CN115797785A - Farmland irrigation frequency determination method and device based on microwave remote sensing - Google Patents
Farmland irrigation frequency determination method and device based on microwave remote sensing Download PDFInfo
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Abstract
The embodiment of the invention provides a farmland irrigation frequency determining method and device based on microwave remote sensing, and relates to the technical field of farmland irrigation, wherein the method comprises the following steps: acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected; soil moisture data are obtained through inversion according to the microwave remote sensing data, and evapotranspiration data are obtained through inversion according to the optical remote sensing data; and inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model, and outputting the irrigation frequency of the irrigation area to be detected by the detection model, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples. The scheme is beneficial to improving the accuracy and timeliness of determining the farmland irrigation frequency.
Description
Technical Field
The invention relates to the technical field of farmland irrigation, in particular to a farmland irrigation frequency determination method and device based on microwave remote sensing.
Background
The accelerated growth of the world population puts tremendous pressure on the global food supply. The expansion of arable land can offset the increase in food shortages in many regions, but is limited due to the adverse and profound effects on biodiversity. Therefore, the enlargement of irrigation areas and the improvement of agricultural technology are expected to make a significant contribution to meeting the growing food demand. Irrigation agriculture accounts for 40% of global crop production and is an important component of world grain production, particularly in semiarid and arid regions. In addition to maintaining sufficient soil moisture to promote healthy growth and optimal yield, irrigation can also protect crops from frost damage in cold seasons and inhibit growth of weeds in the grain fields. Furthermore, irrigation is an important component of global water circulation and land water balance. Currently, it accounts for about 70% of the total global fresh water usage, accounting for about 90% of the water consumed. Therefore, information on the extent, frequency, etc. of irrigation is very important for a wide range of research including food safety and economic development, water and energy exchange between land surface and atmosphere, water resource allocation between agriculture and ecosystem, and agriculture-climate interaction and feedback.
The traditional farmland irrigation information detection method comprises the following steps: (1) A manual investigation statistical method, irrigation information is investigated and recorded by workers through a lower base layer; (2) The sensor monitoring method is characterized in that a soil humidity sensor is arranged in the field, and irrigation information is detected by utilizing the change condition of soil humidity. The traditional detection method is time-consuming and labor-consuming, can only obtain data of a plurality of points, is limited in coverage range and poor in representativeness, cannot reflect space irrigation information quickly, and affects timeliness of farmland irrigation management measures.
Remote sensing is known as an effective tool for extracting irrigation information, and many researches on drawing irrigation maps by using remote sensing are carried out, but the researches mainly focus on drawing irrigation areas and do not consider frequency information. Data such as MODIS and Landsat have been used for regional and global mapping of irrigation areas. The detection of irrigation information using optical remote sensing data can be described with the following assumptions: water generated by irrigation permeates soil, so that the soil is more moist, and water possibly accumulates on the surface, which means that the change of the remote sensing humidity index can be used as an index for detecting irrigation.
However, the problems of detecting irrigation information by optical remote sensing means are as follows: the vegetation cover density is great usually in the farmland, and irrigation moisture is hidden under the vegetation usually, and optical sensor is very difficult accurate moisture information of catching, and then has influenced irrigation information detection's accuracy.
Disclosure of Invention
In view of this, the embodiment of the invention provides a farmland irrigation frequency determination method based on microwave remote sensing, so as to solve the technical problem of low accuracy in determining irrigation information in the prior art. The method comprises the following steps:
acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected;
soil moisture data are obtained through inversion according to the microwave remote sensing data, and evapotranspiration data are obtained through inversion according to the optical remote sensing data;
and inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model, and outputting the irrigation frequency of the irrigation area to be detected by the detection model, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples.
The embodiment of the invention also provides a farmland irrigation frequency determining device based on microwave remote sensing, which aims to solve the technical problem of low accuracy in determining irrigation information in the prior art. The device comprises:
the data acquisition module is used for acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected;
the data inversion module is used for obtaining soil moisture data according to the microwave remote sensing data and obtaining evapotranspiration data according to the optical remote sensing data;
and the irrigation frequency calculation module is used for inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model, and the detection model outputs the irrigation frequency of the irrigation area to be detected, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the arbitrary farmland irrigation frequency determination method based on microwave remote sensing when executing the computer program so as to solve the technical problem of low accuracy in the determination of irrigation information in the prior art.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for executing any farmland irrigation frequency determination method based on microwave remote sensing, so as to solve the technical problem of low accuracy in irrigation information determination in the prior art.
Compared with the prior art, the beneficial effects that can be achieved by the at least one technical scheme adopted by the embodiment of the specification at least comprise: the method comprises the steps that soil moisture data are obtained through inversion of microwave remote sensing data based on an irrigation area to be detected, evapotranspiration data are obtained through inversion of optical remote sensing data based on the irrigation area to be detected, further soil moisture data, evapotranspiration data and water related data of the irrigation area to be detected are input into a detection model, the detection model can output irrigation frequency of the irrigation area to be detected, the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples, the purpose of determining the irrigation frequency of the irrigation area based on the microwave remote sensing data is achieved, compared with a scheme that only optical remote sensing data are adopted to detect the irrigation information in the prior art, the microwave remote sensing has the advantages of being all-day-time, all-weather, high in resolution, high in penetrability and sensitive to soil moisture change, moisture information can be captured more accurately than the optical remote sensing data, and the use of the microwave remote sensing data is beneficial to improvement of accuracy and timeliness of determining farmland irrigation frequency; meanwhile, soil moisture data is adopted to replace a humidity index inverted by optical remote sensing data to serve as an ideal index for detecting irrigation information, and as the field evapotranspiration amount is obviously increased after irrigation, evapotranspiration data is introduced to determine irrigation frequency, and water related data is also introduced, so that the irrigation frequency is determined by combining the soil moisture data, the evapotranspiration data and the water related data, and the accuracy of determining the farmland irrigation frequency is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for determining farmland irrigation frequency based on microwave remote sensing provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a 1D-CNN model structure provided in an embodiment of the present invention;
FIG. 3 is a schematic flow chart of training the detection model according to an embodiment of the present invention;
FIG. 4 is a block diagram of a computer device according to an embodiment of the present invention;
fig. 5 is a block diagram of a structure of an apparatus for determining a farmland irrigation frequency based on microwave remote sensing according to an embodiment of the present invention.
Detailed Description
The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. 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 application.
In an embodiment of the present invention, a method for determining a farmland irrigation frequency based on microwave remote sensing is provided, as shown in fig. 1, the method includes:
step S101: acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected;
step S102: soil moisture data are obtained through inversion according to the microwave remote sensing data, and evapotranspiration data are obtained through inversion according to the optical remote sensing data;
step S103: and inputting the soil moisture data, the evapotranspiration data and the moisture related data of the irrigation area to be detected into a detection model, and outputting the irrigation frequency of the irrigation area to be detected by the detection model, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical moisture related data as samples.
As can be seen from the flow shown in fig. 1, in the embodiment of the present invention, determination of irrigation frequency of an irrigation area based on microwave remote sensing data is implemented, and compared with a scheme in the prior art that irrigation information is detected only by using optical remote sensing data, microwave remote sensing has the advantages of being all-weather, high resolution, strong in penetrability, and sensitive to soil moisture change, and can capture moisture information more accurately than optical remote sensing data, and the use of microwave remote sensing data is beneficial to improving the accuracy of determining farmland irrigation frequency; meanwhile, soil moisture data is adopted to replace a humidity index inverted by optical remote sensing data to serve as an ideal index for detecting irrigation information, and as the field evapotranspiration amount is obviously increased after irrigation, evapotranspiration data is introduced to determine irrigation frequency, and water related data is also introduced, so that the irrigation frequency is determined by combining the soil moisture data, the evapotranspiration data and the water related data, and the accuracy of determining the farmland irrigation frequency is further improved.
In specific implementation, the irrigation area to be detected can be a farmland area with any area and needing to detect irrigation information.
In specific implementation, the microwave remote sensing data is remote sensing data which can be used for soil moisture inversion, for example, SAR radar satellite data
The TerSAR can be used for soil moisture inversion.
In specific implementation, the microwave remote sensing data can be preprocessed before soil water data is obtained through inversion according to the microwave remote sensing data. For example, after a Sentinel-1 Ground Range Detection (GRD) High Resolution (HR) interferometric wide mode (IW) satellite image of a research area (i.e., an irrigation area to be detected) is acquired, the software of Sentinel-1Toolbox (S1 TBX) provided by the European Bureau can be used for preprocessing. The preprocessing step may include (1) applying an orbit file (2) GRD boundary noise removal (3) thermal noise cancellation (4) radiometric calibration (5) to obtain observations and angles of incidence under VV and VH polarizations.
In specific implementation, in order to improve the accuracy of determining the farmland irrigation frequency, in the embodiment, a method for inverting soil moisture data is provided, for example, a soil dielectric constant is determined according to a radar backscattering coefficient, a radar incident angle and a polarization mode of the microwave remote sensing data; and converting the soil dielectric constant into the volume water content of the soil by adopting a dielectric mixing model.
In specific implementation, the radar backscattering coefficient is mainly influenced by factors such as the soil dielectric constant (mainly dependent on soil moisture), the surface roughness and vegetation coverage. Assuming that the surface roughness and vegetation conditions are constant over time, the change in radar backscattering coefficient can be attributed to a change in the soil dielectric constant (soil moisture). The extraction of soil moisture and the change information thereof can adopt a multi-time phase repeated observation method. For 2 time phases T 1 and T2 The acquired SAR images, assuming that the roughness of the earth's surface remains constant during this time interval,and the influence of the surface roughness and the vegetation on the radar backscattering coefficient is multiplicative, the ratio of the radar backscattering coefficients acquired at the 2 time phases can be approximately regarded as a function of the soil dielectric constant, the radar incidence angle and the polarization mode. This model, also known as the Alpha approximation model, can be expressed as:
in the formula 
Represents the radar backscattering coefficient (energy);
is the radar incident angle;
is the relative permittivity of the soil;
representing the time of radar data acquisition;
is the polarization amplitude, which is a function of the radar incidence angle and the soil dielectric constant; PP represents a polarization mode and is HH or VV. The research shows that VV polarization
Is more sensitive to differences in soil water content and is therefore selected
And inverting the soil moisture of the farmland.
If a small disturbance model is adopted to describe the scattering of the earth surface, the PP is VV,
the polarization amplitude can be expressed as:
after acquiring the SAR images of 2 phases, an observation equation can be obtained according to formula (1):
to ensure the validity of the assumption, an observation equation is usually constructed using 2 consecutive images. For SAR images observed continuously with N scenes, the SAR images can be formed
The system of equations is as follows:
for equation set (4), there are N soil moisture unknowns and there are countless multiple solutions. In order to solve the system of equations, it is necessary to solve
The value range of the method is limited, and then a boundary constraint least square algorithm is adopted for solving. For a given radar angle of incidence and soil moisture range,
the value constraint condition is expressed as:
in the formula 
Respectively representing the amplitude of polarization
Minimum and maximum values for a given radar incidence angle and soil moisture range. Solving the equation set by using boundary constraint least square to obtain polarization amplitude
The value of (3) can be further determined from the equation (2)
Finally, a dielectric mixed model is adopted to convert the dielectric constant into the volume water content of the soil
。
In specific implementation, the spatial resolution of the soil water content data obtained by inversion can be determined according to requirements, for example, the spatial resolution of the soil water content data obtained by inversion can be 10m, and the time interval can be 6 days.
In specific implementation, in order to further improve the precision of determining the farmland irrigation frequency, in this embodiment, a method for inverting the evapotranspiration data is provided, for example, the optical remote sensing data and the meteorological data are input into an SEBAL model, and the SEBAL model inverts to obtain the evapotranspiration data of the irrigation area to be detected based on the earth surface energy balance principle.
In a specific implementation, the optical remote sensing data may be MODIS (medium resolution imaging spectrometer) data. Because the original MODIS data is stored by adopting a hierarchical data format hdf format and sinusoidal projection SIN, when in use, MRT software provided by NASA is required to be used for converting the MODIS data in the hdf format into a Geotiff format and carrying out operations such as projection conversion, resampling and the like. For example, when inverting evapotranspiration data in a SEBAL (land energy balance) model, the MODIS data used may include MOD11A1, MOD13A2, and MCD43B3.
In specific implementation, the SEBA (Surface energy balance Algorithms for Land) model is a model for inverting regional evapotranspiration based on the ground Surface energy balance principle, estimates the instantaneous evapotranspiration at the image acquisition time by an energy balance remainder method, and converts the evapotranspiration value into the evapotranspiration of a daily scale by combining the instantaneous evapotranspiration and the evapotranspiration ratio under the assumption that the evapotranspiration ratio is constant within one day, thereby calculating the total evapotranspiration of the whole day. In the method, when in calculation, only an optical remote sensing image and a small amount of meteorological data (air temperature and air speed) are input into the SEBALL model, and the net ground surface radiation quantity (Rn), the soil heat flux (G), the sensible heat flux (H) and the latent heat flux (lambda ET) can be obtained. The calculation formula of the earth surface energy balance principle in the SEBAL model is as follows:
the MODIS product data can directly provide characteristic parameters such as earth surface temperature, normalized vegetation index and earth surface reflectivity for the calculation of the SEBALL model.
The following is a specific calculation method for each map parameter in the SEBAL model.
(1) Net amount of radiation Rn
Rn is the sum of various radiations obtained from the earth surface and can be obtained by calculating the earth surface albedo, the earth surface specific radiance and the like, and the calculation formula is as follows:
Incident solar short wave radiation
The calculation formula of (2) is as follows:
long wave radiation of up going
With long-wave radiation down
Can be obtained by Stefan-Boltzmann law, and the calculation formula is as follows:
wherein 
Is the solar constant, takes value
；
Is the zenith angle of the sun;
is the atmospheric emissivity;
the surface emissivity is used;
is Stefin-Boltzmann constant, takes value
；
Is the air temperature;
is the surface temperature;
is the distance of day and earth in astronomical units; j is the serial number of the image acquisition date arranged in the solar calendar.
wherein Z is the elevation of the research area, and the unit is m, and can be obtained from the remote sensing image.
Reflectivity of earth's surface
Is a quantity that characterizes the reflection of solar radiation from the earth's surface, which varies with time and space. The earth surface albedo can be obtained by correcting the albedo outside the atmosphere through atmospheric radiation. For the MODIS data, the ground albedo is the ground reflectivity of visible light and near infrared bands, where the conversion coefficient is defined as the ratio of incident flux of each narrow band to incident flux of the whole wide band, and a calculation formula of the ground albedo is obtained:
in the formula ：
in order to obtain the color-reflecting rate of the earth surface,
specific emissivity
Is a dimensionless quantity, and the value is between 0 and 1. The emissivity of the object is the expression of the emission capability of the object, and the emissivity is expressed by using a normalized vegetation index NDVI, and the calculation formula is as follows:
(2) Soil heat flux G
Soil heat flux refers to the portion of energy stored in the soil and vegetation by conduction, through the surface temperature
The statistical relationship of (a) is obtained, and the formula is as follows:
wherein ,
is the surface temperature in K.
(3) Sensible heat flux H
Sensible heat flux is a function of atmospheric stability, wind speed and surface roughness, and refers to the fraction of energy lost to the atmosphere by conduction and convection, and is expressed as follows:
The aerodynamic resistance rah needs to be introduced into the wind speed,
And the ground elevation and other parameters are obtained by calculating the air density, the friction wind speed and the surface roughness. The calculation formula is as follows:
wherein, in practical application
The friction wind speed is at neutral stability; k is a Von Karman constant, and the value is 0.41;
is from the ground
The wind speed of the wind turbine is measured,
taking 200m or 100m;
is a meteorological station off the ground
Measured wind speed of (A) is measured in units of
。
The dT is calculated by utilizing the Morin-Obtuff theory, a linear relation between the earth surface temperature and the dT is established by selecting a pixel (namely a 'cold spot') with luxuriant vegetation growth, low temperature and sufficient water supply and a pixel (namely a 'hot spot') without vegetation coverage, dryness and high temperature on a remote sensing image, namely dT = a, and dT is obtained by cyclic recursive calculation, wherein the 'cold spot' can be an area or a water body completely covered by the vegetation, and the evapotranspiration amount on the 'hot spot' is approximately 0 and can be ignored.
(4) Daily evapotranspiration ET24
The Rn, G and H obtained by the above calculation are substituted into an energy balance principle formula, instantaneous latent heat flux lambda ET is obtained by calculation, lambda in the latent heat flux is latent heat of vaporization and is a constant, ET in the latent heat flux is required evapotranspiration, the calculation of ET needs to convert the instantaneous latent heat flux into evapotranspiration of a daily scale through an evapotranspiration ratio, generally, soil heat flux G can be ignored when the daily evapotranspiration is calculated, so the calculation formula of the daily evapotranspiration ET24 is as follows:
wherein ,
is the net amount of radiation during a day,
is the evaporation rate within 24h a day.
In specific implementation, the water related data of the irrigation area to be detected may include data reflecting vegetation water stress and/or data affecting irrigation water, for example, the data may include greenness index and precipitation.
In particular, greenness index
The ratio of near infrared to green spectrum is more sensitive to the influence of chlorophyll content than other vegetation indexes, so that the ratio is considered to be a good index for monitoring the water stress of vegetation. The interference of precipitation on the mapping of the space irrigation information can be eliminated by adding the space precipitation data.
In specific implementation, in order to further improve the accuracy of determining the farm irrigation frequency, in this embodiment, before determining the farm irrigation frequency, data of the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount are matched on a spatial scale, for example, the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount are resampled according to a preset spatial resolution, so that the spatial resolutions of the resampled soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount are all preset spatial resolutions, and then the resampled soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount are input to the detection model.
In practice, the irrigation frequency (village) is usedZhuang Chedu), evapotranspiration rate (1 km), soil moisture (10 m), precipitation
And the greenness index GI (10 m) has different spatial resolutions and data structures, and in order to improve the accuracy of the irrigation frequency, resampling is needed to match these data sets on a spatial scale, so that soil moisture data, evapotranspiration data, greenness index and precipitation can be resampled according to a preset spatial resolution, the specific size of the preset spatial resolution can be determined according to requirements, for example, the preset spatial resolution may be 10m resolution, and data with a resolution of more than 10m is resampled to 10m resolution uniformly.
In one embodiment, each resampled precipitation pixel may be provided by a grid
In order to accurately determine the precipitation of each pixel after resampling and to improve the accuracy of the precipitation data, in this embodiment, it is proposed to calculate the precipitation of each pixel after resampling by the following formula:
wherein ,
for the amount of precipitation of the pixel after resampling,
is the first
The grid comprising the resampled pixel is a percentage of the area of the resampled pixel,
is the first
And (c) forming the precipitation of the grid of the pixel after the resampling, wherein n is the total number of the grids forming the pixel after the resampling.
In specific implementation, after the irrigation frequency (village scale) is resampled to be 10m, partial 10m pixels are still covered by different village grids at the village boundary, and the irrigation frequency of the partial pixels can be calculated and then rounded by using the area percentage weighting method formula (24).
In specific implementation, in order to further improve the precision of determining the farm irrigation frequency, in this embodiment, data such as the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount are interpolated into a data set with an equidistant time sequence, for example, the resampled greenness index and the precipitation amount are linearly interpolated to match the time sequence of the soil moisture data with the greenness index and the precipitation amount after linear interpolation, the evapotranspiration data are available every day without interpolation, and the soil moisture data, the evapotranspiration data, the greenness index after linear interpolation, and the precipitation amount after linear interpolation are sorted according to the time sequence, so that the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount, which are sorted according to the time sequence and have consistent spatial resolution, corresponding to each pixel are obtained, and the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount, which are sorted according to the time sequence and have consistent spatial resolution, are input into the detection model.
In a specific implementation, the time sequence is established on a pixel level. Because the time interval of the soil moisture data of SAR inversion is 6 days, linear interpolation is carried out on the greenness index GI and the rainfall Pre to match the soil moisture data of SAR inversion, evapotranspiration is available every day, and interpolation processing is not needed. In order to remove noise in time series, use
Smoothing the time sequence by a filterAnd finally, normalizing the soil moisture SM, the evapotranspiration amount ET, the greenness index GI and the precipitation amount Pre.
In specific implementation, the detection model can be trained
The network obtains, as shown in fig. 2, three columns of data corresponding to features are soil volume water content, evapotranspiration amount and water related data after being sequenced in time series, data in a bold frame is represented as a data matrix of 3X3 of an input convolution kernel, and a mapping relation of the detection model for detecting Irrigation Frequency (IF) is as follows:
in order to avoid decimal irrigation frequency in the detection process, the output of the detection model needs to be converted into a form of a supervision classification problem instead of a regression problem. And then establishing a relation model of the irrigation frequency IF and soil moisture SM, evapotranspiration ET, greenness index GI and precipitation amount Pre.
In specific implementation, as shown in fig. 3, in the process of training the detection model, microwave remote sensing data of the irrigation area to be detected within a period of time in the past are collected, and the microwave remote sensing data adopt historical data
Data is taken as an example, by
Obtaining historical soil volume water content through data inversion, obtaining historical evapotranspiration through inversion of an SEBA model by adopting MODIS, resampling historical rainfall (rainfall), historical greenness index, historical soil volume water content and historical evapotranspiration according to preset spatial resolution, realizing space size matching, sequencing historical rainfall (rainfall) after resampling, historical greenness index, historical soil volume water content and historical evapotranspiration according to historical time sequence, and realizing sequencingMatching time sequences to obtain a data set corresponding to each pixel time sequence, and finally, taking irrigation range data, irrigation frequency, historical rainfall (rainfall), historical greenness index, historical soil volume water content and historical evapotranspiration of an irrigation area to be detected as samples, and taking different characteristic combinations of the pixels corresponding to the time sequences as a training sample set to be input into the training sample set
The model is trained by using a space cross validation method, the cycle is 10 times, 90% of samples are extracted each time for training, 10% of samples are validated, and finally the average error is calculated.
In specific implementation, in the process of training the detection model, a historical related data set within a first preset time period can be used as a sample for training, in the process of determining the farmland irrigation frequency of an irrigation area to be detected by using the detection model, a related data set within a second preset time period can be used for detection, the first preset time period and the second preset time period can be the same or different, and the specific time periods of the first preset time period and the second preset time period can be determined according to requirements, for example, the specific time periods can be several months, half years, one year and the like.
In the present embodiment, a computer device is provided, as shown in fig. 4, and includes a memory 401, a processor 402, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement any of the above-mentioned methods for determining farmland irrigation frequency based on microwave remote sensing.
In particular, the computer device may be a computer terminal, a server or a similar computing device.
In the present embodiment, a computer-readable storage medium is provided, which stores a computer program for executing any of the above-mentioned methods for determining farmland irrigation frequency based on microwave remote sensing.
In particular, computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable storage medium does not include transitory computer readable media (transmyedia), such as modulated data signals and carrier waves.
Based on the same inventive concept, the embodiment of the invention also provides a farmland irrigation frequency determination device based on microwave remote sensing, which is described in the following embodiment. Because the principle of solving the problems of the farmland irrigation frequency determining device based on microwave remote sensing is similar to that of the farmland irrigation frequency determining method based on microwave remote sensing, the implementation of the farmland irrigation frequency determining device based on microwave remote sensing can refer to the implementation of the farmland irrigation frequency determining method based on microwave remote sensing, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a structure of an apparatus for determining a field irrigation frequency based on microwave remote sensing according to an embodiment of the present invention, as shown in fig. 5, the apparatus includes:
the data acquisition module 501 is used for acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected;
a data inversion module 502, configured to obtain soil moisture data according to the microwave remote sensing data, and obtain evapotranspiration data according to the optical remote sensing data;
an irrigation frequency calculation module 503, configured to input the soil moisture data, the evapotranspiration data, and the water related data of the to-be-detected irrigation area into a detection model, where the detection model outputs the irrigation frequency of the to-be-detected irrigation area, and the detection model is obtained by training a machine learning component using historical soil moisture data, historical evapotranspiration data, and historical water related data as samples.
In one embodiment, a data inversion module, comprising:
the soil moisture data inversion unit is used for determining the dielectric constant of the soil according to the radar backscattering coefficient, the radar incident angle and the polarization mode of the microwave remote sensing data; and converting the soil dielectric constant into the volume water content of the soil by adopting a dielectric mixing model.
In one embodiment, the data inversion module further comprises:
and the evapotranspiration data inversion unit is used for inputting the optical remote sensing data and the meteorological data into an SEBA model, and the SEBA model is used for performing inversion on the basis of the earth surface energy balance principle to obtain the evapotranspiration data of the irrigation area to be detected.
In one embodiment, the irrigation frequency calculation module is configured to resample the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount according to a preset spatial resolution, and input the resampled soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount into the detection model.
In one embodiment, the above apparatus further comprises:
a precipitation determination module for calculating the precipitation of each pixel after resampling by the following formula:
wherein ,
for the amount of precipitation of the pixel after resampling,
is the first
The grid comprising the resampled pixel is a percentage of the area of the resampled pixel,
is the first
And (c) forming the precipitation of the grid of the pixel after the resampling, wherein n is the total number of the grids forming the pixel after the resampling.
In an embodiment, the irrigation frequency calculation module is further configured to perform linear interpolation on the resampled greenness index and the precipitation amount, match the linearly interpolated greenness index and the precipitation amount with the time series of the soil moisture data, sort the soil moisture data, the evapotranspiration data, the linearly interpolated greenness index, and the linearly interpolated precipitation amount according to the time series, and input the soil moisture data, the evapotranspiration data, the greenness index, and the precipitation amount, which are sorted according to the time series and have the same spatial resolution and correspond to each pixel, into the detection model.
The embodiment of the invention realizes the following technical effects: compared with the scheme of detecting irrigation information only by adopting optical remote sensing data in the prior art, the microwave remote sensing has the advantages of all-weather, high resolution, strong penetrability and sensitivity to soil moisture change, can more accurately capture moisture information than the optical remote sensing data, and is favorable for improving the accuracy of determining the farmland irrigation frequency by using the microwave remote sensing data; meanwhile, soil moisture data are adopted to replace a humidity index inverted by optical remote sensing data to serve as an ideal index for detecting irrigation information, and as the field evapotranspiration amount is obviously increased after irrigation, evapotranspiration data are introduced to determine the irrigation frequency, and water related data are also introduced, so that the irrigation frequency is determined by combining the soil moisture data, the evapotranspiration data and the water related data, and the accuracy of determining the farmland irrigation frequency is further improved.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A farmland irrigation frequency determination method based on microwave remote sensing is characterized by comprising the following steps:
acquiring microwave remote sensing data and optical remote sensing data of an irrigation area to be detected;
soil moisture data are obtained through inversion according to the microwave remote sensing data, and evapotranspiration data are obtained through inversion according to the optical remote sensing data;
and inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model, and outputting the irrigation frequency of the irrigation area to be detected by the detection model, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples.
2. The method for determining farmland irrigation frequency based on microwave remote sensing of claim 1, wherein the soil moisture data obtained by inversion according to the microwave remote sensing data comprises:
determining a soil dielectric constant according to the radar backscattering coefficient, the radar incident angle and the polarization mode of the microwave remote sensing data;
and converting the soil dielectric constant into the volume water content of the soil by adopting a dielectric mixing model.
3. The method for determining farmland irrigation frequency based on microwave remote sensing of claim 1, wherein the obtaining of evapotranspiration data from the inversion of the optical remote sensing data comprises:
and inputting the optical remote sensing data and the meteorological data into an SEBA model, and performing inversion on the SEBA model based on the earth surface energy balance principle to obtain evapotranspiration data of the irrigation area to be detected.
4. The method for determining farmland irrigation frequency based on microwave remote sensing according to any one of claims 1to 3, wherein the water related data of the irrigation area to be detected comprises greenness index and precipitation.
5. The method for determining farmland irrigation frequency based on microwave remote sensing of claim 4, wherein the step of inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model comprises the following steps:
resampling the soil moisture data, the evapotranspiration data, the greenness index and the precipitation amount according to a preset spatial resolution, and inputting the resampled soil moisture data, the evapotranspiration data, the greenness index and the precipitation amount into the detection model.
6. The method for determining farmland irrigation frequency based on microwave remote sensing of claim 5, further comprising:
the amount of precipitation per pixel after resampling is calculated by the following formula:
wherein ,
for the amount of precipitation of the pixel after resampling,
is the first
The grid comprising the resampled pixel is a percentage of the area of the resampled pixel,
is the first
And n is the total number of grids constituting the resampled pixel.
7. The method for determining farmland irrigation frequency based on microwave remote sensing of claim 5, wherein the step of inputting the soil moisture data, the evapotranspiration data, the greenness index and the precipitation amount after resampling into the detection model comprises the steps of:
performing linear interpolation on the resampled greenness index and the precipitation amount, matching the greenness index and the precipitation amount after the linear interpolation with the soil moisture data time sequence, sequencing the soil moisture data, the evapotranspiration data, the greenness index after the linear interpolation and the precipitation amount after the linear interpolation according to the time sequence, and inputting the soil moisture data, the evapotranspiration data, the greenness index and the precipitation amount which are sequenced according to the time sequence and have consistent spatial resolution and correspond to each pixel into the detection model.
8. A farmland irrigation frequency determining device based on microwave remote sensing is characterized by comprising:
the data acquisition module is used for acquiring microwave remote sensing data and optical remote sensing data of the irrigation area to be detected;
the data inversion module is used for obtaining soil moisture data according to the microwave remote sensing data and obtaining evapotranspiration data according to the optical remote sensing data;
and the irrigation frequency calculation module is used for inputting the soil moisture data, the evapotranspiration data and the water related data of the irrigation area to be detected into a detection model, and the detection model outputs the irrigation frequency of the irrigation area to be detected, wherein the detection model is obtained by training a machine learning component by taking historical soil moisture data, historical evapotranspiration data and historical water related data as samples.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the method for determining microwave remote sensing-based farm irrigation frequency according to any one of claims 1to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method for determining farmland irrigation frequency based on microwave remote sensing according to any one of claims 1to 7.
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