81 results about "Land surface temperature" patented technology

The invention discloses an agricultural meteorological disaster monitoring and early warning method and system based on a remote sensing technology, and the method comprises the steps: generating a multi-dimensional data set through obtaining and processing multi-source remote sensing data, and generating a comprehensive data set through weighted average fusion. Then, extracting land surface temperature data, comparing the land surface temperature data with a historical value, calculating a temperature deviation value, and generating temperature anomaly distribution data; and calculating a disaster intensity index by combining the soil humidity and vegetation index data change trend, and generating disaster intensity distribution data. The data meteorology is imported into a driving simulation system, disaster evolution is simulated, and the disaster influence range and duration are predicted. And if the prediction data exceeds an early warning threshold, generating early warning data including disaster categories, influence areas and prediction time, and generating disaster risk distribution data by using a spatial interpolation method for dynamic monitoring. According to the invention, the accuracy and timeliness of disaster monitoring and early warning are improved.

The invention relates to the technical field of remote sensing image intelligent processing, in particular to a multi-scale progressive surface temperature fusion downscaling method and device, and the method comprises the steps: preprocessing a surface temperature image in a multi-scale image library; constructing a multi-scale training data set containing the surface temperature and at least one auxiliary parameter; constructing a surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training a surface temperature downscaling network and a domain transformation network based on a progressive dual-network joint training strategy; finely adjusting the downscaling model meeting a preset low-medium resolution condition; and performing low-medium-high progressive downscaling on the low-resolution surface temperature image to be processed to generate a multi-scale progressive surface temperature meeting a target high-resolution condition. The method fully considers the problem of descending scale difference of different resolutions, achieves the maintenance of the physical characteristics of the surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The invention discloses an active city cooling method based on a three-dimensional city form and a circuit theory, and belongs to the technical field of city environment regulation and heat island effect governance, and the method comprises the steps: S1, recognizing long-term stable cold and heat source patches through obtaining multi-temporal surface temperature (LST) data and combining mathematical form spatial pattern analysis (MSPA) and a landscape connectivity index; s2, adopting a spatial principal component analysis (SPCA) method to construct a comprehensive resistance surface fused with the three-dimensional urban form factors; s3, connecting a cold and heat source to simulate an airflow conduction path based on a circuit theory, extracting a cooling gallery and identifying key nodes; and S4, finally, through a stable surface-connecting line-key point mode active cooling network, space optimization is carried out. According to the method, accurate identification and efficient communication of cold and heat sources can be realized, the heat island effect relieving efficiency is improved, the long-term adaptability of a cooling network is ensured, and a popularized urban thermal environment governance path is provided.

The invention relates to a land surface temperature and emissivity joint inversion method and system and a medium. The inversion method comprises the following steps: acquiring radiation brightness vectors observed by a satellite sensor in a plurality of thermal infrared bands as observation radiation brightness vectors; constructing a standard emissivity spectrum dictionary database as a dictionary matrix; defining a generation rule of the surface emissivity; establishing a forward physical model, and taking an analog value of the observed radiance vector as model output; defining a likelihood function of an observation radiance vector, defining sparsity prior probability distribution for the sparse coefficient vector, and defining uniform prior probability distribution for the surface temperature; joint posterior probability distribution of the surface temperature and the sparse coefficient vector is calculated, and multiple groups of samples are extracted; calculating a posteriori estimation value and an uncertainty interval of the surface temperature; and calculating a posteriori estimation vector of the sparse coefficient vector, and multiplying the posteriori estimation vector with the dictionary matrix to obtain a posteriori estimation value of the surface emissivity. The automation level of remote sensing information extraction and the information output efficiency are improved.

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 relates to a multi-source meteorological data fusion technology, and discloses an air temperature reconstruction method fusing data of a micro-meteorological device, which improves the temporal-spatial resolution and precision of ground temperature under a complex terrain. The method comprises the following steps: densely deploying micrometeorological devices in a complex terrain area to obtain high-frequency observation data, carrying out quality control, and carrying out hierarchical processing in two dimensions of space and time by taking pattern forecast grid point data as an initial background field: in the spatial dimension, dynamically updating a fusion weight for grid points with observation stations by using geographical weighted regression, and carrying out data fusion; a residual machine learning model is combined with multi-topographic feature correction for grid points without observation stations, and a high-precision space fusion background field is generated; in the time dimension, errors after space fusion are decoupled into a trend term and a periodic term, an autoregressive integral moving average model is used for predicting a trend, a Fourier algorithm is used for correcting a periodic phase, and then time dimension machine learning correction is carried out on a grid point of an observation-free station. And finally, complete-process automatic, high-temporal-spatial-resolution and low-error complex terrain area air temperature reconstruction is realized.

The invention belongs to the field of land surface temperature reconstruction, and relates to a high-spatial-temporal-resolution land surface temperature reconstruction method used in a cloud environment, which comprises the following steps: calculating a first reference land surface temperature and a first land surface temperature residual error of a clear sky pixel under a low spatial resolution according to a first land surface temperature annual change model; seamless surface reflectance data under high spatial resolution are obtained; defining a relative solar radiation index based on the digital elevation model data with high spatial resolution; screening clear sky pixels participating in modeling of the XGBoost model; an XGBoost model is adopted, and a plurality of model relations between a low-spatial-resolution land surface temperature annual change model coefficient and a land surface attribute and between a day-by-day land surface temperature residual error and the land surface attribute are constructed; obtaining a high-spatial-resolution surface temperature annual change model coefficient and a day-by-day surface temperature residual error based on the constructed multiple model relationships, and obtaining a high-spatial-resolution daily surface temperature; the accuracy and the stability of a surface temperature reconstruction result are improved.

The embodiment of the invention discloses a surface temperature inversion method and device based on remote sensing satellite data, and a medium, and relates to the technical field of remote sensing, and the method comprises the steps: receiving a heat island effect analysis demand triggered by a user, obtaining satellite remote sensing data of a target region and a corresponding metadata file based on the heat island effect analysis demand, and storing the satellite remote sensing data in the target region; processing the satellite remote sensing data to determine earth surface radiation parameters; the atmospheric features and the earth surface features of the target area are evaluated through the metadata file and a pre-obtained normalized vegetation index, a dynamic adjustment factor corresponding to the target area is determined, a preset single-window algorithm model is optimized through the dynamic adjustment factor, and an optimized single-window algorithm model is determined; and inputting the surface emissivity, the atmospheric transmittance, the brightness temperature value and a pre-acquired atmospheric average action temperature into an optimized single window algorithm model for calculation, and determining a surface temperature distribution diagram of the target area for heat island effect analysis.

The invention discloses a crop drought comprehensive assessment method based on multi-source satellite remote sensing data, which belongs to the technical field of remote sensing, converts soil humidity, precipitation, surface temperature and vegetation index into dimensionless parameters through normalization processing, and enables four different types of data to be fused for use. According to the method, the multi-dimensional characteristics of drought can be more comprehensively reflected, the soil humidity (SM) and rainfall (P) directly reflect the water income and expenditure, the land surface temperature (LST) reflects evapotranspiration and energy distribution, the vegetation index (VI) reflects the vegetation physiological state and drought stress, and the four parameters are combined to more comprehensively describe the'water-energy-vegetation 'drought coupling process.

The invention relates to the technical field of ground surface temperature downscaling, in particular to a ground surface temperature airborne downscaling method integrating object-level and pixel-level residual errors, which comprises the following steps of: S1, collecting remote sensing images covering a research area and medium-resolution imaging spectrometer (MODIS) data, and preprocessing; s2, constructing surface temperature downscaling models of TsHARP, RF, SED and the like as a reference downscaling model; and S3, residual compensation of an object-level residual in surface temperature downscaling is considered. And S4, integrating residual compensation of object-level and pixel-level residuals in surface temperature downscaling, and constructing an IOPRCF residual compensation framework to improve the performance and precision of the reference downscaling model. According to the method, an IOPRCF residual error compensation framework is constructed by integrating object-level and pixel-level residual errors in ground surface temperature downscaling work, so that land cover change information in fine-resolution ground surface temperature is recovered, the performance and precision of various reference downscaling models are improved, and the method is suitable for large-scale popularization and application. And the accuracy of the surface temperature downscaling algorithm in a region with relatively strong heterogeneity is improved.

The invention relates to a large-scale region natural lake cooling effect core index extraction method and device, and the method comprises the steps: obtaining the lake data of a research region, and screening out natural lakes with the area, shape index, compactness, altitude and gradient in a preset range; for each natural lake, acquiring the remote sensing surface temperature LST of the natural lake, outwards constructing a multi-stage annular buffer area by taking the lake boundary as the standard, and counting the LST average value of each annular buffer area; and performing cubic polynomial fitting by taking the distance X between the outer edge of each annular buffer area and the lake boundary as an independent variable and the corresponding LST average value as a dependent variable to obtain a fitting model, and extracting the cooling effect core index, the cooling distance, the cooling intensity, the cooling range, the cooling gradient and the cooling efficiency based on the fitting model. According to the method, standardization and quantification of the large-scale lake cooling effect can be efficiently achieved, scientific data support is provided for urban thermal environment regulation and control, ecological planning and lake protection, and the method has wide application prospects.

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 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.

This invention discloses a method for regulating the thermal environment of urban morphology under the coupling of human and bird's-eye view perspectives. It is based on the radiative transfer equation method and Landsat-8 remote sensing imagery to retrieve land surface temperature; it integrates multimodal data for refined extraction of urban feature information; it constructs a three-dimensional comprehensive representation system of urban morphology under the coupling of bird's-eye and human perspective perspectives; it establishes urban land surface temperature prediction models at different spatial scales, integrating two-dimensional urban morphology, three-dimensional urban morphology from bird's-eye view, and three-dimensional urban morphology from human perspective; and it explores the optimal configuration method for regulating the surface thermal environment of urban morphology at both fine and coarse scales. This invention can provide a scientific basis for the quantitative regulation and spatial planning optimization of the urban thermal environment, achieving refined cooling and thermal environment improvement effects through urban spatial structure optimization and greening configuration adjustment.

The invention discloses a high-temporal-spatial-resolution surface temperature integration method and system based on optimal interpolation, and relates to the field of surface temperature integration, and the method comprises the steps: obtaining a surface temperature image, and carrying out the preprocessing of the image; constructing an auxiliary data set and performing preprocessing; performing spatial interpolation by adopting a thin-plate spline function to obtain a high-temporal-spatial-resolution surface temperature interpolation result; establishing a mapping relation between the low-spatial-temporal-resolution surface temperature and the auxiliary variables, and obtaining a high-spatial-resolution surface temperature spatial downscaling result; performing space-time fusion on the surface temperature by adopting a space-time adaptive reflectivity fusion model to obtain a high-space-time resolution surface temperature space-time fusion result; and carrying out weighted integration on the surface temperature space downscaling result and the space-time fusion result by adopting an optimal interpolation method. According to the method, the problem that an existing method still has certain limitations in surface temperature downscaling and space-time fusion technologies is solved.

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

The present invention belongs to the field of climate and environmental monitoring technology and specifically provides a method and system for the simultaneous simulation and inversion of land surface temperature and land surface emissivity. The method comprises: simulating and generating global thermal infrared channel brightness temperature data based on observational parameters of thermal infrared channels from different geostationary meteorological satellites using the thermal infrared radiation transfer equation; adding random Gaussian noise to the brightness temperature data, using this as the observed brightness temperature, and performing simultaneous inversion of land surface temperature and emissivity. By combining observational data from geostationary meteorological satellites from different countries around the world and leveraging the multispectral and high-frequency observational characteristics of the new generation of geostationary meteorological satellites, a physical algorithm for simultaneous inversion of land surface temperature (LST) and land surface emissivity (LSE) is developed. This allows for simultaneous simulation and inversion of LST / LSE on a global scale, providing a reference for quantitative inversion of LST / LSE at high temporal and spatial resolutions worldwide. Satellite observations confirm that underlying surfaces with large diurnal temperature variations, such as deserts, are more conducive to the separation of land surface temperature and emissivity.

The present application relates to a method, device, computer device, readable storage medium and program product for retrieving land surface temperature, which is applied to the field of remote sensing. The method includes: obtaining a multi-spectral image and a thermal infrared image of a to-be-detected area; the spatial resolutions of the multi-spectral image and the thermal infrared image are consistent; for each pixel point at the same position in the multi-spectral image and the thermal infrared image, based on the calibration coefficients of the respective imaging channels of the multi-spectral image and the thermal infrared image and the pixel values of the pixel point on the multi-spectral image and the thermal infrared image, an irradiance information group corresponding to the pixel point is obtained; based on the irradiance information groups corresponding to each pixel point, a land surface temperature image of the to-be-detected area is retrieved. Using this method can improve the retrieval accuracy of land surface temperature products.

The application discloses a kind of based on multilayer neural network ground surface temperature prediction method and system, wherein the method includes: obtaining remote sensing image and its corresponding atmospheric external force data and ground surface temperature data;Remote sensing image is handled to obtain multiple different scale ground surface feature data, and NDVI data and NDBI data of corresponding scale are added to obtain multiple complete ground surface feature data;Ground surface temperature data is handled to obtain ground surface feature data corresponding ground surface temperature data;Ground surface temperature prediction model is constructed, and complete ground surface feature data, ground surface temperature data and atmospheric external force data are used as training set to carry out model training, to obtain trained ground surface temperature prediction model, to carry out ground surface temperature prediction according to trained ground surface temperature prediction model;Therefore, by multilayer perception neural network, since physical information constraint is applied, mapping relationship between input and output is learned from multiple scales, and good prediction effect can be achieved.

The application discloses a frozen soil inversion method based on full-spectrum data of Gaofen-5 satellites, and the method comprises the following steps: acquiring remote sensing image data sets of full-spectrum of Gaofen-5 satellites at different time nodes, and selecting remote sensing image data of specified spectrum corresponding to each time node from the remote sensing image data sets; calculating the land surface temperature grid data corresponding to each time node according to the preset Planck function and the brightness temperature of the remote sensing image data of the specified spectrum, and obtaining the land surface temperature spatial distribution sequence data according to the land surface temperature grid data; calculating the energy sum of each grid in the remote sensing image data in a specified period in the melting state and the frozen state respectively based on the land surface temperature spatial distribution sequence data and the preset frozen top layer calculation model, and determining the distribution information of the frozen soil based on the energy sum of each grid. The application solves the technical problem that the existing frozen soil inversion scheme cannot meet the actual demand.

The present application relates to the technical field of ecological environment quality evaluation, and particularly relates to a method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indexes, comprising the following steps: S1: collecting remote sensing data such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS, and performing pretreatment; S2: extracting human pressure index, biological abundance index, vegetation cover index, land surface temperature and ground relative humidity from the multi-source remote sensing data set obtained in step S1; S3: using the regression Kriging method to downscale the ground relative humidity data to match the resolution of other data; and S4: after standardizing all indexes, using principal component analysis to construct MCEEI to reflect the comprehensive quality of the ecological environment; the present application can quickly quantify the surface ecological environment quality condition from multi-source remote sensing data by integrating five indexes, and can solve the problem that the traditional remote sensing evaluation method for ecological environment quality cannot effectively balance the regional difference in large-scale research.

The invention discloses a remote sensing moisture index construction method combining surface temperature and short wave infrared information. The method comprises the following steps: step 1, collecting satellite remote sensing data in a research area; step 2, meteorological radiation data collection and spatial interpolation of a research area; 3, performing remote sensing inversion on the surface biophysical parameters; step 4, calculating surface net radiation and soil heat flux; step 5, constructing a remote sensing moisture index combined with the surface temperature and the short wave infrared information; and step 6, performing space-time reconstruction on the remote sensing moisture index. The remote sensing moisture index combining the surface temperature and the short-wave infrared information is constructed, the key defect that a traditional moisture index based on the surface temperature information is greatly influenced by albedo is overcome, and the supersaturation effect of the moisture index based on the short-wave infrared information in a high vegetation coverage area is optimized; satellite thermal infrared and short wave infrared observation information can be fully utilized, and large-range, data-area-free, complex and non-uniform underlying surface moisture index calculation is achieved.

The application discloses a kind of generation near real-time hourly all-weather ground temperature method, belong to ground temperature acquisition technical field.The application will near real-time hourly all-weather ground estimation be divided into time fusion and spatial fusion two parts, realize the space-time fusion of two kinds of ground temperature.For the target moment of remote sensing ground temperature serious missing, in time scale, the more accurate remote sensing ground temperature of certain clear sky reference time before target time is initial quantity, using reanalysis data provides the ground temperature variation between target time and clear sky reference time to estimate the near real-time all-weather ground temperature of target time;Since it needs to meet many conditions to execute time fusion algorithm, part of pixel is difficult to meet, when using machine learning model to map the correlation between time fusion all-weather ground temperature and influence factor in space, and the relationship is used for missing pixel, obtains the near real-time hourly all-weather ground temperature of final spatial continuity.

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.

The invention discloses a remote sensing image land surface temperature downscaling method and system based on a deep learning technology, and the method comprises the steps: calculating each remote sensing index and land surface temperature data based on a Landsat-8 image, carrying out the correlation analysis, selecting three remote sensing indexes which have the highest correlation with the land surface temperature and are not collinear, and carrying out the downscaling of the land surface temperature. Constructing a first deep learning model; calculating various remote sensing indexes based on the Sentinel-2 image, selecting three remote sensing indexes which have the highest correlation with the surface temperature and are not collinear, and constructing a second deep learning model; simulating the characteristics of a Landsat-8 remote sensing index by using the Sentinel-2 remote sensing index, fusing multi-source remote sensing data, and inputting the fused data into a first deep learning model to obtain a downscaled surface temperature image; the method is advanced in algorithm, high in precision, small in model data size, capable of effectively reducing the calculation time, and meanwhile suitable for downscaling of the surface temperature LST data of different input ranges.

The application provides a window method for analyzing land surface temperature, comprising: A1 determining the interval of high spatial resolution vegetation coverage in a window area centered on a target pixel; A2 constructing a low spatial resolution vegetation coverage pixel set belonging to the interval of A1, and establishing a window with a data pair of remote sensing land surface temperature and low spatial resolution vegetation coverage of the pixel set; A3 dividing the window of A2 into several sub-windows according to the horizontal axis, and constructing a normal distribution model of land surface temperature and its variation interval in each sub-window; A4 determining the derivative of land surface temperature with respect to vegetation coverage at the target pixel according to the variation interval of land surface temperature in each sub-window of A3; A5 resolving the remote sensing land surface temperature of the target pixel according to the derivative of A4; and A6 spatially downscaling the remote sensing land surface temperature of the target pixel according to the derivative of A4. The application avoids the error caused by using all pixels to calculate the derivative of land surface temperature with respect to vegetation coverage in the feature space method, and realizes the integration of remote sensing land surface temperature decomposition and enhancement.

The invention provides an hour-by-hour all-weather surface temperature reconstruction method based on a stationary satellite. The method comprises the following steps: S1, collecting surface temperature data and multi-source characteristic variable auxiliary data, and constructing a sample data set; s2, constructing a nuclear drive model dynamically constrained by a temperature intraday circulation model, and synchronously realizing spatial downscaling and ideal surface temperature reconstruction; s3, weather interference and sensor system errors are estimated through regression and prediction of a space-time fusion method driven by a hybrid neural network; and S4, the weather interference and the sensor system error are superposed on the ideal surface temperature, and the all-weather surface temperature is reconstructed. According to the method, nuclear driving and space-time fusion methods are organically combined, and all-weather surface temperature with high space-time resolution is reconstructed based on a stationary satellite FY-4A surface temperature product. The method has good stability and interpretability, and the precision of a reconstruction result is high.

The present application relates to the technical field of remote sensing image intelligent processing, and particularly relates to a multi-scale progressive land surface temperature fusion downscaling method and device, wherein the method comprises: preprocessing land surface temperature images in a multi-scale image library; constructing a multi-scale training data set containing land surface temperature and at least one auxiliary parameter; constructing a land surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training the land surface temperature downscaling network and the domain transformation network based on a progressive double-network joint training strategy; fine-tuning a downscaling model meeting a preset low-to-medium resolution condition; and performing low-to-medium-to-high progressive downscaling on a low-resolution land surface temperature image to be processed, so as to generate a multi-scale progressive land surface temperature meeting a target high-resolution condition. The present application fully considers the downscaling difference problem under different resolutions, realizes the physical property retention of a land surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The application provides a land surface temperature decomposition method based on a ternary space, comprising the following steps: S1, establishing a ternary space with vegetation coverage as an x-axis, land surface temperature as a y-axis and specific radiation as a z-axis; S2, dividing the ternary space into a plurality of subspaces along the y-axis land surface temperature and the z-axis specific radiation, and calculating the change rate of land surface temperature with vegetation coverage in each subspace; S3, dividing the ternary space into a plurality of subspaces along the y-axis land surface temperature, and calculating the change rate of specific radiation with vegetation coverage in each subspace; S4, determining the subspace to which the vegetation temperature belongs when the vegetation is fully covered based on S3, and calculating the change rate of vegetation temperature with vegetation coverage in the subspace; S5, determining the subspace to which the soil temperature belongs when the soil is fully covered based on S3, and calculating the change rate of soil temperature with vegetation coverage in the subspace; and S6, decomposing remote sensing land surface temperature into vegetation temperature and soil temperature based on the results of S2-S5. The proposed ternary space and subspace mode can effectively improve the component temperature decomposition precision.

The present invention proposes a method for interpolating the remote sensing land surface temperature in cloud-covered areas by integrating spatial auxiliary variables and conditional generative adversarial networks. First, the CGAN model is pre-trained on monthly composite land surface temperature data with good spatio-temporal integrity, so that the model can evenly consider different regional characteristics during the training process. Then, transfer training is carried out based on daily-scale data to make it applicable to the interpolation of daily land surface temperature. And during the interpolation process, a spatial progressive interpolation strategy is adopted. By considering the background information of the interpolation area, interpolation is carried out gradually from the cloud cover edge to the cloud cover center to eliminate the edge effect, so as to achieve accurate interpolation of the land surface temperature in cloud-covered areas.