50 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 an irrigation water demand calculation method considering fine distribution of crops, electronic equipment and a storage medium, and aims to accurately calculate the irrigation water demand and spatial distribution of the crops in a target area through a remote sensing technology, meteorological data and a soil moisture balance model. The method comprises the following steps: collecting remote sensing crop classification, weather and soil parameter data; reference crop evapotranspiration is calculated at the grid scale; configuring crop parameters; calculating root zone soil moisture change based on a water balance model; and calculating the water demand of crops and the water demand of irrigation by adopting an FAO-56 crop coefficient method. Through refined crop distribution and dynamic soil moisture simulation, high-resolution irrigation water demand distribution information is provided, and a scientific basis is provided for agricultural water resource management.

The invention discloses a precise irrigation decision-making method and system based on a crop growth model and weather forecast, and relates to the field of agricultural irrigation automation control. The method comprises the following steps: calculating reference crop evapotranspiration corresponding to each growth period of crops in an irrigation decision-making area by adopting an FAO56PM formula according to meteorological data, and calculating a theoretical growth period water demand and a water demand proportion of an ith growth period in a whole growth period according to a growth period crop coefficient; the optimal growth period water demand of the crops in the whole growth period in the typical hydrological year is calculated through optimization of a crop growth model, the daily precipitation serves as a reference value of the effective precipitation, the effective precipitation is estimated according to weather forecast, and by comparing the quantitative relation between the effective precipitation and the optimal water demand of the ith growth period, the optimal growth period water demand of the crops in the ith growth period is calculated. Judging whether the irrigation decision-making area needs to be irrigated or not, and when the irrigation decision-making area needs to be irrigated, calculating and determining the optimal irrigation amount of the crops in the irrigation decision-making area in the ith growth period according to a formula.

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 intelligent irrigation dynamic decision-making method based on reference crop evapotranspiration estimation. The method comprises the following steps: firstly, acquiring historical day-by-day weather data of a farmland area, calculating the evapotranspiration of a reference crop, and preprocessing the evapotranspiration; secondly, weather factors are screened to serve as input features, reference crop evapotranspiration serves as a target variable, and a reference crop evapotranspiration estimation model is constructed and trained; and then, through a reference crop evapotranspiration estimation model, obtaining reference crop evapotranspiration estimation results of the current day and the future n days. And finally, according to an evapotranspiration estimation result in combination with a crop coefficient corresponding to the growth stage of the rice, evapotranspiration of the current day and n days in the future is calculated, an irrigation decision is made, and the water storage capacity of the reservoir is adjusted based on the irrigation water consumption of the current day and the irrigation water consumption of the n days in the future. The invention provides an efficient, economic and sustainable technical scheme for intelligent irrigation management of large-scale farmland, and has important value for promoting agricultural precision and ecological development.

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 belongs to the technical field of natural disasters, and discloses a method for evaluating the toughness of a social economic system under drought stress. The method comprises the following steps: collecting monthly rainfall data and potential evapotranspiration data in a region, and calculating a meteorological drought index SPEI under the scale of three months; collecting social and economic data such as runoff data, crop evapotranspiration, industrial added value, population and the like of the region, estimating the available water amount of the region and the water demand in three aspects of agriculture, industry and life, and calculating a social and economic drought index; extracting the recovery time of the historical social economic drought event; calculating the cumulative intensity of meteorological drought during the historical social economic drought event; and fitting the drought recovery time and the accumulated drought intensity, and constructing a toughness curve of the social economic system under drought stress. According to the technical scheme, the drought toughness of the social economic system can be objectively and quantitatively evaluated.

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 discloses a crop water distribution method and device, a medium and equipment, and relates to the field of water resource management, and the method comprises the steps: constructing a multi-target simulation-optimization model; the multi-objective simulation-optimization model comprises an EPI crop growth model and an optimization model which takes maximized economic water productivity, maximized irrigation water productivity and maximized nutritive water productivity as objective functions and takes irrigation water quantity and salt accumulation in a crop growth period as constraints; inputting the acquired meteorological data, crop data, soil data, channel data and groundwater data into a simulation model of a multi-target simulation-optimization model, determining simulation results of actual crop evapotranspiration, final crop yield and straw quantity, taking the simulation results as input of an optimization model, and determining an irrigation water distribution scheme; constructing a water-salt migration model; and updating the simulation result through the water-salt migration model so as to optimize the irrigation water distribution scheme until a preset optimization target is achieved.

The invention provides a crop evapotranspiration prediction method and device, equipment, a storage medium and a program product. The method comprises the steps of determining reference evapotranspiration data based on evapotranspiration input data; determining the relationship between the soil water potential and the soil moisture stress coefficient based on the evapotranspiration measured data and the reference evapotranspiration data; and based on the reference evapotranspiration data and the relationship, predicting the crop evapotranspiration of the to-be-predicted region to obtain a crop evapotranspiration prediction result. According to the method, reference evapotranspiration data is determined through evapotranspiration input data obtained from a to-be-predicted area, evapotranspiration measured data obtained from the to-be-predicted area are used for jointly determining the relation between the soil water potential and the soil moisture stress coefficient, and finally the crop evapotranspiration amount of the to-be-predicted area is predicted based on the reference evapotranspiration data and the determined relation. And obtaining a crop evapotranspiration prediction result. According to the method, the abiotic stress condition of the soil is effectively judged through the soil water potential, and the accuracy of crop evapotranspiration prediction is improved.

The invention is suitable for the technical field of agricultural irrigation, and provides an intelligent calculation method and system for agricultural irrigation water. The method comprises the following steps: dividing a plurality of crop irrigation sub-regions; periodic monitoring shooting is carried out; meteorological monitoring is carried out, and standard crop evapotranspiration is calculated; determining a plurality of crop growth stages and a plurality of crop growth states; carrying out evapotranspiration optimization, and calculating a plurality of optimized crop evapotranspiration amounts and irrigation water consumption of a plurality of sub-regions. According to different crop types, a plurality of crop irrigation sub-regions can be divided, monitoring shooting and meteorological monitoring can be performed, standard crop evapotranspiration can be calculated, evapotranspiration optimization can be performed on different crop irrigation sub-regions, irrigation water consumption of the sub-regions can be calculated, high-precision calculation of agricultural irrigation water can be realized, insufficient irrigation can be avoided, and the irrigation efficiency can be improved. Excessive irrigation can be avoided, so that appropriate moisture support is provided for crop growth, normal growth and development of crops are guaranteed, and the requirement of modern agriculture for efficient utilization of water resources can be met.

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 provides a remote sensing estimation method for high spatial-temporal resolution crop evapotranspiration in an arid and semi-arid region, which comprises the following steps of: acquiring cloudless or less-cloudless high spatial resolution optical remote sensing data of a crop growing season in a research region, and calculating or inverting remote sensing earth surface parameters required by high spatial resolution evapotranspiration; comprising a vegetation index NDVI, a leaf area index LAI, a vegetation coverage FVC, a surface water index LSWI and a surface albedo; the method comprises the following steps: acquiring meteorological reanalysis data of daily scales ERA5-Land and GLADAS of a research area, wherein the meteorological reanalysis data comprises relative humidity RH, average temperature Ta, air pressure Pa, solar short-wave radiation # imgabs0 #, downward long-wave radiation # imgabs1 # and upward long-wave radiation # imgabs2 #; according to the technical scheme, multi-source remote sensing data, an evapotranspiration physical model and a space-time fusion technology based on evapotranspiration characteristics are utilized, and estimation of high space-time resolution evapotranspiration of the arid and semi-arid agricultural areas is achieved.

The invention discloses a reference crop evapotranspiration prediction method, a storage medium and equipment, and relates to the field of reference crop evapotranspiration prediction.The method comprises the steps that historical reference crop evapotranspiration is calculated according to historical meteorological data of an irrigation area; determining the correlation between the evapotranspiration of the historical reference crop 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 periodic feature prediction module and a long-term trend feature prediction module; the seasonal periodic feature prediction module comprises a frequency domain data embedding unit, a self-attention layer unit based on time steps of a transformer encoder, a channel attention layer unit based on channel attention mechanism cross-dimension, and a linear layer unit. According to the scheme, the data periodic features can be better extracted.

The invention discloses an irrigation water demand measurement and calculation and dominant driving factor identification method, and relates to the field of agricultural irrigation, and the method specifically comprises the following steps: S1, data preprocessing; s2, calculating evapotranspiration of a reference crop; s3, correcting the crop coefficient of each stage; s4, calculating the actual water demand of the crops in the growth period; s5, calculating a crop net irrigation quota; s6, calculating the irrigation water demand; s7, constructing an enhanced regression tree model; and S8, identifying main control factors influencing the irrigation water demand. The method provides a reference basis for adjustment of a planting structure and improvement of agricultural water efficiency, provides a new view angle and decision support for agricultural management, and has important guiding significance for water resource saving and modern agricultural production development.

The invention discloses a reference crop evapotranspiration estimation method based on user data information, and the method comprises the following steps: S1, constructing a national database, and calculating the daily-scale reference crop evapotranspiration; s2, a user uploads meteorological data and geographic information, and necessary information of more than two users needs to be provided according to a data template; s3, calculating a cumulative frequency curve of each meteorological factor; s4, performing quantile sequence comparison, selecting 15-20 sites with the highest distribution matching degree, and obtaining a simplified data set; s5, the Euclidean distance is calculated, and a data group with the minimum distance is selected to enter an alternative group; and S6, constructing a reference crop evapotranspiration model to carry out evapotranspiration estimation. According to the method, the optimal machine learning modeling reference crop evapotranspiration method is customized for the user by using the meteorological data owned by the user, the calculation error is lower, the data information owned by the user is fully mined, and the customized and refined reference crop evapotranspiration model is really realized.

The invention, which relates to the technical field of crop evapotranspiration simulation, discloses a paddy rice evapotranspiration estimation method considering the depth of a waterflooding layer, comprising the following steps: acquiring meteorological data in a research area, waterflooding layer depth data of a paddy field, paddy rice growth data and actually measured evapotranspiration data; considering an influence mechanism of the depth of the flooded layer on evapotranspiration, and adding the influence of the depth of the flooded layer on the canopy resistance to a canopy resistance model (such as a Jarvis canopy resistance model); considering the influence of the depth of the waterflooding layer on water layer heat storage, and constructing a water temperature-surface heat flux model; the canopy resistance model and the earth surface heat flux model are coupled into a Shuttleworth-Wallace model to improve the evapotranspiration model; based on an improved Shuttleworth-Wallace model, whole-growth-period evapotranspiration prediction under irrigation of different flooding depths is realized, and the method is used for evaluating the water-saving effect of each irrigation mode. By improving the Shuttleworth-Wallace model, the influence mechanism of the depth of the flooded layer on evapotranspiration is brought into rice evapotranspiration simulation, the rice evapotranspiration simulation precision can be improved, and a basis is provided for formulating a reasonable irrigation system.

The invention discloses a crop evapotranspiration prediction method, system and device, a medium and a product, and relates to the field of crop evapotranspiration, and the method comprises the steps: obtaining the input data of crop evapotranspiration in a research area in real time; the input data comprises meteorological factors, crop factors and soil water potential; predicting crop evapotranspiration by adopting an optimized artificial neural network model based on the input data; the optimized artificial neural network model is obtained by optimizing the artificial neural network model through a food-feeding fish foraging optimization algorithm. According to the method, the soil water potential factor is considered, the crop evapotranspiration is predicted by adopting the optimized artificial neural network model, and the crop evapotranspiration prediction efficiency and accuracy are improved.

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 utility model discloses a crop evapotranspiration monitoring device, which belongs to the technical field of evapotranspiration monitoring and comprises a bottom plate and a monitor mounted on the bottom plate, a plurality of supporting legs are mounted at the bottom of the bottom plate, a sliding rail is mounted on the bottom plate, a first connecting plate is mounted on the sliding rail in a sliding manner, a third connecting rod is mounted at one end of the first connecting plate, and a second connecting rod is mounted at the other end of the first connecting plate. And a connecting block is mounted on the third connecting rod and is in threaded connection with a threaded rod. The first sliding block is slidably connected with the first sliding groove, so that the position of the humidity sensor can be conveniently adjusted, a worker does not need to carry the monitoring device to adjust the position of the humidity sensor, and adjustment is convenient; the first connecting plate can be guided and limited by arranging the first sliding block to be a T-shaped sliding block to be matched with the T-shaped sliding groove, the second connecting plate and the humidity sensor can be driven to move by arranging the threaded rod, and the humidity sensor can be inserted into soil for evapotranspiration monitoring.

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 present invention discloses a greenhouse water-saving irrigation method based on multi-information fusion, which is applied to an irrigation system. The greenhouse water-saving irrigation method based on multi-information fusion includes: collecting data on light intensity, air temperature, air humidity, and soil water content in the greenhouse at fixed intervals. Using weighted normalization, multi-objective centroid method and average silhouette coefficient to optimize clustering for the growth characteristics of greenhouse crops, and obtaining the change in soil water content corresponding to the centroid of each cluster, that is, the water demand of crops in different environments in the greenhouse; establishing a soil water balance model suitable for the greenhouse, determining crop coefficients in different periods according to different crop growth cycles, calculating more accurately the water lost due to crop evapotranspiration, and using the results obtained by optimal clustering as the reference input of the model predictive controller to avoid irrigation waste caused by relying on experience.