CN116415704A - Regional precision irrigation method and system based on multi-data fusion and assimilation - Google Patents
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Abstract
The invention relates to a regional precise irrigation method and a regional precise irrigation system based on multi-data fusion and assimilation, wherein the method comprises the following steps: acquiring real-time monitoring data, remote sensing image data and weather forecast information of a irrigated area; acquiring crop yield estimated value G in real time based on a irrigated area earth surface temperature dataset and a yield prediction model obtained by inversion of remote sensing image data, and judging whether the last soil water storage amount S on the nth day is smaller than the minimum soil water storage amount W min If not, no irrigation is needed at the current moment; if yes, judging rainfall P in the future M days according to weather forecast information pre Whether or not to be largeAt a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment and the irrigation quantity at the current moment is calculated. The invention provides a regional precise irrigation method and a regional precise irrigation system based on multi-data fusion and assimilation, which solve the technical problem that the irrigation time and the irrigation volume are unreasonable due to the fact that the time scale of remote sensing information and field real-time monitoring data are not matched in the prior art.
Description
Technical Field
The invention relates to the technical field of agricultural irrigation, in particular to a regional precise irrigation method based on multi-data fusion and assimilation and a related device.
Background
The farmland irrigation management method of the irrigation area (or large farm) based on the traditional irrigation system gradually changes to precise irrigation decision-making based on real-time monitoring information of agriculture. Real-time monitoring of agricultural condition information based on field data acquisition is a basic method for realizing precise irrigation decision-making; the method focuses on the growth details of crops, reflects the real growth state of the crops in real time, is limited by the number of monitoring instruments/equipment, manual sampling and the like, and can be applied to a smaller range and area. Traditional means based on ground station measurement can not meet the demands far; the remote sensing technology has the advantages of macroscopic, dynamic, real-time and the like, can rapidly acquire the growth state information of large-area crops, and provides a good technical support for the implementation of precise agriculture and water resource optimization allocation in irrigation areas. However, in the remote sensing quantitative inversion, the inversion model and the assumption conditions established by applying the analysis model are too many, too idealized and conceptual, the obtained result has relative uncertainty, and the accuracy of the result may be difficult to meet the actual requirements. Therefore, the remote sensing information and the field real-time monitoring data are combined, the advantages of the remote sensing information and the field real-time monitoring data can be effectively utilized, and data support is provided for precise irrigation management in the irrigation area.
In the past, when irrigation decision is made by utilizing remote sensing information and field real-time monitoring data, water is often used as a single factor, or only the current multi-factor decision is considered, and the prediction of the final yield or benefit possibly achieved by crops in the future under the current condition is lacking, so that the overall economic benefit after the irrigation decision is implemented is difficult to comprehensively evaluate in real time. The ultimate goal of precision irrigation in fact is to increase crop yield and irrigation water use efficiency. Therefore, the irrigation decision and forecast are carried out by comprehensively considering the crop yield estimation and the actual water consumption estimation, and the method has important significance for defining the agricultural water-saving direction of the irrigation area and realizing the maximization of the irrigation benefit.
The crop growth model can be used for describing the growth dynamic change of crops in the whole growth period, including the accumulation of dry matters, water consumption of the crops and the like, estimating the final yield of the crops, and better estimating the future yield of the crops under the continuous condition of the current environmental factors, so that a powerful support is provided for the precision irrigation decision of farmlands according to the real-time yield feedback information. In addition, when a crop growth model is used for estimating the crop yield and water consumption of a large irrigation area, the problem of model scale expansion from point to surface often exists in the application process. This problem is typically addressed by finding and utilizing the scale-up factors associated with remote sensing data and field real-time monitoring data. However, the remote sensing information and the field real-time monitoring data often have the problem of time scale mismatch, namely, the remote sensing data represents instantaneous data and reflects state information at a certain moment in the crop growth process, and the field real-time monitoring data can continuously monitor the growth information of crops. Therefore, in practical application, how to effectively solve the time scale mismatch between the two is of practical significance for developing precise irrigation decision-making research in the irrigation area.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a regional precise irrigation method and a regional precise irrigation system based on multi-data fusion and assimilation, which solve the technical problems of unreasonable irrigation time and irrigation amount caused by mismatching of time scales of remote sensing information and field real-time monitoring data in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the invention discloses a region precision irrigation method based on multi-data fusion and assimilation, which comprises the following steps:
acquiring real-time monitoring data, remote sensing image data and weather forecast information of a irrigated area;
the crop yield estimated value G is obtained in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of remote sensing image data, and the historical optimal yield G is combined max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment;
if m is larger than the first preset value, judging whether the last soil water storage amount S of the nth day is smaller than the minimum soil water storage amount W min If not, no irrigation is needed at the current moment; if so, then
Judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
Preferably, the acquiring of the last soil water storage amount S on the nth day specifically includes:
inversion-acquired irrigation area earth surface temperature dataset and crop water consumption model based on remote sensing image data, and real-time calculation of daily crop evaporation quantity ET n ；
Calculating and obtaining the actual water storage capacity W of the soil by utilizing the real-time monitoring data T ；
Based on daily vapor emission ET of crops n And the actual water storage capacity W of the soil T And (3) combining the real-time rainfall observation value P, and estimating and obtaining the end soil water storage quantity S on the nth day according to a water balance method.
Preferably, the determination is made as to whether the last day of N is less than the minimum soil water storage amount W min If not, after no irrigation is needed at the current moment, the method comprises the following steps:
according to the soil water storage amount S at the end of the N day and the daily evapotranspiration of crops ET n And minimum water storage capacity W of soil min And determining the next irrigation time.
Preferably, the method judges rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the current moment needs to be irrigated,and calculate the current moment and irritate the water yield, including:
judging rainfall P in the future M days according to weather forecast information pre Whether or not it is greater than a second preset value W min -S-M× ET n ；
If so, no irrigation is needed at the current moment;
if not, irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated according to the maximum water storage quantity of the soil, the minimum water storage quantity of the soil and the rainfall in the future M days.
Preferably, the acquiring real-time monitoring data, remote sensing image data and weather forecast information of the irrigation area specifically includes:
analyzing available remote sensing images of the irrigation areas, and inverting to obtain earth surface temperature data sets of the irrigation areas;
monitoring soil moisture data and field meteorological factors in real time;
collecting the accumulation amount of crop dry matters at fixed time, measuring the characteristic points of soil moisture and collecting historical yield data of irrigation areas;
and acquiring near-M weather forecast data including rainfall and air temperature in real time.
Preferably, the method for obtaining the crop yield estimated value G in real time based on the irrigation area surface temperature dataset and the yield prediction model obtained by inversion of the remote sensing image data specifically includes:
acquiring effective crown laminated temperature at any moment by using a crown temperature daily average value monitored in real time in the field and an acquired dry matter accumulation amount, establishing a Logistic normalization model, and determining a coefficient to be determined of the Logistic normalization model;
acquiring a history harvest index HI of a irrigated area, and establishing a yield estimation model based on a Logistic normalization model;
calculating the relative effective accumulated temperature of the surface temperature by using the inversion obtained surface temperature dataset of the irrigation area;
the relative effective accumulation temperature of the surface temperature is used for replacing the effective crown lamination temperature at any time as an input parameter in a Logistic normalization model, and the accumulated dry matter y of the crops on the N th day is collected D Substituting the regional yield estimation value G into a yield estimation model to obtain the regional yield estimation value G.
Preferably, the irrigation area surface temperature dataset and the crop water consumption model obtained based on inversion of the remote sensing image data calculate the daily evaporation quantity ET of the crop in real time n The method specifically comprises the following steps:
acquisition of actual daily crop vapor emission ET C ；
Selecting instantaneous canopy temperature at noon and air temperature at corresponding moment according to local sunlight condition and net radiation daily, and combining measured daily farmland evaporation ET C Substituting the model into an S-I model which is an evapotranspiration model based on the crown temperature difference, and determining a model undetermined coefficient;
substitution of the canopy temperature T with the nth day surface temperature dataset LST obtained by remote sensing inversion c As S-I model input parameters;
collecting the N-th sunshine hours and the air temperature of all sites in the irrigation area, and calculating to obtain the net radiation R by using the sunshine hours n Interpolation is carried out to obtain net radiation and air temperature values of the irrigation area;
substituting the LST, the net radiation and the air temperature of the noon in the N day of the irrigation area into the calibrated S-I model to obtain the estimated value ET of the vapor emission quantity of the N day of the irrigation area n 。
The second object of the invention can be achieved by adopting the following technical scheme: a system for real-time precision irrigation of a region based on multiple data assimilation, the system comprising:
the basic data acquisition system is used for acquiring real-time monitoring data, remote sensing image data and weather forecast information of the irrigation area;
the crop growth evaluation system is used for acquiring a crop yield estimated value G in real time based on a irrigated area surface temperature dataset and a yield prediction model obtained by inversion of remote sensing image data and combining with a historical optimal yield G max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment;
the decision module is used for judging whether the last soil water storage amount S on the nth day is smaller than the minimum soil water storage amount W if m is larger than a first preset value min If not, no irrigation is needed at the current moment; if so, then
Judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
The third object of the present invention can be achieved by adopting the following technical scheme:
the computer equipment is characterized by comprising a processor and a memory for storing a program executable by the processor, wherein the processor realizes the regional precision irrigation method based on multi-data fusion and assimilation when executing the program stored by the memory.
The fourth object of the present invention can be achieved by adopting the following technical scheme:
a storage medium storing a program which, when executed by a processor, implements the above-described area precision irrigation method based on multi-data fusion and assimilation.
The invention acquires the crop yield estimated value G in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of the remote sensing image, estimates whether irrigation is needed or not through the seed yield estimated value G, acquires the crop yield estimated value G in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of the remote sensing image data, and combines the historical optimal yield G max When the grain yield loss evaluation index m is smaller than or equal to a first preset value, crops at the current moment are not required to be irrigated, and if m is larger than the first preset value, whether the last soil water storage amount S of the N day is smaller than the minimum soil water storage amount W is judged min If not, temporarily no irrigation is needed; if yes, judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the current time is needed to be irrigated, and the current time irrigation quantity is calculated, so that the strategy needed to be irrigated and the specific data quantity for making irrigation are judged by combining the soil water storage quantity and weather forecast of a period of time in the future, and the yield of crops can be ensured and accurate irrigation can be realized.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a regional precision irrigation method based on multi-data fusion and assimilation according to the present invention;
FIG. 2 is a schematic diagram of acquiring remote sensing information, real-time monitoring data and weather forecast information of an irrigation area provided by the invention;
FIG. 3 is a flow chart of the real-time estimation of grain yield provided by the invention;
FIG. 4 shows a T-shape according to the present invention canopy And T LST Is a comparison result graph of (2);
FIG. 5 is a flow chart of the evapotranspiration real-time estimation provided by the invention;
FIG. 6 is a schematic diagram of a real-time precision irrigation system based on multiple data assimilation areas provided by the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In the prior art, when a crop growth model is used for estimating the crop yield and water consumption of a large irrigation area, the problem of model scale expansion from point to surface often exists in the application process. This problem is typically addressed by finding and utilizing the scale-up factors associated with remote sensing data and field real-time monitoring data. However, there is often a problem of time scale mismatch between the remote sensing information and the field real-time monitoring data.
The invention acquires the real-time monitoring data, the remote sensing image data and the weather pre-treatment of the irrigation areaReporting information; real-time acquisition of crop yield estimated value G by using irrigation area earth surface temperature dataset and yield prediction model obtained based on inversion of remote sensing image, and combination of historical optimal yield G max When the grain yield loss evaluation index m is smaller than or equal to a first preset value, crops at the current moment are not required to be irrigated, and if m is larger than the first preset value, whether the last soil water storage amount S of the N day is smaller than the minimum soil water storage amount W is judged min If not, temporarily no irrigation is needed; if yes, judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
According to the invention, whether irrigation is needed or not is estimated through the grain yield estimated value G, and the strategy of irrigation is judged and the specific data quantity of irrigation is formulated through combining the soil water storage quantity and weather forecast in a future period of time, so that the yield of crops can be ensured, and accurate irrigation can be realized.
Example 1
Taking Ji Lin Sheng Changchun main crops-spring corns as an example, the embodiment provides a method and a system for real-time farmland precision irrigation based on areas with multiple data assimilation, as shown in fig. 1, comprising the following steps:
(1) As shown in fig. 2, real-time monitoring data, remote sensing image data and weather forecast information of the irrigation area are obtained, specifically:
(1-1) analyzing the available remote sensing images of the irrigation area by using a cloud service platform Google Earth Engine (GEE), and inverting to obtain an irrigation area surface temperature (LST) data set.
(1-2) 5 sets of CTMS-Online crop canopy temperature and environmental factor measuring systems and 10 sets of soil moisture content instruments are uniformly distributed on a typical corn field in a irrigated area, soil moisture data and field meteorological factors (including daily air temperature, relative humidity, net radiation, wind speed, atmospheric pressure and canopy temperature) are monitored in real time, and the acquisition frequency is 30min. It should be emphasized that the acquisition frequency can be adjusted as desired.
(1-3) collecting the accumulated amount of the dry corn matters at fixed time manually, and measuring the characteristic points of soil moistureComprises soil volume weight, saturated water holding capacity and field water holding capacity, collects historical yield data of the irrigation area, analyzes and obtains the maximum value G of the historical yield of corn in the irrigation area max 。
(1-4) acquiring weather forecast data of the irrigation area near M days according to weather forecast information, wherein M is taken as 3 in the embodiment, and the rainfall P is included pre And air temperature.
(2) As shown in fig. 3, the estimated value G of the yield of corn kernels in the irrigation area is obtained in real time based on the yield prediction model and the basic data set obtained in the step (1), specifically:
(2-1) determining the coefficients A, B and K to be determined of a Logistic normalization model (i.e. a dry matter accumulation estimation model) by using the daily average value of the canopy temperature monitored in real time by the CTMS-Online type system in the step (1) and the manually collected dry matter accumulation, wherein the model expression is as follows:
Y D ＝A/(1+Bexp(-KT canopy ))
wherein:
T canopy ＝t canopy /t m
Y D ＝y D /y Dm
wherein t is i The temperature average value of the canopy measured on the ith day is expressed as the unit of the temperature; t is t canopy Is the effective crown lamination temperature on the i day, and the unit is the temperature; t is t base The effective growth basic temperature of crops is expressed as the unit of the temperature t base ＝10℃；t m For effective crown layer temperature at harvest, the unit is °c; t (T) canopy Is a relatively effective crown lamination temperature; y is D The unit is kg/ha of dry matter accumulation quantity at any moment; y is Dm For the dry matter accumulation at harvest, the unit is kg/ha; y is Y D Cumulative amount of relatively dry matter; a. B and K are coefficients to be determined.
(2-2) collecting and researching to obtain a irrigated area historical harvest index HI, and combining a dry matter accumulation amount estimation model to obtain a yield estimation model:
wherein: g is the estimated grain yield value in kg/ha.
(2-3) after the model parameters A, B and K of (2-2) are calibrated, the surface temperature LST (instantaneous value) obtained by remote sensing inversion is used to calculate the surface temperature relative effective accumulation temperature (T) LST ) The calculation formula is as follows:
T LST ＝t LST /t LSTm
wherein t is LSTi Is the instant earth surface temperature value of the remote sensing on the i day, and the unit is the temperature; t is t LST The effective surface temperature is accumulated in the unit of DEG C; t is t base The effective growth basic temperature of corn is the same as (2-1), and the unit is the temperature; t is t LSTm The unit is the temperature accumulation of the effective surface temperature during harvest; t (T) LST Is a relatively effective surface temperature accumulation.
(2-4) T for comparing remote sensing transient data acquisitions LST And T obtained by field monitoring daily average value canopy T for verification LST Directly replace T canopy The feasibility of (2) is verified as shown in FIG. 4, see T LST And T is canopy Has excellent correlation, and in the practical application process, T is used LST Instead of T canopy The method is feasible, and solves the mismatch between the remote sensing instantaneous value and the measured daily average value.
(2-5) T LST Instead of T canopy In combination with the cumulative amount of dry matter y collected on day N D Substituting into the yield estimation model (2-2) to obtain the yield estimation value G of the seed grains in the irrigation area.
(3) As shown in fig. 5, the daily transpiration of crops is estimated in real time based on a crop water consumption model, specifically:
(3-1) calculating the actual daily corn at the monitoring point by using the meteorological factors and the FAO56 Penman-Monteth formula measured in the step (1)Vapor-dispensing ET C . It should be emphasized that the actual evaporation of the crop can be obtained in different ways in different irrigation areas according to actual conditions, for example, the actual daily evaporation ET of the crop can be obtained directly by using a lysimeter or a vorticity relator C 。
(3-2) selecting the measured instantaneous canopy temperature at noon and the air temperature at the corresponding time (11:30 noon data in this example) and the daily net radiation according to the local sunlight conditions, and combining the daily farm evaporative ET obtained in (3-1) C Substituting into an S-I model which is an evapotranspiration model based on a crown air temperature difference, determining undetermined coefficients a and b of the model, and obtaining an evaporation estimation model of an irrigation area, wherein the specific expression of the S-I model is as follows:
ET c -R n ＝a+b(T c -T a )
wherein T is c And T a The temperature of the canopy and the temperature of the air at noon are respectively given in units of DEG C; r is R n Is the net radiation in mm/day; a and b are model undetermined coefficients.
(3-3) substituting the canopy temperature T with the nth day surface temperature data LST obtained by remote sensing inversion c As input parameters for the S-I model.
(3-4) collecting the N-th solar hours and the air temperature of the irrigation area site in the Chinese meteorological data network, and calculating to obtain the net radiation R by utilizing the solar hours n And interpolating to obtain the net radiation and air temperature values of the irrigation area.
(3-5) substituting the N-th LST, the net radiation and the air temperature (approaching the remote sensing satellite transit time) of the irrigation area into the S-I model to obtain the estimated value ET of the vapor emission amount of the N-th day of the irrigation area n 。
(4) As shown in fig. 1, on the basis of steps (1) - (3), a precise irrigation decision method based on yield real-time evaluation, crop moisture status real-time judgment and weather forecast is established, specifically comprising the following steps:
(4-1) combining the historical optimum yield G according to the grain yield estimated value G in the step (2) max The grain yield loss evaluation index m is obtained, and the calculation formula of m is as follows:
m＝(G max -G)/G max
if m is less than or equal to 1%, the method shows that under the current moisture state condition, the yield loss of crops in the future is small, and the crops do not need to be irrigated at the current moment; if m is more than 1%, the loss of crop yield is large in the current moisture state, and the degree of crop water shortage and drought tolerance needs to be further judged according to the actual water consumption condition of the crops.
(4-2) calculating and obtaining the actual water storage capacity W of the soil by utilizing the soil moisture data monitored in real time by the soil moisture content instrument T Combining the estimated value ET of the evaporation quantity of the N day of the irrigation area obtained in the step (3) n And a real-time rainfall observation value P, and estimating and obtaining the end soil water storage quantity S of the nth day according to a water balance method.
(4-3) determining whether S is less than the minimum water storage amount W of the soil min If not, indicating that the end crops on the N day are not deficient in water, and temporarily, not needing to irrigate:
further, the next irrigation time is estimated, and a specific calculation formula is as follows:
k＝(S-W min )/ET n
water irrigation (in the absence of rainfall) is expected after k days based on the estimation.
If so, indicating that the crops are lack of water at the current moment, and further judging whether the crops need to be irrigated at the current moment according to weather forecast information.
(4-4) judging the rainfall P in 3 days in the future according to weather forecast pre Whether or not it is greater than W min -S-3×ET n If so, indicating that the rainfall in the future 3 days can supplement the missing water quantity of the soil, and not requiring irrigation at the current moment; if not, the water should be irrigated at the current moment, and the water should be irrigated at the current moment IW, which is shown by the following specific calculation formula:
IW＝W max -W min -P pre
w in the formula max And (3) determining and calculating the maximum water storage amount of the soil according to the soil moisture characteristic points measured in the step (1).
Example 2
As shown in fig. 6, the present embodiment provides a management system for real-time precision irrigation decision-making of regional corn based on multiple data assimilation corresponding to the present embodiment 1. The system comprises the following subsystems: 1) A basic data acquisition system; 2) A data storage system; 3) A data processing system; 4) A crop growth assessment system; 5) Result analysis and irrigation decision making; 6) And an irrigation decision information release system.
In this embodiment, the subsystem (1) specifically includes:
(1-1) real-time monitoring data: selecting a typical plot in a corn planting area, and uniformly arranging a real-time monitoring system, wherein the real-time monitoring system comprises a multi-element integrated real-time irrigation information acquisition device (CTMS-Online system) and a low-power consumption economic area soil moisture content real-time monitoring system (LESW system) for monitoring field meteorological factors and soil moisture in real time; the field crop growth indexes including plant height, leaf area index, dry matter accumulation and the like are manually and regularly collected.
(1-2) remote sensing data: and directly inverting to obtain the surface temperature dataset of the research area by using the GEE remote sensing platform.
(1-3) weather forecast information: and obtaining the rainfall and the air temperature of the weather forecast of the near M days in real time.
The subsystem (2) specifically comprises:
(2-1) cloud service platform: the data acquired in the subsystem (1) can be uploaded to the cloud service platform in real time, and an operator can directly log in the cloud service platform from a computer end to perform real-time data viewing, downloading, analysis, system parameter setting and the like.
(2-2) hard disk storage: the remote sensing data processed by the GEE platform can be stored in a computer end for subsequent processing and analysis.
The subsystem (3) specifically comprises:
(3-1) a remote sensing data processing analysis module: classifying planting structures in a research area by utilizing multisource remote sensing data and ground planting structure investigation data through an automatic processing algorithm, extracting corn planting distribution, carrying out time sequence analysis on the obtained remote sensing data, supplementing missing values, and further obtaining space-time continuous remote sensing inversion surface temperature.
(3-2) model construction module: the model construction module is a computer processing program, and is expressed by the computer program in the step (1) and the step (2) in the embodiment 1, and by taking real-time monitoring data and manual acquisition data of a irrigated area farmland as program input data, the model construction module automatically processes by using a constructed algorithm and directly outputs crop evaporation model coefficients and yield model coefficients.
The subsystem (4) specifically comprises:
(4-1) an information processing module: the regional planting structure classification and daily surface temperature data obtained in the subsystem (3) are input into the evapotranspiration model and the yield model which are calibrated, and the regional real-time evapotranspiration quantity and the corn kernel yield are automatically obtained by using a model program.
(4-2) a visual analysis module: and displaying the classification result of the regional planting structure on a computer screen, and updating the estimated value of the evaporation quantity and the corn kernel yield in real time.
The subsystem (5) comprises:
(5-1) comprehensive analysis module: the combination subsystem (1) is used for collecting and uploading field soil moisture data in real time by comprehensively analyzing the actual corn evaporation and the field grain yield, so that the crop growth state and the moisture condition can be comprehensively analyzed and evaluated;
and (5-2) the decision output module is a module for conveying decision instructions to the field automatic irrigation management network, automatically optimizes field irrigation management measures through the result output by the information processing module, and outputs decision information to the field management network so as to realize intelligent control of typical farmland irrigation decisions.
The subsystem (6) specifically comprises:
the regional corn precise irrigation decision information release platform is an interactive platform capable of displaying irrigation decision instructions in real time, a manager can manually adjust irrigation decisions through the platform, and meanwhile, automatic irrigation decisions can be carried out through an algorithm built in the platform, so that real-time precise irrigation decisions of irrigated areas and farmlands are realized.
Example 3
A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor implements the data fusion and assimilation based on the program stored in the memory when executing the program stored in the memoryThe regional precise irrigation method comprises the following steps: acquiring real-time monitoring data, remote sensing image data and weather forecast information of a irrigated area; the crop yield estimated value G is obtained in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of remote sensing image data, and the historical optimal yield G is combined max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment; if m is larger than the first preset value, judging whether the last soil water storage amount S of the nth day is smaller than the minimum soil water storage amount W min If not, no irrigation is needed at the current moment; if yes, judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
Example 4
A storage medium storing a program which, when executed by a processor, implements the above-described region precision irrigation method based on multi-data fusion and assimilation, the method comprising: acquiring real-time monitoring data, remote sensing image data and weather forecast information of a irrigated area; the crop yield estimated value G is obtained in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of remote sensing image data, and the historical optimal yield G is combined max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment; if m is larger than the first preset value, judging whether the last soil water storage amount S of the nth day is smaller than the minimum soil water storage amount W min If not, no irrigation is needed at the current moment; if yes, judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A regional precision irrigation method based on multi-data fusion and assimilation, the method comprising:
acquiring real-time monitoring data, remote sensing image data and weather forecast information of a irrigated area;
the crop yield estimated value G is obtained in real time based on the irrigation area earth surface temperature dataset and the yield prediction model obtained by inversion of remote sensing image data, and the historical optimal yield G is combined max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment;
if m is larger than the first preset value, judging whether the last soil water storage amount S of the nth day is smaller than the minimum soil water storage amount W min If not, no irrigation is needed at the current moment; if so, then
Judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
2. The regional precision irrigation method based on multi-data fusion and assimilation according to claim 1, wherein the obtaining of the nth last soil water storage amount S specifically comprises:
inversion-acquired irrigation area earth surface temperature dataset and crop water consumption model based on remote sensing image data, and real-time calculation of daily crop evaporation quantity ET n ；
Calculating and obtaining the actual water storage capacity W of the soil by utilizing the real-time monitoring data T ；
Based on daily vapor emission ET of crops n And the actual water storage capacity W of the soil T And (3) combining the real-time rainfall observation value P, and estimating and obtaining the end soil water storage quantity S on the nth day according to a water balance method.
3. The regional precise irrigation method based on multiple data fusion and assimilation according to claim 2, wherein the determination is made as to whether the last day soil water storage amount S is smaller than the minimum soil water storage amount W min If not, after no irrigation is needed at the current moment, the method comprises the following steps:
according to the soil water storage amount S at the end of the N day and the daily evapotranspiration of crops ET n And minimum water storage capacity W of soil min And determining the next irrigation time.
4. The regional precise irrigation method based on multi-data fusion and assimilation according to claim 3, wherein the method is characterized in that the rainfall P in the future M days is judged according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated, including:
judging rainfall P in the future M days according to weather forecast information pre Whether or not it is greater than a second preset value W min -S-M×ET n ；
If so, no irrigation is needed at the current moment;
if not, irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated according to the maximum water storage quantity of the soil, the minimum water storage quantity of the soil and the rainfall in the future M days.
5. The regional precision irrigation method based on multi-data fusion and assimilation according to claim 2, wherein the acquiring real-time monitoring data, remote sensing image data and weather forecast information of the irrigation area specifically comprises:
analyzing available remote sensing images of the irrigation areas, and inverting to obtain earth surface temperature data sets of the irrigation areas;
monitoring soil moisture data and field meteorological factors in real time;
collecting the accumulation amount of crop dry matters at fixed time, measuring the characteristic points of soil moisture and collecting historical yield data of irrigation areas;
and acquiring near-M weather forecast data including rainfall and air temperature in real time.
6. The regional precision irrigation method based on multi-data fusion and assimilation according to claim 5, wherein the irrigated area surface temperature dataset and the yield prediction model obtained based on inversion of remote sensing image data are used for obtaining a crop yield estimated value G in real time, and specifically comprising the following steps:
acquiring effective crown laminated temperature at any moment by using a crown temperature daily average value monitored in real time in the field and an acquired dry matter accumulation amount, establishing a Logistic normalization model, and determining a coefficient to be determined of the Logistic normalization model;
acquiring a history harvest index HI of a irrigated area, and establishing a yield estimation model based on a Logistic normalization model;
calculating the relative effective accumulated temperature of the surface temperature by using the inversion obtained surface temperature dataset of the irrigation area;
the relative effective accumulation temperature of the surface temperature is used for replacing the effective crown lamination temperature at any time as an input parameter in a Logistic normalization model, and the accumulated dry matter y of the crops on the N th day is collected D Substituting the regional yield estimation value G into a yield estimation model to obtain the regional yield estimation value G.
7. The regional precision irrigation method based on multi-data fusion and assimilation according to claim 5, wherein the irrigation region surface temperature dataset and the crop water consumption model obtained based on inversion of remote sensing image data calculate the daily evaporation and emission ET of crops in real time n The method specifically comprises the following steps:
acquisition of actual daily crop vapor emission ET C ；
Selecting instantaneous canopy temperature at noon and air temperature at corresponding moment according to local sunlight condition and net radiation daily, and combining measured daily farmland evaporation ET C Substituting the model into an S-I model which is an evapotranspiration model based on the crown temperature difference, and determining a model undetermined coefficient;
substitution of the canopy temperature T with the nth day surface temperature dataset LST obtained by remote sensing inversion c As S-I model input parameters;
collecting all sites of the irrigation areaN days of sunshine hours and air temperature, and calculating to obtain net radiation R by using the sunshine hours n Interpolation is carried out to obtain net radiation and air temperature values of the irrigation area;
substituting the LST, the net radiation and the air temperature of the noon in the N day of the irrigation area into the calibrated S-I model to obtain the estimated value ET of the vapor emission quantity of the N day of the irrigation area n 。
8. A system for real-time precision irrigation of a region based on multiple data assimilation, the system comprising:
the basic data acquisition system is used for acquiring real-time monitoring data, remote sensing image data and weather forecast information of the irrigation area;
the crop growth evaluation system is used for acquiring a crop yield estimated value G in real time based on a irrigated area surface temperature dataset and a yield prediction model obtained by inversion of remote sensing image data and combining with a historical optimal yield G max Obtaining a grain yield loss evaluation index m, and if m is smaller than or equal to a first preset value, the crops do not need to be irrigated at the current moment;
the decision module is used for judging whether the last soil water storage amount S on the nth day is smaller than the minimum soil water storage amount W if m is larger than a first preset value min If not, no irrigation is needed at the current moment; if so, then
Judging rainfall P in the future M days according to weather forecast information pre Whether the value is larger than a second preset value; if so, no irrigation is needed at the current moment; if not, the irrigation is needed at the current moment, and the irrigation quantity at the current moment is calculated.
9. A computer device comprising a processor and a memory for storing a program executable by the processor, the computer device performing the method of any of claims 1 to 7 when the processor executes the program stored in the memory.
10. A storage medium storing a program which, when executed by a processor, performs the method of any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116956200A (en) * | 2023-09-19 | 2023-10-27 | 山东辉瑞管业有限公司 | Irrigation pipe production real-time detection system based on machine learning |
| CN117522059A (en) * | 2023-11-23 | 2024-02-06 | 星景科技有限公司 | Afforestation irrigation decision method and system based on multisource information fusion |
| CN118094112A (en) * | 2024-04-18 | 2024-05-28 | 北京市农林科学院智能装备技术研究中心 | Irrigation data assimilation method and device, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116956200A (en) * | 2023-09-19 | 2023-10-27 | 山东辉瑞管业有限公司 | Irrigation pipe production real-time detection system based on machine learning |
| CN116956200B (en) * | 2023-09-19 | 2023-11-24 | 山东辉瑞管业有限公司 | Irrigation pipe production real-time detection system based on machine learning |
| CN117522059A (en) * | 2023-11-23 | 2024-02-06 | 星景科技有限公司 | Afforestation irrigation decision method and system based on multisource information fusion |
| CN118094112A (en) * | 2024-04-18 | 2024-05-28 | 北京市农林科学院智能装备技术研究中心 | Irrigation data assimilation method and device, electronic equipment and storage medium |
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