114 results about "Aridity index" patented technology

The invention belongs to the field of crop growth monitoring, and particularly relates to a crop irrigation decision-making method and system based on crop growth period water demand characteristics, and the method comprises the steps: firstly obtaining a remote sensing satellite image of a to-be-decided crop soil region, and the segmented growth period and transition time period attributes of each crop; secondly, monitoring the soil water content of a crop soil area to be decided by using a temperature vegetation drought index according to a remote sensing satellite image; and finally, combining the soil water content with the related attributes of the crop growth period, and generating a variable irrigation prescription map by means of a preset variable precise irrigation decision model, so as to realize the irrigation decision of the crop soil area to be decided. By comprehensively utilizing the remote sensing data and the crop growth period information, the irrigation scheme can be reasonably planned according to the water demand characteristics of different crops in different growth stages, the water resource utilization efficiency is effectively improved, and the crop growth is guaranteed.

The invention relates to disaster prediction, in particular to a high-temperature and drought composite disaster prediction method and system based on artificial intelligence, and the method comprises the steps: collecting meteorological data of a target region, inputting the meteorological data into a preset artificial intelligence high-temperature and drought prediction model, and predicting the change trend of a standardized rainfall index and a standardized temperature index, the artificial intelligence high-temperature drought forecasting model is obtained based on training of a training set including historical meteorological data of the target area, and the model is constructed by combining wavelet transform with a circulating network. Partitioned analysis is carried out on rainfall and temperature in different regions and different seasons, wavelet transform is introduced on the basis of a deep learning model to construct an artificial intelligence high-temperature drought prediction model, and a complete artificial intelligence high-temperature drought composite disaster prediction method and system are further constructed. And combined prediction of high temperature and drought indexes is realized.

The invention belongs to the technical field of drought risk prediction and vegetation influence evaluation, and particularly discloses a vegetation-to-drought resource utilization coupling response strategy evaluation method, which comprises the following steps: acquiring target data; constructing a drought index according to the target data, and extracting a drought event; extracting vegetation total productivity, vegetation net level productivity and evapotranspiration abnormal data before and after the drought event, and calculating response intensity of vegetation variables; calculating the average variation of the light energy utilization efficiency, the carbon utilization efficiency and the moisture utilization efficiency; comprehensively considering the multivariable resistance-recovery state of the vegetation physiological process and the regulating variable of the resource utilization efficiency of the vegetation in response to drought stress, and analyzing the drought event resistance-recovery response capability of the vegetation. The method can more accurately and objectively understand the physiological mechanism difference of the vegetation to the drought stress response.

The invention discloses a soil drought risk early warning system and early warning method based on machine learning, and belongs to the field of soil drought early warning, and the system comprises a data collection module, a data restoration and fusion module, a space-time drought coupling prediction model construction module, and a real-time early warning and model updating module. Wherein the data acquisition module is used for acquiring wind cloud meteorological data and MODIS optical image data; the data restoration and fusion module is used for generating an all-weather soil humidity chart; the space-time drought coupling prediction model construction module is used for constructing a space-time drought coupling prediction model; and the real-time early warning and model updating module is used for obtaining the dynamic drought index and dynamically updating the dynamic drought index. By adopting the soil drought risk early warning system and early warning method based on machine learning, high-precision drought risk prediction and decision support are realized through multi-source data fusion, a space-time coupling prediction model and a dynamic grading early warning mechanism.

The invention belongs to the technical field of remote sensing agricultural monitoring, and particularly relates to a daily scale monitoring index-based composite dry heat event evaluation method, which comprises the following steps of: collecting data, respectively constructing daily scale high temperature and daily scale drought indexes, and constructing a composite high temperature drought index. And finally, determining the frequency, duration, severity and strength of the composite high-temperature drought event by using a run-length theory, analyzing the composite dry-heat event by combining the run-length theory and the composite high-temperature drought index, and realizing evaluation of the composite dry-heat event, so that the method has good applicability and expandability, is suitable for various climate regions, and has good application prospects. And the method can be popularized and applied to construction of other composite indexes, and support is provided for monitoring and evaluation of multiple composite events.

The invention discloses a rain-fed agricultural area drought index construction and application method, equipment and a medium. The rain-fed agricultural area drought index construction and application method comprises the following steps: S1, determining drought influence coefficients of crops in different growth stages; s2, constructing an APSIM model, and carrying out parameter calibration and model verification; s3, the APSIM model dynamically simulates the phenological period, the yield and soil water in the growing season of the crops in the sufficient irrigation and rain culture mode; s4, calculating the crop yield reduction rate; s5, constructing a drought stress index relational expression including the cumulative drought stress degree, the cumulative drought stress index and the standardized cumulative drought stress index; s6, based on the dynamic simulation result of the APSIM model, the crop yield reduction rate and the accumulated drought stress index are calculated in a fitting mode. According to the method, the soil humidity data acquisition problem is considered, the sensitivity difference of different growth stages to drought is also considered, and accurate quantitative evaluation of the drought stress response in the whole crop growth process can be further realized.

The invention discloses a drought series-parallel connection propagation process analysis method, and relates to the technical field of data processing of drought propagation. The method comprises the steps of obtaining a correlation coefficient sequence according to correlation coefficients between drought indexes of meteorological drought under each week time scale and drought indexes of other drought types under corresponding month time scales; obtaining a first-order forward difference and a second-order forward difference of a first maximum value in the correlation coefficient sequence; if the first-order forward difference and the second-order forward difference are both larger than or equal to 0, the week time scale corresponding to the position is determined as the propagation time from meteorological drought to other drought types; according to the propagation time from meteorological drought to other different drought types, determining a series-parallel connection mode of drought propagation; and according to different drought propagation modes, determining a propagation threshold value from at least one type of drought to another type of drought. The method can accurately describe the propagation process of multiple types of drought.

The invention discloses a drought disaster risk quantitative assessment method fusing an irrigation disaster reduction effect, and belongs to the field of disaster risk assessment, and the method comprises the steps: obtaining different meteorological data of a target region in a three-dimensional space grid scale, and calculating a drought index; a drought event is identified by using an ST-KMeans clustering method; drought events are screened and combined, the space overlapping degree of the clustered drought region and the historical disaster region is calculated, and risk loss data docking is carried out; generating a drought event characteristic dynamic data set, and measuring the drought event; and in combination with the land coverage and utilization data of the target area and the farmland irrigation area data, extracting irrigation response information serving as a drought adaptability measure, and carrying out irrigation disaster reduction capability quantification and risk assessment. According to the method, irrigation information can be integrated on the basis of drought event identification, the drought slow release effect of irrigation in different time and space scales is quantitatively evaluated, and a scientific basis is provided for drought disaster risk management and agricultural adaptability policies.

The invention discloses a dynamic drought transmission threshold identification method and system, and relates to the technical field of drought transmission dynamic monitoring. Time sequence basic data is acquired, the time sequence basic data is segmented through a sliding window with a preset window length, and segmentation data of each window is acquired; calculating each meteorological drought index in a preset continuous time scale and a soil humidity index in a month scale for the segmented data; calculating a correlation coefficient between each meteorological drought index in a preset continuous time scale and a soil humidity index in a month scale based on a Pearson's correlation coefficient, and obtaining a preset time scale corresponding to the maximum correlation coefficient as drought transmission time; and calculating a drought transmission threshold value based on the drought transmission time, the soil humidity index of one month scale, combined with the joint probability distribution and the first-order Bayesian network. According to the invention, dynamic identification of different levels of drought transmission thresholds is realized.

The invention belongs to the technical field of disaster remote sensing monitoring, and relates to an AI technology-based composite high-temperature drought index construction method, which comprises the following steps of: collecting multi-source data, preprocessing the data, and constructing a high-temperature drought factor and an evaluation index; then, a drought monitoring model and a high-temperature monitoring model are respectively constructed by using multiple AI algorithms including machine learning and deep learning, finally, a composite high-temperature drought index is constructed, the constructed composite high-temperature drought index is used for monitoring and evaluating a composite high-temperature drought event, multi-source data are fused, and the accuracy of the composite high-temperature drought event is improved. According to the method, factors influencing high temperature and drought disasters are comprehensively considered, so that the recognition precision of the composite high temperature and drought disasters is remarkably improved, the AI technology is applied, internal association between data is fully explored, and the method has higher generalization ability, can be suitable for different climate regions and time scales, has good expansibility and is suitable for popularization and application. The method can be popularized and applied to construction of other composite disaster indexes, and provides support for monitoring and prediction of various extreme climate events.

The invention discloses an arid region identification method based on a GRACE satellite, and the method comprises the steps: obtaining satellite remote sensing original data, carrying out the median filtering of the satellite remote sensing original data, and obtaining noise reduction data; complementing missing data for the noise reduction data to obtain complemented data; calculating a normalized vegetation index and a temperature vegetation drought index according to the complementation data to obtain a drought index; time sequence features are extracted according to the drought indexes, principal component analysis is carried out on the time sequence features, and comprehensive indexes are obtained; spatial clustering analysis is carried out on the comprehensive indexes, and target areas with similar drought characteristics are divided; calculating a topographic relief degree and a land utilization type proportion based on the target area in combination with the land utilization data and the topographic data; selecting a target area of which the topographic relief degree is greater than a preset threshold value, and performing topographic correction on the drought index of the selected target area to obtain a corrected drought index; and judging the drought grade of the target area according to the corrected drought index and the land utilization type ratio.

The invention discloses an alpine grassland drought monitoring method based on Parmer drought index localization verification, which is applied to the field of meteorological drought monitoring, and comprises the following steps: calculating the day-by-day meteorological drought index of the alpine grassland based on the average day-by-day rainfall data of the alpine grassland, and combining with the average day-by-day soil humidity data, and according to the drought grade division result, calculating the weather drought index of the alpine grassland; calculating the drought monitoring precision of different meteorological drought indexes in the alpine grassland to obtain a to-be-verified Parmer drought index which is most suitable for drought monitoring of the alpine grassland; analyzing the correlation between the Pearson drought index and the soil humidity by using a Pearson correlation coefficient method, constructing a linear regression model of the Pearson drought index and the soil humidity, fitting data by using a linear fitting algorithm, minimizing the difference between a predicted value and an actual value, calculating to obtain a coefficient in the linear regression model, and calculating the soil humidity. And verifying the Parmer drought index according to the coefficient, and carrying out drought monitoring on the alpine grassland. The drought monitoring precision of the alpine grassland is effectively improved.

The invention belongs to the technical field of drought monitoring, and particularly discloses a drought monitoring method and system based on multi-source data and spatio-temporal context embedding, and the method comprises the following steps: obtaining drought index data, multi-source dynamic remote sensing factor data and static geographic data of a meteorological station in a research area, and carrying out the preprocessing, obtaining original remote sensing features; extracting attribute values from the static geographic data according to the longitude and latitude of a site, extracting a space context embedding vector and a time context embedding vector based on a multi-layer perceptron and a long-short-term memory network, generating a modulated space-time context embedding vector, and splicing the modulated space-time context embedding vector with the original remote sensing features to obtain a final feature vector; and inputting the information enhanced final feature vector and the monthly standardized rainfall evapotranspiration index SPEI-3 of each station into a GXGBDM model, and outputting a drought monitoring result. By adopting the technical scheme, the drought monitoring result can be efficiently and accurately generated by fusing the multi-source data and the spatio-temporal context embedding information.

The invention discloses a GIS-based arid and semi-arid region farmland irrigation priority dynamic evaluation and zoning method, system, device and medium, and belongs to the technical field of farmland irrigation priority evaluation and zoning, and the method comprises the steps: collecting various types of remote sensing satellite data of a to-be-irrigated region; performing pretreatment; identifying the boundary and range of the to-be-irrigated area, and extracting a remote sensing image of the to-be-irrigated area; based on the remote sensing image of the to-be-irrigated area and the basic geographic information, parameter calculation is carried out, and normalization processing is carried out; performing superposition calculation on each normalized parameter according to a set weight to obtain an irrigation priority index; partitioning the to-be-irrigated area based on the irrigation priority index to generate an irrigation priority map; and judging whether agricultural irrigation management requirements are met or not according to preset conditions. According to the method, temperature vegetation drought indexes, normalized moisture indexes, water resource accessibility and crop economic value parameters are coupled; the irrigation priority index weight can be automatically adjusted according to a season strategy.

The invention relates to the technical field of intelligent data identification, in particular to a wetland ecological drought condition identification method based on multi-source data. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring a multi-source data set of a target wetland region in a preset time sequence; the multi-source data set at least comprises a remote sensing reflectivity data set, a surface temperature data set, a meteorological reanalysis data set and an on-site monitoring hydrological data set; and data processing: sequentially carrying out space-time registration, denoising and missing value interpolation processing on the multi-source data set to generate a standardized space-time data cube. According to the method, multi-source data can be integrated to generate a standardized spatio-temporal data cube, each ecological drought index threshold value is dynamically determined based on the background of the historical wet season of the wetland, the comprehensive drought intensity is calculated in a weighted mode according to the wetland type, and accurate wetland ecological drought spatio-temporal distribution, grade sequence and early warning information are output after multi-scale verification. And a basis is provided for wetland ecological protection and water resource regulation and control.

The invention discloses an electric power climate risk assessment method for the influence of high temperature and / or drought events on both sides of supply and demand, a medium and a program product, and belongs to the technical field of meteorological disaster risk assessment and energy system safety crossing. Then identifying a high-temperature or drought event based on a temperature percentile threshold value and an SPEI drought index, and constructing a bivariate distribution model by adopting a joint probability density function to identify a high-temperature drought composite event; respectively constructing a power supply side response model and a load side response model, and quantitatively evaluating output changes of different power supplies and load response characteristics of various users in a high-temperature and / or drought scene; key indexes such as a power gap and load risk exposure intensity are calculated through supply-demand coupling offset analysis; and finally, based on the climate mode prediction data, driving the risk model, and outputting a risk evolution trend in a future scene. The method can provide support for climate toughness improvement and scheduling decision making of the power system.

The invention discloses a human drink drought prediction method based on an AE-TT model, and the method comprises the steps: constructing a drought index, constructing a standardized human drink drought index SHI, constructing an AE-TT deep learning model for human drink drought prediction, and outputting a human drink drought prediction result through the AE-TT model. According to the method, aiming at the problem of lack of a systematic index system for human drink drought evaluation in the Da-rock mountainous area in Guangxi, a human drink drought evaluation index system suitable for the area is constructed, accurate quantification and scientific grading of the drought-induced water shortage condition of the area without a stable water source are realized, and a standardized tool is provided for drought degree research and judgment; the method breaks through the applicability limitation of an existing drought prediction technology in the field of human drinks in rocky mountainous areas, develops a high-precision human drink drought index prediction technology based on deep learning, improves the prejudgment capability of drought occurrence trends in different time periods, and provides a scientific basis for deploying water supply safeguard measures in advance.

The invention discloses a three-dimensional meteorological-hydrological drought propagation identification and matching method, a storage medium and a program product, and the method comprises the steps: employing an elbow method to determine a proportion parameter of a minimum drought area in a whole watershed and a proportion parameter of a minimum overlapping area based on a meteorological drought index matrix and a hydrological drought index matrix, and combining with a moderate drought standard threshold value to determine the proportion of the minimum drought area in the whole watershed; identifying all meteorological drought events and hydrological drought events; pairing the identified meteorological drought event and hydrological drought event to generate a meteorological-hydrological drought event pair; grouping the meteorological-hydrological drought event pairs according to whether the drought events repeatedly appear in all the meteorological-hydrological drought event pairs or not, and generating drought event groups; and by taking the drought event group as a unit, extracting the characteristics of the transfer process from the meteorological drought event to the hydrological drought event. According to the method, the characteristic change in the meteorological-hydrological drought propagation process can be quantitatively identified, the propagation characteristics from meteorological drought to hydrological drought are visually revealed, and the propagation mechanism between meteorological drought and hydrological drought is clarified.

The invention belongs to the technical field of data processing, and particularly provides a drought early warning method and device based on artificial intelligence, and the method comprises the steps: collecting the data of a drought early warning drainage basin; constructing a deep learning model considering a hydrological process for daily runoff simulation, and reconstructing a long series of daily runoff data; constructing a monthly-scale vegetation-meteorological hydrological drought regression model; calculating a comprehensive meteorological and hydrological drought index, extracting drought events, and constructing K artificial intelligence models; inputting meteorological and hydrological observation data into K artificial intelligence-based models for simulation, and deducing the weight of each artificial intelligence model by adopting a Bayesian mode averaging method; a joint distribution function of drought index observation and forecast values is constructed based on a Copula function, and an artificial intelligence-Bayesian coupling model is adopted to carry out drought forecast. According to the method, the problems of data scarcity, model uncertainty and the like in drought early warning are solved, and high-precision and quantifiable decision support is provided for water resource management and disaster prevention and reduction.

The invention provides a drought probability forecasting method and device, a medium and electronic equipment. The drought probability forecasting method comprises the steps that calibration processing is conducted on a hydrological model according to meteorological data and flow data measured by a target site so as to obtain calibration parameters of the hydrological model, and interpolation grids of the hydrological model are obtained after spatial interpolation processing is conducted on grids of a target drainage basin through the meteorological data; based on binary normal distribution of the forecast rainfall data and the actually measured rainfall data of the target drainage basin, acquiring a forecast rainfall value associated with a preset probability value; and obtaining a drought index associated with the forecast rainfall value based on the forecast rainfall value, the calibration parameter and the hydrological model. The drought probability forecasting process of the drought probability forecasting method is simple, and the accuracy is high.

The invention discloses a drought propagation time prediction method based on machine learning. The method comprises the following steps: collecting and preprocessing hydro meteorological data used for drought index calculation; calculating accumulated rainfall, potential evapotranspiration and soil water content under a given target time scale by adopting a day-by-day sliding method; fitting a probability distribution function of the cumulative data, and performing normal distribution inverse transformation on the cumulative probability to obtain meteorological drought and agricultural drought index sequences; identifying meteorological drought events and agricultural drought events, and counting soil water content states and meteorological drought event characteristics when the meteorological drought events occur as potential factors influencing drought propagation time; matching the identified meteorological drought event and agricultural drought event, and calculating the propagation time of each matched drought event pair; training a drought propagation time prediction model based on an extreme gradient lifting algorithm to realize prediction of drought propagation time; and a technical support is provided for agricultural drought prediction and drought-resistant water source optimization configuration.

The application provides a drought propagation analysis method and system considering multi-dimensional characteristics of drought events. The method comprises the following steps: calculating a drought duration propagation index based on a meteorological drought total duration and an agricultural drought total duration; calculating a drought intensity propagation index based on meteorological drought intensities of previous meteorological drought events and agricultural drought intensities of previous agricultural drought events; calculating a drought intensity trend propagation index based on meteorological drought intensity trends of previous meteorological drought events and agricultural drought intensity trends of previous agricultural drought events; calculating a drought fluctuation propagation index based on meteorological drought index fluctuation rates of previous meteorological drought events and agricultural drought index fluctuation rates of previous agricultural drought events; calculating a drought probability propagation index based on a meteorological drought occurrence probability and an agricultural drought occurrence probability of a target region within a given time period; and constructing a comprehensive drought propagation index by using a CRITIC objective weighting method. The application can accurately describe the propagation process between meteorological drought and agricultural drought.

The application discloses a kind of wind power resource drought degree evaluation method and related equipment.The method comprises: obtaining the hourly wind speed data of target area in history and evaluation period, and determining hourly wind power output sequence in combination with wind turbine power curve;Based on historical wind power output sequence sample, determine reliable output reference value and recharge threshold value for each duration t based on the statistical distribution of historical sample of each duration t;Calculate the equivalent energy buffer capacity of system in the evaluation window for resisting wind power underproduction;Based on reliable output reference value and recharge threshold value, calculate power gap and power surplus hour by hour;According to the reliable reference gap power that is not satisfied by energy storage and energy storage energy state, calculate operating deficit;Based on equivalent energy buffer capacity and operating deficit, calculate wind power resource drought index.Can solve the problem of the shortcomings of the existing method for quantifying and analyzing wind power resource drought, including fixed threshold method, relative anomaly method and standardized index method.

The invention discloses a mountain fire risk assessment method and system based on meteorological drought, terminal equipment and a storage medium, and belongs to the field of mountain fire risk assessment. Obtaining a weather drought index of the day in combination with the effective precipitation of the day; calculating a drought factor according to the index and historical rainfall data; finally, the drought factor, the ground temperature, the relative humidity and the wind speed of the day are integrated, the mountain fire risk level is determined, multi-dimensional meteorological elements are integrated, data of the day and historical data are combined, the drought change process is accurately tracked, the accuracy of mountain fire risk assessment is effectively improved, and the problem that in the prior art, only a rainfall factor serves as a single factor of drought assessment, and the accuracy of mountain fire risk assessment is poor is solved. Key meteorological factors such as temperature, relative humidity and wind speed are neglected, so that the drought assessment index is inaccurate, and the forest fire risk assessment is inaccurate.

The invention discloses a grassland insurance grade evaluation method and system based on a multi-source time sequence. Based on PCA information entropy, fusing long-time sequence grassland insurance indexes (a meteorological drought index, a vegetation drought index, a soil drought index, a soil-vegetation drought index, a fire index, a flood index, a grassland comprehensive vegetation coverage, a grassland grass yield and a grassland greenness index); the grassland vegetation change condition is detected, the grassland insurance index main feature information limiting the grassland vegetation condition in each time period is extracted, the grassland insurance grade is obtained by using FCM clustering, manual ground patrol and verification work is greatly reduced, the grassland insurance grade condition of a target area can be quickly and accurately mastered, and the loss caused by natural disasters is greatly reduced.

The invention relates to a drought monitoring method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: monitoring the growth state of planted crops in a first irrigation region, the drought state and a ground water source through a plurality of monitoring satellites to obtain satellite monitoring data, and determining a first drought index; acquiring unmanned aerial vehicle monitoring data of the second irrigation area by using the plurality of unmanned aerial vehicles and the intelligent machine nest, determining a second drought index of the second irrigation area based on the unmanned aerial vehicle monitoring data, and determining a preliminary irrigation state of the second irrigation area; acquiring actual measurement data of the second irrigation area through ground drought monitoring equipment, and determining a third drought index according to the actual measurement data; and determining the land parcel drought grade of each land parcel in the second irrigation area by combining the first drought index, the second drought index and the third drought index, and determining the irrigation scheme of each land parcel based on the land parcel drought grade, the initial irrigation state and the actually measured data. According to the invention, the irrigation scheme can be timely and accurately determined.

The invention discloses a method for quantitatively evaluating the dynamic hydrological effect of vegetation, and belongs to the technical field of climate change evaluation. Aiming at the defect that vegetation and hydrological effects cannot be independently and quantitatively separated due to the fact that underlying surface parameters confuse vegetation and non-vegetation factors in an existing Budyko hydrothermal balance attribution model, meteorological, hydrological, vegetation and environmental data are collected and processed; inverting underlying surface parameters based on a Budyko framework; calculating elastic coefficients of runoff to rainfall, potential evapotranspiration and parameters; constructing a linear model taking the drought index and the soil water-holding capacity as variables to obtain vegetation sensitivity parameters; finally, the runoff component caused by vegetation change is quantitatively separated in combination with the elastic coefficient and the vegetation sensitivity parameter. According to the method, the influence of vegetation dynamics on the hydrological effect is independently and accurately quantified, and the transparency of runoff attribution and the application value in ecological hydrological management are improved.

The present application relates to the technical field of drought prediction, and particularly to a method and system for predicting drought in the Yellow River Basin based on deep learning. The method includes: obtaining adjacent meteorological stations of a hydrological station to be measured, and obtaining a fused meteorological parameter sequence of the hydrological station to be measured based on the meteorological parameter sequences of the adjacent meteorological stations; inputting the hydrological parameter sequence and the fused meteorological parameter sequence of the hydrological station to be measured into the drought prediction network of the hydrological station to be measured to predict the future drought index; obtaining the future drought index of the adjacent hydrological stations corresponding to the hydrological station to be measured; calculating the target drought index of the hydrological station to be measured based on the correlation between the hydrological station to be measured and the adjacent hydrological stations, the future drought indices of the hydrological station to be measured and the adjacent hydrological stations, and the determination coefficient of the drought prediction network, and then predicting the drought level of the hydrological station to be measured. Through the technical solution of the present application, the drought level of each hydrological station can be accurately predicted.

The present invention discloses a method for detecting farmland plot irrigation by integrating multi-source remote sensing data, which includes obtaining time-series remote sensing data and auxiliary data and performing preprocessing; based on the above data, obtaining the normalized difference moisture index, land surface temperature and precipitation at the plot scale; constructing a scatter plot of NDMI-LST feature space with the normalized difference moisture index at the plot scale as the abscissa and the land surface temperature as the ordinate, obtaining the upper and lower boundary lines of the NDMI-LST feature space, and calculating the temperature vegetation drought index; constructing a time-series curve of the temperature vegetation drought index at the plot scale, setting a rule set for plot irrigation detection, establishing a plot irrigation detection algorithm with an adaptive dynamic threshold, obtaining the plot irrigation cycle, and obtaining the irrigation information of the analysis and statistical area. Therefore, by using the above method, it is possible to quickly and accurately extract farmland plot irrigation events and cycle information, providing a reference for decision-making such as water resource management and agricultural production activities.

The application provides a hydrothermal soil humidity coupling regional agricultural drought monitoring method and system, which comprises the following steps: 1, collecting soil humidity data and meteorological element data of a research area; 2, determining the time scale value with the highest correlation in different seasons by considering the correlation between the accumulated hydrothermal factors and the soil humidity in different time scales by means of the Pearson correlation coefficient method; 3, calculating the lag time of soil moisture to precipitation and temperature by means of the cross wavelet transform method; 4, constructing a hydrothermal soil humidity coupling drought index RADI by means of a binary Copula model; and 5, dividing different drought grades to represent the regional agricultural drought conditions. The application evaluates the reliability and applicability of RADI in representing regional agricultural drought from the time dimension and the space dimension, provides more fine data support for the evolution characteristics of agricultural drought, and has important significance for regional grain production, ecological safety and environmental protection and the like.