CN112162014A - Cotton field soil profile water data processing method based on electromagnetic induction data - Google Patents
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Abstract
The embodiment of the invention discloses a cotton field soil profile water data processing method based on electromagnetic induction data, which comprises the following steps: acquiring apparent conductivity data of soil in a target area; obtaining and processing a plurality of soil samples in the target area to obtain water data of the plurality of soil samples; establishing a soil apparent conductivity inversion soil moisture model and evaluation indexes according to the soil apparent conductivity data and the moisture data of the plurality of soil samples; and inverting a soil moisture model, the calculation evaluation index and the target cotton field soil according to the apparent conductivity of the soil to obtain the section moisture data of the target cotton field soil. According to the invention, the apparent conductivity and soil moisture model is established by an electromagnetic induction technology, so that the water content data of soil profiles at different depths can be rapidly and nondestructively obtained, and an effective calculation basis is provided for rated irrigation.
Description
Technical Field
The embodiment of the invention relates to the field of soil moisture processing, in particular to a cotton field soil profile moisture data processing method based on electromagnetic induction data.
Background
Soil moisture is a carrier for salt and nutrient transportation, is an indispensable part in soil, and is also a key factor influencing the growth and development of crops. The research on the time dynamic change of the soil moisture is important for predicting and evaluating the soil moisture condition of the field scale in real time, and particularly for improving the irrigation efficiency and the water utilization efficiency of crops[2]And the like.
In recent years, as researchers deeply research electromagnetic induction technical sensors, the advantages of a geodetic conductivity meter for rapidly, efficiently and nondestructively measuring various soil properties become key, and the geodetic conductivity meter is widely involved in the research in the fields of soil salinity, soil moisture, soil texture, soil clay content and the like.
In some arid areas, efficient irrigation is an indispensable technology for increasing the agricultural production efficiency. The traditional spring and winter irrigation mode is mostly empirical flood irrigation, although the salinization problem can be alleviated to a certain extent, because the soil moisture content changes along with rainfall in time, surface runoff, subsurface flow, soil moisture infiltration, evaporation and root moisture absorption, in space, because of soil heterogeneity, topography, vegetation and climate are different, the soil moisture has high spatial variability in the vertical direction, the method is particularly prominent in arid regions, the surface soil moisture condition is difficult to characterize the root layer or deep soil moisture content, the soil moisture difference of different soil layers is large, and the continuous monitoring of the deep soil moisture content can not be effectively realized in four periods in real time, so that the waste of soil water resources is easily caused. In addition, the water and fertilizer integration is widely applied, a new irrigation method is popularized, but people rely on the experience prediction of farmland soil moisture, the moisture content of deep soil is difficult to be effectively evaluated in real time, so that excessive water and fertilizer flows into the underground, a series of problems such as underground water pollution and the like are easily caused, and therefore, the continuous monitoring of the moisture content of profile soil can provide related water storage condition and time dynamic information, and excessive irrigation or insufficient irrigation is avoided.
In view of the defects that the collection of traditional soil samples mainly adopts an invasive method, the traditional soil samples have the defects of time and labor waste, small number of samples, strong destructiveness, poor representativeness and the like, and the requirement of acquiring the soil moisture content data of the root layer of the crop at high frequency and carrying out reasonable irrigation according to the data is difficult to meet.
Disclosure of Invention
The embodiment of the invention aims to provide a cotton field soil profile water data processing method based on electromagnetic induction data, which is used for solving the problems that the existing invasive method is time-consuming and labor-consuming in soil sample collection, small in sample number, strong in destructiveness and poor in representativeness.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
the embodiment of the invention provides a cotton field soil profile moisture data processing method based on electromagnetic induction data, which comprises the following steps: acquiring apparent conductivity data of soil in a target area; obtaining and processing a plurality of soil samples in the target area to obtain water data of the plurality of soil samples; establishing a soil apparent conductivity inversion soil moisture model and evaluation indexes according to the soil apparent conductivity data and the moisture data of the plurality of soil samples; and inverting a soil moisture model, the calculation evaluation index and the target cotton field soil according to the apparent conductivity of the soil to obtain the section moisture data of the target cotton field soil.
According to one embodiment of the invention, obtaining soil apparent conductivity data for a target area comprises: and measuring the earth surface of the target area by adopting a geodetic conductivity meter in a non-contact direct reading mode to obtain the apparent conductivity data of the soil.
According to one embodiment of the invention, the soil apparent conductivity data is obtained by the following formula:
ECa＝4(Hs/Hp)/ωμ0S2
wherein ECa is an apparent conductivity value, Hs and Hp are a primary magnetic field strength and a secondary magnetic field strength respectively, S is a distance between a transmitting end and a receiving end, and mu0Is the spatial magnetic field propagation coefficient.
According to one embodiment of the invention, the geodetic conductivity meter is model number EM38-MK 2.
According to one embodiment of the invention, before acquiring the soil apparent conductivity data of the target area, the method further comprises the following steps: and carrying out zero-returning verification on the geodetic conductivity meter.
According to one embodiment of the invention, acquiring and processing a plurality of soil samples of the target area to obtain water data of the plurality of soil samples comprises: in a sample area of the target area, taking the center of the geoelectric conductivity meter as a sample point position, and respectively collecting soil of 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm in situ by using an earth drilling method to obtain 18 soil samples; collecting 30 soil samples in a sample area of the target area by a cutting ring method; and measuring the 18 soil samples and the 30 soil samples to obtain water data of the plurality of soil samples.
According to one embodiment of the invention, establishing a soil apparent conductivity inversion soil moisture model and calculating an evaluation index according to the soil apparent conductivity data and the moisture data of the plurality of soil samples comprises: acquiring sampling point data of 72 soil samples obtained by an earth drilling method within four specified months, wherein 18 soil samples are obtained by the earth drilling method in each month, and each sampling point takes 20cm as 1 soil layer and has 5 soil layers in total; each month contains 18 sampling point data, a modeling set and an actual measurement set are divided according to the ratio of 2:1, and separate inversion models are established for soil samples in different periods; dividing the 72 sampling point data into a modeling set and an actual measurement set according to the proportion of 2:1, and adopting a cross validation method; providing an evaluation index, wherein the evaluation index comprises a relative analysis error, the relative analysis error is an index for judging the prediction capability of the model, and the relative analysis error is the ratio of the standard deviation of the sample to the root mean square error; acquiring 30 soil samples obtained by a cutting ring method, wherein the first 18 soil samples comprise soil volume weight and field water capacity data, acquiring mass water content by combining soil drilling samples to obtain volume water content, 12 soil samples are used for modeling, and 6 soil samples are used for verification to establish an apparent conductivity inversion soil water content model; and 20 of the 30 soil samples are used for modeling, and 10 of the 30 soil samples are used for verification, so that an apparent conductivity inversion field water capacity model is established.
According to one embodiment of the invention, the value of the relative analysis error is RPD: when the RPD is greater than 2.5, the model is proved to have extremely strong prediction capability; when RPD is more than or equal to 2.0 and less than 2.5, the model has good prediction capability; when RPD is more than or equal to 1.5 and less than 2.0, the model prediction capability is general; when RPD is less than 1.5, the model can only roughly estimate the maximum value and the minimum value of the water content of the sample soil.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
according to the cotton field soil profile water data processing method based on the electromagnetic induction data, provided by the embodiment of the invention, the apparent conductivity and soil water model is established through the electromagnetic induction technology, the soil profile water content data of different depths can be rapidly and nondestructively obtained, and an effective calculation basis is provided for rated irrigation. And respectively establishing a single-period local model and four-period unified global models by combining the soil moisture conditions of the four periods. And inversion is carried out by utilizing the field water capacity and the soil volume weight data to obtain the farmland soil irrigation quota, so that a new reference method is provided for the farmland soil water irrigation high efficiency and maximization.
Drawings
FIG. 1 is a flow chart of a method for processing moisture data of a cotton field soil profile based on electromagnetic induction data according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
FIG. 1 is a flow chart of a method for processing moisture data of a cotton field soil profile based on electromagnetic induction data according to an embodiment of the present invention. As shown in fig. 1, a method for processing moisture data of a cotton field soil profile based on electromagnetic induction data according to an embodiment of the present invention includes:
s1: and acquiring apparent conductivity data of the soil in the target area.
Specifically, EM38-MK2 is a single-frequency multi-coil ground conductivity meter, consists of one transmitting coil and two receiving coils, and can directly measure soil apparent conductivity data on the ground surface in a non-contact direct reading mode, wherein the total length of the meter is 1 m. The EM38-MK2 generates a primary magnetic field changing with time through a transmitting coil, the magnetic field further induces small electron eddy currents in the ground, a secondary magnetic field is induced, the strength of the primary magnetic field and the strength of the secondary magnetic field are received at a receiving end, and the apparent conductivity is obtained through the relationship conversion of a formula:
ECa＝4(Hs/Hp)/ωμ0S2
wherein ECa is an apparent conductivity value with the unit of ms/m, Hs and Hp are respectively the primary magnetic field strength and the secondary magnetic field strength, omega is 2 pi f, f is the emission frequency with the unit of Hz, S is the distance between the emission end and the receiving end with the unit of m and mu0Is the spatial magnetic field propagation coefficient.
S2: and acquiring and processing a plurality of soil samples in the target area to obtain water data of the plurality of soil samples.
Specifically, the soil apparent conductivity acquisition instrument is an EM38-MK2 earth conductivity instrument, a GPS is arranged in the soil apparent conductivity acquisition instrument, and apparent conductivity data and geographical position information of each point position can be automatically acquired. The instrument is preheated and zeroed before use. After calibration is completed, the instrument switches to automatic mode for on-site measurement, and the apparent conductivity information of the study area is extracted (see fig. 1). The instrument is changed into a manual measurement mode, and 18 point position information of certain gradient apparent conductivity is collected in the sample zone by combining the high-low value distribution of the sample zone apparent conductivity in an automatic mode. Taking the center of the instrument as a sample point position, and respectively collecting soil samples of 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm in situ by using a soil drilling method. And (5) filling the soil sample into a self-sealing bag, and taking the self-sealing bag back to a laboratory to measure the soil moisture by a drying method. Because the research area is a war group cotton field experimental area, film mulching planting is adopted, and a mode of digging a soil section and obtaining a deep soil sample by a cutting ring method is not advisable, each sample point only has surface layer data, and the surface layer data is used for replacing root layer data (0-20 cm, 20-40 cm and 40-60 cm). The total number of the soil samples collected by the cutting ring method is 30, the first 18 soil samples are parallel to the soil auger, namely, the soil samples are collected by the cutting ring method near the soil auger sampling point so as to obtain the soil volume weight and the field water capacity data. The last 12 soil samples collected by the ring cutter method have no earth auger and are used for supplementing the apparent conductivity and actually measured field water capacity data.
Xinjiang cotton fields are mostly 'early-promoting' measures of '4-month seedlings, 5-month buds, 6-month flowers, 7-month bolls, 8-month bolls in northern Xinjiang and 9-month bolls in southern Xinjiang' and cultivation modes of mulching film planting. The water demand of the southern Xinjiang cotton in the seedling stage is less and gradually increased later, the maximum peak value of the water demand is in the early stage of flower bolls, and the water demand in the later stage of the flower bolls is gradually reduced[25]. Therefore, the sampling time is the pre-emergence period (31 days in 3 months), the boll period (7 days in 7 months) and the boll opening period (9 days in 9 months) and the sampling time in 27 days in 10 months is designed for avoiding excessive irrigation in winter irrigation.
S3: and establishing a soil apparent conductivity inversion soil moisture model and evaluation indexes according to the soil apparent conductivity data and the moisture data of the plurality of soil samples.
Specifically, the experimental data processing, the soil apparent conductivity inversion soil moisture model establishment and the evaluation index calculation are all completed in Excel 2019 and Unscramble X10.5, and the sample area schematic drawing and the soil layer moisture content interpolation are completed in ARCGIS 10.2 and ENVI 5.3.1. The soil drilling sample total 72 sampling point data, each month contains 18 sampling point data, each sampling point takes 20cm as 1 soil layer, 5 soil layers are provided in total, each soil layer is provided with 1 apparent conductivity inversion soil moisture content model, a modeling set and a verification set are divided according to a ratio of 2:1, 12 modeling sets are provided, and 6 soil sampling points are used for verification. The modeling idea comprises local modeling and global modeling, the local model comprises 18 data per month and is divided into a modeling set and an actual measurement set according to the ratio of 2:1, 12 data are modeled, 6 data are verified, and independent inversion models are established for soil samples in different periods. The global model is that data of 72 sampling points are divided into a modeling set and an actual measurement set according to the ratio of 2:1, 48 modeling and 24 verification are carried out by adopting a cross validation method, and the condition of a soil moisture unified model in four periods is reflected.
The evaluation indexes of the model stability and the prediction precision comprise a decision coefficient, a root mean square error, a relative analysis error and an average relative error. The relative analysis error is an index for judging the prediction capability of the model, is the ratio of the standard deviation of the sample to the root mean square error, and when the value is higher than 2.5, the model has extremely strong prediction capability, and when the RPD is more than or equal to 2.0 and less than 2.5, the model has very good prediction capability, when the RPD is more than or equal to 1.5 and less than 2.0, the model has general prediction capability, and when the RPD is less than 1.5, the model can only roughly estimate the maximum value and the minimum value of the water content of the sample soil.
The method comprises the steps of obtaining 30 sample point data of soil samples obtained by a cutting ring method, obtaining mass water content by combining soil drilling samples according to the first 18 sample point data containing soil volume weight and field water holding capacity data, obtaining volume water content, dividing a modeling set and a verification set according to the ratio of 2:1, wherein 12 sample points are used for modeling, 6 sample points are used for verification, and establishing an apparent conductivity inversion soil water content model. And dividing the modeling set and the verification set by 30 apparent conductivities and actually measured field water capacity data according to a ratio of 2:1, wherein 20 apparent conductivities are used for modeling, and 10 apparent conductivity inversion field water capacity models are established. The model evaluation index calculation method is consistent with the soil sample drilling. And (3) inverting the soil moisture content and field water holding capacity data through the sample apparent conductivity information pulled by the EM38-MK2 in the automatic mode, and obtaining rated irrigation data according to the difference between the soil moisture content and the field water holding capacity data. GMS 10.0.5 draws a rated irrigation distribution map, and the interpolation method adopts common kriging interpolation.
S4: and inverting a soil water model, the calculation evaluation index and the target cotton field soil according to the apparent conductivity of the soil to obtain the profile water data of the target cotton field soil.
Specifically, a soil moisture model is inverted by establishing apparent conductivity at four periods of different soil depths, and a new way and a new method are provided for controlling the soil moisture content at a field scale in real time. And respectively modeling on the premise of ensuring that the freedom degrees of each group of data are consistent and the p values are less than 0.01, wherein the table 1 is a modeling type evaluation index of each soil layer at different periods, and the table 2 is an actually measured soil moisture content statistical table. As can be seen from Table 1, the soil water content model inverted by the 0-20 cm apparent conductivity of the surface layers of April and July has higher precision, R2Above 0.85, RPD above 2.5, the model has strong prediction ability. Through preliminary analysis, from april to july, with the continuous rise of the environmental temperature, particularly, july in each year is the peak period of the mean temperature of the month, the ice and snow in the Tianshan mountain are ablated to enable the Taliemu river to enter the flood season, the underground water level is raised, the cotton field mulching planting mode enhances the transpiration effect of cotton while improving the soil temperature, the water consumption is raised, soluble salts in soil moisture move to the root, and the precision of the soil moisture content model with the surface layer of 0-20 cm is higher. April soil water content model R of 80-100 cm2And RPD highest, RMSE and MAE lowest, with highest accuracy among all models (20 total) in four epochs. According to the table 2, the water content is actually measured in the same soil layer at the same time, the average value and the minimum value of the water content of the soil of 80-100 cm in April are higher than those of other four soil layers, the variation coefficient is minimum, and the spatial variability in the horizontal direction is minimum. The water content model precision of 20-40 cm and 40-60 cm of September is lower than that of other soil layers in the same period, and R is2And when the water content is less than 0.5, the RMSE is higher, the RPD is lower and is below 1, and the soil water content model can only estimate the maximum value and the minimum value of the water content. As can be seen from Table 2, the variation coefficient (0.16) of the actually measured water content of the soil layers of 20-40 cm and 40-60 cm in September isThe highest period indicates that the soil moisture spatial distribution in the period is not uniform enough, so that the apparent conductivity and the moisture content of the soil are lower in correlation than those of the soil layer in other periods.
In order to explore whether the same apparent conductivity inversion soil water content model is suitable for multiple periods, the sampling cost is reduced, and the application efficiency of EM38-MK2 is improved. And respectively establishing a global model and a local model, and comparing evaluation indexes of the models in a table 3. Firstly, through variance analysis, the degrees of freedom of 2 models are consistent, the p values are all less than 0.01, and the models are reliable. Secondly, comparing the water content models R of the soil layers of the same soil layer and the local models2And RPD is higher than the global model, and MAE is lower than the global model. Through preliminary analysis, compared with a local model, the global model has the advantages that evapotranspiration water consumption is caused by environmental temperatures in different periods, crop root systems in different growth periods are distributed, and the soil water content is greatly changed. The highest RPD of the global model is 1.28, the lowest RPD of the local model is 1.35, and therefore the local model with the apparent conductivity inversion soil moisture content has good prediction capability and high stability. The global model has the lowest coefficient of 40-60 cm, and the reason is presumed to be that the soil layer is directionally moved by the absorption of soil moisture by a few main root systems of crops, and the environmental temperature changes.
TABLE 1 soil moisture content model evaluation index
TABLE 2 actual measurement of moisture content statistics
TABLE 3 local model to Global model evaluation index comparison
And (3) performing inversion on the linear apparent conductivity data EM38-MK2 through each model to obtain the predicted soil moisture content data of each soil layer, wherein the table 4 is the water content data after model inversion. As can be seen from Table 4, the coefficient of variation of the soil layers of 0-20 cm in each period is the smallest in the same month, which is caused by the influence of southern Xinjiang climate and mulching, and the effect of the thin film on heat and moisture preservation is favorable for the reduction of the content difference of the surface soil moisture (especially the soil layers of 0-20 cm) relative to the soil layers of other depths. The variation coefficient of the soil water content in April, July and October increases with the increase of the depth, and the average value decreases with the increase of the depth, which shows that the water content of the soil layer of 40-60 cm is not uniformly distributed in the horizontal direction, especially in the seedling stage of the cotton root with the most vigorous growth, because the crop root is used to grow towards water, the too little soil water content is not beneficial to the downward growth of the crop root, and the lodging resistance of the crop can be influenced finally. The average value of the water content of the soil in Nonine months rises along with the increase of the depth of the soil, the spatial variability of 0-20 cm is minimum, but the maximum value of the water content is less than 20%, the spatial variability of the water content of the soil layer of 20-40 cm is maximum, the minimum value of the water content of the soil is lower, the average value of the water content of 0-20 cm is minimum, the average value of the water content of 40-60 cm is maximum, and the difference between the two is.
TABLE 4 prediction of soil moisture statistics
The soil moisture content and the field water capacity are inverted through the apparent conductivity, the maximum field irrigation rating is efficiently and quickly evaluated, an irrigation rating spatial distribution map is drawn, and the soil moisture utilization rate can be improved. Apparent conductivity inversion field water capacity model R2And RPD 0.66 and 1.36, respectively, and RMSE and MAE 1.89 and 1.46, respectively. By a common krillAnd drawing by a grid interpolation method to obtain rating irrigation space distribution maps of 0-40 cm and 0-60 cm. Since the temperature of southern Xinjiang reached the average peak of the month each year, local managers performed the first drip irrigation in 22 days 6 months. Influenced by the proximity of Tarim river in the northern part of the research area, the most of 163m area from the northern part to the southern part of the research area is yellow, which indicates that the soil moisture content in the area is close to the field moisture capacity. The research area is concentrated by blue and dark blue in a 163-403 m area from north to south, and field investigation shows that the area is large-area bare sandy soil, the soil moisture content is low, the cotton plant height is lower than that in the north area, and part of surface soil has obvious salt crystals. The soil in the region has high salt content, the first drip irrigation is carried out for only 15 days after 7-month sampling, the soil moisture is greatly lost due to the reasons of temperature, soil texture and the like, the retention time of the soil moisture in the root layer is short, and the cotton is not beneficial to the growth and development of cotton. Therefore, a large amount of water is needed to rinse the soil salinity in the area, and a series of measures should be taken to improve the soil texture and increase the clay content so as to improve the yield while the soil salinity is reduced to the depth below the root of the crops. The research area is mostly green and yellow from the north to the south in the area 403-877 m, in field investigation, the plant height of cotton in the area is approximately the same as that of the north, for 0-60 cm of straight root system crops, the water content of a soil layer of 40-60 cm should be paid attention to, the deep soil is pricked into by the straight root system crops, particularly the roots of cotton, the lodging resistance of the cotton is improved, the quality of the cotton is enhanced, and the yield of the cotton is improved. If the fibrous root system crops with root layers of 0-40 cm are planted, soil moisture in the east region is considered to be important, and the influence on the growth of the crops due to uneven distribution of the soil moisture is avoided.
Obtaining the soil moisture content state through the electromagnetic induction technology is one of the current methods for realizing accurate management of the soil moisture in the field, and a multivariate linear regression method is used for establishing soil moisture content interpretation models of different depths in the field, and analyzing and evaluating the soil moisture content of the field in southern Xinjiang arid regions. According to the method, the moisture interpretation models of different soil layers in the cotton field in 4 periods are established through multiple linear regression, the determination coefficient is 0.56-0.90, and the models are reliable. However, the local model is superior to the global model in the view of respectively establishing the global model with four uniform periods and the local model with a single period. Therefore, the direct application of multiple linear regression is not favorable for the establishment of the global model to a certain extent. The apparent conductivity can be used for describing the space-time change characteristics of the soil moisture of the average volume of the specific soil depth, more few students successfully predict the volume moisture of the soil of the specific depth, but the apparent conductivity measured by an instrument is the comprehensive condition of the soil attribute of the specific depth, so that the conversion of the apparent conductivity data is required[26]。
The local model is superior to the global model, so that the sampling cost is not favorably controlled in the practical application process. The reason why the evaluation index of the global model is low is probably that firstly, the actual soil moisture content of each soil layer is very different due to the spatial variability of the soil moisture in each period by analyzing the soil moisture content, the actual measured moisture content is statistically measured by combining the table 2, the maximum difference appears in the soil layers of 10 months and 40-60 cm, the highest value of the soil moisture content is 16.31% away from the lowest value, the minimum difference appears in the soil layers of 4 months and 80-100 cm, and the maximum value of the soil moisture content is 5.15% away from the lowest value. And secondly, analyzing from the perspective of the apparent conductivity, and the environmental temperature difference at different periods is the factor which can most influence the change of the apparent conductivity.
The method utilizes the electromagnetic induction principle to invert the soil moisture content, can realize real-time monitoring of the field soil moisture content, researches and selects the apparent conductivity and actually measured soil moisture content data collected in April, July, September and October, combines the synchronous collection of field soil volume weight and field water capacity data, and draws a field rated irrigation distribution diagram. The result shows that the global model built uniformly in four periods is superior to the local model built in a single period, the global model R2 is 0.50 at the maximum, the RPD is 1.28 at the maximum, and the prediction capability and the inversion accuracy of the local model are better than those of the local model R2 and the RPD (the R2 and the RPD are 0.62 and 1.35 at the minimum respectively). In the actual production and application process, the better result of the local model is not beneficial to controlling the sampling cost, so that a nonlinear regression modeling method such as a neural network, a support vector machine or a random forest is required to be adopted to improve the prediction capability of the global model. As can be seen from the rated irrigation profile, more irrigation is required in the middle of the study relative to the north and south. Because the middle part is large-area bare sandy soil, the phenomenon of soil salt segregation is obvious, the irrigation quantity of the area is emphasized when an irrigation strategy is appointed, and the improvement of the quality and the yield of cotton is facilitated.
An embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the method for processing moisture data of a cotton field soil profile based on electromagnetic induction data according to the first aspect.
The disclosed embodiments of the present invention provide a computer readable storage medium, which stores therein computer program instructions, which when run on a computer, cause the computer to execute the above-mentioned cotton field soil profile water data processing method based on electromagnetic induction data.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or may be implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, etc. storage media well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile memory may be a Read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM for short), Dynamic RAM (DRAM for short), Synchronous DRAM (SDRAM for short), Double Data Rate Synchronous DRAM (ddr Data Rate SDRAM for short), Enhanced Synchronous DRAM (ESDRAM for short), Synchronous Link DRAM (SLDRAM for short), and Direct bus RAM (DRRAM for short).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention can be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modification, equivalent replacement, improvement, etc. made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (8)
1. A cotton field soil profile water data processing method based on electromagnetic induction data is characterized by comprising the following steps:
acquiring apparent conductivity data of soil in a target area;
obtaining and processing a plurality of soil samples in the target area to obtain water data of the plurality of soil samples;
establishing a soil apparent conductivity inversion soil moisture model and evaluation indexes according to the soil apparent conductivity data and the moisture data of the plurality of soil samples;
and inverting a soil moisture model, the calculation evaluation index and the target cotton field soil according to the apparent conductivity of the soil to obtain the section moisture data of the target cotton field soil.
2. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 1, wherein the step of obtaining soil apparent conductivity data of the target area comprises the following steps:
and measuring the earth surface of the target area by adopting a geodetic conductivity meter in a non-contact direct reading mode to obtain the apparent conductivity data of the soil.
3. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 2, wherein the apparent conductivity data of the soil is obtained by the following formula:
ECa＝4(Hs/Hp)/ωμ0S2
wherein ECa is an apparent conductivity value, Hs and Hp are respectivelyIs the primary and secondary magnetic field strength, S is the distance between the transmitting and receiving ends, mu0Is the spatial magnetic field propagation coefficient.
4. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 2, wherein the model of the geodetic conductivity meter is EM38-MK 2.
5. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 2, characterized in that before obtaining the soil apparent conductivity data of the target area, the method further comprises:
and carrying out zero-returning verification on the geodetic conductivity meter.
6. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 2, wherein the step of obtaining and processing a plurality of soil samples of the target area to obtain water data of the plurality of soil samples comprises:
in a sample area of the target area, taking the center of the geoelectric conductivity meter as a sample point position, and respectively collecting soil of 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm in situ by using an earth drilling method to obtain 18 soil samples;
collecting 30 soil samples in a sample area of the target area by a cutting ring method;
and measuring the 18 soil samples and the 30 soil samples to obtain water data of the plurality of soil samples.
7. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 2, wherein establishing a soil apparent conductivity inversion soil water model and calculating an evaluation index according to the soil apparent conductivity data and the water data of the plurality of soil samples comprises:
acquiring sampling point data of 72 soil samples obtained by an earth drilling method within four specified months, wherein 18 soil samples are obtained by the earth drilling method in each month, and each sampling point takes 20cm as 1 soil layer and has 5 soil layers in total;
each month contains 18 sampling point data, a modeling set and an actual measurement set are divided according to the ratio of 2:1, and separate inversion models are established for soil samples in different periods;
dividing the 72 sampling point data into a modeling set and an actual measurement set according to the proportion of 2:1, and adopting a cross validation method;
providing an evaluation index, wherein the evaluation index comprises a relative analysis error, the relative analysis error is an index for judging the prediction capability of the model, and the relative analysis error is the ratio of the standard deviation of the sample to the root mean square error;
acquiring 30 soil samples obtained by a cutting ring method, wherein the first 18 soil samples comprise soil volume weight and field water capacity data, acquiring mass water content by combining soil drilling samples to obtain volume water content, 12 soil samples are used for modeling, and 6 soil samples are used for verification, and establishing an apparent conductivity inversion soil water content model;
and 20 of the 30 soil samples are used for modeling, and 10 soil samples are used for verification, so that an apparent conductivity inversion field water capacity model is established.
8. The method for processing cotton field soil profile water data based on electromagnetic induction data as claimed in claim 7, wherein the value of relative analysis error is RPD:
when the RPD is greater than 2.5, the model is proved to have extremely strong prediction capability;
when RPD is more than or equal to 2.0 and less than 2.5, the model has good prediction capability;
when RPD is more than or equal to 1.5 and less than 2.0, the model prediction capability is general;
when RPD is less than 1.5, the model can only carry out rough estimation on the maximum value and the minimum value of the soil moisture content of the sample.
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