最新刊期
卷 21 ， 期 1 ， 2017
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摘要：Bidirectional Reflectance Distribution Function (BRDF) contains directional information on surface reflectance and provides important orientation information in calculating the surface time-space variable elements. The analysis of the sensitivity of BRDF shapes to vegetation structure parameters contributes to understanding the laws of vegetation bidirectional reflectance and increasing the accuracy in inversing vegetation parameters. Numerous scholars have investigated different BRDF models to determine the relationship between vegetation structure parameters and bidirectional reflectance distribution effects of the vegetation surface. Among these models, the two-layer canopy reflectance model (ACRM model) and kernel-driven model are generally accepted as physical and semiempirical models, respectively.This study explores the sensitivity of BRDF shapes to vegetation structure parameters by coupling the ACRM model and kernel-driven Ross-Thick/Li-Sparse-Reciprocal model (RTLSR model). The adopted sensitivity analysis method is the extended Fourier amplitude sensitivity test, one of the global sensitivity analysis methods. The Anisotropic Flat indeX (AFX) is employed as the metric for BRDF shapes to explore the sensitivity change in AFX to vegetation parameters under the condition of different SKY Light ratios (SKYL) and sensitivity with SKYL=0.1. From these variables, this study explores the sensitivity parameters with SKYL=0.1. Then, the relationship between the main sensitivity parameters and AFX is analyzed.Overall, AFX can indicate the change in BRDF shapes. The kernel-driven RTLSR model can fit the data produced by the ACRM model well. Results of the analysis of sensitivity indicate that the change in sky light results in different degrees of sensitivity. Given the influence of sky light, parameter sensitivity at the canopy scale is larger than that at the leaf scale. Under sunny conditions (SKYL=0.1), the total order sensitivity index of the upper Leaf Area Index (LAI) is strongest in the red band, and lower LAI is observed in the near-infrared band. The first-order sensitivity indices of the upper LAI, upper leaf angle distribution, and lower leaf structure parameters are stronger in the red band. Meanwhile, the first-order sensitivity indices of the upper and lower LAI are stronger in the near-infrared band.AFX plays an important role in increasing inversion accuracy. The index is significant in understanding the relationship between AFX and the surface-covering physical parameters for the inversion of the parameters. This study analyzed the sensitivity of AFX to vegetation structure parameters. The results will help researchers understand the effects of changes in vegetation parameters on BRDF shapes.关键词：ACRM model;kernel-driven model;AFX;vegetation structure parameter;sensitivity analysis- 394
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发布时间：2021-06-07 -
摘要：A spectrum is the distribution of electromagnetic radiation that has been reflected, emitted, or transmitted from ground objects because of their inherent physical characteristics. Remote sensing measurements provide a solid theoretical basis for applications such as target detection and identification fields considering that different materials possess distinct abilities for reflection, emission, and scattering mechanisms over different wavelength ranges. To support such studies, a spectral library is necessary for quantitative remote sensing modeling, land surface parameter inversion, and ecological environment monitoring. For nearly half a century, domestic and international research institutes have designed various spectral libraries by assembling spectral data of typical materials. These spectral libraries include a long-term, reliable, accumulative observation for a certain period and indispensable standard ancillary data. Such spectral data are essential resources for ground cover classification and target identification.This study reviews the history of the development of spectral libraries by analyzing and comparing each spectral library with those of different wavelength ranges, types, ancillary data, and resources. The applications of two kinds of spectral libraries for different disciplines have been summarized. In this study, the general spectral library refers to a database with more comprehensive and abundant ground objects, including the United States Geological Survey Spectral Library, Advanced Spaceborne Thermal Emission and Reflection Radiometer Spectral Library, and Spectral Database System of Typical Objects in China. Meanwhile, the professional spectral libraries that were established focus mainly on the specific disciplines.Comparison results reveal that spectrum data are relatively insufficient in the microwave band because previous research focused more on visible and near-infrared bands during the last century, especially on geological and ecological applications. This observation can also account for the over whelming quantity of minerals, rocks, and crops spectral data in existing data bases. However, most of the available spectra data that exist still exclude canopy or urban grassland spectra, although Nonetheless, vegetation spectra data have been considerable. Based on the research demand investigation for spectral library, the study should mainly focus on data management, quality control standard in measurements, and data utilization efficiency.The establishment and full sharing of a comprehensive spectral library are crucial steps for domestic research. The waveband and the spatial and temporal gradients of the ground objects present the issues for accurate classification and identification applications. Thus, the full-wave band, spatial scales, or phenological information are significant in the completion of future spectral libraries.关键词：spectra;database;visible-NIR;thermal infrared;microwave- 812
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发布时间：2021-06-07 -
Retrieval of clear sky temperature and humidity profiles over land using measurements of FY-3C/MWHTS
摘要：Land surface emissivity has a significant effect on the atmospheric temperature and humidity sounding from space. Given the complicated land surface, the calculated emissivity is generally with low accuracy. A one-dimensional variational retrieval system was built using measurements of microwave humidity and temperature sounder onboard the Fengyun-3C satellite (FY-3C/MWHTS)to improve retrieval accuracy and to reduce the computation complexity in retrieving atmospheric temperature and humidity profiles over land under clear sky.By analyzing the a priori information affecting the accuracy of inversion, a hybrid retrieval approach based on united and individual matrices of background covariance is proposed. The method established a better correlation relationship between temperature and humidity profiles, reduced the error propagation of the retrieval temperature and humidity, and prevented the complicated land emissivity calculations according to different surface types. From the correlation between the observed values from FY-3C/MWHTS and those simulated by the forward radiative transfer model, a statistical regression method was also adapted in pixel-by-pixel correction procedure to correct the bias between observed and simulated values.This retrieval system obtains temperature and humidity profiles over a part of China’s land under clear sky and validates the retrieval results with respect to the European Centre for Medium-Range Weather Forecasts(ECMWF) reanalysis data, National Centers for Environmental Prediction(NCEP) analysis data, and Radiosonde Observation(RAOB) data. With respect to the ECMWF reanalyzed data, the maximum root mean square errors of the resulting temperature and relative humidity are 2.59 K and 11.87%, respectively. With respect to the NCEP analyzed data, the maximum root mean square errors of the resulting temperature and relative humidity are 1.88 K and 21.50%, respectively. With respect to the RAOB data, the maximum root mean square errors of the resulting temperature and relative humidity are 3.43 K and 25.48%, respectively. The comparison of the retrieval results with those measured by AMSU using the physical and statistical retrieval methods shows that the MWHTS has higher accuracy. The comparison of the retrieval results with the NCEP 6 h forecast profiles shows that the retrieval humidity profiles can improve the accuracy of the forecast profiles, particularly in the upper atmosphere.The proposed hybrid approach using the united and individual matrices of background covariance can provide satisfactory retrieval accuracy, although land emissivity is calculated without classifying the surface types in the retrieval system built in this study. The retrieval system, whose retrieval results are evaluated by root mean square errors with respect to the three data sources, has high accuracy and high reliability. The comparison of the retrieval results with the measurements of AMSU indicates that MWHTS has a greater ability to probe the temperature in the upper atmosphere and humidity in the entire atmosphere. The comparison of the retrieval results with the NCEP 6 h forecast profiles indicates that MWHTS can obtain high-quality data for sounding atmosphere and is of significance to numerical weather prediction radiance assimilation.关键词：Microwave Humidity and Temperature Sounder (MWHTS);one-dimensional variational retrieval;temperature and humidity profiles;pixel by pixel correction;forecast profiles- 325
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发布时间：2021-06-07 -
摘要：Land Surface Temperature (LST) derived from geostationary satellites (GEO-LST) is one of the key parameters in analyzing diurnal climate and environment changes. Compared with polar-orbiting satellite data, the LST of geostationary satellites has become increasingly attractive because of its high temporal resolution. GEO-LST has been widely applied in meteorological, climatological, and hydrological studies. However, the existing GEO-LST products can suffer from cloud cover, cloud contamination, and atmospheric disturbance, resulting in missing data. The objective of this study is to develop a new method for reconstructing Feng Yun geostationary satellite (FY-2F) LSTs based on Diurnal Temperature Cycle (DTC) and robust regression.The hourly LSTs from FY-2F with 5 km spatial resolution are adopted for this study, and different types of missing pixels are simulated and reconstructed based on a new model. This model is composed of polynomial, Fourier, and Gaussian functions (PFG), and a robust regression called Levenberg–Marquardt (LM) algorithm is adopted for solving and optimizing the modelparameters. A performance test is conducted by comparing the PFG model based on the LM algorithm with the other two methods implemented on FY-2F data, namely, a method based on Least Square (LS) algorithm modeling of the previous model (van den Bergh. F VAN2006) and a method based on LS algorithm modeling of the PFG. Moreover, the new method is also tested for real LST products (with missing data).By comparing the reconstructed results of different methods (LM-PFG, LS-PFG, and LS-VAN2006) with the real LST values, the simulated experiment indicates that the LM-PFG method obtains the lowest root mean square compared with the other two methods. The stability analysis of these methods shows that LM-PFG is the least sensitive to missing samples, which validates that LM-PFG is more stable than LS-PFG and LS-VAN2006. Simultaneously, experimental results show that LM-PFG can also reconstruct the missing values with high accuracy and better than the other methods.In this study, an approach for reconstructing GEO-LSTs based on DTC and robust regression is proposed. The method is evaluated using simulated and real FY-2F LST products and can obtain high scores on quantitative evaluation and visual connectivity. Further work can be conducted to expand this method to other potential GEO-LSTs, such as Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager LSTs or Geostationary Operational Environmental Satellite Imager LSTs. Similar to previous studies, the reconstructed LST values of the proposed method are estimated under cloud-free conditions. LSTs under cloudy conditions should be investigated in future studies.关键词：land surface temperature;geostationary satellite;diurnal temperature cycle;robust regression;FY-2F- 325
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发布时间：2021-06-07 -
摘要：This study adopts the Infrared Atmospheric Sounder of Feng-Yun and the 3rd(B) Weather Satellite(FY-3B/IRAS) brightness temperature to investigate the generalized variational assimilation, which combines the advantages of classical variational assimilation and robust M-estimators. Classical variational assimilation is based on the model variables and satellite observations of the brightness temperature to yield a quadratic functional minimization. The observational errors are needed to follow a Gaussian distribution and subsequently apply the least-square principle. The least-squares method is sensitive to outliers; if the analyzed data contain gross errors, the parameter estimation will be inaccurate. The classical variational assimilation consists of two stages. First, an appropriate algorithm is used to identify and address outliers in the data and then, the assimilation. This approach may result in the loss of useful data because the outliers are not always harmful; some outliers may represent new information, such as weather phenomena. At present, the quality control is generally based on a certain threshold value if the subjective uncertainty is too strong. If outliers persist after the quality control, the optimal parametric results that are obtained through classical variational assimilation become meaningless.M-estimators are added to the framework of classical variational assimilation to obtain a generalized variational assimilation, which is coupled with quality control in the process of assimilation. The main idea is to use the weight factor of M-estimators to re-estimate the contribution rate of the observation items to the objective function in each process of objective function minimization based on classical variational assimilation. The cost function consists of M-estimators to guarantee the robustness to outliers. Thus, the assimilation results are improved. Humidity is an important dynamic variable in the NWP model. It does not only determine the occurrence of precipitation but also changes the temperature through evaporation and condensation, and it also influences the wind field by changing the pressure gradient. In addition, the nonlinearity of humidity is stronger than temperature, which causes the humidity to follow a stronger non-Gaussian distribution. Thus, humidity was used as an assimilation experiment effect validation, and the correlation coefficient of humidity was compared with FNL and GDAS, which are assimilated by different M-estimator weights. The specific operation process that is based on the FNL as the background field adopts classical and different weight factors of M-estimators to the variational assimilation of FY-3B/IRAS. In addition, the correlation between the analyzed field, and the GDAS is compared. The correlation between 13 GPS/PWV stations of Anhui province and the integral humidity profile of the relevant field from both GDAS/PWV and FNL/PWV is evaluated. Furthermore, based on the information entropy of freedom degrees, the contribution of IRAS 20 channels were determined to analyze the field for nearly a month. Preliminary results demonstrate the potential application value of the generalized variational assimilation.关键词：generalized variational assimilation;M-estimators;NCEP/FNL;precipitable water vapor;freedom degrees of information entropy- 248
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发布时间：2021-06-07 -
摘要：The techniques of satellite remote sensing has been providing massive remote sensing data, however there’s no integrated storage structure specially designed for the characters of remote sensing’s multi-dimensions. In this context, a method of organizing multi-dimensional remote sensing data is proposed, and an integrated storage structure of SPAtial-Temporal-Spectral (SPATS) multi-dimensional remote sensing images is designed with five multi-dimensional storage format defined: Temporal Sequential in Band (TSB)、Temporal Sequential in Pixel (TSP)、Temporal Interleaved by Band (TIB)、Temporal Interleaved by Pixel (TIP) and Temporal Interleaved by Spectrum (TIS). In addition, Based on this structure, a multi-dimensional data analysis module, namely MDA, is designed, which could implement the SPATS integrated storage of long time-series remote sensing imagery, perform data analysis and display, and build temporal image cubes of a variety of spectral indices, providing a solution of organizing data for the synthesis and characterization of SPATS multi-dimensional remote sending images.关键词：remote sensing;integrated storage;multi-dimensional data structure;SPATS;MDA- 276
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发布时间：2021-06-07 -
摘要：Finding the ideal fractal of the objective world based on pure mathematics is difficult, but the statistical significance of random fractal is an objective existence such that fractal and fractal dimension has some uncertainty. The length-radius dimension model is a fractal dimension method used to describe the complex and uncertain phenomenon of urban traffic network. However, the uncertainty of the model is frequently neglected, and related research are rarely reported. Therefore, theories and methods of uncertainty for fractal dimension should be developed and improved urgently. Aiming at the uncertainty existing in the measuring process of length-radius dimension model, we first systematically conduct research on the analysis, quantitative estimation, and quality control of fractal dimension for the urban traffic network.The uncertainty estimation and analysis of this model are conducted from the aspects of data source, vector processing, measuring center, scale selection, and fractal dimension estimation. In particular, the uncertainty measurement interval of fractal dimension (i.e., the corresponding regression coefficient) under a certain confidence level is first provided quantitatively. Then, based on the theory of error propagation, we describe the propagation and accumulation of errors. Meanwhile, a method of quality control is proposed using least median of squares (LMedS) to remove the gross error (i.e., outliers) and to determine the scale less range simultaneously.In this study, the experimental data were selected from the traffic network distribution map of Lhasa City in 2011, which is the map of the Lhasa road network with a scale of 1∶370000 published by the China Cartographic Publishing House. The main road includes national, provincial, county, and township roads. The Lhasa City traffic network distribution map was acquired by registration and vectorization using ArcGIS. Experimental results show that vectorization of the traffic network and selection of the measuring center and scale cause uncertainty in the fractal dimension. The road is vectorized under different scale environments, e.g., 1∶1000, 1∶10000,$cdots$ 关键词：traffic network;length-radius dimension model;uncertainty;LMedS;quality control;error propagation- 264
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发布时间：2021-06-07 -
摘要：Considering the existence of both detailed information and interfering objects, research on recognition and extraction of roads from high-resolution remote sensing images remains in the exploration phase. The assistance of vector data during road extraction solves the difficulty of obtaining initial information. Thus, reliable training data can be selected. Therefore, a road extraction method that is supported by vector data is proposed, which effectively provides information that is included in the vector data and extract roads automatically from high-resolution remote sensing images. After the use of the mean shift filter to preprocess images, candidate seed points are acquired from vector data, among which false candidate points are later excluded via shape features of homogeneous areas. Then, negative sample points are selected for the conduct of Bayesian classification. After which, Neighborhood Centroid Voting is adopted to extract road centerlines from classified images. Finally, road centerlines are transformed into vector data by using a method that combines pixel tracking and direction estimation, and a segment linking and burr removing method is proposed based on geometric analysis of vectors. Experiments are conducted on a collection of high-resolution images in which roads have diverse types and distribution features. For an ideal case with clear and continuous roads, the extraction quality is up to 97.35%. For images with occlusions and interferences, the extraction quality stays above 80%, thereby indicating the effectiveness and robustness of the proposed method. A road extraction method that is supported by vector data has been proposed in this study. This method uses the information concluded based on vector data to guide the road extraction process, thus free of manual operation. Experimental results suggest that this method can automatically extract roads from high-resolution remote sensing images with accuracy and robustness, thereby being able to adapt to roads with diverse radiation and distribution features.关键词：road extraction;vector data;naïve Bayesian classification;Neighborhood Centroid Voting;vectorization;segment linking- 359
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发布时间：2021-06-07 -
摘要：Satellite laser altimeter has a unique advantage in obtaining the global elevation control points. The Geoscience Laser Altimetry System (GLAS) loaded on the Ice, Clouds, and Land Elevation Satellite(ICESat) has collected numerous high-accuracy terrain elevation points from 2003 to 2009, which can be selected as elevation control points. In this study, an algorithm is proposed to select ICESat/GLAS data as elevation control points based on multi-criteria constraint. The Shuttle Radar Topography Missions-Digital Elevation Model (DEM) data is proposed to initially eliminate the gross error of GLAS points. Then, the parameters regarding laser ranging in the GLA14 product, such as cloud, attitude quality mark, and energy saturation parameters, are introduced to implement coarse selection and to obtain highly reliable GLAS points, which are less influenced by cloud, atmosphere, or reflectivity of the ground. Finally, the waveform characteristic is presented to extract high-accuracy laser points as elevation control points. The experiment is implemented in Tianjin and Hebei, and the algorithm is validated by the DEM reference data. From waveform analysis, the relationship between elevation accuracy of GLAS points and terrain relief can be described in detail. In addition, the selected result in Tianjin experimental region is better than 0.725 m; another result is better than 3.288 m. Moreover, if the criterion is strict, then the elevation accuracy may be improved. Preliminary test results show that the elevation accuracy of laser points is favorable after selection by the multi-criteria constraint method, which can be used as elevation control points for 1∶50000 and 1∶10000 stereo mapping. These conclusions will be valuable for global mapping using domestic satellite without ground control points.关键词：ICESat/GLAS;Multi-criteria constraint;Laser Altimeter Satellite;elevation reference;waveform characteristic- 341
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发布时间：2021-06-07 -
摘要：Soil taxonomy plays a significant role in soil remote sensing. Soil spectral reflectance is the comprehensive representation of soil’s physical and chemical parameters. The study of soil spectral reflectance features is the physical basis for soil remote sensing, and it provides new ideas and methods for soil classification.To quickly classify soil based on topsoil reflectance spectral characteristics and provide an effective method, the room spectral reflectance in the visible and near-infrared region (400—2500 nm) of 148 soil samples, including black, chernozem, blown, and meadow soils,were collected from Songnen plain, which is located in Heilongjiang province. Given that the high-frequency noise of reflectance spectrum is relatively strong in the range of 400 nm to 430 nm and 2450 nm to 2500 nm, we chose the visible and near-infrared region of 430 nm to 2450 nm.The spectral reflectance of soil samples were measured using ASDFieldSpec 3 in the laboratory. Resampling and continuum removal techniques were used to process spectral data and extract the spectral characteristic parameters (i.e., the absorption positions of the spectral curve, the vale’s area, the slope of the spectral curve, the distance between adjacent absorption positions, the depth of the vale, and the width of the vale), respectively. When the K-means clustering results based on spectral reflectance were compared with the K-means clustering results based on the spectral characteristic parameters, the spectral characteristic parameters were found to be more suitable for soil classification. Finally, the spectral characteristic parameters were used to constructsoil classification model that is based on the decision tree. The classification accuracy of black, chernozem, blown, and meadow soilsare 97.22%, 94.2%, 85.29% and 55.56%, respectively.These results were obtained by using the decision tree model. The most effective spectral characteristic parameters include the second absorption positions of the spectral curve, the first vale’s area, the first two vales’ area, and the slope of the spectral curve at 500 nm to 600 nm and 1340 nm to 1360 nm. Meadow soilis often distributed in the lower area, and the spectral curve of the topsoil of meadow soil is similar to its adjacent soil, which is regarded asa trend toward its adjacent soil. The spectral characteristic parameters that were extracted could be used to study the soil classification, and the decision tree model that is based on the spectral characteristic parameters of topsoil reflectance has achieved excellent results. This paper provides a convenient, rapid, and nondestructive approach for soil classification, which helps in soil mapping.关键词：soil taxonomy;spectral characteristic parameters;K-means clustering;decision tree;Songnen plain- 280
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发布时间：2021-06-07 -
摘要：Cotton is a significant economic crop, and cotton extraction plays an important role in effective and controllable agricultural management. Multi-temporal remote sensing images have been widely used in cotton extraction, but these studies mainly focused on sole features, such as the Normalized Difference Vegetation Index (NDVI). An effective method of integrated multi-features based on multi-temporal Landsat 8 images was proposed to extract cotton information.In this study, we chose north-central Shawan County in Xinjiang Uygur Autonomous Region as the study area. Nine images taken by Landsat 8 in 2013 were collected for cotton extraction. NDVI time series were generated to characterize the phenological pattern of each land cover type. The optimal temporal reflectance image was selectedby analyzing the difference in NDVI profile between cotton and other crop types. Texture features were calculated by the gray-level co-occurrence matrix method. NDVI time series, optimal temporal reflectance image, and texture features were combined as the original classification features. When the training samples were sufficient,butthe featureswereexcessive, the classification accuracy may decrease because of redundant information. We completed feature selection by using the rough set method and then obtained the selective features of the original features. The NDVI time series, original features, and selective features were used for classification by the support vector machine. The cotton distribution map was generated based on the classification result of the highest accuracy. Finally, we evaluated the accuracy of classification results by confusion matrix.①The selection of the optimal temporal reflectance image for cotton identification is important, and the optimum phase of this studyis on September 4. In this period, wheat was harvested; corn and sunflower were in the mature period; andcotton was in the blossom period. Significant differences were observed among these crops in the optimum phase. ②The original features achieved accuracies of 87.4% and 87.93% for cotton producers and users, respectively, and the overall accuracy was 92.81%. Compared with the classification results of the NDVI time series, the overall accuracy increased by 5.53% and the accuracy of cotton producers increased by 5.05%.Moreover, the classification accuracies of other land cover types increased to varying extents. ③The selective features achieved accuracies of 92.73% and 90.36% for cotton producers and users, respectively, and the overall accuracy was 93.66%. Compared with the classification results of the original features, the overall accuracy increased by 0.85% and the accuracy of cotton producers increased by 5.33%.Experiments showed that feature selection by the rough set method not only improved the classification accuracy but also effectively reduced the classification complexity. The proposed method achieved an accuracy of 92.73% for cotton extraction. The method of integrated multi-features based on multi-temporal Landsat 8 images is promising for crop extraction, even for land cover classification.关键词：multi-temporal Landsat 8 images;integrated multi-features;NDVI;cotton extraction;feature selection- 522
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发布时间：2021-06-07 -
摘要：We use CE-318 sun photometer, MPL lidar, and satellite to measure aerosols in Nanjing and employ the spectral light extinction method, Fernald method, and MODIS dark dense vegetation method, respectively, to calculate Aerosol Optical Depth(AOD).The spatial distribution maps of AOD from satellite shows that AOD is higher in March 3 and 6 in the vicinity of the Yangtze River and urban district, respectively(in addition to the Laoshan, Sun Yat Sen, and other mountainous areas). The calculated AOD values in March 3 from the CE-318 sun photometer, MPL lidar, and satellite data were 0.455, 0.289, and 0.4, respectively, and in March 6, the values were 0.373, 0.267, and 0.25, respectively, in site location(Nanjing University of Information Science and Technology; 118.7°E, 32.2°N).By comparing three kinds of AOD data from March to September, we observed that the difference of AOD is small, and the AOD values calculated from satellite and lidar are relatively reliable. In addition, in March 3 and 6, the sun photometric data showed two kinds of AOD variations: the first variation shows that AOD is high in the morning and evening and is low at noon; the second variation indicates that AOD is low in the morning and high in the evening. Moreover, results of the lidar data varied significantly with time, and lidar can be measured during cloudy weather to detect aerosol. This study indicates that the mean value of AOD by lidar below 9 km is approximately 0.3.In March 3 and 6, the area below 2 km was dirty, indicating that it contains some aerosols, whereas the area above 6km was relatively clean, which implies the presence of some clouds. Compared with ground-based observations, although the temporal resolution of satellite is low, satellite has an absolute advantage in case of trend analysis of a large area. In the development of aerosol observation in the future, combined with the advantages of the three observation methods, the proposed method can be helpful in the observation of large areas with high precision to retrieve the atmospheric aerosol spatial distribution and obtain more accurate aerosol parameters.关键词：aerosol;MODIS;MPL LiDar;Sun Photometer- 288
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发布时间：2021-06-07 -
摘要：Hyperspectral remote sensing technology has recently been a topic of interest in remote sensing studies, which attracts attention. Previous studies focus mainly on data processing, mineral and lithological mapping, and modeling, and have paid little attention on regional metallogenic background. The regional metallogenic background, especially the area that is favorable for mineralization, is significant to ore exploration. Without regional metallogenic background, hyperspectral remote sensing cannot be used effectively. This paper discusses about ore exploration criteria and mineralization elements. This study establishes structural framework of the Liuyuan-Fangshankou area. This study is based on the airborne hyperspectral remote sensing image obtained by using the CASI/SASI/TASI imager of the National Key Laboratory of Science and Technology on Remote Information and Image Analysis. The structural framework was established based on visual interpretation, and the geological background was analyzed based on expert knowledge. These information were successfully used to explore mineralization-favorable areas and provided several prospects for the Liuyuan-Fangshankou area. Hyperspectral remote sensing is important in understanding regional and geological background and exploration.关键词：airborne hyperspectral remote sensing;mineralization condition and ore controlling factors;metallogenic regulation;metallogenic background- 270
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发布时间：2021-06-07 -
摘要：To quantitatively analyze the accuracy of atmospheric parameters in near space using satellite data from TIMED and ENVISAT for more than 10 years and the density and wind calculated through the ideal gas equation of state and geostrophic wind formula, the atmospheric temperature, density, and zonal, meridional, and resultant winds are statistically calculated. These parameters are compared with China Reference Atmosphere to analyze the seasonal variation of atmospheric parameter deviation along the altitude, latitude, and longitude, which is significant for the application of satellite data, analysis of environmental characteristics, and support of meteorology in near space. The results are shown bolow. A temperature deviation above 55 km in the altitude decreases along the zonal direction during spring and autumn. Density deviation increases as altitude decreases, whereas in an altitude below 30 km, some longitudinal belts manifest large deviations along the meridional direction. The deviation of zonal wind speed has significant differences during summer as altitude increases, and it decreases along the zonal direction in altitudes between 40 km and 70 km. At an altitude below 40 km, the deviation of meridional wind speed has a uniform distribution along the zonal direction, and the difference between each season is not significant. The deviation of resultant wind speed evidentlyoscillatesas altitude increases. Along the zonal direction, it decreases and increases in altitudes between 40 km and 60 km during autumn, whereas along the meridional direction, it shows a decreasing trend in altitudes between 30 km and 45 km during spring.关键词：near space;TIMED;ENVISAT;geostrophic wind;China reference atmosphere- 273
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发布时间：2021-06-07 -
摘要：Changes in Fractional Vegetation Cover (FVC) is one of the core research fields in eco-environment assessment. FVC estimation from satellite image is significantly influenced by the terrain, soil, and atmosphere. Among of them, terrain variation can arouse miscalculation of biomass information of similar kind of vegetation, and it is a main bottleneck of quantitative remote sensing of vegetation. Therefore, it is urgent to find a method of FVC estimation that can eliminate or reduce the effects of terrain variation, in order to improve the FVC estimation using the satellite data over mountainous and hilly areas.Taking Landsat 8 OLI image as a data source. Firstly, three vegetation indexes, including the normalized difference vegetation index (NDVI), Cosine-C corrected NDVI, and the normalized difference mountain vegetation Index (NDMVI), are estimated. Then FVC are calculated from three vegetation indexes, using a linear unmixing model. Finally, the impact of terrain variation of three estimation methods of FVC are assessed and compared, considering altitude, slope, aspect, and solar incident angle. five altitude ranges, (i.e. 100—400 m, 400—600 m, 600—800 m, 800—1000 m and ≥1000 m), 72 aspect ranges (from 0 to 360° with a 5° interval) , four slope ranges (i.e. 0—10°, 10°—20°, 20°—30° and ≥30°), and ten solar incident angle ranges (i.e. 0—10°, 10°—20°, …, ≥90°) are considered, and the Coefficient of Variation (CV) is used to measure the impacts.The results show that the CV is minimal when the altitude is lower than 400 m, and the CV increases with the increasing altitude in three method. The minimal CV is 0.39%, –8.15% and –3.14% for NDVI-based method, NDVI-C based method and NDMVI-based method, respectively. The CV also increases with increasing slope. The CV of NDVI-based method reaches 11.5% when the slope is larger than 30°, indicating a significant impact of terrain variation. The CV of NDMVI-based method is slightly larger than that of NDVI-C based method when the slope is lower than 10°. The CV of NDMVI is the nearest to 0 when slope ranges from 20° to 30°, indicating a little impact of terrain variation.The terrain effects are reduced for all three VI-based methods. The NDMVI-based method has a best performance, and the terrain variation has little effect on FVC estimate. However, there is over-corrected phenomenon when the Cosine-C is corrected. The NDVI-based method has the poorest result, indicating that it is heavily influenced by the terrain variation when the slope is larger than 10°. The estimated FVC in shady slope is significantly lower than that of sunny slope. Based on the analysis above, it is shown that the NDMVI-based method can effectively eliminate or reduce the effects of terrain variation to improve the FVC estimation from the remote sensing data over mountainous and hilly areas in southern China. However, only Landsat 8 image with spatial resolution of 30 m is employed in this study. The magnitude of terrain effects may also depends on the spatial resolution of image. Therefore, further studies should be focused on terrain correction for high spatial resolution satellite image.关键词：fractional vegetation cover;NDVI;NDMVI;Cosine-C correction;terrain effect- 392
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发布时间：2021-06-07
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