143 results about "Atmospheric correction" patented technology

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention discloses a photovoltaic panel surface pollution degree image evaluation system. The system comprises an image acquisition module, an image processing module, a multi-task evaluation module and a pollution degree determination module. Multi-spectral image data of the surface of a photovoltaic panel are collected, after radiation calibration and atmospheric correction processing, spectral reflectivity features and spatial texture features are extracted and fused, a multi-task deep learning model is input, a pollution type classification result and a power generation efficiency loss weight of each pixel region are output, and a pollution type classification result of each pixel region is obtained. And finally, generating a pollution type spatial distribution diagram, calculating the overall power generation efficiency loss percentage, and determining the pollution degree grade. According to the method, the problem that the mixed pollution type cannot be distinguished and the differentiation influence cannot be evaluated in the prior art is solved, accurate quantitative evaluation of the pollution degree is realized, and a reliable basis is provided for fine operation and maintenance of a photovoltaic power station.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention discloses a deep learning inversion AOD-assisted atmospheric correction-based method and device and a medium, and relates to the field of atmospheric remote sensing and deep learning cross technologies, and the method comprises the steps: constructing a multi-type training sample set fusing actual measurement and physical simulation data, and carrying out mass screening and layering processing to obtain a multi-type training sample set; obtaining a pre-training sample, a fine tuning sample and an extreme scene sample matched with the satellite remote sensing data; establishing a deep learning network model taking data loss and inverse operator constraint loss weighted fusion as a total loss function, and adopting a cross-satellite transfer learning strategy to sequentially complete pre-training, satellite exclusive fine tuning and extreme scene enhancement training to obtain a trained aerosol optical thickness inversion model; radiometric calibration, cloud detection, geometric correction preprocessing and input model reasoning calculation are carried out on satellite remote sensing original data, post-processing and quality grading are carried out on an output result, and an aerosol optical thickness inversion result used for assisting atmospheric correction is obtained.

The invention relates to the technical field of mine slope deformation monitoring, in particular to a mine slope absolute displacement field monitoring and error self-correcting method and system. The method comprises the following steps: according to Kriging synergetics interpolation and adaptive weighted fusion, processing GB-SAR relative displacement, a projection coefficient matrix and a motion mode partition after atmospheric correction to obtain an absolute displacement field calibrated by a GNSS (Global Navigation Satellite System); processing the displacement period characteristic parameters and the area displacement gradient field by utilizing multi-characteristic fuzzy comprehensive evaluation to obtain a slope local stability comprehensive index field; and high-precision absolute displacement field monitoring and error self-correction are completed by combining a GNSS early warning level, an absolute displacement field calibrated by the GNSS and a local stability comprehensive index and utilizing graded early warning and displacement field traceability analysis. According to the invention, high-precision planar absolute displacement field monitoring, accurate displacement abnormal cause identification and error self-correction can be realized.

The invention discloses an active and passive fusion water depth inversion method based on adaptive spectrum weighting, and the method comprises the steps: carrying out the atmospheric correction of a multispectral satellite image, extracting a target region, and removing a land region and a deepwater region; the method comprises the following steps: acquiring a seawater point cloud of a satellite-borne photon counting laser radar in a target area, extracting sea surface and seabed information, and performing refractive index correction and tide correction to obtain a reference water depth; matching each pixel in the multispectral satellite image with a reference water depth, and dividing the multispectral satellite image into a reference pixel and a to-be-measured pixel; according to the spectral value of the multispectral satellite image, the Euclidean distance between each to-be-measured pixel and all reference pixels is calculated, and the reciprocal of the distance is used as the weight of each reference pixel; and introducing a weight-to-weighted least square method, and fitting a relationship from the spectral value to the water depth for each pixel to be measured to realize water depth inversion of the pixels to be measured. The method can effectively improve the active and passive fusion water depth inversion precision when the water depth distribution is uneven and the shallow sea area environment is complex.

This disclosure relates to machine learning models for performing atmospheric correction on an input image. To train a machine learning model to perform atmospheric correction on an input image comprising atmospheric distortion, a processor applies a first trained machine learning model to a training image to determine a first output image, the first trained machine learning model being configured to perform atmospheric correction on an input image comprising atmospheric distortion. The processor applies a second machine learning model to the training image to determine a second output image, wherein the second machine learning model has a smaller model architecture than the first trained machine learning model. The processor trains the second machine learning model by minimising a loss based on the first output image and the second output image to determine a second trained machine learning model.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention provides a water color remote sensing parameter inversion-oriented hyperspectral data quality evaluation method, which relates to the technical field of optical remote sensing and comprises the following steps of: dividing a plurality of hydrological response unit sub-graphs from a hyperspectral remote sensing image of a target area by taking a preset multi-scale biological optical fingerprint wave band as a reference; performing water color noise transmission analysis on the plurality of hydrological response unit sub-graphs, and outputting a plurality of groups of water color inversion signal-to-noise ratio fingerprints; performing atmospheric correction distortion field diagnosis on the hyperspectral remote sensing image, and outputting a near-infrared negative distortion field and a spectral fidelity error field; dynamically fusing to generate a real-time quality entropy field; and confidence domain convergence mapping of the real-time quality entropy field is carried out, and a water color parameter inversion uncertainty thermodynamic diagram is output. The technical problems that in the prior art, a targeted diagnosis mechanism for distortion in the atmospheric correction process is lacked, systematic interference of atmospheric correction on water color parameter inversion precision cannot be captured, and consequently a final water quality monitoring result is unreliable and errors are large are solved.

The invention discloses a remote sensing data-based cultivated land resource dynamic monitoring and checking method and system, and the method comprises the steps: introducing atmospheric scattering parameter estimation in an atmospheric correction process, carrying out the refined estimation of the atmospheric path radiation through combining with the water vapor content and aerosol concentration, and optimizing the atmospheric correction parameters, thereby achieving the dynamic monitoring and checking of cultivated land resources. And the interference of the atmospheric effect on the surface reflectance is effectively weakened. Spectral response function matching and spectral feature standardization processing are implemented, and a spectral difference correction model is constructed, so that the spectral features of the multi-temporal images tend to be consistent. The spectral deviation caused by atmospheric condition fluctuation is remarkably reduced, and the comparability of spectral response between images is improved, so that the accuracy and the stability of cultivated land change detection are improved. And finally, change feature extraction and region identification are performed on the basis of spectrum consistency enhancement, so that the actual change condition of cultivated land resources can be reflected more truly, misjudgment caused by atmospheric noise is reduced, and the reliability of a dynamic monitoring result is enhanced.

The invention relates to the technical field of ocean remote sensing, in particular to an offshore water quality remote sensing inversion and classification method based on small sample deep learning, which comprises the following steps: acquiring offshore in-situ observation, satellite remote sensing and ocean reanalysis data; carrying out atmospheric correction, carrying out space-time reconstruction on the missing remote sensing reflectivity by adopting a data interpolation empirical orthogonal function, carrying out mathematical transformation on input features, and screening out an optimal feature subset by adopting a strategy based on an average absolute percentage error; constructing a deep learning network based on a generative small sample to invert the offshore soluble inorganic nitrogen and inorganic phosphorus concentration, and determining the water quality grade; and analyzing an inversion mechanism by using an SHAP method. According to the method, the problem of data space-time discontinuity is solved through the data interpolation empirical orthogonal function, the capture capability and inversion precision of the nonlinear relation under the small sample condition are remarkably improved by utilizing the generative network, the model interpretability is realized in combination with SHAP analysis, and scientific support is provided for offshore water quality fine management.

The invention provides a carbon monoxide plume identification and quantification method based on a domestic hyperspectral satellite, and the method comprises the steps: carrying out the radiometric calibration and atmospheric correction of the data of a visible short-wave infrared hyperspectral camera AHS I carried by the domestic hyperspectral observation satellite (GF-5B, Gaofen-5 B satellite); generating a carbon monoxide unit absorption spectrum by using a medium-resolution atmospheric transmission model MODTRAN; inverting a carbon monoxide enhancement value by adopting a matched filtering method; generating a binary plume mask through threshold segmentation; and constructing a deep learning model based on a residual neural network ResNet network, and realizing plume pixel-level identification and carbon monoxide column concentration enhancement value prediction. The method can identify the carbon monoxide plume with high precision and high efficiency, and is suitable for atmospheric pollution monitoring and pollution source quantification.

The embodiment of the invention provides a remote sensing image atmospheric correction method and system for an ocean near-shore scene, and relates to the technical field of ocean remote sensing image atmospheric correction. According to the method, after remote sensing data is obtained, solar flare correction is executed by extracting a target mask and using a secondary simulation model of a satellite signal in a solar spectrum. And then a multi-band darkness index is calculated, and an optimal aerosol optical thickness value is inverted through a radiation transmission model. And calculating the atmospheric correction coefficient of each wave band according to the optimal aerosol optical thickness value so as to generate correction result data. According to the method, on the basis of robust flare correction of the SWIR proxy, scene-adaptive flare proxy is constructed by utilizing water body reflection and path radiation physical characteristics of the SWIR under a deepwater condition, and flare deduction is realized. Inversion is carried out through multiband combined darkness indexes, and the spectral characteristics of the deep water body reflected in the visible spectrum are utilized, so that single-band random noise is smoothed, and the accuracy of an AOT estimation result is improved.

The invention relates to an underwater topographic feature quantitative monitoring method based on satellite-borne active and passive fusion sounding. The method comprises the following steps of performing preprocessing such as atmospheric correction, flare correction and water-land separation on a passive optical remote sensing image data set; the method comprises the following steps: extracting an active laser point sounding value from a satellite-borne single-photon laser radar point cloud data set, and carrying out water depth correction including refraction correction, tide correction and the like on the sounding value; geospatial matching is carried out on the preprocessed remote sensing image and the depth sounding sample points, and a spectrum-water depth data set is constructed; performing machine learning and model prediction on the active and passive fusion sounding sample set by using an MLP model to obtain a three-dimensional DBM of the research area; and 12 types of underwater topographic feature factors derived from the DBM are calculated by combining a geographic information system technology and a landform operator, so that an underwater topographic feature quantitative monitoring data set is constructed. According to the invention, through fusion of active and passive optical remote sensing sounding data, large-area, low-cost and high-precision quantitative monitoring of underwater topographic features is realized.

The invention relates to a global land comprehensive improvement monitoring method based on artificial intelligence and remote sensing images, and belongs to the technical field of environment management. According to the method, multi-band high-resolution remote sensing images of a target area in two time phases before and after improvement are obtained for radiation and atmospheric correction to ensure data quality, and land coverage classification maps of the two time phases are generated respectively by using a pre-trained deep convolutional neural network land coverage classification model. Through pixel-level change detection and confidence analysis, a reliable land coverage change area is identified, vectorization and spatial filtering are carried out, multi-dimensional change features are extracted, ecological influence and a spatial distribution mode of the multi-dimensional change features are comprehensively evaluated, and a comprehensive monitoring result including a spatial distribution diagram, a statistical report and trend prediction is generated. And the information is integrated to an interactive geographic information system platform for visual display and early warning. According to the invention, automatic, precise and systematic monitoring and evaluation of the global land comprehensive remediation process are realized.

This invention belongs to the field of hyperspectral remote sensing image processing technology, specifically relating to a noise removal method suitable for satellite hyperspectral data, comprising: Step 1, acquiring domestic hyperspectral satellite image data, performing atmospheric correction on the images, and obtaining ground reflectance image data; Step 2, resampling the image data to extract band images within the 1956-2462nm band range of the Gaofen-5 satellite or the Ziyuan-02D satellite; Step 3, resampling the image data to obtain pixel values ​​of three adjacent bands N1, N2, and N3 within the 1956-2462nm band range of the Gaofen-5 satellite or the Ziyuan-02D satellite; Step 4, obtaining image pixel values ​​H1 and H2 based on the pixel values ​​of bands N1, N2, and N3; Step 5, obtaining image pixel value H based on image pixel value H1 or H2. This invention overcomes the shortcomings of existing techniques, reduces the impact of noise, and improves the accuracy and efficiency of data extraction.

The invention discloses a duckweed type eutrophication water body monitoring method based on sentinel No.2 satellite images, and relates to the technical field of water body monitoring, and the method comprises the following steps: carrying out atmospheric correction processing on sentinel No.2 L1 C-level satellite images in a non-duckweed growth season and a duckweed growth season, and then re-sampling all wave bands to the same resolution; performing mean filtering on a blue wave band, a green wave band, a red wave band, a near-infrared wave band and a narrow near-infrared wave band of the multi-wave-band surface reflectance image after re-sampling in the non-duckweed growth season and the duckweed growth season, and then performing wave band synthesis to obtain a non-duckweed growth season wave band synthesis image and a duckweed growth season wave band synthesis image; and calculating the proportion of the duckweed growth range in the water body range by using the non-duckweed growth season waveband synthetic image and the duckweed growth season waveband synthetic image. The duckweed type eutrophication water body can be rapidly monitored in a large range at low cost.

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

The invention discloses a small-scale deformation InSAR time sequence inversion method and system based on multiple stable points, and the method comprises the following steps: S1, carrying out the small baseline interference processing of multi-temporal synthetic aperture radar data, and screening high-coherence pixel points, S2, recognizing a stable point candidate set with dense spatial distribution; s3, constructing an initial atmospheric delay phase model covering a full interferogram range; s4, obtaining an interferometric phase after atmospheric correction; and S5, inverting the deformation rate and the deformation time sequence parameters of the InSAR again. According to the method, dense stable points are screened based on a deformation rate threshold value, and a space atmosphere model is constructed, so that the problem that atmosphere delay and nonlinear deformation are difficult to separate in InSAR time sequence analysis is effectively solved, and high-signal-to-noise-ratio and high-precision time sequence monitoring of small-scale weak deformation of the earth surface is realized.

The application discloses a kind of based on sentinel-2 satellite image's duckweed type eutrophication water monitoring method, it is related to water monitoring technical field, including the following steps: after the atmospheric correction processing of sentinel-2 L1 C level satellite image of non-duckweed growing season and duckweed growing season, all wavebands are resampled to the same resolution, after mean filtering to the blue band, green band, red band, near infrared band, narrow near infrared band of multi-band surface reflectance image after resampling of non-duckweed growing season and duckweed growing season, then band synthesis is carried out, obtain non-duckweed growing season band synthesis image and duckweed growing season band synthesis image;The proportion of duckweed growing range in water range is calculated using non-duckweed growing season band synthesis image and duckweed growing season band synthesis image.The present application can realize the fast, large-scale, low-cost duckweed type eutrophication water monitoring.

The invention belongs to the technical field of remote sensing, and relates to an atmospheric calibration instrument data processing method using active and passive cooperative remote sensing, which comprises the following steps: S1, carrying a synchronous atmospheric calibration instrument integrating a multispectral polarization camera and a multi-beam laser radar on the same satellite with a high-resolution imaging load to realize complete coverage of an observation area and time-space synchronous observation, acquiring multispectral polarization data and laser radar data; s2, on the basis of laser radar data, atmospheric vertical profile parameters are inverted through a parameter inversion method; s3, based on the multispectral polarization data, combining with a ground surface polarization reflectivity model to invert aerosol related parameters; s4, performing space-time matching and uniformity judgment on the laser radar data and the multispectral polarization data, and screening effective synchronous observation points; s5, collaborative inversion and iterative optimization are carried out based on the active and passive data of the synchronous observation points, and geogas three-dimensional space-time distribution parameters are obtained; and S6, performing atmospheric correction on the remote sensing data of the high-resolution imaging load.

This invention discloses a remote sensing classification method and device for periglacial landforms. The method includes: establishing remote sensing classification rules for periglacial landforms, dividing periglacial landforms into primary and secondary periglacial landform types; collecting remote sensing images and digital elevation model data of the target area in permafrost regions at various resolutions, and performing radiometric calibration and atmospheric correction on the remote sensing images; performing first-scale segmentation and second-scale segmentation on the processed data to generate two-level image object layers, with the first-scale segmentation generating a primary image object layer corresponding to the primary periglacial landform type and the second-scale segmentation generating a secondary image object layer corresponding to the secondary periglacial landform type; extracting and analyzing features for each image object in the primary and secondary image object layers; and, based on the remote sensing classification rules for periglacial landforms, classifying the image objects in the primary and secondary image object layers into the corresponding primary and secondary periglacial landform types, respectively, and outputting a remote sensing classification map of periglacial landforms.

This invention discloses a fully automated atmospheric and topographic radiometric correction method and device for high-resolution remote sensing imagery, belonging to the field of remote sensing science and technology. The method includes: constructing a fully automated batch processing framework; developing an atmospheric correction module based on the 6S radiative transfer model to automatically retrieve atmospheric parameters and eliminate their influence; and developing a topographic radiometric correction module based on the digital elevation model (DEM) and VECA model to compensate for radiometric distortion caused by complex terrain. The key innovation lies in constructing a joint correction framework and integrating a geomorphic feature recognition algorithm, which can automatically identify and select to execute either single atmospheric correction or a combined atmospheric-topographic correction process based on the degree of topographic relief in the image. This invention achieves fully automated batch processing from data input to result output, requiring no manual intervention, significantly improving the processing efficiency, intelligence level, and adaptability to different terrain scenarios in high-resolution image radiometric correction.

This invention provides a method and apparatus for modeling and analyzing the detection performance of an airborne infrared system with cloud interference. The method includes: dividing the cloud landscape into three different cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and detector, and constructing an input parameter set; determining the equivalent target brightness and equivalent background brightness based on the input parameter set, and constructing a unified signal-to-noise ratio (SNR) model by combining the target radiance, background radiance, and atmospheric correction factor under clear-sky conditions, and solving the model; determining the SNR change rate and the detection distance change rate based on the solution results to construct a cloud interference model; comparing the SNR change rate and the detection distance change rate with measured data from field tests, and correcting the input parameter set in reverse based on the comparison error until the comparison error meets a preset threshold, thus completing the calibration of the cloud interference model. This improves the diversity, applicability, and practicality of the model, makes the analysis results traceable, and supports the evaluation of field test environments.

The application discloses a GNSS enhanced positioning method based on self-defined RTCM electric text and belongs to the field of GNSS high-precision navigation and positioning. The method comprises the following steps: a data processing center generates and broadcasts a standard RTCM electric text used by a conventional network RTK at the same time, and generates a self-defined RTCM electric text containing various GNSS enhanced information, wherein the enhanced information comprises high-precision regional atmospheric correction information, ionospheric flicker flag information, regional atmospheric model configuration and quality indexes. After a user positioning terminal receives the self-defined RTCM electric text, the quality indexes are used to evaluate the advantages and disadvantages of a current observation environment, and a positioning second-order polynomial model is adaptively optimized, so that the positioning precision and reliability are effectively improved, especially during an ionosphere active period. The application solves the technical problems that traditional enhanced service information is single and cannot transmit refined quality control information to a terminal, and provides a strong guarantee for the stable application of high-precision GNSS service under various conditions.

This invention relates to the field of atmospheric correction technology, and in particular provides a method for atmospheric correction of marine remote sensing images based on multi-scale Transformer. The method includes acquiring multi-source remote sensing observation data and auxiliary parameters, performing joint preprocessing, and constructing a standardized training dataset; constructing a multi-scale Transformer atmospheric correction model containing parallel convolutional branches; constructing a composite loss function containing physical soft constraints, and training the atmospheric correction model using the training dataset to obtain the trained model; and using the trained model to perform high-throughput end-to-end atmospheric correction inference. In complex large-scale marine environments, this method achieves high-precision, robust, and physically consistent atmospheric correction.

This invention provides a method, device, terminal, and storage medium for soil Munsell color discrimination based on high-resolution multispectral remote sensing. The invention acquires multispectral remote sensing images and metadata, performs radiometric calibration and atmospheric correction on the images to obtain surface reflectance; generates a quality mask including clouds, cloud shadows, shading, water bodies, and anomalous pixels, and removes unusable pixels; estimates soil purity by combining a bare soil candidate mask and gates mixed pixels; performs geometric and BRDF normalization on bare soil candidate pixels, and constructs soil state indicators from SWIR bands; establishes a mapping from reflectance to CIE tristimulus values ​​based on the sensor spectral response and converts it to CIELAB; obtains the equivalent color of dry soil by segmental compensation according to the soil state indicators; performs color difference matching with the Munsell color library and calculates the confidence level; outputs an undecidable condition and cause code when the confidence level is low, achieving stable discrimination and reliable output under complex observation conditions.

The invention relates to a remote sensing estimation method and system for grassland above-ground biomass and fixed carbon content thereof. The method comprises the following steps: acquiring a hyperspectral remote sensing image, and carrying out atmospheric correction to obtain a surface reflectance image; identifying grassland functional end members to obtain a localized grassland end member spectrum library; performing linear spectrum mixed decomposition in combination with the surface reflectance image and the localized grassland end member spectrum library to obtain a grassland plant functional abundance set; calculating a pixel-level dynamic specific carbon content in combination with time sequence multispectral image data to obtain a dynamic specific carbon content field; performing regression prediction calculation by combining the surface reflectance image, the radar data and the grassland plant functional abundance set to obtain a grassland above-ground biomass distribution map; and carrying out pixel-by-pixel multiplication calculation to obtain spatial distribution data of the overground carbon sequestration amount. By adopting the method, the problem of estimation deviation caused by using a fixed carbon content coefficient in the prior art can be solved, and the estimation precision is improved.