227 results about "Ground surface temperature" patented technology

The invention discloses a method for processing unmanned plane optical remote sensing image data. The method comprises the following steps of: A) preprocessing unmanned plane optical remote sensing image data; B) carrying out atmosphere moisture inversion on the unmanned plane optical remote sensing image data so as to obtain an atmosphere moisture content product; C) carrying out ground surface reflectance inversion on the unmanned plane optical remote sensing image data by using the atmosphere moisture content product in the step B; and carrying out ground surface temperature inversion on the unmanned plane optical remote sensing image data by using the atmosphere moisture content product in the step B and a ground surface reflectance product in the step C, so as to obtain a ground surface temperature product.

The invention relates to a method for inverting surface temperature and emissivity from remote sensing data ASTER, comprising three steps: step one: a training and testing database is established by utilizing that an atmospherical radiation transmission simulating software carries out positive simulation aiming at the areas and seasons of the 11th, 12th, 13th and 15th thermal infrared wave bands of the obtained thermal sensing data ASTER and an atmospherical model; step two: a neural network is utilized for carrying out repeated training and test to a training and testing data set; step three: inversion calculation is carried out to the ASTER actual image data, and actual earth surface verification and application analysis are carried out. The products obtained by the method have high precision, especially when the surrounding atmospherical changes greatly.

The invention discloses a method for reducing that scale of surface temperature space. At first, that method quantitatively analyzes the surface temperature and the surface parameters including the impervious surface coverage, vegetation coverage, soil coverage, NDVI, NDBI, MNDWI, DEM, and the correlation between building density and its spatial distribution difference, Then the regression model of low spatial resolution land surface temperature products and related land surface parameters is established by using machine learning stochastic forest algorithm, land surface temperature with highspatial resolution can be predicted by combining the land surface parameters with high spatial resolution, Then the area-to-point Kriging interpolation method of geostatistics is used to reduce the residuals of the random forest regression model to improve the spatial resolution of the residuals, Finally, the high spatial resolution stochastic forest regression model and the surface-to-point Kriging interpolation residuals are added to generate high resolution and high precision surface temperature products to make up for the lack of spatial resolution of the existing surface temperature products.

The invention discloses a drought remote sensing monitoring method suitable for high altitude areas. The method comprises: S10, collecting data of a target area, S20, preprocessing the data obtained in step S10 to obtain an enhanced vegetation index, a surface temperature index, a land cover type and downscaling rainfall data, S30, calculating a vegetation state index, a temperature condition index, a rainfall state index, a reclassified land cover type and elevation by the data in step S20, and S40, constructing a drought remote sensing monitoring model based on spatial principal component analysis. The method comprehensively considers various factors affecting drought, wherein the various factors include a vegetation factor, a surface temperature factor, a rainfall factor, a land cover type factor and a topographic factor, a drought monitoring model is constructed by a spatial principal component analysis method, can effectively eliminate variables with large correlation in the selected variables and can extract few unrelated comprehensive indicators.

The invention discloses a regional earth surface sensible heat/latent heat flux inversion method based on remote sensing data. The method comprises the following steps that a research area and the remote sensing data are determined; remote sensing earth surface and regional meteorological parameters are prepared according to the remote sensing data, wherein the remote sensing earth surface parameters comprise a normalized difference vegetation index NDVI, vegetation coverage f, albedo, earth surface emissivity Emiss, earth surface temperature Ts and a leaf area index LAI, and the regional meteorological parameters comprise air temperature Ta and relative humidity RH; net radiation flux Rn inversion is conducted according to the remote sensing earth surface and regional meteorological parameters; soil heat flux G inversion is conducted according to the leaf area index LAI, the air temperature Ta and net radiation flux Rn; radiation-convection impedance rae inversion is conducted according to theoretical two-dimensional space of the net radiation flux Rn, soil heat flux G, the vegetation coverage f and the earth surface temperature Ts and a temperature profile equation estimated through sensible heat flux; regional earth surface sensible heat H/latent heat LE flux inversion is achieved according to radiation-convection impedance rae.

The invention relates to a superglacial moraine covering type glacier identification method based on optical and thermal infrared remote sensing images, and belongs to the field of remote sensing geoscience application. According to the method, the thermal infrared wave band and the optical wave band of Landsat TM/ETM and remote sensing images are utilized, the data of a DEM (dynamic effect model) and the like is combined for carrying out identification on the superglacial moraine covering type glacier, firstly, the topical wave band of the TM/ETM images is subjected to atmosphere radiation correction and topography radiation correction, the thermal infrared wave band is used for carrying out ground surface temperature inversion, in addition, the normalization is carried out, then, the illumination region and the shade region are distinguished, and finally, a maximum likelihood method is utilized for respectively classifying the illumination region and the shade region of the images to obtain the types of ice, superglacial moraine covering type glacier, ice and rock debris mixed regions, shade, rock and the like. Compared with a traditional method, the superglacial moraine covering type glacier identification has the advantages that the thermal infrared waveband is introduced, the identification on the superglacial moraine covering type glacier is more accurate and efficient, and the results obtained by adopting the method provided by the invention are more reasonable in the aspect of data space resolution ratio resampling.

The invention discloses a method for estimating soil and vegetation evapotranspiration based on an end metainformation model. The method comprises the four steps of 1, determining a structure based on the end metainformation model, building the end metainformation model, and preliminarily constructing an input dataset based on the end metainformation model; 2, performing decomposition of energy balance subitems in a soil-vegetation system based on the end metainformation model in soil and vegetation; 3, utilizing a ground surface balance theory and a Penman-Monteith equation, and calculating the ground surface temperature of four limit end members based on the end metainformation model; 4, estimating and obtaining the surface temperature and evapotranspiration of other elements among the end members in the end metainformation model. According to the method, different response processes of the underlying surface layer water content and root zone water content on soil evaporation and vegetation transpiration can be effectively considered, and the estimation precision of underlying surface ground surface soil and vegetation evapotranspiration is improved.

The invention discloses a remote sensing model method for regional vegetation transpiration and soil evaporation inversion based on thermal infrared remote sensing. The remote sensing model method comprises the steps of 1, collecting and spatializing meteorological radiation data of a research area; 2, acquiring optical and thermal infrared remote sensing data; 3, preprocessing the optical remotesensing data; 4, conducting thermal infrared remote sensing data preprocessing and surface temperature inversion; 5, carrying out inversion calculation of surface biophysical parameters; 6, performingnight surface temperature downscaling calculation; 7, calculating the surface flux based on a single-layer remote sensing evapotranspiration model with a physical mechanism; 8, carrying out sensitiveheat flux cutting calculation based on a double-temperature-difference model; 9, calculating latent heat flux of vegetation and soil components; and 10, performing daily expansion calculation of vegetation transpiration and soil evaporation. According to the method, the problems of high difficulty and uncertainty of regional vegetation transpiration and soil evaporation inversion based on the surface temperature at present are solved.

The invention provides a passive microwave remote sensing instant ground surface emitting rate estimation method and device. The method or device comprises a step or a device for calculating the instant ground surface emitting rate under the clear sky condition through simultaneously observing the ground surface data by a satellite-borne passive microwave sensor and a middle-resolution imaging spectrograph, a step or a device for obtaining the horizontal and vertical polarization instant ground surface emitting rate at different frequencies, a step or a device for determining a microwave polarization difference index and the polarization rate according to the same frequency horizontal and vertical polarization according to thebrightening temperature of the same frequency horizontal and vertical polarization, a step or a device for calculating the horizontal emitting rate of the ground surface emitting rate under the cloud covering condition through building a quadratic polynomial and a linear regression equation according to the horizontal polarization emitting rate of the instant ground surface emitting rate, the microwave polarization difference index and the polarization rate. By using the method and the device in the technical scheme, the zoned local blocking test is performed in the global range; the estimation of the ground surface emitting rate under the global cloud layer covering condition is realized; important data basis is provided for the ground surface temperature reversion and atmospheric correction calculation.

The invention belongs to the technical field of remote sensing applications and particularly relates to an improved diurnal temperature cycle model (IDTCM) based remote sensing ground surface temperature and time normalization method. The IDTCM based remote sensing ground surface temperature and time normalization method comprises the following steps that data pretreatment is performed, improvement is performed based on DTCM to obtain an IDTCM, remote sensing ground surface temperature and time normalization is performed and TASI aerial remote sensing ground surface temperature and time normalization is performed. By means of the method, the improved diurnal temperature cycle model is established, and the shortcoming that an existing model cannot overcome wind speed fluctuation is overcome. The remote sensing ground surface temperature and time normalization method is established based on the improved diurnal temperature cycle model.

The invention relates to a construction method and application of a remote sensing comprehensive ecological model RSIEI for evaluating a ground surface thermal environment differentiation effect of amining development dense area. The construction method comprises the following steps: inverting the surface temperature of a research area by using a radiation conduction equation method; representingthe earth surface vegetation biomass by using the photosynthetic vegetation coverage parameter and the non-photosynthetic vegetation coverage parameter; characterizing the soil moisture content by using the soil humidity index parameters; representing the bare soil and building density by using bare soil and building index parameters; and integrating the four basic ecological parameters based ona principal component analysis method to construct RSIEI. The application comprises the step of quantitatively analyzing the contribution of four ecological parameters to the ground surface thermal environment differentiation effect of the mining industry development dense region by means of a statistical method. The method is suitable for evaluating the thermal environment differentiation effectof mining development dense cities, and evaluating the surface thermal environment differentiation effect of small-scale mining development dense villages and towns with administrative boundaries as boundaries is also feasible.

The invention discloses an all-weather surface temperature inversion method and system, and the method comprises the steps: obtaining surface temperature-related data which comprises thermal infraredsurface temperature data, passive microwave data, surface temperature reanalysis data, a normalized vegetation index, and digital elevation model data; if the passive microwave data has a value, predicting the surface temperature based on a random forest model; and if the passive microwave data is missing, predicting the surface temperature based on the temperature annual cycle model. According tothe technical scheme, the problem of inaccurate surface temperature inversion data caused by passive microwave data loss of the surface temperature is solved, and the all-weather surface temperaturecan be efficiently and accurately predicted.

The invention is related to a pavement construction technology in the direction of bridge engineering in the field of highway engineering, and discloses a construction method of a T-shaped girder bridge deck pavement by means of the heating principle of a graphene heating film. At present, thawing speed of snow on a bridge deck is increased by spreading a snow-melting agent usually, which occupies large amount of manpower and resources; and what is more, using the snow-melting agent for long time may cause corrosion on the bridge deck and bridge structure and further causes reduction of the service life of the bridge. The method includes the steps of: paving a reinforced concrete layer by 10 cm; 2) removing foreign matters from a bridge base layer; 3) paving the graphene heating film; 4) detecting heating performance of the graphene heating film; 5) paving a waterproof layer by 1 cm; 6) paving a cement concrete protective layer by 4 cm; 7) paving modified asphalt concrete by 5 cm; 8) testing ground surface temperature by an infrared temperature detector. The method solves problems of slow thawing speed of ice and water and corrosion on the bridge body due to the snow-melting agent after the T-shaped girder bridge pavement ices in winter. The method increases the service life of the T-shaped girder bridge pavement and reduces expenses of pavement maintenance and the snow-melting agent.

The invention provides a thermal effect quantitative evaluation method and system based on the surface type. comprise the following steps: acquiring thermal infrared remote sensing image data, and inverting the actual surface temperature of a to-be-measured area; taking reflectivity data of visible light, near infrared and other wavebands as a reference image, and carrying out panchromatic multispectral waveband fusion on the reference image to obtain a fused image; carrying out surface classification, and determining components and grades of the surface classification; based on the elastic network regression model, obtaining regression coefficients of different surface types to the surface temperature; determining the influence of the components of the surface type on the regional heat effect; and determining the theoretical average surface temperature of different surface types under each component level and the influence of the spatial pattern on the regional heat effect. Accordingto the embodiment, the optimized elastic network regression model is introduced, and the influence of the surface type components on the LST is stripped from the influence of the surface type space structure on the LST, so that the quantitative evaluation of the surface main type heat effect is realized, and the evaluation accuracy is effectively improved.

The invention discloses a remote sensing data-based high-precision agricultural region ground surface temperature retrieval method, which comprises the following steps: firstly, calculating relationship between thermal radiation intensity and temperature, and performing non-linear approximation on Planck function by utilizing exponential function; and on the basis, carrying out derivation of a high-precision retrieval method of the ground surface temperature of the agricultural region by combining ground surface thermal radiation transmission equation. According to the method, the ground surface emissivity and atmospheric transmittance are required: NDVI of the ground surface of the agricultural region can be extracted by utilizing the data of ASTER visible light waveband and near infrared waveband so as to further calculate the emissivity data; the content of atmospheric water vapor can be retrieved by utilizing the data of MODIS near infrared waveband, and accurate atmospheric transmittance data can be obtained by fitting the relationship between atmospheric transmittance and the content of atmospheric water vapor through utilizing segmental cubic polynomial fitting. The ground surface temperature of the agricultural region can be accurately obtained, the space-time distribution of the ground surface temperature can be accurately analyzed, the space difference of the regional temperature can be analyzed, thus providing basic data for agriculture, weather, hydrology, ecology, biogeochemistry and the like.

The invention relates to a method for acquiring the ground surface temperature through NPPVIIRS data. By means of the method, appropriate ground surface temperature data can be provided for weather, agriculture drought and environment monitoring. The method overcomes the defect that due to the fact that a VIIRS sensor is not provided with a moisture channel, the inversion precision is limited. According to the method, MODIS sensor moisture data carried by an Aqua satellite operating at a sun-synchronous orbit with the descending node being 13: 30 are used for acquiring the moisture data of VIIRS data observation swath. The ground surface temperature inversion algorithm suitable for 11 microns and 12 microns of the VIIRS sensor is derived, an atmospheric radiation transfer model MODTRAN4 is used for simulating and calculating the transmittance of an M15 channel and the transmittance of an M16 channel of the VIIRS sensor under different atmospheric moisture contents (0.4-6.0 g/cm<2>, and increasing is carried out with 0.1 g/cm<2> as the step size), the emissivity of ground features corresponding to the M15 channel and the emissivity of ground features corresponding to the M16 channel of the VIIRS sensor are estimated according to International Geosphere Biosphere Program (IGBP) data, and finally the inverting of the ground surface temperature is obtained. According to the method, study is carried out on the inversion of the ground surface temperature based on the VIIRS sensor, and stable transmission from the MODIS data to the VIIRS data in the agriculture environment data monitoring is facilitated.

The invention discloses a system for the real-time monitoring and prediction of soil moisture content, and a detection method for the soil moisture content. The system comprises a monitoring module and a prediction module. The monitoring module comprises a detection system, a data collector, a wireless transmitter, and a data receiver. The detection system comprises at least two detectors, and the detectors comprise a soil moisture content detector disposed at a to-be-detected point, a precipitation detector, a ground surface wind speed detector, a ground surface temperature detector, a ground temperature detector, and a ground surface air moisture detector. The data collector is connected with the detection system, and the detection system enables the detected data to be transmitted to the data collector and to be transmitted to the data receiver through the wireless transmitter. The prediction module comprises a comprehensive cloud computing platform and an information publishing and self-determining inquiry platform. The comprehensive cloud computing platform is connected with the data receiver, and receives the detection data transmitted by the data receiver. After processing, the detection data is transmitted to the information publishing and self-determining inquiry platform. The system is simple in operation, is stable in performance, is safe in data, is diversified in function, and is high in adaptability.

The invention discloses an assimilation method for multi-scale data of a ground surface temperature field on the basis of photovoltaic power station measurement data. The method comprises steps as follows: firstly, photovoltaic power station measurement points are clustered with a DBSCAN (density-based spatial clustering of applications with noise) algorithm, a multi-scale grid of the temperaturefield is established according to a clustering result, finally, the multi-scale grid is subjected to data assimilation, and an assimilation result of the multi-scale data of the temperature field is obtained. According to the method, the problem that calculation accuracy and calculation efficiency cannot be realized simultaneously due to fixed grid resolution of a common data assimilation method can be solved, and accuracy of automatic numerical weather prediction calculation of the electric power industry is improved; the multi-scale grid is established according to photovoltaic weather station data easily obtained by the electric power industry as well as the distribution condition of the photovoltaic power stations, a high-resolution data assimilation result is obtained in the area nearby the photovoltaic power stations, and the accuracy of automatic numerical weather prediction calculation of the electric power industry is improved.

The invention discloses an all-weather surface temperature near-real-time inversion method fused with multi-source satellite remote sensing, and belongs to the technical field of satellite remote sensing surface temperature. The method comprises the following steps: firstly, constructing a regression mapping model between brightness temperature data of a microwave radiometer and passive microwave brightness temperature data, then constructing an estimation model of passive microwave brightness temperature in an orbit gap region, and obtaining seamless passive microwave brightness temperature in a specified period of a target region; and constructing a random forest regression relationship based on the microwave brightness temperature data of the passive microwave data of the target area and the surface temperature of the medium-resolution imaging spectrometer so as to obtain an all-weather surface temperature estimation value in the reconstruction period, and performing system error correction processing based on the surface temperature of the corresponding medium-resolution imaging spectrometer. The method does not need to depend on complete cycle data, can meet different application requirements and data production requirements of monitoring periods with different lengths, is good in precision, is higher in image quality, and can display more space detail information of the surface temperature.

The invention provides an all-weather surface temperature generation method integrating thermal infrared and reanalysis data, which is realized on the basis of random forest integrated thermal infrared remote sensing and reanalysis data. The method specifically comprises the following steps: performing data preprocessing and space-time matching; implementing a time algorithm; implementing a spacealgorithm; and generating the final 1km all-weather surface temperature. According to the invention, reanalysis data is introduced, a long surface temperature and related observation data can be provided, the data is integrated with thermal infrared satellite remote sensing to obtain all-weather surface temperature with long time sequence and high temporal-spatial resolution. The obtained surfacetemperature has good consistency with a traditional thermal infrared satellite remote sensing surface temperature product, and through the inspection of the actually measured surface temperature, theprecision is within an acceptable range, the image quality is higher, more space detail information of the surface temperature can be displayed, and the overall quality and precision are superior to those of a method for integrating thermal infrared satellite remote sensing data and passive microwave data.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to an aviation mid-infrared hyperspectral data temperature and emissivity inversion method, which comprises the following steps: 1, preprocessing aviation mid-infrared hyperspectral remote sensing data to select a minimum noise wave band; 2, analyzing an intermediate infrared radiation transmission process, and deriving an intermediate infrared radiation transmission equation; 3, carrying out space-time interpolation by utilizing the atmospheric profile data to simulate each atmospheric parameter; 4, eliminating solar radiation and atmospheric thermal radiation effects according to an intermediate infrared atmospheric radiation transmission equation to obtain off-ground radiation; 5, iteratively eliminating downlink radiation in the ground clearance by using an NEM module to obtain the surface temperature and emissivity; 6, calculating the relative emissivity by using the RAT module, and keeping the emissivity spectrum shape inverted by the NEM module unchanged; and 7, acquiring the surface temperature and emissivity by using the empirical relationship between the emissivity spectrum shape parameters of the MMD module and the RAT module and the minimum value of the channel emissivity.

The invention discloses an improvement method of a Bevis model for regional weighted average temperature based on sounding data. The method includes the following steps: S1, preprocessing the soundingdata of a station to obtain a true value T<m0> of the weighted average temperature and a true value T<s0> of ground surface temperature; S2, using the Bevis model to obtain a calculating value T<m> of the weighted average temperature; S3, adding a periodic term based on the Bevis model by taking annual periodic variations of the calculating value T<m> of the weighted average temperature into consideration, and establishing a nonlinear equation; and S4, determining coefficients of the nonlinear equation by using a least square method, determining a final improvement model equation, and verifying the accuracy of the final improvement model equation. Compared with a conventional Bevis model, the calculating accuracy of the Bevis model of the invention is effectively improved.

The invention provides a soil moisture measurement method and device based on multi-source remote sensing data fusion, which are used for measuring moisture contained in soil in a target area. The method includes: determining a soil moisture estimation result of the target area by acquiring original data of the soil in the target area and measured data of the soil in the target area through a cyclone global navigation satellite system; performing space-time fusion on the obtained first surface temperature data of the target area soil and the obtained second surface temperature data of the target area soil to obtain a surface temperature estimation result; obtaining auxiliary parameters influencing soil moisture, wherein the auxiliary parameters comprise vegetation coverage indexes, the ground elevation, the vegetation coverage, th soil roughness, the ground gradient and the rainfall; and performing data fusion on the soil moisture estimation result, the surface temperature estimation result and the auxiliary parameters to obtain a multi-source remote sensing data fusion model, and obtaining a soil moisture measurement result through the multi-source remote sensing data fusion model. By means of the measuring method, the soil moisture measuring result can be more accurate.

The invention discloses a method for estimating ground surface temperature daily range based on MSG2-SEVIRI data. The method comprises the following steps: obtaining the MSG2-SEVIRI data of a research region, carrying out radiometric calibration on the MSG2-SEVIRI data, and converting DN values into radiance; converting the radiance into on-satellite brightness temperature according to a Planck equation; calculating ground surface temperature difference at any two moments by utilizing a surface temperature split-window algorithm; coupling a ground surface temperature diurnal variation model and the split-window algorithm, and through a least squares fitting method, calculating parameters of the ground surface temperature diurnal variation model; and calculating ground surface temperature daily range according to the parameters of the ground surface temperature diurnal variation model obtained in the step 3. The method is to estimate the ground surface temperature daily range based on the stationary satellite data by fully utilizing the characteristics of stationary satellite data high time resolution, by coupling the split-window algorithm with the ground surface temperature diurnal variation model, and under the condition of not needing inversion of the surface temperature and emissivity.