31 results about "Crop evapotranspiration" patented technology

The invention discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, and relates to the field of irrigation water consumption estimation. Multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information are fused, and observation and model errors are reduced through data assimilation; estimating crop evapotranspiration under an energy balance principle; estimating daily-scale irrigation water consumption by adopting a water balance method; a crop actual irrigation area identification model based on deep learning is introduced, actual irrigation areas of different crop types are effectively distinguished, and the influence of temporal and spatial changes on irrigation efficiency is dynamically reflected. According to the method, high-precision and rapid estimation of the comprehensive unit area irrigation water consumption of a single crop and multiple crops on different regional scales can be realized, the timeliness and accuracy of irrigation water quota evaluation are remarkably improved, and the method has strong adaptability to climate change, soil difference and crop diversity and is suitable for popularization and application. Powerful technical support is provided for precise irrigation and intelligent water conservancy management.

The invention discloses a method for predicting the irrigation amount of crops in an arid saline-alkali soil area and related equipment. The method comprises the steps that the potential evapotranspiration of a target crop is calculated through a double-source evapotranspiration mechanism model based on key meteorological parameters, obtained through prediction, of a target area, and the leaf area index and the plant height are dynamically updated according to the accumulated temperature process; respectively constructing different stress inhibition functions aiming at the attention salt ions and the pH value in the saline-alkali soil solution, and applying composite inhibition at the stomatal conductance and / or the apparent evapotranspiration resistance site to correct the potential evapotranspiration so as to obtain the ideal crop evapotranspiration corrected by the salinity and the pH value; and based on the corrected ideal crop evapotranspiration, predicting day-by-day irrigation amount and water distribution schedule suggestions in a future preset period. The method can solve the problem that current crop irrigation of saline-alkali soil in an arid inland region depends on experience, influences on different salt types and contents are caused, and quantitative analysis is lacked.

The invention discloses an irrigation index prediction method based on meteorological data and field moisture capacity, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data and future precipitation forecast data, and carrying out quality control and time alignment processing to obtain a multi-source data set; the method comprises the following steps: calculating evapotranspiration of a reference crop according to real-time meteorological data, obtaining actual water demand of the crop in combination with a crop coefficient, calculating a moisture stress coefficient according to real-time soil volumetric water content data and a field moisture capacity static parameter value, and obtaining a basic irrigation demand index in combination with future precipitation forecast data. According to the method, the evapotranspiration amount is separated by adopting the Shuttleworth-Wallace model, the irrigation index integrating the crop evapotranspiration amount, the water deficit amount and the effective precipitation amount is generated on the basis of multi-period rolling water balance calculation and risk decision rules, and online calibration of soil parameters and optimization of irrigation decision are achieved.

The invention discloses a reference crop evapotranspiration prediction method and device, a medium and equipment, and relates to the field of agrometeorology, water resource management and intelligent computation.The reference crop evapotranspiration prediction method comprises the steps that a historical reference crop evapotranspiration ET0 sequence and historical day-by-day meteorological factor data are obtained, and day-by-day meteorological data are determined according to the day-by-day meteorological factor data; empirical mode EMD decomposition is carried out on the historical ET0 sequence, and historical ET0 multi-scale time-frequency features are determined; serializing the day-by-day meteorological data, and carrying out weight dynamic allocation and fusion on the day-by-day meteorological data processed by adopting a multi-head self-attention mechanism to generate meteorological factor data dynamic characteristics; performing time sequence feature analysis and fusion on the meteorological factor data dynamic features and the historical ET0 multi-scale time-frequency features to generate meteorological factor features and historical ET0 features; and carrying out dynamic weighted fusion on the meteorological factor features and the historical ET0 features, determining fused features, carrying out time sequence feature analysis on the fused features, and obtaining a reference crop evapotranspiration ET0 prediction result.

The invention discloses an intelligent irrigation regulation and control system and method for dynamic balance of water and salt in saline-alkali soil, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting dynamic monitoring data influencing the water-salt dynamic state and storing the dynamic monitoring data in a basic database; calculating the crop evapotranspiration water demand based on the data of the basic database; in combination with the calculated crop evapotranspiration water demand and data of the basic database, salt profile estimation is achieved, a dynamic target salt threshold value is set, the self-adaptive leaching water amount is calculated, the salt return risk is evaluated, and an evaluation report at the end of the growth season is generated; based on all calculation and evaluation results, key strategy control parameters for guiding irrigation are generated and optimized by adopting a reinforcement learning algorithm; generating a self-adaptive irrigation strategy and auxiliary measure suggestions by combining all the obtained results and fusing weather forecast data; the irrigation strategy is converted into an irrigation and drainage executable instruction, and meanwhile, the execution state of irrigation and drainage is monitored to be used for achieving double-path feedback.

The invention belongs to the technical field of agricultural remote sensing and monitoring, and provides an agricultural medium-short-term water shortage intelligent estimation method based on remote sensing, which comprises the steps of data collection and preprocessing, crop spatial distribution and current growth cycle extraction in a research area, medium-short-term meteorological element prediction based on a WRF model, and medium-short-term crop evapotranspiration prediction and medium-short-term agricultural water deficit dynamic intelligent estimation are realized. According to the method, the time-space continuous monitoring advantage of the remote sensing image is fully utilized, and rapid identification and early warning of the water shortage condition of the crops in a large range are achieved; meanwhile, by means of simulation of a numerical weather forecasting model on complex meteorological conditions, the future weather trend is effectively predicted, and a scientific basis is provided for agricultural water resource management.

The invention discloses a method for simulating regional runoff based on a coupling model VMD-CNN-LSTM-Attention of irrigation water consumption of main crops. The method comprises the following steps: 1, calculating daily reference crop evapotranspiration ET0 according to meteorological station data of a target region; 2, calculating the irrigation water consumption of different crops in different periods by utilizing the crop growth coefficients of the different crops; and 3, taking the main crop irrigation water consumption, rainfall, temperature, evapotranspiration, soil water content and other monthly elements in the target area calculated in the step 2 as driving factors, and inputting the driving factors into the coupling model for monthly runoff prediction. The method has the following beneficial effects that the irrigation water consumption of main crops with high correlation with the runoff in the research area is calculated, and driving factors with higher correlation with the runoff can be found out according to local conditions as input, so that the prediction precision is improved.

The invention discloses an irrigation water demand dynamic prediction method based on a water balance equation and a crop water production function. The method comprises the following steps: step 1, multi-source data arrangement; 2, the net irrigation water amount is calculated based on a water amount balance equation; 3, determining an evapotranspiration demand based on a crop moisture production function; step 4, determining precipitation threshold values of different drought degrees; and step 5, predicting regional irrigation water demand under different drought situations. According to the method, spatial and temporal dynamic changes of regional scale farmland irrigation water are estimated, crop evapotranspiration requirements under different yield levels are determined in combination with a crop moisture production function, and then spatial and temporal changes of irrigation water requirements under different drought degrees are predicted according to a water balance principle; compared with a traditional irrigation system, the method considers the potential influence of insufficient water on the crop yield, distinguishes the space-time difference of the irrigation water demand, and provides an effective technical support for the dynamic prediction of the agricultural irrigation water and the meeting of the agricultural drought defense requirements.

This invention discloses an irrigation index prediction method based on meteorological data and field capacity, belonging to the field of smart agriculture technology. The method includes collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data, and future precipitation forecast data, and performing quality control and time alignment processing to obtain a multi-source dataset. Based on the real-time meteorological data, a reference crop evapotranspiration is calculated, and combined with the crop coefficient, the actual water requirement of the crop is obtained. Based on the real-time soil volumetric water content data and the static parameter value of field capacity, a water stress coefficient is calculated, and combined with future precipitation forecast data, a basic irrigation demand index is obtained. This invention uses the Shuttleworth-Wallace model to separate evapotranspiration and, based on multi-period rolling water balance calculation and risk decision rules, generates an irrigation index that integrates crop evapotranspiration, water deficit, and effective precipitation, achieving online calibration of soil parameters and optimization of irrigation decisions.

The application discloses an oil crop on-demand precise supplemental irrigation system and method, the supplemental irrigation system comprises a sensor module, a cloud platform and a water and fertilizer machine; the sensor module is connected with the cloud platform and is used for detecting meteorological data and growth data of the oil crops; the cloud platform is connected with the sensor module and an action execution module, is used for making decisions according to the data detected by the sensor module and issuing instructions to the water and fertilizer machine; the water and fertilizer machine is connected with the cloud platform and is used for receiving the instructions and completing water and fertilizer integrated supplemental irrigation operation in cooperation with the cloud platform. The supplemental irrigation method is to establish a relative water content estimation model of different depths of soil in a regional plot, and to formulate a phased oil crop irrigation and fertilization scheme based on future crop evapotranspiration and rainfall. The application enables the oil crops to realize water and fertilizer regulation and control dynamically in the growth process by on-demand precise supplemental irrigation, and realizes efficient production of the oil crops.

The application discloses a crop irrigation amount prediction method in arid and saline-alkali regions and related equipment. The method comprises the following steps: calculating the potential evapotranspiration of the target crop based on the predicted key meteorological parameters of the target region through a dual-source evapotranspiration mechanism model, and dynamically updating the leaf area index and plant height according to the accumulated temperature process; constructing different stress inhibition functions for the concerned salt ions and the pH value in the soil solution of the saline-alkali soil, respectively, applying a composite inhibition at the stomatal conductance and / or apparent evapotranspiration resistance site to correct the potential evapotranspiration, and obtaining the ideal crop evapotranspiration corrected by the salt and pH value; and predicting the daily irrigation amount and water distribution schedule suggestion within a future preset period based on the corrected ideal crop evapotranspiration. The method can solve the problem that the current crop irrigation in arid inland saline-alkali areas relies on experience and lacks quantitative analysis of the influence caused by different salt types and contents.

This invention discloses a method for estimating rice evapotranspiration considering flooded layer depth, belonging to the field of crop evapotranspiration simulation technology. The method includes the following steps: acquiring meteorological data, flooded layer depth data of paddy fields, rice growth data, and measured evapotranspiration data within the study area; considering the influence mechanism of flooded layer depth on evapotranspiration, adding the influence of flooded layer depth on canopy resistance to the canopy resistance model (such as the Jarvis canopy resistance model); considering the influence of flooded layer depth on water layer heat storage, constructing a water temperature-surface heat flux model; coupling the canopy resistance model and the surface heat flux model into the Shuttleworth-Wallace model to improve the evapotranspiration model; and using the improved Shuttleworth-Wallace model to predict evapotranspiration throughout the entire growth period under irrigation at different flooded depths, for evaluating the water-saving effects of various irrigation methods. This invention, by improving the Shuttleworth-Wallace model and incorporating the influence mechanism of flooded layer depth on evapotranspiration into rice evapotranspiration simulation, helps improve the accuracy of rice evapotranspiration simulation and provides a basis for formulating reasonable irrigation systems.

This invention discloses a method for dynamic prediction of irrigation water demand based on the water balance equation and crop water production function, comprising the following steps: Step 1, multi-source data processing; Step 2, calculation of net irrigation water volume based on the water balance equation; Step 3, determination of evapotranspiration demand based on the crop water production function; Step 4, determination of precipitation thresholds for different drought levels; Step 5, prediction of regional irrigation water demand under different drought scenarios. The method of this invention estimates the spatiotemporal dynamic changes of farmland irrigation water volume at a regional scale, combines this with the crop water production function to determine crop evapotranspiration demand at different yield levels, and then predicts the spatiotemporal changes of irrigation water demand under different drought levels based on the water balance principle. Compared with traditional irrigation systems, this method considers the potential impact of insufficient water on crop yield and distinguishes the spatiotemporal differences in irrigation water demand, providing effective technical support for dynamic prediction of agricultural irrigation water use and meeting the requirements of agricultural drought prevention.

The invention discloses a multi-source remote sensing daily-scale plot-level crop irrigation water demand forecasting method and system, and relates to the technical field of agricultural water-saving irrigation, remote sensing data processing and WebGIS online services, and the method comprises the following steps: S1, data preprocessing; s2, modeling with reference to crop evapotranspiration deviation; s3, rainfall deviation modeling is carried out; s4, carrying out model migration and forecast correction; s5, performing net radiation correction and ET0 calculation; s6, crop evapotranspiration and irrigation water demand calculation; and S7, outputting and issuing a result. According to the multi-source remote sensing daily scale plot-level crop irrigation water demand forecasting method and system, remote sensing data, reanalysis / forecast meteorological data, crop parameters and a high-resolution crop distribution map layer are combined, irrigation water demands of different crops at different time periods can be dynamically calculated on the basis of correcting meteorological data deviation, and the irrigation efficiency is improved. And the result is visually published through the platform, so that scientific decision support is provided for agricultural irrigation scheduling.

The application discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, relates to the field of irrigation water consumption estimation, and through fusing multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information, uses data assimilation to reduce observation and model errors, estimates crop evapotranspiration under the principle of energy balance; adopts a water balance method to estimate daily scale irrigation water consumption; introduces a crop actual irrigation area identification model based on deep learning, effectively distinguishes actual irrigation areas of different crop types, and dynamically reflects the influence of spatial and temporal changes on irrigation efficiency. The application can realize high-precision and rapid estimation of unit area irrigation water consumption of single crops and multiple crops in different regional scales, significantly improves the timeliness and accuracy of irrigation water quota evaluation, has strong adaptability to climate change, soil differences and crop diversity, and provides strong technical support for precise irrigation and intelligent water conservancy management.

The invention belongs to the technical field of agricultural water conservancy and water-saving irrigation, and provides a winter wheat daily crop coefficient calculation method. According to the method, key meteorological factors influencing the evapotranspiration of typical crops in each growth period of the Huaibei plain are extracted through a geographic detector method, a winter wheat growth period crop coefficient model based on meteorology and underground water burial depth is constructed, and the crop evapotranspiration process under different underground water burial depths is simulated. According to the technical scheme provided by the invention, the crop evapotranspiration simulation precision can be improved, a scientific basis is provided for efficient utilization of regional agricultural water resources and daily evapotranspiration simulation, and meanwhile, the blank of considering the underground water level burial depth influence in an existing crop evapotranspiration empirical model is filled.

The invention discloses an irrigation method for increasing the yield of medicago sativa under water deficit. The irrigation method comprises the following steps of 1, medicago sativa planting, wherein strip sowing with the row spacing being 20 cm, the sowing rate being 20 kg / ha and the sowing depth being 5 cm is adopted, and 2, medicago sativa irrigation is conducted, wherein the irrigation rate is kept twice a week from sowing to the seedling stage, and the total irrigation amount is 50 mm. Water deficit irrigation is started in the alfalfa branching period, the irrigation amount is calculated according to the local actual crop evapotranspiration amount, the moderate water deficit and the severe water deficit are 2 / 3 and 1 / 3 of the actual crop evapotranspiration amount respectively, and the irrigation frequency is one time per week. And (3) irrigation system: when an underground drip irrigation system is designed, a drip irrigation belt is buried at a position 20cm underground before alfalfa is sown. The alternate subsurface drip irrigation comprises a subsurface drip irrigation system 1 and a subsurface drip irrigation system 2. One set of underground drip irrigation system is used for one-time irrigation, and the other set of underground drip irrigation system is used for next-time irrigation. The alternate underground drip irrigation method is an effective way for improving the yield of alfalfa under the condition of water deficiency.

The invention provides a method for obtaining the contribution rate of a driving factor of agricultural irrigation water demand in an arid region, and belongs to the field of agricultural water resource management, and the method comprises the steps: constructing a research region SWAT model database; utilizing an improved SWAT model to simulate hydrological cycle components to calculate effective precipitation and crop evapotranspiration; calculating a net irrigation water demand by using effective rainfall and crop evapotranspiration, and calculating a gross irrigation water demand by combining an irrigation water utilization coefficient; and quantitatively solving the driving contribution of each factor based on an LMDI factor decomposition method. Through integration of precipitation temperature correction, distributed hydrological process simulation and an LMDI factor decomposition method of an SWAT model sub-basin according to an elevation zone, high-precision estimation of regional irrigation water demand and quantitative characterization of a driving factor contribution rate are realized, and a scientific regulation and control basis is provided for agricultural water resource management in an arid region.

The application discloses a kind of equivalent water-off days rice drought index determination method, comprising: S1.Calculation daily crop evapotranspiration data;S2.APSIS-Oryza model is constructed, and the model is parameter calibrated and model verified;S3.In the model, the phenophase of crop under full irrigation and no irrigation scenario, yield are simulated dynamically, and long sequence dynamic simulation results are output;S4.Calculation crop specified time period's yield reduction rate;S5.Statistics rice ear differentiation after original water-off days, fitting calculation rice yield and yield reduction rate and the mathematical relationship of original water-off days;S6.Based on the linear mapping of daily evapotranspiration data standardization processing value, obtain drought action coefficient, to calculate the equivalent water-off days of rice drought;S7.Calculation obtains the rice drought index of equivalent water-off days.The application couples crop evapotranspiration and water-off days, proposes equivalent water-off days index, can represent the degree of rice drought.

The application discloses a kind of irrigation water requirement calculation method considering crop fine distribution, electronic equipment and storage medium, to accurately calculate the crop irrigation water requirement and its spatial distribution in target area by remote sensing technology, meteorological data and soil moisture balance model.The method comprises the following steps: collecting remote sensing crop classification, meteorological and soil parameter data;Calculate the reference crop evapotranspiration at the grid scale;Configure crop parameters;Calculate the root zone soil moisture change based on the water balance model;Calculate the crop water requirement and irrigation water requirement using the FAO-56 crop coefficient method.The application provides high-resolution irrigation water requirement distribution information by fine crop distribution and dynamic soil moisture simulation, and provides a scientific basis for agricultural water resource management.

The invention discloses a cut-flower chrysanthemum growth decision-making method and system based on multi-model fusion. The method comprises the steps that greenhouse environment parameters and soil parameters are collected in real time through an environment sensor; calling a corresponding calibration model parameter set in a preset model parameter library according to the planted cut-flower chrysanthemum variety; synchronously executing the following sub-models through a multi-model fusion decision engine; and decision output: outputting a comprehensive decision result containing the model calculation result. According to the method, a growth and development index (DVI) is adopted as a key driving variable, and a growth prediction and management decision model of the cut-flower chrysanthemum is constructed. According to the method, the calculation complexity is reduced, and the prediction precision, timeliness and scientificity are remarkably improved; by coupling the leaf area index of the cut-flower chrysanthemum and referring to key parameters such as crop evapotranspiration, moisture evaporation and soil moisture content, accurate irrigation decision suggestions are output in real time; the blindness of irrigation with traditional experience is effectively avoided, the utilization efficiency of water resources is remarkably improved, and resource waste is reduced.

The application discloses a reference crop evapotranspiration prediction method, a storage medium and equipment, relates to the reference crop evapotranspiration prediction field, and the method comprises the following steps: calculating historical reference crop evapotranspiration according to historical meteorological data of an irrigation area; determining the correlation between the historical reference crop evapotranspiration and different meteorological factors by using a Pearson correlation coefficient, and screening the first k meteorological factors with relatively large correlation; constructing a reference crop evapotranspiration prediction model composed of a seasonal trend decomposition module, a seasonal cycle characteristic prediction module and a long-term trend characteristic prediction module; the seasonal cycle characteristic prediction module comprises a frequency domain data embedding unit, a time step self-attention layer unit based on a transformer encoder, a channel attention layer unit based on cross-dimension channel attention mechanism and a linear layer unit. The scheme can better extract the periodic characteristics of data.

The application discloses a water-saving and emission-reducing potential analysis method and system for an irrigation system. The method comprises the following steps: extracting a water deficit feature, and constructing a water deficit-yield response relationship feature vector; calculating a reference crop evapotranspiration, and determining an optimal irrigation water volume in combination with the feature vector; determining an initial water-saving potential according to an actual irrigation water volume and the optimal irrigation water volume; obtaining energy structure information and a real-time carbon emission factor time sequence library, and calculating actual carbon emissions and theoretical carbon emissions based on an actual irrigation time node and an optimal irrigation time window respectively; and combining a crop yield loss upper limit value, and determining a target water-saving potential and a target emission-reducing potential. The application realizes the collaborative optimization of water saving and emission reduction under the constraint of yield loss by constructing the water deficit-yield response relationship feature vector and introducing the time sequence fluctuation of the marginal carbon emission factor, and solves the problems of isolated water-saving and emission-reducing analysis, insufficient consideration of crop water response and inaccurate carbon emission evaluation in the prior art.

The invention discloses a closed-loop adaptive precise irrigation method based on a prediction model. The closed-loop adaptive precise irrigation method comprises the following steps: obtaining predicted meteorological data through LSTM and calculating to obtain reference crop transpiration; obtaining soil / plant prediction state data through XGBoost, and calculating to obtain actual crop transpiration; and finally calculating the water demand and the water shortage risk index of the crops. Compared with'hysteresis control 'in the prior art, the closed-loop self-adaptive precise irrigation method based on the prediction model disclosed by the invention has the advantages of time sequence prediction and nonlinear interactive modeling by utilizing the LSTM-XGBoost hybrid model, realizes rolling prediction 12 hours ahead of time, and has relatively high accuracy. Predictive driving is combined with the crop water demand and the water shortage risk index, it is guaranteed that the irrigation amount is kept within a reasonable range, the situation that due to lag control, interference irrigation of rainfall after irrigation and insufficient irrigation amount, water shortage of plants is caused is avoided, waste of irrigation water can be avoided, and it can be guaranteed that the crop growth water demand is met.

The present application relates to the field of agricultural meteorology and precision irrigation technology, and specifically discloses a reference crop evapotranspiration probability prediction method and system based on bias correction. The method comprises: obtaining meteorological ensemble prediction data of a target site in a future prediction period, and preprocessing the meteorological ensemble prediction data to match the spatial and temporal scales of the target site; using at least one strategy in a preset bias correction strategy library to correct the system error of the prediction data of each ensemble member after preprocessing; inputting the corrected meteorological data of each ensemble member into at least one reference crop evapotranspiration calculation model respectively to calculate a set of reference crop evapotranspiration prediction values corresponding to the future prediction period; and statistically analyzing the set of reference crop evapotranspiration prediction values to generate and output a probability prediction product quantifying the prediction uncertainty. With this method, the accuracy and reliability of the final probability prediction can be improved, thereby improving the effect of precision irrigation.

The invention belongs to the technical field of dry land agricultural water saving, and provides a dry land agricultural water saving equivalent calculation method, which comprises the following steps: S1, calculating the average moisture productivity WPave of a research area under the same type of soil and traditional management conditions; s2, the actual crop yield Ya and the actual crop evapotranspiration Eta after the target water saving technology is adopted in the research area are obtained; s3, according to the average moisture productivity WPave and the actual crop yield Ya, calculating the theoretical evapotranspiration EThheo of the crop yield Ya; s4, the difference between the theoretical evapotranspiration EThheo and the actual crop evapotranspiration Eta is obtained, and the agricultural water saving equivalent Wsav of the arid region after the water saving technology is adopted is calculated through unit conversion. According to the dry land agriculture water saving equivalent calculation method provided by the invention, the technical defect that the physical water saving amount cannot be calculated in dry farming and rain farming is broken through for the first time, so that the water saving technology can be converted into a visual water saving equivalent with a physical index. The blank that the field lacks direct water volume indexes for a long time is filled.

The application belongs to the field of dryland agricultural water-saving technology, and provides a dryland agricultural water-saving equivalent calculation method, which comprises the following steps WP ave S1, calculating the average water productivity of a research area under the condition of the same type of soil and traditional management Y a S2, obtaining the actual crop yield of the research area after the target water-saving technology is used ET a S3, calculating the theoretical evapotranspiration of the crop yield according to the average water productivity and the actual crop yield WP ave S4, calculating the dryland agricultural water-saving equivalent after the water-saving technology is used by subtracting the actual crop evapotranspiration from the theoretical evapotranspiration and performing unit conversion Y a Y a ET Theo ET Theo ET ET a W sav The dryland agricultural water-saving equivalent calculation method provided by the application breaks the technical deficiency that the dryland rain-fed agriculture cannot calculate the physical water-saving amount for the first time, so that the water-saving technology can be converted into an intuitive and physical water-saving equivalent. The application fills the long-term blank of lacking direct water amount index in the field.​​​​​

The invention discloses a reference crop evapotranspiration estimation method based on heterogeneous integrated feature selection and a stacking model, and relates to the technical field of agricultural water resource management, and the method comprises the following steps: obtaining and preprocessing the historical data of a meteorological station; calculating the evapotranspiration standard value of the reference crop by adopting an FAO-56 Penman-Monte's formula; screening dominant meteorological factors through a heterogeneous integrated feature selection method in combination with a fuzzy Borda method; an HIFS-Stacking model is constructed, RF, XGBoost and MLP are taken as base models, LR is taken as a meta model, and K-fold cross validation is adopted to generate meta features; real-time data are subjected to same preprocessing and then input into the model to output a prediction result. According to the method, the meteorological factors having significant influence on prediction of the evapotranspiration of the reference crop can be effectively determined, the complex nonlinear relationship between the key meteorological factors and ET0 is accurately fitted in combination with the optimized model architecture, and high-precision prediction is realized under limited meteorological data.

The invention discloses an irrigation scheduling method based on remote sensing fusion and automatic linkage of an unmanned aerial vehicle, and the method comprises the steps: building a spatial incidence relation among a canal system, a measurement and control integrated gate, a farming land parcel and crop planting distribution; specific association can be realized between crop species in different farming land parcels and the control relation of the measurement and control integrated gate to the specific farming land parcels. The unmanned aerial vehicle is used for collecting low-altitude remote sensing data, the evapotranspiration inversion model and the field moisture dynamic balance model are combined, the actual crop evapotranspiration and the net irrigation water demand are calculated for the land parcel level water demand control unit, and the limitation that in the prior art, water demand prediction depends on fixed parameters, and spatial heterogeneity is difficult to reflect is overcome. The water distribution scheduling model is constructed based on the land parcel level net irrigation water demand and the management attribute, and the optimal scheme is executed through the integrated gate, so that the problems that the water distribution scheme is disjointed with actual execution and automatic linkage is lacked in the prior art are solved, and the overall efficiency and the collaboration of irrigation scheduling are improved.

The present application relates to a kind of method and system for improving the precision of remote sensing evapotranspiration, comprising the following steps: according to two years of LAI data measured in field, combining sentinel image to retrieve crop growth period vegetation red light edge index CI red‑edge , monitor the two-stage precision retrieval LAI model parameters of crop are calibrated and verified;Based on the two-stage precision retrieval LAI model established, using Landsat8 remote sensing image and sentinel image data as the driving data of typical remote sensing dual-source evapotranspiration model, the surface temperature obtained by remote sensing retrieval is divided into components, and canopy temperature and soil temperature are obtained;Based on the results of LAI precision retrieval and component temperature division, the typical remote sensing dual-source evapotranspiration model is used to estimate crop evapotranspiration and its components in irrigation area.The present application can be widely applied in the field of water-saving irrigation technology.