35 results about "Enhanced vegetation index" patented technology

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The invention discloses a sky-ground integrated rice growth vigor monitoring method, and relates to the technical field of remote sensing, and the method comprises the steps: carrying out the modeling of each growth stage of rice, and forming a rice growth model diagram; collecting data to form a rice remote sensing big data image library; training a deep learning feature extractor, and extracting the features of the rice image; estimating rice growth based on remote sensing image big data; estimating rice growth based on the enhanced vegetation index; and carrying out multi-scale cross validation on the accurate identification degree of the rice growth vigor, and finally forming a set of sky-ground integrated rice growth vigor monitoring method. According to the method, a deep learning algorithm and a vegetation enhancement index method are adopted, the growth vigor estimation is performed on remote sensing data of different sources, and the rice growth vigor estimation result is determined through cross validation, so that high-precision rice growth state monitoring is realized; the three remote sensing technologies complement one another to form a comprehensive and multi-angle rice growth monitoring method, and real-time and efficient farmland monitoring management can be achieved.

The invention discloses a drought assessment method considering phenological characteristics of vegetation, relates to the technical field of ecological engineering assessment, and solves the problem that the actual influence of drought on the key growth stage of the vegetation cannot be accurately reflected in the vegetation drought condition assessment condition. The method includes the following steps that S1, Savitzky-Golay filtering and enhanced vegetation index (EVI) are adopted to identify a vegetation growth period SOS and a vegetation growth withered and yellow period EOS, and then a growth season time window of each pixel is determined; and S2, extracting a vegetation condition index VCI, a temperature condition index TCI and a vegetation health index VHI in the growing season pixel by pixel in combination with enhanced vegetation index EVI data and land surface temperature LST data, calculating a mean value, and constructing a growing season health index GSHI. According to the method, vegetation phenology is brought into drought monitoring, the influence of drought on vegetation can be evaluated more comprehensively and dynamically, and the method has an important value for improving scientificity and accuracy of regional agricultural risk management and ecological protection.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention provides a novel method for improving the spatial resolution of the vegetation optical thickness (VOD) by using a random forest method. According to the method, dynamic and static variables related to VOD, such as optical vegetation indexes, terrain parameters, soil humidity and rainfall, are comprehensively considered, and the memory effect (lag VOD) of vegetation optical thickness is introduced. By means of the method, researchers can downscale 0.1 degree VOD data based on the multi-frequency radiometer to the VOD data with the resolution of 0.01 degree. The research shows that the accuracy of the model is remarkably improved by adding the lag VOD variable. Besides, a normalized vegetation index (NDVI), an enhanced vegetation index (EVI) and a leaf area index (LAI) are used for evaluating the downscaled 0.01-degree VOD, a result shows that the downscaled 0.01-degree VOD is highly consistent with the spatial-temporal pattern of the optical vegetation indexes, and spatial-temporal dynamic characteristics of vegetation can be better captured.

The invention specifically relates to a forest stock dynamic exploration system and method based on unmanned aerial vehicle multispectral fusion, and relates to the technical field of forest resource monitoring. A data preprocessing module; a feature extraction module; a model construction and estimation module; and a result output and visualization module. According to the method, a multi-dimensional data fusion and dynamic calibration mechanism changes the limitation of a traditional fixed soil regulation coefficient; when the soil regulation coefficient is obtained, four core factors of soil type, terrain illumination, soil humidity and texture partition distance are comprehensively considered; interference of different soil backgrounds, terrain illumination and humidity changes on vegetation index calculation is effectively eliminated, the enhanced vegetation index can reflect the coverage and health condition of forest vegetation more truly, a high-precision data basis is provided for subsequent forest stock estimation based on the vegetation index, and compared with a traditional method, the method has the advantage that the method is more accurate. And the data accuracy and reliability are improved.

The invention relates to the technical field of agricultural carbon neutralization, in particular to a cultivated land vegetation NEP simulation and prediction method and device and a medium, and the method comprises the steps: obtaining cultivated land vegetation observation data in a region, and calculating an enhanced vegetation index and a surface water index based on the cultivated land vegetation observation data; calculating the net primary productivity and the soil heterotrophic respiratory capacity in the vegetation growth season; nEP space-time simulation is carried out; obtaining a cultivated land vegetation NEP impact factor in the region, constructing a ridge regression model, and calculating the relative contribution of the cultivated land vegetation NEP impact factor to the NEP; and calculating a regression coefficient by adopting a ridge regression model, establishing a multiple linear regression prediction equation based on the regression coefficient, and performing NEP simulation prediction by adopting the multiple linear regression prediction equation. According to the method, the carbon absorption capacity of cultivated land vegetation in the growing season is accurately simulated by combining climatic change scenes and agricultural management practices, and the relationship among grain production, carbon emission reduction and ecological protection can be balanced.

The invention provides a sunlight-induced chlorophyll fluorescence reconstruction method and system based on satellite observation data. Comprising the following steps: collecting and processing ground site observation data, air temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir correction reflectivity data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, and OCO-2 and TROPOMI satellite observation SIF data; performing data reconstruction by using the OCO-2 SIF and the TROPOMI SIF as original data respectively by using a machine learning method; then, an empirical formula between the two kinds of reconstructed SIF data and the site observation SIF is constructed, and estimation parameters on the site are obtained through optimization; then, calculating estimation parameters of the empirical formula on each land coverage type; and finally, substituting the two types of reconstructed SIF data into an empirical formula to obtain a reconstructed SIF with site SIF information.

The present invention discloses a forest unstructured scene segmentation method based on multispectral image fusion. Aiming at the situations of complex background, multiple light and shadow interference in forest unstructured scenes, the method introduces enhanced vegetation index (EVI) data, designs a parallel dual encoding structure to extract RGB and EVI features, and forms multimodal complementary features in the encoding process. In addition, the encoding stage also uses dilated convolution to increase the receptive field of the network and optimize the feature extraction process. Then, the decoded features are fused with the fused features generated by the encoding for a second time to obtain a multispectral fusion convolutional neural network, and then the network is trained and RGB and EVI images are input to realize the semantic segmentation of forest unstructured scenes. The present invention effectively solves the problems of poor adaptability and mis-segmentation of unstructured segmentation methods based on RGB images, and improves the accuracy and robustness of semantic segmentation of forest unstructured scenes.

The application provides a tea garden identification method and device based on satellite images, which comprises the following steps: determining a evergreen vegetation area of a to-be-identified area based on Sentinel-2 image data and Landsat image data of the to-be-identified area; and identifying a tea garden area from the evergreen vegetation area through a decision tree model; wherein the decision tree model is constructed based on the following features and the classification threshold values corresponding to the features: a tea leaf phenology feature index, a terrain feature, and a spectral index determined by a separability index, wherein the SI is used to reflect the spectral reflectance separability of the tea garden and other evergreen vegetation; wherein the tea leaf phenology feature index is determined by an enhanced vegetation index in month N and a land surface water index in month M; the SI between the enhanced vegetation index in month N and the land surface water index in month M is the largest compared with the SI between the EVI in any other month and the LSWI in any other month, and N and M are integers greater than 0 and less than 13.

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The invention relates to the technical field of crop spectrum, and discloses a crop spectrum and photosynthesis monitoring device, which comprises a main control unit, a sensing unit and a spectrograph unit, and the main control unit comprises a microcontroller and is used for realizing acquisition, calculation, storage and transmission of spectrum and temperature data of the sensing unit and further controlling the temperature control unit to adjust the temperature of the equipment, and a framework of an embedded core board and a bottom board is adopted as a whole. By integrating a single wide-spectrum and high-resolution optical fiber spectrometer, crop spectrum information of 400-900 nm can be obtained, high-resolution detection smaller than or equal to 1 nm is achieved, wavelength, irradiance, radiance, reflectivity and light intensity information within the wide-spectrum range of crops are output, and the spectrum information of the crops can be obtained through an algorithm library integrated in the main control unit. And inversion output of indication parameters such as sunlight-induced chlorophyll fluorescence, a novel leaf chlorophyll index, a normalized vegetation index, an enhanced vegetation index, a normalized red edge index and a photochemical vegetation index of crops is completed.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The present invention provides a method and system for reconstructing sunlight-induced chlorophyll fluorescence based on satellite observation data, comprising: collecting and processing ground site observation data, temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir-corrected reflectance data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, OCO-2 and TROPOMI satellite observation SIF data; then using a machine learning method to reconstruct data using OCO-2 SIF and TROPOMI SIF as original data respectively; then, constructing an empirical formula between the two reconstructed SIF data and the site observation SIF, and optimizing to obtain estimated parameters at the site; then, calculating the estimated parameters of the empirical formula for each land cover type; finally, substituting the two reconstructed SIF data into the empirical formula to obtain a reconstructed SIF with site SIF information.

The invention discloses a salt marsh vegetation classification method, system and device, a medium and a product, and relates to the field of salt marsh vegetation classification, and the method comprises the steps: generating a vegetation index time sequence according to satellite remote sensing data; dividing the coastal zone latitude into a plurality of classification latitude zones as classification intervals, and calculating the monthly mean value and variance index of each vegetation index in each interval and the annual mean value index and variance index in the current year; based on the normalized difference vegetation index time sequence and the enhanced vegetation index time sequence, calculating growth period and decline period indexes of vegetation by using a double logistic function; calculating the J-M distance of each candidate classification index on different vegetation classification samples; on the basis of J-M distances of the indexes on different vegetation classification samples and preliminary classification contribution degree evaluation, the classification index which can be most indexed is screened out, a final random forest classification model is constructed, salt marsh vegetation in the coastal zone salt marsh wetland is classified, and the coastal plant classification task can be efficiently completed with low cost.

The invention discloses a wheat identification method and device based on automatic sample collection, and a storage medium. The method comprises the following steps: calculating a wheat feature enhanced vegetation index according to a first remote sensing image before wheat ripening and a second remote sensing image after wheat ripening in a to-be-identified area; extracting a first infrared band of the first remote sensing image, a second infrared band of the second remote sensing image and a green light band of a vegetation coverage area of the first remote sensing image; the kNDVI value, the kNDVI maximum value, the green light wave band maximum value and the green light wave band minimum value of the first remote sensing image and the kNDVI difference value of the first remote sensing image and the second remote sensing image are calculated; constructing a ground feature recognition condition according to the third information and the wheat feature enhanced vegetation index to obtain a training sample set; training a preset random forest model according to the training sample set to obtain a wheat recognition model; and inputting the combined remote sensing image into a wheat recognition model to obtain a wheat recognition result. According to the method, interference data are screened out, the adaptability is better, and manual annotation of a large amount of data is avoided.

The application discloses a kind of vegetation cover index algorithm and system based on unmanned plane, wherein algorithm includes the following steps: S1, data acquisition and multi-source data preprocessing;S2, dynamic environment correction and multi-source data fusion;S3, red edge enhanced vegetation index calculation and terrain correction;S4, vegetation coverage intelligent prediction and precision verification;S5, result visualization and decision support: generate vegetation coverage spatial distribution map and statistical report, support resource management decision.The application provides efficient, high-precision technical support for precision agriculture, ecological resource management and disaster monitoring by integrating unmanned plane, multispectral imaging sensor, dynamic environment correction model and data fusion algorithm.

This application provides a method and system for identifying the operational boundary of an agricultural drone. When the index difference value between vegetated and non-vegetated areas in farmland is lower than the hard scenario threshold of the agricultural drone, vegetation endmember spectra and soil endmember spectra are extracted from the multispectral image. Based on the vegetation and soil endmember spectra, spectral mixing decomposition is performed on each pixel in the multispectral image to obtain the vegetation endmember abundance of each pixel. A vegetation abundance map is constructed based on all vegetation endmember abundance values. The original vegetation index map is enhanced using the vegetation abundance map to obtain an enhanced vegetation index map. The enhanced vegetation index map is input into a pre-trained semantic segmentation network model, which outputs a binarized operational area mask. The contour of the operational area mask is used as the boundary coordinates of the operational area. Based on the above scheme, spectral identification of crop boundaries in low-contrast farmland scenes can be achieved.

The invention relates to the technical field of wetland ecology, in particular to a wetland ecology monitoring method which comprises the following steps: 1) image acquisition; 2) image preprocessing: preprocessing the collected image; 3) image classification: selecting different ground feature classification samples, extracting features of spectral brightness, texture and space structure of similar ground features in different wavebands to create a training classifier, and further dividing the whole image to obtain a ground feature classification map; 4) constructing monitoring indexes including a water eutrophication NDVI, a black and odorous water recognition index BOI, an enhanced vegetation index EVI, an optimized soil adjustment vegetation index OSAVI and a normalized difference red edge index NDRE; by constructing the monitoring indexes and the ecological index formula, the wetland ecology is systematically monitored and judged, the monitoring method is stable and reliable, and a scientific judgment basis can be provided for protection and restoration of the wetland.

The invention relates to a novel drought index construction method based on multi-factor coupling, and belongs to the technical field of agricultural drought monitoring. The method comprises the following steps: calculating vegetation coverage FVC, land surface temperature LST, soil water content SMC and enhanced vegetation index EVI by using multi-source remote sensing image data, calculating chaos characteristic parameters of each factor, determining sensitivity weight of each factor to drought according to the chaos characteristic parameters, adopting an improved random forest algorithm as a multi-factor coupling model, inputting a weighted characteristic matrix for training, and determining drought sensitivity of each factor. And optimizing key parameters of the random forest by adopting a particle swarm optimization algorithm, determining nonlinear mapping from a weighted feature to a chaos coupling multi-factor drought index CCMDI, and judging a drought level according to an index value. According to the method, the comprehensive process of drought formation and development is represented more truly, and high-precision inversion of the drought grade is realized.

The present invention provides a global rice mapping method based on phenology-based integration of optical and radar data, comprising: obtaining VH polarization backscatter coefficient time series data from synthetic aperture radar (SAR) and land surface moisture index and enhanced vegetation index (EVI) from optical images; obtaining phenological time windows for rice flooding and transplanting based on the VH time series data; obtaining a rice flooding signal based on the SAR according to the phenological period and normalizing it to obtain a normalized rice flooding signal; obtaining a rice flooding signal based on the optical image using the vegetation index; obtaining a rice flooding signal based on the integrated optical and radar data based on an integration formula; obtaining a rice rapid growth signal based on a vegetation rapid growth formula; and integrating the rice flooding signal and the rapid growth signal to obtain rice spatial distribution information. The present invention solves the problem in the prior art that rice planting information relies on sample point acquisition, which makes it difficult to extract rice spatial distribution on a large scale.

The present invention discloses a method, system and medium for identifying biodiversity conservation hotspots, relating to the field. The method includes: obtaining remote sensing image data within a preset year segment in the area to be identified, calculating the inter-annual variation index and spatial variation index of the enhanced vegetation index, as well as the inter-annual variation index and spatial variation index of the land surface temperature, and then determining the spatio-temporal remote sensing index and the hotspots identified based on the spatio-temporal remote sensing index; determining the human activity factors within the area to be identified, calculating the human footprint index and the hotspots identified based on the human footprint index; and determining the biodiversity conservation hotspots within the area to be identified according to the hotspots identified based on the spatio-temporal remote sensing index and the hotspots identified based on the human footprint index. By integrating the spatio-temporal remote sensing index and the human footprint index, the present invention respectively identifies the areas protected due to climate change and human pressure, and accurately identifies the areas that need to be protected preferentially.

The application discloses a salt marsh vegetation classification method, system, device, medium and product, relates to the field of salt marsh vegetation classification, and comprises the following steps: generating a vegetation index time sequence according to satellite remote sensing data; dividing a coastal zone latitude into several classification latitude zones as classification intervals, calculating the mean value and variance index of each vegetation index in each interval per month, and the mean value index and variance index of the current year; based on the normalized difference vegetation index time sequence and the enhanced vegetation index time sequence, calculating the growth period and decay period indexes of the vegetation by using a double logistic function; calculating the J-M distance of each candidate classification index on different vegetation classification samples; based on the J-M distance of the indexes on different vegetation classification samples and the preliminary classification contribution evaluation, screening out the most separable classification index, and constructing a final random forest classification model to classify the salt marsh vegetation in the coastal salt marsh wetland. The application can efficiently complete the coastal plant classification task at low cost.

The present application relates to a kind of irrigation farmland crop low temperature cold injury monitoring and diagnosis method, device, equipment and medium, comprising: collecting irrigation farmland multi-source data;Key indicators are calculated based on farmland multi-source data, wherein, key indicators include: land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF and solar declination delta;The key indicators calculated are calculated by the air temperature estimation model of pre-constructed irrigation farmland to obtain farmland daily average air temperature, and based on the low temperature cold injury determination standard of farmland crop, the monitoring and diagnosis of farmland crop low temperature cold injury are carried out.The present application can be based on the actual situation of irrigation farmland crop growth, utilize multi-source remote sensing data, consider the influence of surface temperature, crop growth, canopy physiology, solar radiation and other multi-factor, accurately estimate the air temperature of irrigation farmland, have strong operability, practicality is strong, it is easy to promote.

The application belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, which comprises: a preprocessing module that acquires a remote sensing image sequence and obtains an initial cultivated land segmentation sub-region and edge features; a feature extraction module that extracts spatial features such as normalized vegetation index and enhanced vegetation index and wave values based on the same, and constructs a space-time coordinate system; a region determination module that determines a true cultivated land area and a cultivated land area wave value; an inversion module that inverts vegetation coverage, water content and soil fertility, and obtains an ecological degradation wave value in combination with abnormal identification of a phenological period; and an early warning module that performs real-time early warning based on the cultivated land area wave value and the ecological degradation wave value, and visually marks the position, type and severity of a changed region through a GIS platform; and the application realizes accurate monitoring and dynamic early warning of land ecological conditions.

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The application provides a farmland forest network rate monitoring method and device, electronic equipment and storage medium, and relates to the technical field of remote sensing. The method comprises the following steps: determining a vegetation area in a target remote sensing image based on the spectral information of the target remote sensing image of a region to be monitored; obtaining a characteristic parameter value corresponding to the vegetation area; determining a forest belt area in the vegetation area based on the characteristic parameter value corresponding to the vegetation area; obtaining the farmland forest network rate of the region to be monitored based on the forest belt area and a farmland area in the target remote sensing image; the characteristic parameters comprise a normalized vegetation index and an enhanced vegetation index, and a length-width ratio and / or information entropy; and the farmland area is determined according to the geographic information of the farmland in the region to be monitored. The farmland forest network rate monitoring method and device, electronic equipment and storage medium provided by the application can more accurately and efficiently monitor the farmland forest network rate of a large area, and can reduce the cost input of farmland forest network rate monitoring.

The invention discloses a wheat field grain water content prediction method and system based on a PlanetScope image. The method comprises the following steps: acquiring 8-waveband multispectral surface reflectance data of PlanetScope, screening an image of which the cloud cover is lower than a preset percentage from the 8-waveband multispectral surface reflectance data, and calculating an enhanced vegetation index (EVI) pixel by pixel to obtain an EVI time sequence; obtaining the heading period of the winter wheat based on the preprocessed EVI time sequence; based on the heading period of the winter wheat, predicting the physiological mature period of the winter wheat through an effective accumulated temperature accumulation model; an actually measured wheat grain moisture content sample is obtained, and a field grain drying model combining a moisture diffusion mechanism, saturation vapor pressure difference adjustment and Gaussian function constraint is constructed based on the wheat grain moisture content sample; and by taking the predicted physiological mature period as a priori and combining daily meteorological data and a field grain drying model, simulating the dynamic change of the moisture content of the wheat grains day by day, and generating a spatial distribution diagram of the moisture content of the wheat grains. The method can predict the water content of the wheat grains in the field in a high-time-efficiency and high-precision manner.

The invention provides an ecological corridor heat island mitigation function monitoring method and system, and the method comprises the steps: obtaining a remote sensing image of a monitoring region which comprises a target ecological corridor and a plurality of buildings in a buffer interface adjacent to the target ecological corridor; the method comprises the following steps: calculating an openness index, a height-width ratio index, an enhanced vegetation index and a building density index; respectively carrying out weighted summation on the openness index, the aspect ratio index, the enhanced vegetation index and the building density index to obtain a heat island mitigation function comprehensive index of the target ecological corridor; and when the heat island mitigation function comprehensive index is smaller than a preset heat island mitigation function comprehensive index threshold value, alarm information is sent out. By acquiring and analyzing the remote sensing image covering the target ecological corridor and the adjacent buffer interface thereof in the monitoring area, the monitoring efficiency and accuracy of the heat island mitigation function of the ecological corridor can be effectively improved.