168 results about "Ground surface temperature" patented technology

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The invention relates to the technical field of alpine sand soil moisture leakage estimation technologies, and particularly discloses an alpine sand soil moisture leakage estimation method which comprises the following steps: S1, multi-source data acquisition: acquiring 0-110cm layered soil moisture content and temperature data by using a cold-resistant soil hydrothermal sensor array, and calculating the moisture content and temperature data of the layered soil; synchronously acquiring Sentinel-2 remote sensing data to extract vegetation coverage and land surface temperature, and recording regional freezing and thawing cycle times and biological crust coverage types; s2, key parameter inversion: calculating a permeability coefficient correction factor kFTC = alpha * N (1 / 2) through a multi-expression programming model based on the freezing and thawing cycle number N. According to the method for estimating the soil moisture leakage amount of the high and cold sand land, the limitation of a traditional model based on steady-state water flow and empirical parameters is broken through by quantifying the dynamic effect of freezing and thawing cycles on the porosity of a 30-80 mm soil layer and the water blocking effect of 23.41%-30.39% when the biological crust coverage degree exceeds 30%; and the influence of the low-temperature freezing and thawing cycle and the biological crust on the leakage mechanism of the high and cold sand land is accurately depicted.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

The invention provides a drought monitoring method and device for a target area, electronic equipment and a storage medium, and relates to the technical field of drought monitoring, and the method comprises the steps: for each pixel corresponding to the target area, obtaining meteorological reanalysis data, a normalized vegetation index and a target land surface temperature corresponding to the pixel in a first preset time period; for each pixel, determining a hot boundary and a cold boundary corresponding to the pixel based on the meteorological reanalysis data and the normalized vegetation index corresponding to the pixel; and determining a drought monitoring result corresponding to the target area based on the hot boundaries, the cold boundaries and the target surface temperature corresponding to all the pixels. According to the technical scheme, for the target area, the hot boundary and the cold boundary are determined pixel by pixel through the meteorological reanalysis data, the situation that the weather conditions of the large area with the complex terrain are different is considered, accurate drought monitoring can also be carried out in the large area with the complex terrain, the applicability of drought monitoring is widened, and the drought monitoring accuracy is improved. Moreover, the method does not need to divide the region, and is simpler in steps.

The invention relates to the technical field of remote sensing technology and ecological environment restoration evaluation, in particular to a mine ecological restoration project effect evaluation method based on multi-source remote sensing data. The method comprises the following steps: carrying out multi-source remote sensing data collaborative acquisition and preprocessing on an acquired mine restoration area to obtain a noise reduction data set; performing multi-dimensional cooperative sensing processing on the noise reduction data set to obtain a multi-dimensional cooperative sensing data set; performing soil spectral feature separation and extraction on the multi-dimensional collaborative sensing data set to obtain soil spectral feature data; calculating a surface temperature distribution diagram according to the soil spectral characteristic data; performing surface soil water content inversion according to the soil spectral characteristic data and the surface temperature distribution diagram to obtain a surface water content distribution diagram; estimating the water content of the deep soil according to the surface water content distribution diagram to obtain soil water profile data. According to the method, the remediation input benefit is improved by quantitatively inverting the physical parameters of the soil and dynamically analyzing the rainfall response process.

The invention provides a surface water heat flux integrated estimation method, system and equipment, and relates to the technical field of hydro meteorological remote sensing, and the method comprises the steps: according to the target surface temperature data, target net radiation data, target air temperature data, target vegetation coverage data and target soil moisture data of a target region, calculating the target surface temperature data; constructing to obtain target surface environment key parameters corresponding to the target region; and inputting the target earth surface environment key parameters into the surface water heat flux integrated estimation model to obtain target earth surface sensible heat, latent heat and soil heat flux estimation values which are simultaneously output by the surface water heat flux integrated estimation model and correspond to the intra-day time scale of the target region, the surface water heat flux integrated estimation model is obtained by training a physical information neural network machine learning model based on the sample surface water heat flux observation data corresponding to the sample surface water heat flux key parameters and the sample surface water heat flux key parameters. According to the method, the surface water heat flux estimation accuracy is improved.

The invention discloses a method for evaluating rock mass fracture freeze-thaw fracture damage risk based on surface temperature, which comprises the following steps: S1, carrying out data acquisition on a target area based on a multi-source sensor carried by an unmanned aerial vehicle platform, and obtaining fracture geometric parameters, daily surface minimum temperature, daily surface maximum temperature, daily temperature difference and fracture moisture conditions; s2, on the basis of the geometric parameters of the crack, constructing a crack frost heaving force analytical model; obtaining a quantitative relation between the stress intensity factor and the environment temperature; s3, the I-type fracture toughness of the target rock mass is measured, and the critical crack initiation temperature of the rock mass is determined; determining the number of critical freezing and thawing cycles; s4, on the basis of the daily earth surface minimum temperature, the daily temperature difference, the fracture moisture condition and the critical crack initiation temperature, the annual accumulated effective freezing and thawing cycle number is obtained; and calculating a freeze-thaw damage ratio, and carrying out risk quantification and grading. According to the invention, the problem of inaccurate evaluation caused by limitation to local monitoring and lack of an environment factor and material parameter integrated evaluation framework in the prior art is solved.

The invention discloses a surface temperature medium and long term forecasting method based on a multi-scale TransFuse pyramid network. The surface temperature medium and long term forecasting method comprises the steps of S1, acquiring original LST time sequence data and performing preprocessing; s2, constructing a three-dimensional time delay embedded feature tensor for the data preprocessed in the S1 by adopting a stepped cross-node tensor feature reconstruction method; s3, on the basis of the three-dimensional time delay embedded feature tensor constructed in the S2, meteorological features of different time scales are extracted in parallel through a dynamic multi-resolution convolution kernel group; s4, performing five-stage cascade operation on the meteorological features of different time scales extracted in the S3 to realize deep fusion of cross-scale features, and performing high-level semantic information integration through a global feature pyramid; and S5, finally outputting a multi-step prediction result through a regression prediction framework based on the fusion features obtained in the S4. According to the invention, an extensible technical normal form is effectively provided for high-precision meteorological prediction in a limited computing power scene, and important application potential is shown in actual business deployment.

The invention belongs to the technical field of data processing, and discloses a Mars rover non-contact trafficability estimation method and system based on thermal inertia, and the method comprises the steps: obtaining a ground surface temperature time sequence and environment parameters of a target region; constructing a thermal inertia estimation model, and inputting the earth surface temperature time sequence and the environmental parameters into the thermal inertia estimation model to obtain a thermal inertia estimation value of the fire soil; constructing a thermal inertia and trafficability index relation model, inputting the thermal inertia estimation value of the fire soil into the thermal inertia and trafficability index relation model, and outputting a predicted trafficability index; and determining a trafficability state grade according to the predicted trafficability index, and generating a navigation control suggestion, so that the Mars rover passes through the target area according to the Mars rover navigation control suggestion. According to the method, the trafficability index of the Mars rover is predicted by extracting the thermal inertia estimation value of the fire soil in the target area, the evaluation precision of the Mars rover passing through the target area can be effectively improved, and the driving safety of the Mars rover is ensured.

The invention provides a high-resolution surface soil moisture estimation method and device, a terminal and a medium. The method comprises the following steps: obtaining space-time seamless coarse resolution moisture data and space-time seamless surface temperature data according to obtained soil moisture data, brightness temperature data and downscaling factor data; processing the obtained downscaling factor data and the space-time seamless surface temperature data through space aggregation resampling to obtain coarse resolution downscaling factor data and high resolution downscaling factor data; inputting the space-time seamless soil moisture data, the coarse resolution scale factor data and the high resolution downscaling factor data into various machine learning models to obtain various high resolution space-time seamless moisture estimation data; and performing fusion processing on the multiple high-resolution space-time seamless moisture estimation data through a Bayesian triangular cap fusion method to obtain high-resolution space-time seamless surface soil moisture estimation data. And high-resolution and time-space seamless estimation of the surface soil moisture is realized.

The invention discloses a land utilization change comprehensive feature analysis method based on multi-source heterogeneous data fusion, and relates to the technical field of remote sensing and geographic information processing. Building volume, building density, night light, surface temperature and population density are aligned under unified coordinates and resolution; constructing an average building height according to volume / density and coding and recording quality; uniform noctilucence is realized through partition three-order mapping; interpolation is carried out by adopting fractional learning under the missing measurement mask, and pixel-level uncertainty is output; uniformizing the annual temperature; extrapolation is carried out through multi-scale space-time small block coding-decoding containing flux conservation and Laplace constraint, and uncertainty is given; implementing uncertainty weighted principal components according to the five indexes to obtain a comprehensive strength index; concentric buffering, set splitting regression and partial response confidence band recognition threshold values are combined. The method has the advantages of cross-period comparability, physical consistency, traceable uncertainty and the like.

The invention discloses a debris flow prediction method based on multi-source remote sensing, and the method comprises the following steps: 1, data acquisition and preprocessing: obtaining target region data through satellite remote sensing, and eliminating terrain shadow interference; step 2, feature extraction: extracting vegetation coverage and surface temperature from the optical image, extracting deformation magnitude and time sequence from InSAR data, and extracting gradient, slope direction, curvature and terrain humidity index from DEM terrain data; 3, performing multi-dimensional feature fusion, and constructing a multi-dimensional feature vector; 4, model construction: establishing a debris flow susceptible area identification model, and fusing the vegetation-terrain coupling characteristics of the optical image, the deformation dynamic characteristics of the InSAR and the terrain stability characteristics of the terrain data; 5, carrying out susceptibility partitioning and early warning, dividing risk grades based on probability values output by the model, and triggering graded early warning responses; according to the method, the multi-dimensional feature space is constructed through the collaborative interaction of the multi-source remote sensing data and the fused data.

The invention discloses a method for confirming the width of a heat island corridor of an urban heat island network, and belongs to the technical field of urban ecological intelligent analysis. The method comprises the following steps: inverting the surface temperature of a research area; in combination with morphological spatial pattern analysis, landscape connectivity indexes and circuit theories, screening a heat island source ground and generating a resistance surface, and constructing a multi-grade urban heat island corridor network; constructing a multi-level buffer zone for different levels of galleries, and respectively extracting surface temperature mean values; extracting a buffer zone temperature extreme value of each grade of gallery, calculating a temperature difference parameter, and quantifying the heat exchange intensity; calculating heat transfer efficiency per unit area by combining the area of the buffer zone; analyzing the nonlinear attenuation characteristics of the heat transfer efficiency along with the offset distance of the buffer zone, extracting the inflection point of the heat transfer efficiency, and taking the offset distance of the buffer zone corresponding to the inflection point as the space action width of the heat island gallery, thereby realizing the scientific definition of the space action boundary of the heat island network. The method can also provide a scientific basis for relieving the urban heat island effect, optimizing the green land layout and the like.

The invention relates to the technical field of surface temperature measurement based on an infrared radiation principle, and discloses a non-contact surface temperature measurement device which realizes surface temperature measurement through cooperation of a plurality of components. The optical system is located in front of the infrared detector, the field angle is determined, infrared radiation is focused, and a 3D printing black sealing shell of the optical system is rainproof and windproof. The infrared radiation detector of the MLX90614 chip converts infrared energy into a voltage signal and processes data, the data is sent to the main processor through the signal conditioning circuit for analog-to-digital conversion and other operations, and the sensor is endowed with digital and intelligent characteristics. The communication module comprises an RS-232 serial port and a ZigBee wireless communication module and is used for data transmission and connection with an upper computer, and the state monitoring module monitors the work of the processor. Hardware design is completed by using an embedded system and a ZigBee technology, software design is realized by IAR and Z-Stack protocol stacks, and the surface temperature can be accurately measured. The device also has two-way communication and online upgrading functions, the emissivity and the distance are corrected through an algorithm, the efficiency is improved, the standard requirements are met, and meteorological observation and ground temperature measurement are assisted.

The invention discloses a marking method and system for a geothermal abnormal target area, and the method comprises the steps: generating a surface temperature distribution diagram according to the remote sensing thermal infrared data of a target area, and marking a first-stage geothermal abnormal target area; a temperature measurement network is arranged in the first-stage geothermal abnormal target area, ground temperature measurement data are collected, and a shallow ground temperature distribution diagram is generated; marking a secondary geothermal abnormal target area by combining the shallow ground temperature distribution diagram and the ground surface temperature distribution diagram; and obtaining geothermal information of the second-stage geothermal abnormal target area, marking a drilling position according to the geothermal information, drilling the drilling position, collecting deep geological data, and inputting the deep geological data into a pre-trained geothermal geological model to obtain a target geothermal abnormal target area output by the geothermal geological model. According to the method, through comprehensive analysis and processing of multiple data, the accuracy and reliability of marking of the geothermal abnormal target area are improved.

The invention discloses a space remote sensing image analysis method for detecting surface water and underground water exchange in a river region based on surface temperature, and belongs to the field of remote sensing detection surface temperature anomaly analysis. According to the method, background field construction is carried out on pixels in a space by combining single-scene high-resolution surface temperature data and land cover classification data, and temperature thermal anomaly of a river main stream is extracted by adopting an adjacent pixel temperature anomaly analysis method, namely, the temperature thermal anomaly of the river main stream is extracted by analyzing subtle change of surface temperature in a remote sensing image; and identifying abnormal pixels which are obviously different from the temperature characteristics of the surrounding area. The abnormal pixels often correspond to areas where surface water and underground water exchange is active, such as permeable zones, spring water exposure points and the like. The method can be applied to drainage basin water resource management and ecological protection, and the conversion path and intensity between surface water and underground water can be deduced by further analyzing and processing the abnormal pixels extracted by the method and combining background information such as geology and hydrology.

The present invention discloses a surface temperature estimation method, system, device and storage medium, and relates to the technical field of surface temperature estimation. The method comprises the following steps: obtaining a daily surface temperature dataset with low spatial resolution and an underlying surface dataset with high spatial resolution; reprojecting the daily surface temperature dataset with low spatial resolution to obtain a new daily surface temperature dataset; using a sliding window clipping method to process the spatial resolutions of the new daily surface temperature dataset and the underlying surface dataset to be consistent; and inputting the processed new daily surface temperature data and the underlying surface data as feature data into a surface temperature estimation model to obtain daily surface temperature data with high spatial resolution. The model can provide reliable long-time series, high-spatial-resolution surface temperature data when performing resource and environmental monitoring.

The application discloses a kind of urban heat island network construction method based on multilevel thermal field recursive decomposition in the technical field of urban climate analysis, including obtaining seamless summer average ground surface temperature grid data of initial area, the multidimensional environmental driving factor data required for building thermal resistance surface;Based on mean-standard deviation threshold method, the continuous ground surface temperature field of the initial area is multilevel recursive decomposition, and the main level heat island source is identified, and the nested secondary heat island source is recursively identified inside the main level heat island source, and the hierarchical nested heat island network structure is constructed.The application realizes the accurate decomposition of multilevel heat island source by using mean, standard deviation threshold value for recursive calculation of continuous ground surface temperature field, can effectively identify cross-scale heat island connection pinch point and heat island connection barrier, can realize the hierarchical construction of urban heat island network, and is conducive to improving the management efficiency of urban heat island.

The embodiment of the application discloses a dynamic current-carrying capacity prediction method of a direct-buried cable and related devices, and the method comprises the following steps: obtaining historical weather data and historical cable load current of a direct-buried cable in a historical time period in a preset area; the historical weather data comprises historical ground temperature and historical precipitation; determining soil dynamic characteristic parameters based on the historical ground temperature and the historical precipitation; determining the historical dynamic current-carrying capacity of the direct-buried cable based on the soil dynamic characteristic parameters and the historical cable load current; obtaining future weather data and future cable load current of the preset area in a future time period; determining a target quantile point; determining a regression coefficient vector corresponding to the target quantile point based on the target quantile point, the historical weather data, the soil dynamic characteristic parameters, the historical dynamic current-carrying capacity and the historical cable load current; and determining the future dynamic current-carrying capacity of the direct-buried cable corresponding to the target quantile point in the future time period based on the future weather data, the future cable load current and the regression coefficient vector.

The invention discloses a method for regulating and controlling a thermal environment by an urban form under coupling of human eyes and a bird's-eye view angle. The method comprises the following steps of: inverting a surface temperature based on a radiation transfer equation method and a Landsat-8 remote sensing image; carrying out fine extraction on urban ground feature information fused with multi-modal data; constructing a three-dimensional comprehensive representation system of the urban form under the coupling of a bird's-eye view angle and a human eye view angle; establishing an urban surface temperature prediction model fusing an urban two-dimensional form, an urban three-dimensional form in a bird's-eye view angle and an urban three-dimensional form in a human eye view angle under different spatial scales; and an optimal configuration method for regulating and controlling the surface thermal environment by the urban form under the fine scale and the rough scale is explored. According to the method, a scientific basis can be provided for quantitative regulation and control and space planning optimization of the urban thermal environment, and the effects of refined cooling and thermal environment improvement under urban space structure optimization and greening configuration adjustment are achieved.

This invention relates to the field of image sensing data processing and discloses a method for diagnosing the thermal environment of blue-green spaces in cold-region cities based on multispectral remote sensing. The method includes the following steps: acquiring multispectral remote sensing images, three-dimensional building elevations, and solar apparent shape parameters; calculating the illumination shading coefficient of blue-green space pixels to form a three-dimensional dynamic shadow mask; constructing a microscopic phase transition evolution operator using the sensitivity gradient of radiation intensity in different polarization directions and mid-infrared reflectivity; inputting the mask and operator into the thermal infrared radiation measurement equation to reconstruct the dynamic surface temperature; solving the surface energy balance equation based on the dynamic surface temperature, separating the latent heat storage of phase transition energy, and constructing a latent heat buffer index; and performing a comprehensive diagnosis of the thermal environment health based on this index. This invention effectively overcomes the temperature measurement errors caused by complex shading and phase state interweaving, accurately isolates the latent heat energy of phase transition, and achieves precise positioning and objective quantitative diagnosis of microclimate anomalies in cold-region cities.

The present invention relates to the field of remote sensing data analysis, and particularly to a method for measuring surface temperature based on an unmanned aerial vehicle, including: obtaining a thermal infrared image, meteorological data, building height data corresponding to each land type, and building area data of a target area through the unmanned aerial vehicle; converting the thermal infrared image into a surface temperature image, extracting a plurality of sample data corresponding to each land type from the surface temperature image, and obtaining temperature data corresponding to the sample data; constructing a surface temperature measurement model according to the temperature data, meteorological data, building height data corresponding to each land type, and building area data; in response to a measurement instruction, the measurement instruction includes a thermal infrared image, meteorological data, building height data corresponding to each land type, and building area data of an area to be classified, and obtaining a surface temperature measurement result of the area to be classified according to the thermal infrared image, meteorological data, building height data corresponding to each land type, and building area data of the area to be classified.

The invention discloses a surface temperature and downscaling temperature inversion method based on remote sensing data, and belongs to the technical field of remote sensing data processing. The method comprises the following steps: 1) fusing measured data of a weather station at an interval of three hours all day with Landsat 8 / 9 satellite imaging time through a cubic spline interpolation method, generating a space-time matched land surface temperature (LST) inversion result, and eliminating abnormal values by using a Kutta distance method to optimize model precision; (2) the surface emissivity is calculated based on an NDVI threshold value method, and the vegetation coverage (Pv) is dynamically determined through an NDVI empirical threshold value (NDVIsoil = 0.05, and NDVIeg = 0.70); and 3) in a Google Earth Engine (GEE) platform, in combination with Landsat 8 / 9 (30 meters) and Sentinel-2 (10 meters) multi-source satellite data, constructing a random forest model with NDVI, NDBI and NDWI as inputs, downscaling the LST to a resolution of 10 meters, and smoothly improving detail precision through regression residual error. The problems that a traditional method is low in resolution, large in space-time matching error and remarkable in abnormal value interference are solved, the inversion precision index R is larger than or equal to 0.85, RMSE is smaller than or equal to 1.5 DEG C, and the method is suitable for urban heat island effect analysis, environment monitoring and climate research.

The invention relates to the technical field of infrared detection, and discloses a high-precision infrared surface temperature measuring device and method, and the device comprises at least one infrared sensor which is used for measuring the target temperature and the cavity temperature; the infrared optical lens penetrates through an infrared radiation signal and prevents mosquitoes from entering the device; a camera unit that captures and acquires an underlying surface image; the light intensity measuring unit is used for measuring environment radiation; and the control part is used for reading the target temperature and the cavity temperature measured by the infrared sensor, acquiring the underlying surface image shot by the camera, acquiring the voltage value of the light intensity measuring unit and acquiring the real-time time of the system. An infrared optical lens is additionally arranged on the structure, and the target temperature is corrected through an infrared optical lens energy compensation algorithm. The camera device based on underlying surface identification identifies the type of the underlying surface through an image and estimates a corresponding emissivity value; through linear fitting of environmental radiation energy, the target temperature is corrected by using the emissivity and the environmental radiation, and the measurement precision is improved.

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 invention relates to the technical field of remote sensing data processing, and provides a soil humidity data missing filling method, which comprises the following steps: acquiring a soil humidity data sequence corresponding to ESA CCI soil humidity data to be filled, the multi-source soil humidity related data comprises surface temperature data, a normalized vegetation index, rainfall data, digital elevation model data, soil texture data, soil humidity reanalysis data and ground verification data. And inputting the soil humidity data sequence into a trained soil humidity data missing filling model to obtain a filling data result corresponding to the ESA CCI soil humidity data to be filled, the soil humidity data missing filling model being obtained by carrying out transfer learning training according to a pre-training set and a fine tuning training set. The soil humidity data missing filling model comprises a time convolutional network module, a bidirectional long short-term memory network module, a self-attention mechanism module and an output module.

The invention discloses a carbon dioxide sequestration leakage monitoring method and device based on unmanned aerial vehicle multi-source data fusion. The method comprises the following steps: collecting spectral data and surface temperature distribution data of surface vegetation in a sequestration area range through an unmanned aerial vehicle; preprocessing the spectral data, and identifying a vegetation growth abnormal area within the range of the sequestration area based on the preprocessed spectral data; pre-processing the temperature distribution data, and identifying a temperature abnormal area within the range of the storage area based on the pre-processed temperature distribution data; performing spatial correlation analysis on the vegetation growth abnormal region and the temperature abnormal region to determine a highly suspected leakage region; and based on the geological structure data of the sequestration area range, performing ground verification on the highly suspected leakage area to determine a target leakage area. According to the invention, air patrol is carried out on the sealing area range through the unmanned aerial vehicle, the vegetation growth abnormal area and the temperature abnormal area are comprehensively analyzed, the highly suspected leakage area is determined, the target leakage area is determined, and the leakage monitoring precision is improved.