CN115937711A - Soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing - Google Patents
Soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing Download PDFInfo
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Abstract
A soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing relates to the technical field of soil moisture data monitoring; the method comprises the following steps: determining a target plot; acquiring remote sensing images of the target plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data; the unmanned aerial vehicle transmits acquired remote sensing image acquisition data to a ground control center by using a network signal; the ground control center processes data according to the obtained remote sensing image acquisition data to obtain extracted spectral reflectivity, salinity index and vegetation index; the obtained spectral reflectivity, salinity index and vegetation index are led into a salinity monitoring model for calculation, and specific soil salinity data of a target plot is obtained; and uploading the obtained soil salinity data, the remote sensing image acquisition data, the spectral reflectivity, the salinity index and the vegetation index to a cloud platform for storage. According to the invention, the soil salinity data of a large-range plot can be rapidly measured by using the unmanned aerial vehicle equipment so as to monitor.
Description
Technical Field
The invention relates to the technical field of soil moisture data monitoring, in particular to a soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing.
Background
The traditional method for acquiring the salt content of the soil is fixed-point sampling and is measured by a conductivity meter, so that time and labor are wasted; with the close combination of the remote sensing technology and agriculture, the satellite remote sensing data is commonly used for carrying out effective monitoring inversion on soil salinization, however, the satellite remote sensing data has the problems of low precision, high possibility of being influenced by the thickness of a cloud layer, unfavorable revisit time and the like, and due to the problems, when the soil salinization is monitored, the obtained data is inaccurate, and the actual salinization condition of the soil cannot be accurately reflected.
In recent years, unmanned aerial vehicle remote sensing and soil salinization monitoring combine more and more closely, and unmanned aerial vehicle easily deploys, and remote sensing data acquisition's is with low costs, and the accuracy is high, has higher spreading value.
Disclosure of Invention
The invention aims to provide a soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing comprises the following steps:
a1, determining a target plot;
a2, acquiring remote sensing images of the target land parcel by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B1;
a3, the unmanned aerial vehicle transmits the remote sensing image acquisition data B1 obtained in the step A2 to a ground control center by using a network signal; the ground control center processes the data of the B1 according to the obtained remote sensing image acquisition data B1 to obtain an extracted spectral reflectivity B2, a salinity index B3 and a vegetation index B4;
a4, introducing the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 obtained in the step A3 into a salinity monitoring model for calculation to obtain specific soil salinity data of the target plot;
and A5, uploading the soil salinity data obtained in the A4 and the remote sensing image acquisition data B1 obtained in the A3, the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 to a cloud platform for storage and remote calling.
As a further scheme of the invention: in the step A2, instruments used for collecting remote sensing images of the target plot are a multispectral sensor, a laser radar sensor, an RTK camera and an SD card for storing data.
As a still further scheme of the invention: the laser radar sensor is of a Zen L1 type.
As a still further scheme of the invention: the ground control center comprises a solar power supply module, a server, a display and a wireless communication module;
the solar power supply module is connected with the server, the display and the wireless communication module and used for supplying electric energy;
the server receives, calculates and stores the data;
the display is used for displaying the data in the server and presenting the result of data calculation in the server;
and the wireless communication module realizes data connection between the server and the unmanned aerial vehicle.
As a still further scheme of the invention: the salinity monitoring model is one of a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model.
As a still further scheme of the invention: the determination method of the salinity monitoring model comprises the following steps:
c1, selecting one of the target plots as a sample plot;
c2, performing soil salinity measurement in the sample plot by using a soil salinity measurer by adopting a five-point sampling method, and obtaining measurement data B5;
c3, carrying out remote sensing image acquisition on the sample plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B6;
c4, the unmanned aerial vehicle transmits the remote sensing image acquisition data B6 obtained in the step C3 to a ground control center by using a network signal; the ground control center collects data B6 according to the obtained remote sensing image and processes the data of the data B6 to obtain extracted spectral reflectivity B7, a salinity index B8 and a vegetation index B9;
c5, introducing the spectral reflectivity B7, the salinity index B8 and the vegetation index B9 obtained in the step C4 into a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model for model calculation respectively, and obtaining soil salinity data B10 by each model;
and C6, comparing the data of the B10 and the data of the B5 respectively obtained by the support vector machine model, the back propagation neural network model, the random forest model and the extreme learning machine model to obtain a model with the closest data, and determining that the closest model is a salinity monitoring model.
As a still further scheme of the invention: and the soil salinity measurer in the C2 is a TRIME-PICO-IPH TDR section soil salinity measurer.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the monitoring of the salinity data in the soil can be realized by using the unmanned aerial vehicle equipment, the method is particularly suitable for monitoring the soil salinity data of a large-range plot, and after the model selection is completed, the remote sensing image acquisition data can be acquired by using the unmanned aerial vehicle remote sensing image acquisition mode, so that the specific soil salinity data of a target plot can be rapidly acquired; compared with the traditional measuring mode, the method is more convenient and faster to operate, larger in monitoring range and higher in efficiency; compared with the satellite remote sensing measurement, the measurement accuracy is higher.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, an element of the present invention may be said to be "fixed" or "disposed" to another element, either directly on the other element or with intervening elements present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only and do not represent the only embodiments.
The method aims to solve the problems that the traditional soil salinity acquisition method is fixed-point sampling and is determined by a conductivity meter, time and labor are wasted, the satellite remote sensing data measurement is low in precision and is easily influenced by the thickness of a cloud layer and unfavorable revisit time, and the like.
The invention provides a soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing, which comprises the following steps:
a1, determining a target plot;
a2, acquiring remote sensing images of the target plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B1; instruments used for collecting remote sensing images of the target plot are a multispectral sensor, a laser radar sensor, an RTK camera and an SD card used for storing data; in order to ensure the measuring effect, the model of the laser radar sensor is Zen Si L1;
a3, the unmanned aerial vehicle transmits the remote sensing image acquisition data B1 obtained in the step A2 to a ground control center by using a network signal; the ground control center processes the data of the B1 according to the obtained remote sensing image acquisition data B1 to obtain an extracted spectral reflectivity B2, a salinity index B3 and a vegetation index B4;
the ground control center comprises a solar power supply module, a server, a display and a wireless communication module;
the solar power supply module is connected with the server, the display and the wireless communication module and used for supplying electric energy;
the server receives, calculates and stores the data;
the display is used for displaying the data in the server and presenting the result of data calculation in the server;
the wireless communication module is used for realizing data connection between the server and the unmanned aerial vehicle;
a4, introducing the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 obtained in the step A3 into a salinity monitoring model for calculation to obtain specific soil salinity data of the target plot;
the salinity monitoring model is one of a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model;
the determination method of the salinity monitoring model comprises the following steps:
c1, selecting one of the target plots as a sample plot;
c2, performing soil salinity measurement in the sample plot by using a soil salinity measurer by adopting a five-point sampling method, and obtaining measurement data B5; the soil salinity measurer is a TRIME-PICO-IPH TDR section soil salinity measurer;
c3, carrying out remote sensing image acquisition on the sample plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B6;
c4, the unmanned aerial vehicle transmits the remote sensing image acquisition data B6 obtained in the step C3 to a ground control center by using a network signal; the ground control center collects data B6 according to the obtained remote sensing image and processes the data of the data B6 to obtain extracted spectral reflectivity B7, a salinity index B8 and a vegetation index B9;
c5, introducing the spectral reflectivity B7, the salinity index B8 and the vegetation index B9 obtained in the step C4 into a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model for model calculation respectively, and obtaining soil salinity data B10 by each model;
c6, comparing the data of the B10 and the data of the B5, which are respectively obtained through a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model, to obtain a model with the closest data, and determining the closest model as a salinity monitoring model;
and A5, uploading the soil salinity data obtained in the step A4 and the remote sensing image acquisition data B1 obtained in the step A3, the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 to a cloud platform for storage and remote calling.
Example 1
A sample monitoring module: the method comprises the steps of monitoring according to different planting crop types in a farm of an irrigation area, wherein a soil salinity monitoring module is a TRIME-PICO-IPH TDR profile soil salinity measurer, and the monitoring method further comprises a solar power supply module, an SD card and a 5G module.
Unmanned aerial vehicle remote sensing image acquisition module: the unmanned aerial vehicle carries a multispectral sensor, a laser radar sensor, an RTK camera and an SD card, wherein the type of the laser radar sensor is Zen Si L1.
A ground control center: receiving unmanned aerial vehicle remote sensing image data and soil salinity data of different crop coverage areas in an irrigation farm through 5G network signals, completing remote sensing image data processing by a control center, extracting spectral reflectivity, calculating vegetation indexes and salinity indexes, and completing construction of a monitoring model; the control center comprises a solar power supply module, a server, a display and a wireless communication module.
A digital platform: the unmanned aerial vehicle remote sensing image data and the soil salinity data are uploaded and stored in the cloud platform according to time and space respectively through the 5G data network, and remote access and calling are achieved.
According to the invention, the multispectral remote sensing of the unmanned aerial vehicle is used for rapidly monitoring the soil salinity covered by different crops in the irrigation area, the 5G cloud platform and the big data cloud platform are used for storing data, and the salinity variation analysis of the space and time sequence of the soil salinity under a long time sequence can be realized. And the real-time soil salinity data is used for carrying out precision evaluation and real-time correction on the unmanned aerial vehicle monitoring soil salinity model, and the monitoring precision is guaranteed.
Example 2
The method comprises the following steps that firstly, building of an unmanned aerial vehicle remote sensing image acquisition module, field arrangement and building of a soil salinity acquisition module, and building of a ground control center server are completed;
secondly, communicating with each module through a 5G communication module of the ground control center to complete a data transmission function and build a data cloud platform;
thirdly, operating the unmanned aerial vehicle by ground workers at a ground control center, planning a path, carrying a multispectral sensor, an RTK sensor and a laser radar sensor by the unmanned aerial vehicle, acquiring remote sensing images covered by crops such as film-covered cultivated land, salt-returning bare land, wheat, alfalfa and the like in different time and different spaces in a target block, transmitting data to the ground control center in real time through a 5G module, and acquiring field soil salinity and transmitting the data to the control center through a 5G communication module;
fourthly, the ground control center processes the remote sensing data transmitted back by the communication module, extracts reflectivity, calculates vegetation indexes and salinity indexes, guides the vegetation indexes and the salinity indexes into a server for modeling, and completes real-time, rapid and high-precision monitoring of soil salinity according to a salinity inversion model;
and fifthly, the ground control center compares the soil salinity actually measured in the field with the monitored salinity of the inversion model for analysis, corrects the precision of the model and further improves the precision of the model.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.
Claims (7)
1. A soil salinity rapid monitoring method based on unmanned aerial vehicle remote sensing is characterized by comprising the following steps:
a1, determining a target plot;
a2, acquiring remote sensing images of the target plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B1;
a3, the unmanned aerial vehicle transmits the remote sensing image acquisition data B1 obtained in the step A2 to a ground control center by using a network signal; the ground control center processes the data of the B1 according to the obtained remote sensing image acquisition data B1 to obtain an extracted spectral reflectivity B2, a salinity index B3 and a vegetation index B4;
a4, introducing the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 obtained in the step A3 into a salinity monitoring model for calculation, and obtaining specific soil salinity data of the target plot;
and A5, uploading the soil salinity data obtained in the step A4 and the remote sensing image acquisition data B1 obtained in the step A3, the spectral reflectivity B2, the salinity index B3 and the vegetation index B4 to a cloud platform for storage and remote calling.
2. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method of claim 1, wherein the instruments used for remote sensing image acquisition of the target plot in the step A2 are a multispectral sensor, a laser radar sensor, an RTK camera and an SD card for storing data.
3. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method according to claim 2, wherein the type of the lidar sensor is zensi L1.
4. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method according to claim 1, wherein the ground control center comprises a solar power supply module, a server, a display and a wireless communication module;
the solar power supply module is connected with the server, the display and the wireless communication module and used for supplying electric energy;
the server receives, calculates and stores the data;
the display is used for displaying the data in the server and presenting the result of data calculation in the server;
and the wireless communication module realizes data connection between the server and the unmanned aerial vehicle.
5. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method according to claim 1, wherein the salinity monitoring model is one of a support vector machine model, a back propagation neural network model, a random forest model, and an extreme learning machine model.
6. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method of claim 5, wherein the salinity monitoring model is determined by a method comprising the following steps:
c1, selecting one of the target plots as a sample plot;
c2, measuring the soil salinity in the sample plot by using a soil salinity measurer by adopting a five-point sampling method, and obtaining measurement data B5;
c3, carrying out remote sensing image acquisition on the sample plot by using an unmanned aerial vehicle to obtain remote sensing image acquisition data B6;
c4, the unmanned aerial vehicle transmits the remote sensing image acquisition data B6 obtained in the step C3 to a ground control center by using a network signal; the ground control center collects data B6 according to the obtained remote sensing image and processes the data of the data B6 to obtain extracted spectral reflectivity B7, a salinity index B8 and a vegetation index B9;
c5, introducing the spectral reflectivity B7, the salinity index B8 and the vegetation index B9 obtained in the step C4 into a support vector machine model, a back propagation neural network model, a random forest model and an extreme learning machine model for model calculation respectively, and obtaining soil salinity data B10 by each model;
and C6, comparing the data of the B10 and the data of the B5 respectively obtained by the support vector machine model, the back propagation neural network model, the random forest model and the extreme learning machine model to obtain a model with the closest data, and determining that the closest model is a salinity monitoring model.
7. The unmanned aerial vehicle remote sensing-based soil salinity rapid monitoring method according to claim 6, wherein the soil salinity measurer in the C2 is a TRIME-PICO-IPH TDR profile soil salinity measurer.
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