14 results about "Land surface temperature" patented technology

The application discloses a kind of spatiotemporal modeling ground surface temperature downscaling method and system considering energy constraint, comprising: 1) acquisition and preprocessing of multi-source remote sensing and meteorological data;2) feature grouping and spatiotemporal feature tensor construction;3) time feature and spatial feature extraction;4) spatiotemporal feature modeling and high-resolution ground surface temperature estimation;5) loss construction and cross-scale consistency constraint;6) model iterative training and result output.The application breaks through the modeling limitation of time variation law and spatial detail description in the prior art, realizes the collaborative promotion of time continuity and spatial fine expression of ground surface temperature.On this basis, the overall temperature deviation and the problem of insufficient physical rationality commonly existing in the prior art are also effectively avoided, and the application is significantly superior to the prior art in terms of timing stability, spatial accuracy and physical reliability.

The application provides a kind of urban green land cooling benefit evaluation and optimization method, system, equipment and medium based on interpretable space machine learning, method includes: based on the city boundary data of target area, land use data and land surface temperature data, constructs urban green land data set and corresponding city land surface temperature data set;For each spatial unit in the city green land data set, calculate its corresponding several urban green land characteristic indexes;Geographic weighted random forest model is constructed, for fitting the longitude and latitude of all spatial units and several urban green land characteristic indexes and the nonlinear relationship of the land surface temperature data of the corresponding spatial unit in the city land surface temperature data set;The target area is divided into zones, and based on the geographic weighted random forest model, the interpretable modeling of the cooling benefit of each urban green land characteristic index in the target area and in each zone of the target area is constructed;According to local conditions, obtain the green land cooling benefit optimization decision of target area.

This invention relates to the field of meteorological forecasting technology and discloses a method for predicting surface temperature and evaporation. The invention first performs multi-scale mode decomposition on the raw meteorological data, separating high-frequency noise components from mid- and low-frequency effective information components. This eliminates interference from high-frequency random disturbances at the data preprocessing level, effectively solving the inherent non-stationarity and strong volatility problems of meteorological data. Subsequently, a CNN-BiLSTM-Attention hybrid model is constructed to perform parallel predictions on the selected mode components. The core of this invention lies in introducing a barrel theory optimization algorithm to adaptively and jointly optimize the mode decomposition parameters and deep learning hyperparameters, overcoming the limitations of manual parameter tuning. Finally, the prediction results of each component are reconstructed, and long-term predicted values ​​are output. This effectively solves the problems of insufficient accuracy, difficulty in parameter tuning, and weak cross-site generalization ability of traditional methods and existing combined models when dealing with non-stationary, multi-scale meteorological sequences, significantly improving the accuracy, stability, and automation level of prediction.

The application provides a kind of angle consistent global MODIS ground surface temperature product development method, comprising: according to the initial global scale MODIS remote sensing image data and initial reanalysis data, the database of MODIS remote sensing data and ERA5-Land reanalysis data is constructed;The MODIS zenith observation direction remote sensing data and reanalysis data in the database of MODIS remote sensing data and ERA5-Land reanalysis data are extracted using MODIS observation zenith angle information, and the training data set is constructed according to the MODIS zenith observation direction remote sensing data and reanalysis data;Based on the training data set, a global scale vertical ground surface temperature estimation model is established using a random forest machine learning algorithm;Based on the global scale vertical ground surface temperature estimation model, the corresponding MODIS vertical ground surface temperature data is estimated using MODIS non-vertical observation time remote sensing data and ERA5-Land reanalysis data.The application helps to improve the application value of ground surface temperature product in climate change, resource environment monitoring, ground surface energy balance and the like.

Provided are a proxy server and a positioning method. The proxy server applies to a mesh network. The proxy server connects to the mesh network and a cloud service platform. The mesh network includes multiple wireless access points. The wireless access points include a first wireless access point and a second wireless access point. The proxy server receives a longitude, a latitude, an air pressure value, and a temperature value from the second wireless access point and transmits the longitude and the latitude to the cloud service platform. The proxy server receives a ground air pressure value and a ground temperature value from the cloud service platform. The proxy server calculates an altitude of the second wireless access point according to the air pressure value, the temperature value, the ground air pressure value, and the ground temperature value, and transmits the altitude to the second wireless access point.

This invention discloses an urban land cover classification system based on multispectral remote sensing technology, belonging to the field of spectral spatial data calculation technology. This system acquires land surface temperature data of the target area and sub-regions and calculates their rate of change differences. First, based on a comparison of the absolute value of the difference with a preset threshold, a first-level correction is performed on the initial contribution using a material thermal tuning coefficient. Then, based on the correlation coefficient between the temperature changes of the marked sub-region and adjacent regions, a second-level spatial coupling correction is performed, ultimately generating an accurate urban land surface thermal contribution report. This invention realizes a transformation from static identification to dynamic quantitative assessment of urban thermal environment contributions, significantly improving the accuracy, precision, and practical value of the assessment results. It solves the technical problem of insufficient accuracy in the spatial correlation dynamic assessment and adjustment of urban land surface classification thermal contributions.

This invention discloses a method for correcting the inversion of surface temperature and humidity from remote sensing satellites by incorporating surface roughness parameters. The method includes the following steps: acquiring multi-channel observed brightness temperature data, a set of surface roughness parameters, and auxiliary meteorological data from a passive microwave remote sensing satellite; calculating initial values ​​of surface temperature and soil moisture based on the observed brightness temperature data using a benchmark inversion model that does not consider surface roughness details; constructing a radiative transfer forward model incorporating the set of surface roughness parameters, and using the forward model to simulate and generate simulated brightness temperature data corresponding to the current estimated values ​​of surface temperature and soil moisture; performing iterative optimization with the goal of minimizing the difference between the observed brightness temperature data and the simulated brightness temperature data until a preset convergence condition is met, thereby obtaining the corrected surface temperature and soil moisture.

The application provides a kind of based on stationary satellite hourly all-weather ground temperature reconstruction method, comprising the following steps: S1. collect ground temperature data and multi-source characteristic variable auxiliary data and construct sample data set;S2. construct temperature intra-day cycle model dynamic constraint kernel driven model, simultaneously realize spatial downscaling and ideal ground temperature reconstruction;S3. by hybrid neural network driven spatio-temporal fusion method regression and prediction, estimate weather interference and sensor system error;S4. superimpose weather interference and sensor system error on ideal ground temperature, reconstruct all-weather ground temperature.The method of the application organically combines kernel driven and spatio-temporal fusion method, realizes the reconstruction of all-weather ground temperature with high spatio-temporal resolution based on stationary satellite FY-4A ground temperature product.The method has good stability and interpretability, and the reconstruction result has high precision.

This application provides a method and system for temporal reconstruction of land surface temperature considering energy distribution mechanisms. The method includes acquiring an energy feature dataset of a target area, wherein the energy feature dataset includes at least one of the following: temporally continuous land surface temperature data, net radiation data, latent heat flux data, sensible heat flux data, albedo data, and snow depth data for each pixel in the target area; performing abrupt change detection on the energy feature dataset based on the energy distribution of each pixel to obtain an identifier matrix corresponding to different stage types, wherein the stage types include snow cover stage, snowmelt stage, and snowless stage; performing regression fitting on the identifier matrix of any stage type to obtain the land surface temperature sequence of the stage type; generating a temporal optimization model based on constraints and the land surface temperature sequences of different stage types; and solving the objective function of the temporal optimization model to obtain the temporal land surface temperature.

This application provides a method, apparatus, and equipment for predicting the impact of marsh vegetation change on land surface temperature, belonging to the field of ecological remote sensing and climate effect assessment technology. Based on long-term series leaf area index, land surface temperature, digital elevation model, and marsh wetland distribution data, it first determines the vegetation pixel distribution as the study area within the unchanged marsh wetland distribution range; it then constructs annual and multi-year average growing season datasets using the maximum value synthesis and arithmetic mean methods; within the grid cells, pixels are divided into high and low value groups based on the multi-year average growing season leaf area index, and the difference between the multi-year average growing season leaf area index and the average land surface temperature of all pixels in the high and low value groups is calculated to quantify the degree of land surface temperature change caused by marsh wetland vegetation cover change; finally, by combining the vegetation change trend at the pixel scale, it achieves accurate prediction of the future impact on land surface temperature.

This invention relates to the field of image processing technology, and more particularly to a method and system for evaluating grassland ecosystem function based on multi-source data. The method includes the following steps: acquiring remote sensing image data and aerial images of the target area, and performing image geometric registration; extracting vegetation spatial distribution indicators of the target area based on the spectral characteristics of ground features obtained from the geometric registration results; retrieving land surface temperature indicators, and combining the land surface temperature indicator retrieving results with acquired meteorological observation data to construct a land surface energy balance curve; identifying functional trends in each season based on the land surface energy balance curve; dividing functional feature domains according to the spatial clustering results of functional trends in each season, and determining the distribution of functional feature domains in each temporal phase; and evaluating the grassland ecosystem function score based on the distribution of functional feature domains in each temporal phase. Compared with traditional evaluation methods based on single-source data, this invention achieves a more accurate, quantitative, and dynamic evaluation of ecological function.

The present invention relates to: a ground solidification system capable of rapidly and efficiently solidifying soil on the ground in order to construct roads, takeoff and landing sites, and the like in environments, such as lunar surfaces, in which it is difficult to use cement, asphalt, or the like; and a ground solidification method using same. The ground solidification system according to the present invention may comprise: a moving body that moves along a floor surface; a sintering module that discharges energy required for sintering soil downward to the moving body, thereby raising the temperature of soil on the floor surface to a temperature equal to or below the melting point and sintering the soil; and a control module that controls the output of the sintering module.

This application relates to the field of artificial intelligence technology, and in particular to a pixel-level dual-branch collaborative coding method for land surface temperature prediction. Addressing the problem that existing land surface temperature prediction models struggle to effectively integrate multivariate features and spatiotemporal information, this application proposes a dual-branch coding architecture capable of simultaneously modeling multivariate interactions and spatiotemporal evolution features. In the multivariate interaction coding branch, to address the issue of traditional multivariate prediction methods neglecting differences in feature importance and dynamic coupling relationships between variables, a feature interaction and filtering mechanism based on cross-attention and selective attention is designed. In the spatiotemporal sequence coding branch, to address the insufficient modeling of local spatial details and multi-scale temporal dependencies in existing spatiotemporal prediction methods, local spatial perception aggregation and multi-scale temporal convolutional networks are introduced. The aim is to solve the problem of how to construct a land surface temperature prediction model that can simultaneously integrate multivariate interactions and spatiotemporal sequence information.