121 results about "Evapotranspiration" patented technology

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses an irrigation method and system based on a crop growth model, and relates to the technical field of farmland irrigation, and the method comprises the steps: training a growth model based on a time sequence neural network through employing the obtained historical meteorological data, the crop growth process data of a corresponding time period, and the moisture state of a soil root zone; calculating the actual evapotranspiration of the crops by combining the actual moisture state of the soil and the moisture stress coefficient predicted by the growth model, and determining the irrigation amount according to the water deficit and the root depth by combining the current moisture state of the root zone with the stage target moisture content. And the irrigation frequency adaptive to the current environmental condition is calculated by using the meteorological prediction data and the actual evapotranspiration. According to the invention, by introducing the crop growth model based on time sequence modeling, correlation learning among meteorological conditions, soil moisture dynamic changes and crop moisture stress states is realized, so that irrigation decisions can reflect periodic physiological needs of crops on the basis of real-time data.

The invention relates to the technical field of resource scheduling, in particular to a wheat irrigation optimization system based on big data, which comprises a leaf index identification module, a water consumption feature reconstruction module, a time period supply and demand pre-judgment module, a decision water distribution generation module and a dynamic duration execution module. According to the method, the continuous change structure is constructed according to the leaf area amplification characteristics in the wheat growth stage, the water demand characteristic dynamic reduction in the crop growth process can be realized, the trend curve is established by pairing the evapotranspiration rate and the leaf area change to identify the water consumption change node, and the accurate time period water demand description can be realized in the growth stage; the evapotranspiration supply trend is established in combination with meteorological prediction data, pre-judgment identification of the water supply and demand state in the future time period is achieved, time period deviation analysis and supply and demand difference judgment are introduced in the actual water distribution process, the scheduling flexibility and the resource matching precision are improved, and the adaptability and high efficiency of field water regulation and control in different growth stages are ensured.

The invention belongs to the technical field of inter-basin underground water circulation flux estimation, and particularly relates to an infiltration and lateral inflow separated underground water flux remote sensing estimation method and system. The method includes: acquiring a multi-source remote sensing data set of a research area; performing partition correction on evapotranspiration data of different land utilization type areas in the research area; the method comprises the following steps: dividing a research region into equal-area units, identifying a vertical dominant unit and a lateral dominant unit, and further determining a cross-basin underground water exchange zone; and solving a local vertical infiltration amount by using the vertical dominant unit and interpolating to form a background field, establishing a combined equation set which takes the background field as an initial value and takes the dynamic characteristic parameters of the underground water as additional constraints for the lateral dominant unit, solving to obtain an external lateral inflow amount, and further obtaining a cross-basin underground water circulation flux. According to the method, the vertical dominant unit and the lateral dominant unit are identified, and the cross-basin groundwater circulation flux is quantitatively estimated.

The invention discloses an efficient identification method for a drought and flood sudden turning event. The method comprises the following steps: step 1, calculating a standard evapotranspiration index of a daily scale; step 2, constructing a drought and flood sudden turning event identification method; 3, determining characteristic indexes of drought and flood sudden change events; and 4, calculating a drought and flood sudden turning event intensity index. According to the method, the drought and flood state is judged grid by grid, the false flood phenomenon in the dry season is avoided in event recognition, higher recognition precision is achieved compared with a traditional method, the false flood recognition problem in the dry season is reduced, and the starting and ending time and the conversion direction of drought-to-flood and flood-to-drought events can be accurately recognized; and by determining the characteristic indexes of the drought and flood sudden change event, objective weighting is performed on multiple indexes by adopting an entropy weight method, a drought and flood sudden change intensity index is established, quantitative evaluation of the drought and flood sudden change event is realized, and scientific reference is provided for disaster prevention and reduction.

The invention discloses an AIoT water and fertilizer integrated regulation and control method and system based on a crop growth dynamic model, and relates to the technical field of water and fertilizer regulation and control, and the method comprises the steps: firstly obtaining multi-dimensional environment parameters and soil characteristics through an AIoT sensing network, and deeply analyzing a restriction mechanism of soil moisture on nutrient effectiveness based on the crop growth dynamic model; and quantifying the nutrient effectiveness coefficient. And then, inputting the coefficient into a water-fertilizer coupling prediction model, and dynamically deducing the actual nutrient absorption amount and the moisture evapotranspiration amount of the crop in a specific water-fertilizer interaction environment, so as to realize digital representation of the physiological demand. And further, in combination with future meteorological information and cost constraints, multi-target collaborative optimization is carried out by using a rolling time domain optimization algorithm, and an optimal regulation and control strategy considering growth requirements and economic benefits is generated and driven to be executed. In this way, quantitative and accurate regulation and control of the water and fertilizer synergistic effect can be achieved, and therefore the resource utilization efficiency and agricultural production economic benefits in the whole growth cycle of crops are greatly improved.

The invention relates to the technical field of underground water simulation, in particular to an underground water numerical simulation method coupled with vegetation ecology. The method realizes bidirectional coupling between groundwater and vegetation ecology through iteration, and specifically comprises the following steps: inverting vegetation coverage based on groundwater burial depth; inversion of actual evapotranspiration is carried out based on underground water burial depth and vegetation coverage; and finally, updating the net supply amount of the groundwater model based on evapotranspiration until convergence. According to the method, a statistical function relation is established to replace a complex physical model, and drainage and overflow cell water levels are corrected in iteration to ensure calculation stability.

The invention discloses a method for quantifying the influence of vegetation change on regional rainfall and a related device, and the method comprises the steps: carrying out the evapotranspiration simulation of a target region through a remote sensing evapotranspiration model according to meteorological driving data and vegetation and land utilization data, and obtaining an evapotranspiration data set under different vegetation utilization scenes; performing monthly scale correction on the water vapor tracking input data through the evapotranspiration data set to obtain a corrected water vapor tracking input data set; the meteorological driving data and the corrected water vapor tracking input data set are adopted to drive the water vapor tracking model to perform forward tracking, and water vapor tracking results under different vegetation utilization scenes are output; and quantitatively analyzing the downwind rainfall influence of the vegetation change on the target area through the water vapor tracking results under different vegetation utilization scenes. According to the invention, based on the water vapor source-sink relationship of the region, the evapotranspiration change caused by the vegetation change is spatially redistributed, the influence on downwind rainfall is tracked, and the quantitative relationship of the influence of the vegetation change on the regional rainfall is revealed.

The invention relates to the field of climate change influence assessment, and discloses a method for quantizing contribution of natural internal variability to drought intensity based on a circulation similarity method, and the method comprises the steps: data collection; evaluating the drought strength based on the difference between the rainfall distance and the potential evapotranspiration distance, and quantifying the relative contribution of rainfall deficit and evapotranspiration demand to the drought strength; based on a circulation similarity method, a regional large-scale atmospheric circulation background is obtained, and contribution of a natural internal variation rate to drought intensity, rainfall deficit and evapotranspiration requirements is evaluated; estimating long-term evolution trends of drought intensity, rainfall deficit and evapotranspiration demands under different carbon emission scenes in the future; the method has the advantages that the natural internal influence mechanism of drought intensity change is defined, the relative contribution of historical and future rainfall deficit and evapotranspiration requirements to the drought intensity is quantified, the reliability of future drought trend estimation is enhanced, theoretical support is provided for making disaster prevention and reduction policies, and the accuracy of drought risk prediction is improved.

The invention relates to the technical field of intelligent water conservancy, in particular to an irrigation area water demand prediction and scheduling system based on intelligent water conservancy, which comprises a disturbance identification module, a humidity sensing module, a deviation judgment module, an irrigation sequencing module and an instruction generation module. According to the method, the change degree of future weather forecast and weather data on that day is quantified to pre-judge the disturbance of the future environment, the soil moisture sensitive areas which are violently responded to the disturbance are dynamically screened out from the global irrigation area on the basis of the pre-judge, and the soil moisture sensitive areas which are violently responded to the disturbance are selected for the screened areas. Further combining with the ideal moisture demand of the crops at the current growth stage, analyzing the dynamic deviation trend between the actual soil humidity and the ideal value in the continuous time, thereby accurately identifying the land parcels really in the moisture supply deviation state, abandoning a fixed irrigation plan, and improving the irrigation efficiency. And acquiring physiological indexes such as evapotranspiration rate and leaf area index of the crops in the deviated area in real time, thereby judging the real urgent degree of the crops in each area to moisture and carrying out irrigation sequencing.

The invention relates to the technical field of intelligent agriculture, and discloses a traditional Chinese medicine organic planting management method based on big data analysis. A space coordinate system is established in a planting area, regular partitioning is carried out, a unique land parcel identifier is formed, a soil humidity sensor is arranged in the center of each land parcel, meteorological observation points are matched to continuously collect air temperature, wind speed and illumination, and an evapotranspiration potential index is generated through sliding window statistics and normalization. The system performs dynamic balance prediction and state correction according to the soil moisture content, the irrigation volume and the evapotranspiration loss, extracts a reference evapotranspiration rate by using an unirrigated sample, determines a threshold interval and a real-time deviation in combination with a historical quantile, and smoothly improves the stability through a sliding window. The threshold value is updated hour by hour within a feasible range, irrigation is triggered when the water content is lower than the threshold value, and the irrigation amount is limited within a reasonable range through depth and flow conversion. Structured logs are generated in the whole process, and irrigation whole-process trace reserving is achieved.

The invention relates to the technical field of agricultural irrigation, in particular to a crop actual evapotranspiration calculation method and device, electronic equipment and a storage medium. Time-space units are established based on objective division rules, and a stable reference cold and hot image element set is constructed for each unit by utilizing historical data; therefore, the pixel selection process depending on subjective experience in a traditional SEBAL model is converted into a standardized and data-driven process, the reference set of the corresponding unit of the day to be calculated is automatically matched and called for calculation during calculation, and deviation caused by artificial subjective judgment and random selection is avoided. Therefore, the calculation result has higher precision, stability and repeatability, meanwhile, the comparability of the calculation results at different time and in different areas is ensured, and the scientificity and reliability of agricultural water management are remarkably improved.

The invention discloses a regional groundwater supply simulation method based on a rainfall intensity dynamic response mechanism. The method comprises the following steps: firstly, collecting and preprocessing data of a research area; then simulating a rainfall redistribution process; calculating surface runoff production and initial infiltration; calculating dynamic infiltration response based on rainfall intensity; calculating actual evapotranspiration; and finally outputting the groundwater replenishment amount. According to the method, the key technical problem that underground water supply under high-intensity rainfall is systematically underestimated due to the fact that an existing regional underground water supply simulation method is insufficient in rainfall intensity dynamic response and ignores a rapid deep seepage mechanism induced by high-intensity rainfall is solved.

The present application relates to the field of intelligent agriculture and farmland hydrological information control technology, and more particularly to a rice water demand prediction and irrigation method based on physical mechanism characteristic engineering, comprising: collecting multi-source monitoring data and preprocessing into standard monitoring data; extracting Penman core intermediate parameters based on evapotranspiration physical mechanism, constructing weather interaction and water coupling characteristics, and obtaining a set of physical constraint extended characteristics; extracting a target minimal feature combination according to the prediction contribution, inputting the prediction model to output the actual evapotranspiration prediction value; combining the previous hydrology, current rainfall leakage and the prediction value, calculating the daily field water layer depth through a dynamic balance model; comparing the depth with the upper and lower threshold values to generate irrigation instructions, and collecting feedback data to calibrate the model. The present application overcomes the passive water level response hysteresis through mechanism dimension reduction and feedforward deduction, and realizes pre-precise irrigation control under limited edge computing power.

This invention provides a method and system for optimizing soil moisture microwave data based on the influence of geographical environment. The method includes: collecting multi-source soil moisture microwave product data and geographical environmental factor data of a target area, performing standardization processing, and then conducting error assessment; selecting an optimal product combination based on the error assessment results; analyzing the response intensity of each soil moisture microwave product in the optimal product combination to geographical environmental factors, dividing the target area into strong response areas and weak response areas according to the response intensity, calculating the fusion weight of each soil moisture product using different strategies, and fusing the optimal product combination according to the fusion weight; based on the fused data, partitioning the target area according to the coupling strength between soil moisture and evapotranspiration, and downscaling using different downscaling models; then using a residual correction method to perform residual correction on the preliminary downscaled data to obtain optimized soil moisture microwave data for the target area.

The invention provides a meteorological data-based land water reserve abnormal time sequence missing value filling method, and belongs to the related field of hydrological remote sensing and earth information science, and the method comprises the steps: S1, obtaining a monthly rainfall, potential evapotranspiration and land water reserve abnormal time sequence, and completing the initial prediction of a water reserve abnormal missing value through periodic trend decomposition; s2; a standardized multi-scale rainfall evapotranspiration index and land water reserve index sequence is calculated, correlation analysis is carried out, and a regression model between index sequences under maximum correlation is established; s3, predicting a water reserve index of a value-lacking month, and reconstructing a water reserve index sequence; s4, inversely indexing the water reserve index into land water reserve abnormity, and complementing the land water reserve abnormity missing value; iterative optimization is carried out through S2 and S3, and accurate filling of the land water reserve abnormal missing value is achieved; the method is high in interpretability, the accuracy and integrity of the abnormal time sequence of the land water reserves are effectively improved, and the filled sequence can be widely applied to hydrological event monitoring and dynamic research of water resources.

The invention discloses a WSRS framework-based ecological safety assessment method for a drainage basin in an arid region, relates to the technical field of ecological assessment, and solves the technical problems that the prior art is not adaptive to a coupling process of water, salt, evapotranspiration and underground water and lacks key factors, and an assessment method lacks weight uncertainty description and robustness analysis. The method comprises the steps of S1, acquiring multi-source data of a target drainage basin, and performing data preprocessing; s2, constructing a WSRS evaluation index system, and carrying out standardization processing on indexes; s3, based on the standardized indexes, determining the comprehensive weight of each index by adopting a subjective and objective combination weighting method; s4, performing comprehensive evaluation based on the comprehensive weight, and quantifying uncertainty of an evaluation result by adopting a cloud model to obtain an ecological safety index; s5, constructing a composite stress model and correcting an ecological safety index; and S6, carrying out risk grade division and result visualization. The method has the advantages that the arid region process adaptability is high, the evaluation result is stable and explainable, and the like.

The application provides a soil salinization response simulation method and device, the method comprises the following steps: obtaining the soil type, terrain grid data and meteorological data of a target area; inputting the terrain grid data into a first processing model to obtain a sub-basin division result; inputting the meteorological data and soil type into the first processing model according to the sub-basin division result to obtain sub-basin daily precipitation data and sub-basin evapotranspiration; inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into a second processing model to obtain soil salt content and crop yield in combination with sub-basin irrigation; obtaining soil salinization prevention and control measures according to the soil salt content and crop yield; wherein the first processing model is obtained by calibrating a preset processing module according to the difference between the monthly runoff of a basin and the measured calibration data. The application can improve the accuracy, applicability and operability of soil salinization prevention and control.

The application discloses a root zone soil moisture estimation method based on remote sensing driving and moisture hysteresis characteristic simulation, an electronic device and a medium. The method can comprise: acquiring remote sensing data with the same time resolution and spatial resolution, including remote sensing surface soil moisture data, remote sensing evapotranspiration data, potential evapotranspiration data, rainfall data and saturated soil humidity; calculating root zone soil humidity based on the remote sensing surface soil moisture data, and then calculating an estimated value of the evaporation ratio based on the root zone soil humidity and the saturated soil humidity; calculating the remote sensing evaporation ratio based on the remote sensing evapotranspiration data and the potential evapotranspiration data; establishing a target function to calculate an error, and when the error is less than a set threshold, outputting the root zone soil humidity. The application introduces the remote sensing evaporation ratio to exert physical constraints to embody the influence of vegetation and improve physical rationality, and constructs a multi-constraint target function optimization model parameter to improve the estimation accuracy and reliability of the remote sensing evaporation ratio.

This invention discloses a method and device for optimizing and controlling the hydrothermal environment of green roofs based on modified matrix synergy, belonging to the field of green building technology. The method involves acquiring meteorological parameters and roof structure requirements of the target area to determine the hydrothermal control objectives, and customizing a modified matrix formula composed of garden soil, gravel, and wood chip biochar, pretreated for porosity optimization. A layered structure is constructed, and sensors collect data such as matrix moisture content, roof temperature, and rainfall intensity in real time. Based on a coupling algorithm, the porosity and height of the water storage and drainage layer are dynamically adjusted to achieve hydrothermal control through the synergistic effect of the modified matrix's water retention and thermal insulation properties and vegetation evapotranspiration. The control effect is periodically evaluated, and the matrix ratio and water storage and drainage layer parameters are iteratively updated. The device employs an intelligent modular adjustable ventilated water storage and drainage layer, with built-in sensors and a drive motor, and a distributed data acquisition and control module to achieve intelligent control. This invention solves the problems of poor adaptability and weak hydrothermal control capabilities of traditional green roofs.

The application discloses a vegetation restoration influence on evapotranspiration quantitative evaluation method and device and a storage medium, relates to the technical field of ecological environment, and comprises the following steps: collecting remote sensing leaf area index data in a target area, and preprocessing the remote sensing leaf area index data to obtain a vegetation parameter time sequence; based on the vegetation parameter time sequence, separating a trend component dominated by vegetation restoration and a fluctuation component dominated by climate fluctuation; inputting the trend component and the fluctuation component into an evapotranspiration model trained, to obtain first evapotranspiration data corresponding to the trend component and second evapotranspiration data corresponding to the fluctuation component; and calculating a difference value between the first evapotranspiration data and the second evapotranspiration data as an independent contribution value of vegetation restoration on evapotranspiration. The application can realize accurate quantification of the independent influence of vegetation restoration on evapotranspiration.

This invention provides a method for estimating oasis water consumption based on multi-source evapotranspiration fusion and dynamic weight optimization, relating to the fields of hydrological remote sensing and arid zone water resource management. The method involves evaluating multi-source evapotranspiration data to obtain a data quality index; dividing multi-temporal high-resolution remote sensing images into multiple functional zones to obtain a partitioning mask; calculating spatiotemporally adaptive dynamic weights based on the data quality index and multi-source evapotranspiration data; combining multi-source evapotranspiration data and the error covariance matrix to obtain a preliminary fused evapotranspiration field and a fused uncertainty field; and calculating the total water consumption of the oasis and its confidence interval. This invention addresses the problem of how to maximize the complementary advantages of multi-source evapotranspiration data under highly heterogeneous underlying surface conditions in oases through dynamic weight adjustment, uncertainty control, and physical constraint embedding, thereby improving the accuracy and reliability of regional water consumption estimation.

The present application relates to the field of water resource management, and particularly relates to a zero-carbon water resource comprehensive management system for agricultural irrigation areas, comprising: an irrigation area basic data calculation module which calculates soil latent heat flux and crop basic evapotranspiration of the irrigation area respectively according to detected air temperature, air pressure, solar radiation and wind speed; an irrigation area data prediction module which generates predicted water storage and utilization amount of the irrigation area through an irrigation area prediction model based on a ConvLSTM architecture; a water demand fusion prediction module which generates comprehensive predicted water demand of the irrigation area through a fusion mapping model based on a gating mechanism and multi-scale convolution; and a zero-carbon water resource management module which calculates predicted net irrigation amount of the irrigation area based on the comprehensive predicted water demand to perform irrigation management on photovoltaic water pumps and photovoltaic valves of the irrigation area. The present application improves the precision of water resource management of the irrigation area, optimizes the scheduling energy consumption of the zero-carbon photovoltaic water pumps and photovoltaic valves, and realizes the optimization of the zero-carbon transformation of the agricultural irrigation area from the head of irrigation energy consumption.

This application provides a method, apparatus, equipment, and storage medium for identifying agricultural irrigation districts based on the irrigation-enhanced evaporation stress index (IESI), relating to the fields of agricultural water resources monitoring and management and remote sensing applications. The method includes: constructing a multi-source remote sensing and auxiliary dataset for the target area; calculating potential evapotranspiration and actual evapotranspiration and coupling them with a vegetation growth stability factor to obtain quarterly IESI data for the target area; determining irrigation identification thresholds applicable to plains and mountainous areas respectively; and discriminating the IESI of the target area to generate a binary spatial distribution map of irrigated and non-irrigated areas. This application solves the problems of existing irrigation area identification methods in large-scale applications, such as reliance on statistical data or large training samples, strong empirical thresholds, insufficient adaptability to terrain and regional differences, and complex calculation processes. It achieves irrigation behavior identification centered on evaporation stress response, ensuring computational efficiency while also considering spatial accuracy and regional adaptability.

The application relates to a water resource pressure assessment method integrating social economy and climate change. It relates to the technical field of water resource pressure assessment in arid river basins. The method comprises the following steps: obtaining historical values and predicted values of social economic data, correcting the predicted values of the social economy to obtain corrected predicted values; calculating potential evapotranspiration in different scenarios based on historical scenario meteorological data and corrected future climate mode data; calculating future basin water resources based on the correlation between basin rainfall and water resources; extracting drought intensity characteristics according to the change trend of the historical scenario and the future scenario basin drought index; constructing a basin water resource pressure index calculation formula; and according to the calculated water resource pressure index of the water resource region, standardizing the index to obtain a standardized index, and evaluating the change trend of the water resource pressure of the region in different scenarios according to the standardized index. The method solves the problem of quantification and evaluation of basin water resource pressure under complex conditions.

The invention relates to a greening spray irrigation control method and system based on soil humidity feedback and a terminal, and relates to the technical field of greening irrigation, and the method comprises the steps: collecting the plant evapotranspiration, plant type and soil humidity parameters of a preset plant; analyzing plant evapotranspiration, plant types and soil humidity parameters to determine dynamic water demand conditions; judging whether the dynamic water demand condition meets the requirement of a preset water demand threshold condition or not; if not, continuing to collect the plant evapotranspiration, the plant type and the soil humidity parameter of the plant for circular judgment; if yes, the dynamic water demand condition, the plant type and the soil humidity parameter are analyzed to determine the plant water supplementing amount; and controlling a preset irrigation device to irrigate the plants according to the plant water supplementing amount. The method has the effect of improving the precision of greening irrigation.

The invention discloses a mechanism process-based frozen soil thawing water flow fluidization method. The frozen soil thawing water amount is dynamically calculated based on the soil ice content output by a hydrological model; determining that the soil moisture and evapotranspiration loss threshold value needs to be met preferentially before the frozen soil melts to form runoff; calculating surface runoff generated by frozen soil melting water according to comparison between the melting water amount and a loss threshold value; the base flow is recalculated by updating the soil humidity, and the contribution of frozen soil melt water to the base flow is quantified; and finally, integrating the original model runoff with the frozen soil runoff to obtain an integrated total runoff. According to the method, the continuous path of frozen soil melt water-soil water-evapotranspiration-runoff is completely constructed and quantified, the loss threshold value of the frozen soil melt water runoff process is defined, the defect that the frozen soil hydrological process is depicted to be static by an existing hydrological model, and the contribution of frozen soil melt water cannot be separated is overcome, and the method is suitable for popularization and application. The simulation precision of runoff, soil moisture and evapotranspiration is remarkably improved, and a key technical support is provided for accurate evaluation and decision-making of water resources in cold regions.

Disclosed are a monitoring method and a monitoring device for irrigation water use (IWU), a computer device and a non-transitory computer readable medium. The method includes: determining a linear correlation between a first evapotranspiration (ET), a first soil water storage, and a monthly reference ET of a target region according to a first data set in response to a monitoring instruction from a user; determining multiple second soil water storages of the target region based on a second data set, and the linear correlation of the first ET, the first soil water storage, and the monthly reference ET of the target region; determining the IWU of the target region according to the first ET, the second ET, the first soil water storage, the second soil water storage of the target region, and the first preset correlation.

The present application relates to the field of ecology, water resources management and remote sensing technology, and provides an integrated estimation method and device for ecological water demand and water supplement. The method comprises: performing deviation correction or fusion processing on precipitation, evapotranspiration and soil moisture content data respectively; determining a reference evapotranspiration based on the obtained consistent driving field and constructing a baseline vegetation correction coefficient, performing residual correction on the correction coefficient under the condition of no water stress, and determining the ecological water demand according to the corrected vegetation correction coefficient and the reference evapotranspiration; inputting the consistent driving field, the ecological water demand and the remote sensing vegetation observation information into multiple discrimination gates, performing logical operation on the discrimination results, and dividing each pixel into a multi-level water supplement priority label; determining the total loss term based on the consistent driving field, the ecological water demand and the water supplement priority label, and determining the ecological water supplement. The problem that the ecological water supplement accounting in the prior art is difficult to meet the water conservation constraint and is prone to systematic bias is solved.

The invention discloses a soil water content correction method based on an XAJ and LSTM coupling model, and the method comprises the steps: extracting watershed DEM data, and constructing an input set; running XAJoptuna, outputting a time sequence containing three-layer evapotranspiration, runoff production and initial soil water content, and calculating a process file; constructing an LSTM (Long Short Term Memory) taking daily evaporation capacity, net rainfall and the current three-layer soil water content as input, outputting a next moment to correct the three-layer soil water content, and training by adopting a loss function; the corrected soil water content predicted by the LSTMoptuna is fed back in real time to replace an XAJopyuna internal state variable; performing three-water-source division and confluence calculation on the coupled runoff result; according to the method, the advantages of a physical mechanism and data driving are fused, the simulation precision of the soil water content is remarkably improved, dynamic correction from points to sites is achieved, and high-temporal-spatial-resolution state variable support is provided for drainage basin drought early warning, water resource scheduling and refined hydrological simulation.