unmanned aerial vehicle remote sensing data processing method 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 a multispectral image, an RGB visible light image and a thermal infrared image of a target land block;
Registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land block respectively by using the known high-precision reference image of the land block to obtain registered multispectral image, RGB visible light image and thermal infrared image;
Based on the registered RGB visible light images, a full-color image synthesis formula is adopted to generate full-color image graduate:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
Wherein: r red is red band data in the registered RGB visible light image; r green is the green band data in the registered RGB visible image; r blue is blue band data in the registered RGB visible light image;
And fusing the registered excessive spectrum images with the full-color image gradeale to obtain fused multispectral images.
The invention ensures the accuracy of the subsequent processing by preprocessing the data.
Step 2: separating vegetation and bare soil boundaries in the cultivated land range based on the image data obtained in the step 1 to obtain the cultivated land range and the image data of the cotton field of alfalfa;
The step2 comprises the following steps:
Step 2.1: distinguishing soil properties by using the registered RGB visible light images, and dividing different soil properties to extract a cultivated land range;
Step 2.2: based on the registered RGB visible light image, a gray segmentation method is adopted to preliminarily obtain a classification image of a vegetation growing area and bare soil;
Step 2.3: the green-blue vegetation index GBRI and the simple vegetation index SRI are determined based on the following formulas:
Wherein: r Green is green band data in the multispectral image; r Blue is blue band data in the multispectral image;
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
The numerical range of the vegetation index is [ -1,1], and negative values indicate that the ground coverage is cloud, water, snow and the like, and the vegetation index has high reflection on visible light; 0 represents rock, bare soil or the like, and NIR and R are approximately equal; positive values indicate vegetation coverage and increase as coverage increases. Because the growing period of alfalfa is different from that of cotton, the ground coverage is different, so that the cotton and the alfalfa planting range in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (3) superposing the results output in the step (2.2) and the step (2.3), confirming that the final distribution area vector file of alfalfa, cotton and bare soil is obtained, and finishing drawing output results.
The invention fully considers the condition of bare soil water content, realizes the monitoring of farmland soil water content, and can not only reflect the soil water content of a vegetation area, but also fully reflect the soil water content of the bare soil area.
Step 3: calculating a vegetation index and a vegetation coverage based on the alfalfa cotton fields 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:
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
step 3.2: the alfalfa and cotton vegetation coverage VFC was calculated using the following formula:
Wherein: NDVI veg is the 95% confidence value in the vegetation index grading NDVI, and NDVI soil is the 2% confidence value in the vegetation index grading NDVI.
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 obtained in the step 2 and the image data;
The step 4 comprises the following steps:
Step 4.1: superposing the distribution area vector files of the alfalfa and the cotton into the registered thermal infrared image, and performing mask processing by using ENVI software to obtain the alfalfa and cotton canopy mask files; performing mask processing and data statistics on the registered thermal infrared images to respectively obtain a canopy temperature T leaf corresponding to each pixel of the alfalfa distribution area and the cotton distribution area, and a canopy temperature maximum value T l_max, a minimum value T l_min and an average value T l_c of 1% data at two ends of the thermal infrared images; the average value T l_c of the canopy temperature refers to the average value of the canopy temperature of the bare soil removed from the corresponding area;
Step 4.2: superposing the bare soil distribution area vector file into the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; performing mask processing on the infrared image, and performing data statistics to obtain a bare soil temperature T soil corresponding to each pixel of the bare soil distribution area; removing 1% of data at 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: subtracting the bare soil temperature average value from the canopy temperature T leaf to obtain a canopy-bare soil temperature difference data value T ls;
step 4.4: water stress indexes of canopy and bare soil are calculated based on the following formula:
Wherein: CWSI leaf is the water stress index of the canopy; t leaf is the canopy temperature; t l_max is the maximum value of the canopy temperature; t l_min is the minimum value of the canopy temperature;
Wherein: CWSI soil is the water stress index of bare soil; t soil is the bare soil temperature; t s_max is the maximum value of the bare soil temperature; t s_min is the minimum value of bare soil temperature;
step 4.5: and (3) calculating a canopy-soil temperature difference and vegetation coverage CSTI index:
wherein: VFC is alfalfa and cotton vegetation coverage; t ls canopy-bare soil temperature difference data values.
Step 5: based on the calculation results in the step 3 and the step 4, establishing a alfalfa cotton field soil water content monitoring model;
said step 5 comprises the steps of:
Step 5.1: taking the water stress index CWSI leaf of the canopy and the water stress index CWSI soil of bare soil as independent variables, taking the water content of the canopy soil as a dependent variable, and establishing a unitary linear regression model;
Cotton ground moisture content model:
ycotton＝k1*CWSIleaf+β1；
Wherein k 1、β1 is slope and constant, y cotton is cotton soil water content, and the water stress index of CWSI leaf canopy;
alfalfa ground soil moisture content model:
yalfalfa＝k2*CWSIleaf+β2；
Wherein k 2、β2 is the slope and constant, ya lfalfa is the alfalfa soil moisture content, and the water stress index of the CWSI leaf canopy;
taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable, and establishing a unitary linear regression model;
Bare soil moisture content model:
ysoil＝k3*CWSIsoil+β3；
Wherein k 3、β3 is the slope and constant, and y soil is the moisture content of bare soil;
Two factor soil moisture content model:
constructing a linear regression model by taking canopy-bare soil temperature difference and vegetation coverage index CSTI as independent variables and soil water content as dependent variables:
y4＝k4*CSTI+β4；
Wherein k 4、β4 is slope and constant, and y 4 is cotton soil moisture content;
Step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of the cotton field soil of alfalfa, The model weight is a single factor model weight, and eta is a double factor model weight; phi is constant.
Step 6: and verifying the alfalfa cotton fields soil water content monitoring model based on the ground survey data, and selecting an optimal model.
In the step 6, the soil moisture content is predicted through an alfalfa cotton field soil moisture content monitoring model to obtain a predicted value, error analysis and correlation analysis are carried out on the predicted value and an actual value actually measured in ground investigation data, and the accuracy of inversion of the soil moisture content of the comprehensive soil moisture content model is verified by comparing the determination coefficients R 2 and Root Mean Square Error (RMSE) of two groups of variables.
According to the method, remote sensing data are processed through unmanned aerial vehicle remote sensing data, vegetation indexes, vegetation coverage, water stress indexes, layer-bare soil temperature difference and vegetation coverage indexes are calculated based on the processed data, an alfalfa cotton field soil water content monitoring model is built based on the calculation results, accuracy of the alfalfa cotton field soil water content monitoring model is verified through ground actual measurement data, an optimal alfalfa cotton field soil water content monitoring model is selected, accordingly the establishment of the alfalfa cotton field soil water content monitoring model is achieved, the water content can be obtained by means of calculation after the remote sensing data actually monitored by the unmanned aerial vehicle are preprocessed and placed into the alfalfa cotton field soil water content monitoring model, scientific guidance is provided for scientific water and accurate irrigation, and important significance is provided for water conservation and stable production of cotton planting. The application collects data based on unmanned aerial vehicle remote sensing technology, and calculates the cotton field water content based on an alfalfa cotton field soil water content monitoring model, so that monitoring equipment is not required to be installed on a planting field, and the technical problems in the prior art are effectively solved.
The invention is further described with reference to the following implementations:
The method for establishing the bare soil temperature average value comprises the following steps:
Step 1: collecting unmanned aerial vehicle remote sensing data and ground investigation data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground investigation data to obtain preprocessed image data;
the collection of unmanned aerial vehicle remote sensing data in step 1 is as follows:
Ten times of irrigation operations are needed according to the water and fertilizer management operation requirements of the traditional cotton fields, and the first time of irrigation operation is started at the beginning of 6 months. The unmanned aerial vehicle remote sensing platform is adopted to collect remote sensing images of irrigation operation plots in the first three days, the first day, the last day and the last three days of each cotton field irrigation, and 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours of data are collected every day.
The unmanned aerial vehicle remote sensing platform of this embodiment includes: the system comprises a four-rotor unmanned plane, a flight control system, a Buddhist Zenmuse H T 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 miniature portable computer.
Wherein the quadrotor unmanned aerial vehicle is a Dajiang longitude and latitude M300, and the duration is 30 minutes; the Buddhist Zenmuse H T imaging system is a product of Dajiang Innovation, the visual field angle of an RGB visible light camera 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 angle of view is 40.6 degrees, and the temperature measuring range is-40 ℃ to 150 ℃ in a high gain mode. The RedEdge-MX airborne multispectral imager is manufactured by MICASENSE company, adopts a hovering scanning imaging mode, has a spectral range of 400-900nm (the central wavelength of blue wave band is 475nm, the wave width is 40nm, the central wavelength of green wave band is 560nm, the wave width is 20nm, the central wavelength of red wave band is 668nm, the wave width is 10nm, the central wavelength of red wave band is 717nm, the wave width is 10nm, the central wavelength of near infrared wave band is 840nm, the wave width is 40 nm), and the horizontal angle of view is 47.2 degrees, and the spatial resolution is 0.02m@60 m.
The unmanned aerial vehicle remote sensing operation is clear and cloudless on the same day, the wind speed is less than 3 levels, the navigational speed is 1m/s, the navigational height is 50m, the course overlapping degree is 75%, and the side overlapping degree is 75%. After data are collected according to the regulations, PIX4D software is utilized to perform data preprocessing work.
The unmanned aerial vehicle remote sensing data processing method 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 a multispectral image, an RGB visible light image and a thermal infrared image of a target land block;
the original thermal infrared image was analyzed and corrected by DJI ThermalAnalysis Tool tools available from the Xinjiang Innovative company.
After the original acquired cotton field unmanned aerial vehicle remote sensing data set is processed by Pix4D software, a multispectral image with the ground resolution of 3.6cm, an original RGB visible light image with the ground resolution of 1.8cm and an original thermal infrared image are obtained, standard preprocessing data are obtained, and ENVI software is used for cutting according to the range of a 500 m buffer area, so that the multispectral image and the RGB visible light image of a target land block are obtained.
Registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land block respectively by using the known high-precision reference image of the land block to obtain registered multispectral image, RGB visible light image and thermal infrared image;
Based on the registered RGB visible light images, a full-color image synthesis formula is adopted to generate full-color image graduate:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
Wherein: r red is red band data in the registered RGB visible light image; r green is the green band data in the registered RGB visible image; r blue is blue band data in the registered RGB visible light image;
And a NNDiffuse PAN SHARPING module of ENVI software is adopted to fuse the registered excessive spectrum images with the full-color image graduate to obtain fused multispectral images.
The acquisition and processing of the ground investigation data:
After the unmanned aerial vehicle image acquisition is completed, the ground and ground data acquisition is synchronously carried out, and the unmanned aerial vehicle mainly contains the water content of soil. The soil moisture content is measured by adopting a traditional soil sampling and drying method, soil is sampled by a soil drill at the center of each square sampling area, the soil sampling depths are 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100cm, the soil sample is quickly put into an aluminum box for weighing after being taken out, and the soil sample is dried for 8 hours at 105 ℃ in a drying oven and then is weighed, and the soil moisture content is calculated and multiplied 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 alfalfa, cotton and bare soil are fully considered, moisture sensors are arranged at 0-15cm, 0-30cm and 0-60cm, and real-time soil moisture information of the alfalfa, the cotton and the bare soil is obtained.
The invention ensures the accuracy of the subsequent processing by preprocessing the data.
Step 2: separating vegetation and bare soil boundaries in the cultivated land range based on the image data obtained in the step 1 to obtain the cultivated land range and the image data of the cotton field of alfalfa;
Referring to fig. 2, the step 2 includes the following steps:
step 2.1: distinguishing soil properties by using the registered RGB visible light images, and dividing different soil properties to extract a cultivated land range; the soil mass properties include: cultivated land, cultivated land roads, buildings, ditches, protective forests, etc.
Step 2.2: based on the registered RGB visible light image, a gray segmentation method is adopted to preliminarily obtain a classification image of a vegetation growing area and bare soil;
Specific:
according to the invention, 50 typical representative areas of cotton plants, alfalfa plants and bare soil are selected from RGB visible light images obtained by each test, gray scales of the cotton plants, the alfalfa plants and the bare soil in a green wave band are counted respectively, and whether crossing areas exist between vegetation and the bare soil or not is analyzed through histogram comparison, so that classification images of the alfalfa, the cotton and the bare soil are obtained.
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); is affected by the leaf cell structure of vegetation, and has strong Near Infrared (NIR) reflection effect. The green-blue vegetation index GBRI and the simple vegetation index SRI are determined based on the following formulas:
Wherein: r Green is green band data in the multispectral image; r Blue is blue band data in the multispectral image;
wherein: r NIR is near infrared band data in the multispectral image; r red is red 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 negative values indicate that the ground coverage is cloud, water, snow and the like, and the vegetation index has high reflection on visible light; 0 represents rock, bare soil or the like, and NIR and R are approximately equal; positive values indicate vegetation coverage and increase as coverage increases. Because the growing period of alfalfa is different from that of cotton, the ground coverage is different, so that the cotton and the alfalfa planting range in the vegetation growing area can be distinguished according to the vegetation index.
Step 2.4: and (3) superposing the results output in the step (2.2) and the step (2.3), confirming that the final distribution area vector file of alfalfa, cotton and bare soil is obtained, and finishing drawing output results.
The invention fully considers the condition of bare soil water content, realizes the monitoring of farmland soil water content, and can not only reflect the soil water content of a vegetation area, but also fully reflect the soil water content of the bare soil area.
Step 3: calculating a vegetation index and a vegetation coverage based on the alfalfa cotton fields 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:
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
step 3.2: the alfalfa and cotton vegetation coverage VFC was calculated using the following formula:
Wherein: NDVI veg is the 95% confidence value in the vegetation index grading NDVI, and NDVI soil is the 2% confidence value in the vegetation index grading NDVI.
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 obtained in the step 2 and the image data;
The step 4 comprises the following steps:
Step 4.1: superposing the distribution area vector files of the alfalfa and the cotton into the registered thermal infrared image, and performing mask processing by using ENVI software to obtain the alfalfa and cotton canopy mask files; performing mask processing and data statistics on the registered thermal infrared images to respectively obtain a canopy temperature T leaf corresponding to each pixel of the alfalfa distribution area and the cotton distribution area, and a canopy temperature maximum value T l_max, a minimum value T l_min and an average value T l_c of 1% data at two ends of the thermal infrared images; the average value T l_c of the canopy temperature refers to the average value of the canopy temperature of the bare soil removed from the corresponding area;
Step 4.2: superposing the bare soil distribution area vector file into the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; performing mask processing on the infrared image, and performing data statistics to obtain a bare soil temperature T soil corresponding to each pixel of the bare soil distribution area; removing 1% of data at 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: subtracting the bare soil temperature average value from the canopy temperature T leaf to obtain a canopy-bare soil temperature difference data value T ls;
step 4.4: water stress indexes of canopy and bare soil are calculated based on the following formula:
Wherein: CWSI leaf is the water stress index of the canopy; t leaf is the canopy temperature; t l_max is the maximum value of the canopy temperature; t l_min is the minimum value of the canopy temperature;
Wherein: CWSI soil is the water stress index of bare soil; t soil is the bare soil temperature; t s_max is the maximum value of the bare soil temperature; t s_min is the minimum value of bare soil temperature;
Through the calculation, the canopy and bare soil water stress indexes at different times of the day are obtained, and the water stress index change information at different times of 11-21 days is analyzed.
Step 4.5: and (3) calculating a canopy-soil temperature difference and vegetation coverage CSTI index:
wherein: VFC is alfalfa and cotton vegetation coverage; t ls canopy-bare soil temperature difference data values.
Step 5: based on the calculation results in the step 3 and the step 4, establishing a alfalfa cotton field soil water content monitoring model;
said step 5 comprises the steps of:
Step 5.1: taking the water stress index CWSI leaf of the canopy and the water stress index CWSI soil of bare soil as independent variables, taking the water content of the canopy soil as a dependent variable, and establishing a unitary linear regression model;
Cotton ground moisture content model:
ycotton＝k1*CWSIleaf+β1；
Wherein k 1、β1 is slope and constant, y cotton is cotton soil water content, and the water stress index of CWSI leaf canopy;
alfalfa ground soil moisture content model:
yalfalfa＝k2·CWSIleaf+β2；
Wherein k 2、β2 is slope and constant, y alfalfa is alfalfa ground soil water content, and the water stress index of CWSI leaf canopy;
taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable, and establishing a unitary linear regression model;
Bare soil moisture content model:
ysoil＝k3*CWSIsoil+β3；
Wherein k 3、β3 is the slope and constant, and y soil is the moisture content of bare soil;
Two factor soil moisture content model:
constructing a linear regression model by taking canopy-bare soil temperature difference and vegetation coverage index CSTI as independent variables and soil water content as dependent variables:
y4＝k4*CSTI+β4；
Wherein k 4、β4 is slope and constant, and y 4 is cotton soil moisture content;
Step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of the cotton field soil of alfalfa, The model weight is a single factor model weight, and eta is a double factor model weight; phi is constant.
Step 6: and verifying the alfalfa cotton field soil water content monitoring model based on the ground survey data, and selecting a model meeting preset conditions.
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 synchronously collecting ground data.
In the step 6, the soil moisture content is predicted through an alfalfa cotton field soil moisture content monitoring model to obtain a predicted value, error analysis and correlation analysis are carried out on the predicted value and an actual value actually measured in ground investigation data, and the accuracy of inversion of the soil moisture content of the comprehensive soil moisture content model is verified by comparing the determination coefficients R 2 and Root Mean Square Error (RMSE) of two groups of variables.
According to the method, remote sensing data are processed through unmanned aerial vehicle remote sensing data, vegetation indexes, vegetation coverage, water stress indexes, layer-bare soil temperature difference and vegetation coverage indexes are calculated based on the processed data, an alfalfa cotton field soil water content monitoring model is built based on the calculation results, accuracy of the alfalfa cotton field soil water content monitoring model is verified through ground actual measurement data, an optimal alfalfa cotton field soil water content monitoring model is selected, accordingly the establishment of the alfalfa cotton field soil water content monitoring model is achieved, the water content can be obtained by means of calculation after the remote sensing data actually monitored by the unmanned aerial vehicle are preprocessed and placed into the alfalfa cotton field soil water content monitoring model, scientific guidance is provided for scientific water and accurate irrigation, and important significance is provided for water conservation and stable production of cotton planting. The application collects data based on unmanned aerial vehicle remote sensing technology, and calculates the cotton field water content based on an alfalfa cotton field soil water content monitoring model, so that monitoring equipment is not required to be installed on a planting field, and the technical problems in the prior art are effectively solved.
The above examples merely illustrate specific embodiments of the application, which are described in more detail and are not to be construed as limiting the scope of the application. It should be noted that it is possible for a person skilled in the art to make several variants and modifications without departing from the technical idea of the application, which fall within the scope of protection of the application.
Claims (5)
1. The method for establishing the alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing is characterized by comprising the following steps of:
Step 1: collecting unmanned aerial vehicle remote sensing data and ground investigation data, and preprocessing the unmanned aerial vehicle remote sensing data and the ground investigation data to obtain preprocessed image data;
step 2: separating vegetation and bare soil boundaries in the cultivated land range based on the image data obtained in the step 1 to obtain the cultivated land range and the image data of the cotton field of alfalfa;
Step 3: calculating a vegetation index and a vegetation coverage based on the alfalfa cotton fields cultivated land range and the image data obtained in the step 2;
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:
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
step 3.2: the alfalfa and cotton vegetation coverage VFC was calculated using the following formula:
Wherein: NDVI veg is the 95% confidence value in the vegetation index NDVI, and NDVI soil is the 2% confidence value in the vegetation index grading NDVI.
Step 4: calculating a water stress index, a temperature difference between a canopy and bare soil 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 distribution area vector files of the alfalfa and the cotton into the registered thermal infrared image, and performing mask processing by using ENVI software to obtain the alfalfa and cotton canopy mask files; performing mask processing and data statistics on the registered thermal infrared images to respectively obtain a canopy temperature T leaf corresponding to each pixel of the alfalfa distribution area and the cotton distribution area, and a canopy temperature maximum value T l_max, a minimum value T l_min and an average value T l_c of 1% data at two ends of the thermal infrared images; the average value T l_c of the canopy temperature refers to the average value of the canopy temperature of the bare soil removed from the corresponding area;
Step 4.2: superposing the bare soil distribution area vector file into the registered infrared image, and performing mask processing by using ENVI software to obtain a bare soil mask file; performing mask processing on the infrared image, and performing data statistics to obtain a bare soil temperature T soil corresponding to each pixel of the bare soil distribution area; removing 1% of data at 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: subtracting the bare soil temperature average value from the canopy temperature T leaf to obtain a canopy-bare soil temperature difference data value T ls;
step 4.4: water stress indexes of canopy and bare soil are calculated based on the following formula:
Wherein: CWSI leaf is the water stress index of the canopy; t leaf is the canopy temperature; t l_max is the maximum value of the canopy temperature; t l_min is the minimum value of the canopy temperature;
Wherein: CWSI soil is the water stress index of bare soil; t soil is the bare soil temperature; t s_max is the maximum value of the bare soil temperature; t s_min is the minimum value of bare soil temperature;
step 4.5: and (3) calculating a canopy-soil temperature difference and vegetation coverage CSTI index:
wherein: VFC is alfalfa and cotton vegetation coverage; t ls canopy-bare soil temperature difference data values.
Step 5: based on the calculation results in the step 3 and the step 4, establishing a alfalfa cotton field soil water content monitoring model;
said step 5 comprises the steps of:
Step 5.1: taking the water stress index CWSI leaf of the canopy and the water stress index CWSI soil of bare soil as independent variables, taking the water content of the canopy soil as a dependent variable, and establishing a unitary linear regression model;
Cotton ground moisture content model:
ycotton＝k1*CWSIleaf+β1；
Wherein k 1、β1 is slope and constant, y cotton is cotton soil water content, and the water stress index of CWSI leaf canopy;
alfalfa ground soil moisture content model:
yalfalfa＝k2*CWSIleaf+β2；
Wherein k 2、β2 is slope and constant, y alfalfa is alfalfa ground soil water content, and the water stress index of CWSI leaf canopy;
taking the bare soil water stress index as an independent variable and the bare soil water content as a dependent variable, and establishing a unitary linear regression model;
Bare soil moisture content model:
ysoil＝k3*CWSIsoil+β3；
Wherein k 3、β3 is the slope and constant, and y soil is the moisture content of bare soil;
Two factor soil moisture content model:
constructing a linear regression model by taking canopy-bare soil temperature difference and vegetation coverage index CSTI as independent variables and soil water content as dependent variables:
y4＝k4*CSTI+β4；
Wherein k 4、β4 is slope and constant, and y 4 is cotton soil moisture content;
Step 5.2: comprehensively constructing an alfalfa cotton field soil water content monitoring model:
wherein y is the water content of the cotton field soil of alfalfa, The model weight is a single factor model weight, and eta is a double factor model weight; phi is constant.
Step 6: and verifying the alfalfa cotton field soil water content monitoring model based on the ground survey data, and selecting a model meeting the conditions.
2. The method for building the alfalfa cotton field soil moisture content monitoring model by unmanned aerial vehicle remote sensing according to claim 1, wherein,
The unmanned aerial vehicle remote sensing data processing method 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 a multispectral image, an RGB visible light image and a thermal infrared image of a target land block;
Registering the multispectral image, the RGB visible light image and the thermal infrared image of the target land block respectively by using the known high-precision reference image of the land block to obtain registered multispectral image, RGB visible light image and thermal infrared image;
Based on the registered RGB visible light images, a full-color image synthesis formula is adopted to generate full-color image graduate:
grayscale＝0.2126*Rred+0.7152*Rgreen+0.0722*Rblue；
Wherein: r red is red band data in the registered RGB visible light image; r green is the green band data in the registered RGB visible image; r blue is blue band data in the registered RGB visible light image;
And fusing the registered excessive spectrum images with the full-color image gradeale to obtain fused multispectral images.
3. The method for building the alfalfa cotton field soil moisture content monitoring model by unmanned aerial vehicle remote sensing according to claim 2, wherein,
The step2 comprises the following steps:
Step 2.1: distinguishing soil properties by using the registered RGB visible light images, and dividing different soil properties to extract a cultivated land range;
Step 2.2: based on the registered RGB visible light image, a gray segmentation method is adopted to preliminarily obtain a classification image of a vegetation growing area and bare soil;
Step 2.3: the green-blue vegetation index GBRI and the simple vegetation index SRI are determined based on the following formulas:
Wherein: r Green is green band data in the multispectral image; r Blue is blue band data in the multispectral image;
wherein: r NIR is near infrared band data in the multispectral image; r red is red band data in the multispectral image;
step 2.4: and (3) superposing the results output in the step (2.2) and the step (2.3), confirming that the final distribution area vector file of alfalfa, cotton and bare soil is obtained, and finishing drawing output results.
4. The method for building the alfalfa cotton field soil moisture content monitoring model by unmanned aerial vehicle remote sensing according to claim 1, wherein in the step 6, the alfalfa cotton field soil moisture content monitoring model is used for predicting the soil moisture content to obtain a predicted value, the predicted value is subjected to error analysis and correlation analysis with the actual value actually measured in ground investigation data, and the accuracy of inverting the soil moisture content by the comprehensive soil moisture content model is verified by comparing the determination coefficients R 2 of two groups of variables with the root mean square error RMSE.
5. The method for building the alfalfa cotton field soil moisture content monitoring model by unmanned aerial vehicle remote sensing according to claim 1, wherein the collection of unmanned aerial vehicle remote sensing data in the step 1 is as follows:
The remote sensing image of the irrigation operation land is acquired 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 irrigation of the cotton field in the last ten days of 4 months to 8 months, and the data of 11 hours, 13 hours, 15 hours, 17 hours, 19 hours and 21 hours are acquired every day.
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