最新刊期
卷 20 ， 期 4 ， 2016
- 摘要：Infrared limb sounding technique can detect many species of trace gases; it is an important method forth remote observation of the middle and upper atmosphere of the Earth. The development process of infrared limb sounding and its applications are reviewed in this study to make people focus on limb observation technology and to promote the technology’s development in China. This study focuses on eight infrared limb sounders, namely, LRIR, LIMS, SAMS, ISAMS, CLAES, MIPAS, HIRDLS, and TES, and introduces the characteristics of these instruments and their detectable species in chronological order. The inversion algorithms of infrared limb sounding are also described. Lastly, the applications of these limb sounders are summarized. Since 1975, after more than 40 years of development, the detection capability, refrigeration technology, forward model, and inversion algorithm mofinfrared limb sounders have significantly improved. Currently, these instruments can detect numerous species and have high inversion accuracy, long in-orbit life, and broad application scope. The applications of infrared limb sounding are divided into four parts, namely, detection of trace gas, cloud and aerosol, gravity wave, polar vortex, and the non-local thermodynamic equilibrium effect, especially its outstanding contributions to O3 and its related trace gas detection.The development of infrared limb sensors has provided a rich dataset of global coverage atmosphere profile. Infrared limb sensors play an irreplaceable role in studying the stratosphere ozone, polar ozone loss, and the ozone chemical reaction mechanism with stratospheric clouds and aerosols. However, being affected by clouds and aerosols, the limb detection range hardly reaches the lower troposphere. Infrared limb instruments also have a short on-orbit life. Data fusion technology should be investigated to form a long sequence of observed data for environment and climate change research.关键词：infrared limb sounders;trace gases;forward model;inversion algorithm;application
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发布时间：2021-06-10 - 摘要：Retrieval of Aerosol Optical Depth（AOD）over land through satellite remote sensing is difficult. Traditional methods to retrieve aerosol over land involve the use of the Dense Dark Vegetation（DDV） algorithm. However, this algorithm requires the support of an infrared band and dark pixels. Compared with DDV, the structure function method can be utilized to retrieve AOD over bright areas with high surface reflectance. However, the precision of retrieving AOD through the structure function method can be significantly influenced by pixel distance. Not all pixels can be retrieved with high precision; this condition results in a large amount of unnecessary computations. As a result, determining the pixel distance settings and the pixels with a small retrieval error is important in improving the retrieval precision and efficiency of the structure function method.To acquire the best pixel distance settings, we calculated the structure function values with different pixel distances and obtained the final structure function value of the target pixel through single-pixel distance, average, slopeheight, and average linear area methods to retrieve AOD at 550 nm over Kiaochow Bay. The first band of nine-scene MODIS L1 B data with resolutions of 250 m and 500 m was used.Surface reflectance was the minimum value of the first band of MOD09Q1 in 2012. By validating the AOD data measured by CE318 and by analyzing the retrieval result and spatial distribution of AOD, we obtained the pixel distance settings with high precision and minimal effects of resolution. The experiment indicated that the average linear area method has a small retrieval error whether the resolution is 250 m or 500 m. According to statistics and by analyzing the structure function value of the surface reflectance of pixels with an acceptable error（the absolute value of the absolute error is 0.1 for measured AOD less than 0.6 and 0.2 for measured AOD greater than 0.6）, we found that the ratio of pixels with are solution of 500 m is obviously larger than that with 250 m resolution. Pixels with a surface reflectance structure function value larger than 0.02 have a good result and areaways distributed over areas with a sudden change in geographical elements, such as piedmonts, mountain streams, coasts, rivers, and rural–urban continua. In this study, we utilized different methods and historical surface reflectance data with two resolutions to calculate the final structure function value. The retrieval precision and distribution of AOD were analyzed to acquire the best pixel distance settings, and its features were identified through the statistics of the structure function values with high retrieval precision. The average linear area method is stable,and its result has an insignificant relationship with data resolution. The retrieved AOD with high precision is concentrated on pixels with a surface reflectance structure function value larger than 0.02. The efficiency of this method increases when the measured AOD is greater than0.5.关键词：structure function method;surface reflectance;apparent reflectance;pixel distance;AOD（Aerosol Optical Depth）
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发布时间：2021-06-10 - 摘要：This study aims to assess the quality of retrieved stratospheric aerosol data from the Stratospheric Aerosol and Gas Experiment（SAGE） in China. Data from 10-year observation of ground Li DAR in Hefei, China were comparedwith the corresponding SAGE aerosol dataset. This work focused on the 20—40 km aerosol extinction profile and AOD to elucidate stratospheric aerosol. Two methods were used to form matching pair for Li DAR and SAGE because of the limited observation samples in Hefei for SAGE limb detection. The first method is singe profile matching, which matches the observed aerosol profile within the same day with the temporal and 10° spatialdistancetoobtain approximately 24 matching pairs. The second method is multi-profile matching, which matches the aerosol profiles with the 10° spatial distance within a certain period, such as per season; subsequently, differences in the averaged profiles were compared among the four seasons.Comprehensive comparisons show that aerosol variation trends are consistent when derived from Li DAR and SAGE in 10—30km. The positions of peaks and valleys of the aerosol extinction profile are similar. In addition, aerosol seasonal differences in 20—35km from Li DAR and SAGE, respectively, are relatively smaller, which reflects the stability of stratospheric aerosol. However, the detailed value distribution shows that the SAGE results are generally smaller than those of Li DAR, and their corresponding AODs differ within 20—40km. The quantitative results show that the aerosol AOD within 20—40 km as observed by Li DAR is approximately 0.004; the corresponding results retrieved by SAGE is approximately 0.002, which is only half of the former. The evident differences in Li DAR and SAGE results for stratospheric aerosol AOD and their extinction profiles are mainly attributed to their different measurement and retrieval methods. Furthermore,large temporal and spatial scales are required to match the Li DAR and SAGE observations, which may cause uncertainties in the compared results because of different detection technologies employed. Moreover, the SAGE aerosol profile was retrieved from Mie scattering theory.By contrast, the Li DAR aerosol profile was calculated using Fernald method, which requires the adjustment of several important constants,such as back scattering ratio and reference height. Artificial choices for these constants increase the possibility of errors for Li DAR aerosol retrieval. Therefore, different and complex retrieval methods can increase bias in the compared results. Comparisons of aerosol within20—40 km from SAGE and Li DAR in Hefei, China show that variation in stratospheric aerosol is generally reasonable as reflected by SAGE retrieval. The quantitative AOD value is systemically lower than half of that detected by ground Li DAR.关键词：stratospheric aerosol;SAGE;ground LiDAR;extinction coefficient;optic depth
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发布时间：2021-06-10 - 摘要：There still had some uncertainty when satellite aerosolproducts were applied in northeast China for lack of effective verification method. The sources and seasonal variations of aerosol were analyzed based on the HYSPLIT-4 model using the ground-based observation provided by the Chinese Sun Hazemeter Network（CSHNET） in Shenyang during 2009—2011. The quality of MERSI and MODIS Aerosol Optical Depth（AOD） products, and the source of error at four seasons were also discussed. The results showed distinctive seasonality for AOD and Angstrom exponent in northeast China. The aerosol came from remote transmission and regional emission with abundant anthropogenic and natural components. Aerosol sizes and AODs had large differences in four seasons, which leaded to a large error of aerosol model in the satellite retrieval algorithm. The accuracy of the satellite AOD products decreased due to the improper aerosol model. The comprehensive utilization ratio of MERSI and MODIS AOD products accounted for 68% and 83% of the ground-based AOD during the observing period. When AOD was lower than 0.35, the MODIS products underestimated and FY3-A products overestimated. Meanwhile, the relative error range of MODIS and FY3-A AOD products from the ground-based AOD was –46%—54% and 7%—135%. Both MODIS and FY3-A products underestimated with the relative error–34%—54% and –21%—75% when AOD was in the range from 0.35 to 0.75. A worse underestimation of MODIS and FY3-A products was found when AOD was more than 0.75 with the relative error of –34%—100%and –9%—58%. The utilization ratios of MODIS and MERSI had large differences in four seasons, 69% and 80% in spring, 73% and 70%in autumn, 69% and 73% in summer, only 2% and 49% in winter, respectively. On the whole, MODIS AOD products were more consistent with the ground-based observation than MERSI. There representativeness both of MODIS and MERSI were very poor in winter in northeast China. The percentages of MODIS and MERSI products falling within the expected errors were largest in spring（22%,25%）, moderate in autumn（19%, 16%）, least in summer（6%, 5%）; MERSI retrieval algorithm has better capability to retrieve coarse mode aerosols（α≤0.5）,while MODIS has more advantage in retrieving mixed mode aerosols（0.5≤α<1.5）. The comparability between MERSI and MODIS was relatively good in spring and autumn, but poor in summer. MERSI products were more severe underestimated than MODIS in spring and autumn. Comparisons of regional distribution between MERSI and MODIS AOD products in Liaoning province showed that MERSI AOD product covered a broader range than MODIS AOD product. Although the general characteristic of AOD distribution was consistent, but there still existed obvious deviation in some areas.关键词：HYSPLIT-4 model;aerosol optical depth（AOD）;Angstrom exponent;FY-3A/MERSI;Terra /MODIS
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发布时间：2021-06-10 - 摘要：Surface emissivity primary influences the accuracy of land surface temperature retrieval using remote sensing methods. This parameter is important in thermal infrared remote sensing. This paper aims to investigate the effects of soil water content and roughness on the thermal infrared emissivity of bare soil through an experimental method. This work can provide useful information for developing a soil emissivity retrieval method via remote sensing. The spectral emissivity of two soils（loam and sandy loam） with different soil water contents was measured by a Nicolet IS10 Fourier-transform infrared spectrometer with an integrating sphere. Data were used to analyze the effects of soil water content on soil spectral emissivity. The average emissivity of both soil types with different particle sizes of 8—14 μm was determined using an active-passive diffuse emissivity measuring device. This device can determine the effects of soil water content on the average soil emissivity for 8—14 μm particles. Soil emissivity increases with increasing soil water content because of the high emissivity of water.The average emissivity of loam soil with water contents of 0%, 13.7%, 26.1%, and 43.4%are 0.81, 0.92, 0.93, and 0.95, respectively, for wavelengths from 3 μm to 11 μm, as well as 0.73, 0.89, 0.93, and 0.96, respectively, for wavelengths from 3.3 μm to 5.3 μm. Moreover, the average emissivity of sandy loam soil with water contents of 0%, 8.8%, 25.3%, and 28.6% are 0.82, 0.92, 0.94, and 0.95,respectively, for the wavelengths from 3 μm to 11 μm, as well as0.75, 0.89, 0.95, and 0.96, respectively, for wavelengths from 3.3 μm to 5.3 μm. For the effects of soil roughness on emissivity, the results show that the average emissivities from 8—14 μm are 0.916, 0.934, and 0.937 for particles with sizes less than 0.6, 0.6—1, and 1—2 mm, respectively, for the dry sandy loam soil, as well as 0.936, 0.951, and 0.959, respectively, for the dry loam soil. Soil emissivity increases with increasing soil moisture. Wavelengths from 3.3 μm to 3.5 μm are the most affected by soil moisture. In this wavelength range, the band-average difference in soil emissivity between dry and wet soils is higher than 0.2. The wavelengths with minimal effect include 11 μm to 15 μm, when the band-average difference in soil emissivity between dry and wet soils is lower than 0.015. For the thermal infrared atmospheric window waveband（8—14 μm）, wavelengths from 8 μm to 9.5 μm are the most influenced by soil moisture. In this range, the band-average difference for soil emissivity is 0.05. Soil emissivity slightly increases with increasing particle size for dry and wet soils. Furthermore, soil emissivity increases by more than 0.05 for bands 10, 11, and 12 of ASTER data with increasing soil water content.关键词：soil emissivity;soil water content;soil roughness;soil spectrμm
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发布时间：2021-06-10 - 摘要：As a canopy vertical structure parameter, the Leaf Area Density（LAD） of horizontal layers is generally applied to quantify the leaf area within a given volume. It is important in characterizing the vertical distribution of leaf chemical components and canopy biomass.Light Detection And Ranging（Li DAR） has been widely applied to obtain the three dimensional structural properties of vegetation in the past ten years. Terrestrial Li DAR data can be utilized to estimate LAD. The objective of this studyisto develop a new method for woody canopy LAD estimation through the use of terrestrial Li DAR data.High-resolution point clouds and true-color images of Magnolia woody were acquired with Leica Scan Station C10 and a digital camera device, respectively.The study sites arelocated in the University of Electronic Science and Technology of China. Leaves were labeled through supervised classification of true-color images based on the registration of point clouds and true-color images.The laser-scanning points over leaves were accurately extracted with the classification acquired from the corresponding image pixels. Through the voxelization of point clouds, the two dimensional convex hull algorithm was introduced to determine the boundaries of each vertical layer of tree crowns and the contact frequency of laser canopy. With the Eigenvalue method, leaves at different tree heights were randomly selected to fit leaf planes and estimate leaf angles.Together with the zenith angle, the adjustment factor of leaf angles was identified. Finally, the vertical profiles of LAD were retrieved with the Voxel-based Canopy Profiling（VCP） method.The voxelization of point cloud data can be used to accurately determine forest canopy boundaries and the statistical contact frequency of LAD inversion. The LAD profile is in agreement with the vertical distribution of canopy leaves. The LAD values increased with the height of trees in the middle and lower canopies, and the maximum LAD was 1 m2/m3 at a height of 4 m.The LAD valuesdecreased in the upper canopy as the height increased. The Leaf Area Index（LAI） calculated from LAD was 3.20 m2/m2. Comparison with the field measurements acquired by LAI-2200 suggested that the relative error was 1.26%.By combining true-color image classification and canopy point cloud registration, point clouds over leaves can be accurately extracted from original Li DAR data. The LAD of woody canopy can be estimated from point clouds over leaves through the VCP method;in particular, the blade of the point cloud voxel can accurately determine the forest canopy boundaries and statistical contact frequency and can also be used in LAD estimation. However, the accuracy of LAD estimation cannot be evaluated directly because LAI does not reflect the vertical layer of LAD. LAD values need to be investigated in future studies.关键词：terrestrial LiDAR;Voxel-based Canopy Profiling（VCP） method;Leaf Area Density（LAD）;forest canopy
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发布时间：2021-06-10 - 摘要：The Bohai Sea is located in the sea area of China with the highest latitude. This region is an important economic development zone in China. However, sea ice and its drift often block shipping vessels, destroy offshore structures, and limit development of the marine industry. Thus, sea ice monitoring techniques must be developed. Phase correlation based on polar transformation is extensively applied to image registration and sea ice tracking because of its robustness; however, this method results in various forms of noise and is insensitive to illumination changes. In addition, this approach can capture the translational and rotational motions of sea ice. Nevertheless, polar coordinate transformation and its subsequent operation extract amplitude information alone and ignore phase characteristics, which determine the position of each frequency. Consequently, errors in the matching results significantly increased. Moreover, traditional phase correlation involves several Fourier transformation and inverse transformation, which may increase computational burden and reduce tracking efficiency.By considering the limitations of sea ice tracking through phase correlation, this paper presents another approach for phase correlation based on projection transformation by combining the unique characteristics of sea ice in the Bohai Sea based on GOCI to track and monitor sea ice motion.First, sea ice samples with identified; stable characteristics were quantitatively selected and projected into the one-dimensional space to minimize the function of rotational components. The characteristics of individual images and the relationship among them were fully considered. This step is crucial for setting appropriate correlation values to discriminate good angles from mixed ones. Next, the measured errors generated from the two methods were compared and analyzed based on the manual tracking results. The translation components can be determined from differences among the phase representatives of the cross power spectral function and extended to the sub-pixel level. Outliers should be excluded during the entire process by using correlation coefficients, visual inspection, and two-way matching. Finally, the derived sea ice motion in the Bohai Sea was analyzed with insitu data and historical records.Results show that the maximum/average of root-mean-square error between the rotation angles via the proposed method and manual measurements is 0.59/0.50, whereas that between the traditional technique and manual values is 1.41/0.94. The running speed increased by50.6%. In terms of translation, the sea ice motion field agrees well with insitu data and historical records of the Bohai Sea.Recent developments in local research on sea ice motion usually ignore the rotational condition. This paper presents a method that combines phase correlation and projection transformation based on GOCI to simultaneously track and monitor sea ice rotation and translation in the Bohai Sea. Projection transformation replaces the polar coordinates, thereby improving the matching errors generated from the traditional method. Sea ice translation is derived from phase information in terms of image feature windows. This method can efficiently track sea ice motion.关键词：GOCI;projection transform;phase correlation;sea ice of Bohai Sea;rotation;translation
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发布时间：2021-06-10 - 摘要：False Top Ographic Perception Phenomena（FTPP）are visual obstacles in applications of remote sensing images. Thid study aims to correct FTPP for non-professionals who use these images. After the causes of FTPP were analyzed, a method was proposed for topographic normalization models to correct FTPP in remote sensing images. Four FTPP correction formula were developed based on the Lambert an model, Cosine-Civco Model, c correction, and b correction; these equations were then compared in three test areas. In addition, the FTPP correction results were compared among the four topographic normalization models and the five existing methods. Results showed that the four topographic normalization models could successfully remove FTPP in images, but introduced some spectral distortions. The FTPP correction results of the Lambert and Cosine-Civco model showed good stereoscopic effects but inferior color balance and hues. The c and b correction produced similar images with good visual effects, while retaining spectral information of ground features. Overall, the quantitative index showed that b correction was slightly better than the three other models for FTPP correction. Comparison with the five existing methods indicated that c and b correction were superior. In conclusion, the c and b correction can effectively remove FTPP in images, regardless of the number of bands involved, there by preserve the spectral information of the images for further quantitative analysis.
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发布时间：2021-06-10 - 摘要：Radiometric calibration is a procedure to obtain at-sensor radiance from digital number records. Accurately calibrated radiation is critical in retrieving brightness temperatureand Land Surface Temperature（LST）. Uncertainty in radiometric calibration typically becomes increasingly obvious because of the decay of the sensor during in-orbit operation. In practice, radiometriccalibration is determined by calibration parameters. In this study, radiometric calibration of the HJ-1B thermal channel and the effects of calibration uncertainty on LST retrieval are investigated.A sensitivity model based on the radiative transfer model is proposed to assess the effects of radiometric calibration on LST retrieval.The calibration parameters for HJ-1B imagery are not updated in a timely manner（always annually）; this lack of timeliness inevitably results in errors in radiometric calibration. Therefore, two methods are proposed for two specific cases to estimate radiation properly. These two methods are linear interpolation and linear extrapolation.The sensitivity model shows that in terms of the numerical value, the ratio of radiometric calibration deviation to the LST error is approximately 1:11. Under a moderate condition, a 0.1（W·m–2·sr–1·μm–1） uncertainty in radiometric calibration may result in an error of 1.1 K in the final LST result. Simulations based on the calibration parameters of the HJ-1B thermal channel are conducted. The errors in radiometric calibration result from the improper selection of calibration parameters. Generally, even the calibration differences between two neighboring years are considered. The mean absolute errors of LST that approximate 1.0—2.0 K and highly significant LST errors（larger than 5.0K） could be obtained under poor conditions. Case studies show that the calibration parameters in the header file of the HJ-1B imagery are always the parameters updated in the previous year or even two years ago. The calibration parameters for the HJ-1B thermal channel have not been updated since 2012. Thus, two estimation methods based on linear models are proposed to obtain radiometric calibration properly. A case study illustrates that under the current situation, the proposed estimation methods are simple but effective. Therefore, they are recommended to general users as references and for application.The calibration parameters in the header file of the HJ-1B imagery are always not updated timely and frequently. Thus, uncertainty in the radiometric calibration of the HJ-1B thermal channel is inevitable. The LST errors resulting from radiometric calibration uncertainty are influenced by several factors, including surface characteristics（i.e., temperature and emissivity） and atmospheric conditions. The actual effects depend on seasonal and regional variations, which emphasize the challenge of accurately obtaining LST from the HJ-1B thermal channel. Two methods are proposed and assessed for the radiometric calibration of the HJ-1B thermal channel. Although the estimation methods are generally acceptable because of their simplicity and effectiveness, additional efforts are required to clarify the actual performance of the sensor. General users should consider radiometric calibration uncertainty in quantitative applications of the HJ-1B thermal imagery.关键词：radiometric calibration;thermal remote sensing;land surface temperature;sensitivity;HJ-1B
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发布时间：2021-06-10 - 摘要：Calibration of Light Detection And Ranging（LiDAR） intensity data can improve classification reliability. The normalized algorithm based on laser transmission distance and scanning angle is the most common algorithm for LiDAR intensity data calibration.However, this study determined that the influence of the tilt angles of several round objects, such as triangular and arched roofs, on echo intensity can be hardly calibrated by the normalized algorithm.Therefore, an optimized algorithm that calculates the tilt angle and calibrates the corresponding influence on LiDAR intensity data was developed. Eight typical ground objects in the study area were selected and sampled. The laser reflectivity of the samples was measured with Lamada 950 and regarded as a reference to evaluate the intensity calibration results. The steps of the optimized algorithm are as follows:（1）calibrate the original intensity value based on laser transmission distance;（2） determine whether adjacent laser points belong to the same tilted object by comparing their plane distance, elevation, and intensity with cut-off values;（3） calculate the tilt angle when the adjacent laser points belong to the same tilted object;（4） propose a rule by analyzing the LiDAR data collection mode and features of tilted ground objects and use this rule to determine whether the tilt angle is negative or positive;（5） calculate the corresponding reflection angle by summing the tilt angle and absolution of the scanning angle; and（6） recalibrate the intensity based on the reflection angle and laser transmission distance to eliminate the influence of reflection angle. The differences in the mean intensity of ground objects calculated with the existing normalized algorithm are consistent with the laser reflection measurement results, except for the blue-and-white-painted iron from different roofs. By contrast, the differences in the mean intensity of all eight ground objects are in agreement with the laser reflection measurement results obtained by using the optimized algorithm.For instance, the range and mean-square deviation of the intensity of triangular and arched roofs with blue-painted iron are both reduced. A few unreliable high intensity values correspond to the laser points, such as the wall of buildings and street lamp. Although these points meet the cut-offs, they have a small plane distance value. Thus, the calculated tilt angles for these points are large, and the cosine values are small;this condition results in unreliable high intensity values. The optimized algorithm can identify the laser points of homogeneous ground objects with a certain area and the same material and calculate the corresponding tilt angles. The influence of tilt angle can be effectively eliminated after calibration based on the reflection angle obtained by summing the tilt and scanning angles. The optimized algorithm can therefore improve classification feasibility based on the intensity value. With regard to the few unreliable high intensity values, two possible solutions are proposed for further study. Given the reliable empirical cut-off values presented by numerous experiments, LiDAR data can be preliminarily classified prior to separate the laser points. The optimized algorithm can then be utilized to calibrate intensity.关键词：airborne LiDAR;intensity;tilt angle;reflection angle;laser reflectivity;normalization calibration
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发布时间：2021-06-10 - 摘要：In this paper, we improved the original Haze-Optimized Transformation（HOT） method to solve the limitations of RGB synthetic images, such as sensitivity, over-correction, and color distortion. First, we combined the Normalized Difference Vegetation Index（NDVI）with the Red-Blue Spectral Difference（RBSD） to design a general mask that is insensitive to water bodies, bare soil, and man-made features.The mask was used to extract dense vegetation areas from the original image,and the corresponding regions of the initial HOT map were treated as a valid pixel set to assess haze intensity. The HOT values of invalid pixels based on the valid pixel set were inferred to generate a final valid HOT map. Finally, with the valid HOT map as reference to implement Dark Object Subtraction（DOS）, the influence of haze can be eliminated. The corrected value of the starting band was determined during DOS by calculating the percentile values of the histograms.The scattering model was then used to produce the correction values of other bands. The scatter plots of the blue and red bands before and after haze removal show that the hazed images share the same characteristics with the clear regions while maintaining differences between different objects. The plaques and halo artifacts are also significantly minimized. Furthermore, experimental results reveal that the improved HOT method can effectively remove haze and thin cloud, as well as resolve the limitations of synthetic images.关键词：image dehazing;HOT transform;mask;dark object subtraction;scattering model
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发布时间：2021-06-10 - 摘要：This paper presents a comprehensive analysis of the patterns of spatio-temporal variations in the Mars Surface Brightness Temperature（SBT） by using thermal infrared data acquired by three orbiters. Inter-annual changes in SBT are determined by comparing temperature data from different thermal infrared sensors, with spectral and local time corrections. Seasonal variations in the northern（NH） and southern（SH） hemisphere night SBT are presented with four Mars-year Thermal Emission Spectrometer（TES） data. The influences of the latitude and altitude on night SBT are analyzed based on TES measurements. The discrepancies between the adjusted Infrared Thermal Mapper（IRTM） band-B and the Mars Climate Sounder（MCS） band-A4 measurements are 1.3 K and 1.0 K for day and night SBT, respectively.The differences between the IRTM band-B and the average TES Mars Year（MY） 24—26 are 3.1 K and 2.1 K for day and night SBT, respectively. During the aphelion period, seasonal changes in SH night SBT conform well to the sine curve, and the inter-annual variation is lower than 3 K. However, the seasonal variation greatly diverges from the sine curve; the inter-annual difference is significant during the perihelion period. By contrast, NH night SBT exhibits no clear seasonal trend; the variation of this parameter is smaller than that of SH. No signs of the sine-curve pattern are displayed. Night SBT increased by approximately 17 K in NH and SH during the MY 25 global dust storm. When Ls is 115°—125°,NH SBT is obviously influenced by topography, whereas the SH SBT isotherms are nearly parallel to the latitude lines. Some plains, such as Acidalia Planitia, Chryse Planitia, Isidis Planitia, and Utopia Planitia, are warmer than their surrounding areas; moreover, high-altitude regions, including Arsia Mons, Alba Patera, Elysium Mons, Terra Sabaea, and Olympus Mons, have almost the lowest SBT, except for the Polar regions. When Ls is 295°—315°, the SH isotherms are fragmentized, which corresponds to the cratered surfaces. The NH isotherms in winter are only parallel to the latitude lines north of 50° N. The maximum SBT gradually decreased from the equator to the poles, except for 35° S. The minimum SBT between 15° S and 35° N are lower than that of the higher latitude regions because of the influence of to pography. The SBT standard deviations and differences between maxima and minima reveal the same law, that is, the closer to the equator, the higher are both values. Conclusions: The following conclusions are reached.（1） The 5 Mars-year inter-annual variation in SBT is within the precision range of measurement and processing; thus, the detection of Mars climate change is not feasible using current thermal infrared measurements.（2） The inter-annual difference in SBT during the perihelion period is higher than that during the aphelion period. The amplitude of seasonal variations in NH is smaller than that in SH. Increased SBT in NH caused by dust storm is higher than that caused by increased solar radiation in summer. Low-altitude topographies, such as basin sand canyons, exhibit higher temperatures than high-altitude topographies, such as mountains, terrains, and plateaus. Night SBT exhibits a strong negative correlation with altitude in Olympus Mons. The linear fitting result indicates that night SBT decreases by approximately 1.4 K as the altitude increases by 1km. Night SBT at low latitudes is generally higher than that at high latitudes. Influenced by factors, such as topography, the SBT maximum exists at 35° S instead of the equator. The SBT minima at the latitudes close to the equator are smaller than those at high latitudes. The lowlatitude SBT varies more intensely than those near the polar latitudes.关键词：Mars;thermal infrared;surface brightness temperature;seasonal variation;inter-annual variation;spatial variation
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发布时间：2021-06-10 - 摘要：Identification of stable pointwise target is an important procedure in multi temporal multi-temporal INSAR analysis and application in monitoring regional surface deformation. The accurate identification result helps to improve the land subsidence inversion precision.Various methods for pointwise target detection have been proposed during the past two decades from different respective. The methods can be divided into some main categories according to the criteria for coherence point selection, such as amplitude dispersion index DA, signalto-Clutter ratio and phase stability. The DA method performs a time series analysis on amplitude instead of phase, and reflects the stability of series amplitude. The advantage of coherence point selection by phase stability can identify some special objects with a stable phase, which further increases the density of the stable pointwise target points, but ignores the highly scattering reflection characteristics of the coherent point. The existing methods take insufficient account of the overall features of stable pointwise targets. For ensuring stable scattering mechanism and temporal stability of pointwise target, an improved method with subaperture correlation was proposed in this paper. First, the subaperture correlation properties IHP of SAR images were obtained by spectral decomposition. Then the stability of targets is evaluated based on series two-aperture spectral coherence, by which the coherence points with high scattering could be identified and detect as PSC1.The DA threshold is utilized as the second criterion, which means any pixel in PSC1 with amplitude dispersion less than 0.4 can be determined as PSC2 Then phase stability analysis was carried out to screen out the true stable points from PSC2 with the Characteristics of high scattering mechanism and temporal stability. The experiments of stable pointwise target detection were performed using 33 high resolution SAR images collected by the Terra SAR X-band radar sensor covering Beijing. The detection results demonstrated that the improved method can detect more accurate and reliable pointwise targets than those identified by traditional methods To further confirm the effectiveness of the proposed method, the small subset INSAR technique based on the proposed coherence point detection method was adopted to retrieve the ground deformation by 40 scenes dataset acquired from 2003 to 2009 in Beijing. The vertical surface displacement rates during this period was validated by the leveling observations, with RMSE =1.36 mm/a, indicating two types of subsidence matched very well. The maximum subsidence rate of Beijing in investigated area has reached –92.25 mm/a, with an obvious uneven spatial distribution. Subaperture correlation is sensitivity to the high scattering body and can ensure stable scattering mechanism and temporal stability of pointwise target. Both coherence point detection results and the primary surface deformation proved the effectiveness of the proposed method. The deformation result during 2003–2009 has undergone severe land subsidence with high spatial aggregation characteristic, and the regional subsidence and the groundwater exploitation reveal good corresponding relationship, the more exploitation, the higher deformation rate.关键词：Subaperture decompositions;high coherence point detection;small subset INSAR;Temporal-spatial distribution;ground water
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发布时间：2021-06-10 - 摘要：Considering that a large area of Pinus tabulaeformis suffers from Dendrolimus tabulaeformis in West Liaoning, this study monitored Dendrolimus. tabulaeformis disaster promptly, efficiently, and precisely through remote sensing technology. Information on the influencing factors, such as geography and meteorology, was collected to provide a reference for future prediction. This study adopted the method of Thematic Mapper（TM）, Enhanced TM Plus data source, and Geographic Information System technology to examine the image gray histograms of injured Pinus tabulaeformis with various damage degrees and monitor the damage degrees by using the Ratio Vegetation Index（RVI） [near infrared（NIR）/red]. To analyze the imposing factors, the classification results from RVI analysis were overlapped with geography and meteorology statistics, with cross reference to previous studies on the biological characteristics of Dendrolimus tabulaeformis. By analyzing the image gray histograms, this study determined that in the Pinus tabulaeformis spectrum, the NIR band shows high sensitivity to mild infection, whereas the red band shows high sensitivity to severe infection. Therefore, using RVI,which combines the two bands, prompts the disaster monitoring. The monitoring results are in accordance with the biological characteristics of Dendrolimus tabulaeformis, which prefer dry and warm environments, thus proving the efficiency of the remote sensing monitoring. For the factors that influence the damage, Dendrolimus tabulaeformis prefers south gentle slopes. Areas with long sunshine time, minimal rain,and low accumulative temperature suffer from severe damage. This finding provides a basis to predict the probability of disaster outbreak. By applying a novel converse method to test the reliability and accuracy of monitoring results, this study showed that researchers could take advantage of the biological and ecological characteristics of remote sensing objects to assess the reliability of results when forestry damage field investigations are lacking and there isno access to fieldwork sample statistics to estimate the monitoring results. This procedure could reduce the load and difficulty of field investigations.关键词：remote sensing;Dendrolimus tabulaeformis;monitor;spatial analysis;biological characteristics
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发布时间：2021-06-10 - 摘要：Accurate estimation of forest biomass is critical for modeling the carbon cycle and mitigating climate changes. Integration of multi-spectral satellite data and airborne LiDAR data can accurately estimate the biomass. However, the application of this strategy is limited in subtropical forests, particularly in China. In this study, a novelapproach was assessed using one strip of LiDAR point cloud and "wallto-wall" Landsat OLI free multi-spectral data combined with field-measured plot data to generate a low-cost and high-accuracy forest biomass map in a subtropical secondary forest in southeast China. Sixty square plots（30 m ×30 m） were established across the study site. First,the OLI data were processed by atmospheric and geometric correction, and LiDAR point clouds were extracted from the raw full-waveform LiDAR data. Second, fivesets of OLI and three sets of LiDAR metrics were extracted, and correlation analysis was performed with the field estimates of above-and below-ground biomass foroptimal metrics selection. Third, the "LiDAR biomass model" was fitted to LiDAR metrics extracted from the strip of LiDAR point cloud and the field plots within the strip. The "LiDAR-OLI biomass model" was fitted tothe OLI metrics and forest biomass estimated by the LiDAR data. Finally, the performance of the predictive models and the accuracy of the cross-validation results were evaluated through comparison with the accuracy assessment results of the "OLI biomass model." [Result] The"LiDAR-OLI biomass model"（R2 of above-and below-ground biomass estimation=0.69 and 0.56, respectively） exhibited improved performance than the "OLI biomass model"（R2 of above-and below-ground biomass estimation=0.69 and 0.56, respectively）. The relative biases of above-and below-ground biomass estimation increased by 14% and 15%, respectively. The mean differences in the cross-validation results for the "LiDAR-OLI biomass model"（mean differences in above-and below-ground biomass estimation=-12.9 and-0.15, respectively） were more accurate than the "OLI model"（mean differences in above-and below-ground biomass estimation=-18.99 and-0.33, respectively）. The ranges of above-and below-ground biomasses were 49.9—214.6 and 15.6—59.0 t·hm-2, respectively, in the entire study site. Moreover, the spatial distributions of above-and below-ground biomasses were similar to each other. Forests with high biomass were located in valleys and flat areas, whereas those with low biomass were located in the mountain ridge. This study provides an experimental basis for estimation of medium-scale forest parameters by synergizing active and passive remote sensing technologies. The study also explores the technological route of using one strip of LiDAR point cloud and "wall-to-wall" Landsat OLI free multi-spectral data for biomass mapping. These methods are relatively inexpensive and exhibit potential in supporting management and policies for addressing carbon stocks and understanding the effect of subtropical forest ecosystems under climate changes in China and elsewhere.关键词：upscale;forest biomass inversion;subtropical forest;airborne LiDAR data;Landsat 8 OLI imagery;multiple regression model
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发布时间：2021-06-10
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