基于并行计算的气溶胶定量遥感反演模型实现

为了加快气溶胶光学厚度(AOD)反演计算速度,基于SYNTAM串行算法,提出了循环分块划分和聚合通信的策略,利用消息传递模型,在中国气象局的IBM Cluster 1600高性能计算机系统上,并行实现了从MODIS双星(TERRA和AQUA)卫星数据反演AOD。试验结果表明该方法大大减少了计算时间,与地面太阳光度计实测AOD数据进行对比验证,发现所有站点处的AOD反演相对误差小于22%,表明这种并行方法可以满足高精度监测空气质量要求。     

利用MODIS遥感监测可吸入颗粒物PM10的研究

为了研究利用卫星遥感方法监测大范围可吸入颗粒物的空间分布,本文在利用MODIS卫星资料反演河北省晴天大气气溶胶光学厚度的基础上,将河北省11个城市地面监测站的可吸入颗粒物PM10浓度值与对应的大气气溶胶光学厚度AOD值作了相关分析,建立了大气气溶胶光学厚度AOD与PM10的关系模型,相关系数为O．5390,达到了O．001以上的显著水平。经多次应用效果检验,平均误差为17％。结果表明,利用晴天AOD与地面PM10质量浓度的关系模型可以有效地监测PM10的空间分布。     

利用MODIS遥感监测PM10的方法研究

本文为了研究利用卫星遥感方法监测大范围可吸入颗粒物的空间分布,在利用MODIS卫星资料反演河北省晴天大气气溶胶光学厚度的基础上,将河北省11个城市地面监测站的可吸入颗粒物PM10浓度值与对应的大气气溶胶光学厚度AOD值作了相关分析,建立了大气气溶胶光学厚度AOD与PM10的关系模型,相关系数为O．5590,达到了0．001以上的显著水平。经多次应用效果检验,平均误差为17％。结果表明,利用晴天AOD与地面PM10质量浓度的关系模型可以有效地监测PM10的空间分布。     

基于HJ-1卫星数据反演长江三角洲地区气溶胶光学厚度

探索利用我国HJ-1卫星CCD数据,运用深蓝算法开展长江三角洲地区气溶胶光学厚度反演的可行性,并将结果与其他气溶胶光学厚度产品进行比较。针对HJ-1A和HJ-1B数据,反演结果分别与MODIS气溶胶光学厚度产品以及AERONET地基观测数据进行对比验证。结果表明:深蓝算法得到A星、B星的反演结果与MODIS气溶胶产品呈显著相关,但在数值上普遍高于MODIS产品;反演结果与AERONET站点数据之间的误差介于0.008~0.364之间,研究时段内站点数据缺乏,未对误差进行严格的统计分析。基于深蓝算法的HJ-1卫星数据反演结果虽然在数值上与MODIS气溶胶光学厚度产品存在系统性偏差,但在空间上能够较好地反映长江三角洲地区大气气溶胶分布状况,且具有空间分辨率高的优势。     

利用MODIS资料计算不同云天条件下的地表太阳辐射

利用MODIS气溶胶和云产品卫星数据与大气辐射传输模式RSTAR,进行了晴空和有云条件下地表太阳辐射计算,并与香河综合辐射站的地基辐射测量值相比较。分析表明,晴空下二者相关性较好,相关系数平方R~2值为0.95,均方根误差RMSE为38.8 W/m~2。有云条件下,计算结果较差于晴空条件下,R~2值为0.88,RMSE为88.2 W/m~2。观测显示,香河站云—气溶胶共存现象较多,而MODIS仅按单一层的云进行微物理参数反演,导致模式输入参数误差,给地表太阳辐射计算结果引入误差。为了分析云-气溶胶共存状态对计算地表太阳辐射的影响,利用RSTAR计算了不同光学厚度的云和气溶胶在特定波段卫星观测的辐亮度值,并对于特定波段卫星接收的辐亮度值,用不同垂直结构的云和气溶胶分别反演其光学和微物理参数,再利用反演的结果分别计算相应的地表太阳辐射。结果表明:相对于单一云层的反演结果,云下气溶胶光学厚度(AOD)为0.1时,由反演误差所导致的地表太阳辐射估算误差较小;而随着AOD增加影响明显增大,在AOD为1.2时,相对误差达17.79%~18.38%。对于污染较重的华北地区而言,分析云覆盖下的气溶胶对地表太阳辐射的影响,有助于提高有云条件下地表太阳辐射的计算精度。     

高分六号卫星遥感估算大气细颗粒物方法研究北大核心CSCD

针对目前利用高分六号卫星开展地区高空间分辨率大气细颗粒物监测研究较少的问题,提出了基于高分六号卫星宽幅相机数据的气溶胶光学厚度及大气细颗粒物遥感反演的技术方法,并在京津冀及周边地区开展了应用实验和对比分析。首先,基于改进暗像元法反演了高分六号卫星数据的气溶胶光学厚度;然后,结合地面大气细颗粒物监测数据与多种气象辅助数据,基于随机森林算法,构建了多参量综合的大气细颗粒物估算模型,对京津冀地区的大气细颗粒物浓度进行了估算。研究表明:高分六号气溶胶光学厚度反演结果与地基站点监测结果的相关系数为0.94,反演精度较高;大气细颗粒物估算结果与地基站点监测结果的决定系数达到0.79以上,较好地反映了京津冀地区的大气细颗粒物空间分布。     

2008~2016年MODIS多角度大气校正气溶胶产品在中国的时空分布及变化趋势

探究全国大气气溶胶分布及变化特征,准确了解中国地区气溶胶光学特性对研究大气环境污染、应对全球气候变化是非常重要的。对2008~2016年的MODIS MAIAC气溶胶光学厚度数据在中国的适用性进行验证,并采用Mann-Kendall方法,从不同的时空尺度和气溶胶类型上分析中国地区AOD值的时空变化特征。结果表明：①验证表明C6的MAIAC反演结果在中国AERONET匹配点上表现良好,C6的MAIAC反演AOD结果适用于中国区域;②从年际尺度上看,2008~2016年AOD年均值整体呈波动下降;从季节尺度上看,AOD季节变化呈春季整体高、夏季中心高、秋冬季水平低的特点,各省AOD平均值及各省份划区AOD平均值随季节变化趋势相似。③在空间上,AOD呈东南高、西北低、高值中心聚集的特征。④中国AOD变化整体呈现出东部减少且集聚,西部增加且分散的变化特征。可进一步探究不同种类气溶胶分布和气溶胶与典型大气污染物分布关系,以期为中国环境污染治理提供更好的决策指导。     

利用MODIS资料反演杭州市500米分辨率气溶胶光学厚度

反演城市/区域范围内高空间分辨率的气溶胶光学厚度时,如果气溶胶类型选取的不合理造成的反演误差会很大,甚至超过地表反射率确定误差导致的反演误差。针对这一问题,本文提出了一种结合MODIS L1B资料和AERONET(Aerosol Robotic Network)的气溶胶光学厚度产品,基于6S大气辐射传输模型的计算,确定杭州市在2008年12月16日的气溶胶类型的方法。利用得到的气溶胶类型,结合改进的暗像元法,反演了杭州市500m空间分辨率的气溶胶光学厚度。将气溶胶光学厚度反演结果与采用标准气溶胶类型时的反演结果进行比较,结果表明,本文确定的气溶胶类型更符合杭州市当天的情况,应用到气溶胶光学厚度反演中,精度也最好,相对误差的绝对值在20%以内。     

双通道算法与MODIS算法反演中国近海气溶胶光学特性的对比研究

利用双通道和IMAPP气溶胶反演算法处理TERRA/MODIS L1B数据得出中国近海气溶胶的光学厚度,与AERONET太阳光度计的反演结果作对比分析,验证了反演方法的可行性。同时,对各海域的反演结果及表征粒子谱宽度的Angstrom指数(α)的变化情况进行了分析,结果表明：在东海和日本以南等广阔海域,两种反演算法的结果同AERONET太阳光度计的观测结果基本一致,相关性较好;在渤海和黄海近海岸一带两者气溶胶光学厚度的反演值均偏高,其原因主要是由这些海域的二类水体的影响导致的。探讨分析了这些海域的水域特征及光学特性,为研究发展适合中国近海气溶胶特性的反演算法提供了依据。     

一种基于混编的气溶胶光学厚度(AOD)反演的新方法

目前国内AOD反演专业性很强,需要通过使用者自己编程处理才能得到结果,速度很慢,无法实现异地处理。本文介绍了一种利用C#和IDL混合编程技术开发对CE318太阳光度计测量得到的直射太阳辐射数据进行处理,从而最终得到550nm气溶胶光学厚度的网络处理系统的新方法。文章给出了新方法的处理流程,详细介绍了混编机制在AOD反演系统开发中的实现方式,并且通过野外实验实测CE318数据对应用平台进行验证,从而提出了C#和IDL进行混合编程开展基于网络版的遥感数据定标和大气测量应用研究的优势。     

Aerosol retrieval with satellite image and correlation analyses between aerosol distribution and urban underlaying surface

Human activity is one of the most important aerosol sources. Because the underlaying surface feature records most human activities, it is important to recognize the correlation between aerosol distribution and the underlaying surface. In this research, the dark object algorithm and a second-generation operational algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval are used to estimate aerosol optical depth from Enhanced Thematic Mapper Plus (ETM+) images acquired by the Landsat 7 satellite system in urban regions, and the correlations between aerosol distribution and urban underlaying surface features (including landform, land cover and urbanization level) is analysed. Results show that (1) it is feasible to apply a second-generation algorithm to retrieve aerosol optical depth with ETM+?images. When a validation is performed with the ground observation meteorological range converted into aerosol optical depth with the correlation model acquired by a Moderate-Resolution Atmospheric Transmission (MODTRAN) simulation, the retrieval error is about 0.0094. For higher spatial resolution of an ETM+?image, it is better to study the aerosol distribution features in the urban regions. Additionally, (2) there are obvious variations in spatial distribution of aerosol over the different features of the underlaying surface. For the landform, aerosol optical depth is mountain < hill < plain; for the land cover, aerosol optical depth is dense vegetation < sparse vegetation < water < bare soil < residential area; for the different urbanization-level regions, there is bigger and bigger aerosol optical depth with increasing of the urbanization level. On the whole, as human activities increase, so too does the aerosol optical depth.     

Retrieving Aerosol Optical Depth Over Land basedon GOCI Data Onboard Geostationary Satellite

The aerosol optical depth were retrieved using deep\|blue algorithm based on the GOCI data of 72 clear\|sky days in 2013 over the Yangtze River Delta，and then were validated by field data from two measured sites.The results show that the GOCI data can be used for inversion of land aerosols，and both sites show high accuracy.The fitting degree of Beichenlou station of Nanjing University of Information Science and Technology is 0.659，and that of AERONET station is 0.747，which shows good agreement compared with MODIS aerosol products.At the same time，it is found that the aerosol products based on the GOCI data of the stationary satellite have the ability to monitor the diurnal variation of aerosol and provide the basis for the aerosol dynamic diffusion and the climate effect study.      

Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS

Aerosol retrieval over land remains a difficult task because the solar light reflected by the Earth-atmospheric system mainly comes from the ground surface. The dark dense vegetation (DDV) algorithm for MODIS data has shown excellent competence at retrieving the aerosol distribution and properties. However, this algorithm is restricted to lower surface reflectance, such as water bodies and dense vegetation. In this paper, we attempt to derive aerosol optical thickness (AOT) by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including for high-reflective surface. Preliminary validation results by comparing with Aerosol Robotic Network (AERONET) data show good accuracy and promising potential.     

Influence of land surface effects on MODIS aerosol retrieval using the BAER method over Korea

As satellite receiving signals are affected by complex radiative transfer processes in the atmosphere and on land surfaces, aerosol retrieval over land from space requires the ability to determine surface reflectance from the remote measurements. To use the Bremen Aerosol Retrieval (BAER) method for aerosol optical thickness (AOT) retrieval over land at a spatial scale of 1×1 km² from Moderate Resolution Imaging Spectroradiometer (MODIS) data, a linear mixing model with a vegetation index was used to calculate surface reflectances. As the vegetation index is affected by the aerosol present in the atmosphere, an empirical linear relationship between short wavelength infrared (SWIR) channel reflectance and visible reflectance was estimated to calculate a modified aerosol free vegetation index (AFRI) value. Based on a modified AFRI obtained from MODIS SWIR channel reflectance, an improved linear mixing model was applied for aerosol retrieval. A comparison of results between calculated and apparent surface reflectance was satisfactory, with a linear fit slope above 0.94, correlation coefficients above 0.84, and standard deviation below 0.008 for the study area. These results can therefore be used for improved aerosol retrieval over land by the BAER method with MODIS Level 1 data.     

基于高分五号DPC数据的细模态气溶胶光学厚度反演

偏振遥感技术监测细模态气溶胶光学物理特性的优势,是监测大区域大气污染的有效手段。基于高分五号（GF-5）携带的多角度偏振成像仪（DPC）的多角度偏振观测数据开展全球陆地上空的细模态气溶胶光学厚度（AOD_f）反演研究。主要通过地表二向偏振反射（BPDF）模型估算出地表偏振反射率,结合评价函数得出了最优气溶胶模型以及AOD_f反演结果,将反演结果与AERONET地基观测数据进行了对比验证。结果显示： 地基数据与反演结果相关性系数达到0.903,平均绝对误差,平均相对误差、均方根误差分别为0.026、0.43%、0.060,反演结果总体可靠,反演方法具备可行性。     

The inter-comparison of AATSR dual-view aerosol optical thickness retrievals with results from various algorithms and instruments

The Advanced Along-Track Scanning Radiometer (AATSR) dual-view (ATSR-DV) aerosol retrieval algorithm is evaluated for a single scene over Germany (49–53? N, 7–12? E) on 13 October 2005 by comparison of the aerosol optical thickness (AOT) at 550 nm with products from Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS), in addition to the Atmospheric Aerosol Retrieval using Dual-View Angle Reflectance Channels (AARDVARC) algorithm developed at Swansea University. The AOT was retrieved from the AATSR using the ATSR-DV algorithm, for the pixel size of 1 km × 1 km (at nadir). Then these values were meshed to be consistent with the sampling of the products from the other satellite instruments. The ATSR-DV results compare favourably with the products from orbiting optical instruments dedicated to aerosol retrieval, such as MODIS and MISR, which leads to the conclusion that AATSR is well suited for aerosol retrieval over land when the dual view is used with the ATSR-DV algorithm.     

Improved estimation of aerosol optical depth from MODIS imagery over land surfaces

Estimation of aerosol loadings is of great importance to the studies on global climate changes. The current Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol estimation algorithm over land is based on the “dark-object” approach, which works only over densely vegetated (“dark”) surfaces. In this study, we develop a new aerosol estimation algorithm that uses the temporal signatures from a sequence of MODIS imagery over land surfaces, particularly “bright” surfaces. The estimated aerosol optical depth is validated by Aerosol Robotic Network (AERONET) measurements. Case studies indicate that this algorithm can retrieve aerosol optical depths reasonably well from the winter MODIS imagery at seven sites: four sites in the greater Washington, DC area, USA; Beijing City, China; Banizoumbou, Niger, Africa; and Bratts Lake, Canada. The MODIS aerosol estimation algorithm over land (MOD04), however, does not perform well over these non-vegetated surfaces. This new algorithm has the potential to be used for other satellite images that have similar temporal resolutions.     

Distribution and transport of aerosols in the south Indian region and surroundings

The weather/climate of south India is entirely different from that of north India. So the aerosol loading and variability in the regions also show considerable difference. The present study investigates the aerosol distribution over the south Indian region. The transport of aerosols over the region is studied in detail and climate features of the region are investigated to understand the aerosol distribution of the region. In situ observations available in two stations in the west coast and equatorial Indian region and satellite data available for the region are used for the analysis. Aerosol concentration is less in south India compared to that in north India with AOD (aerosol optical depth) values above 0.5 in north India and below 0.4 in south India. The studies reveal the seasonal variability in aerosol loading with high aerosol concentration during summer (>0.4) and less loading during winter season (<0.3) in south India. In situ aerosol observations taken for the first time in Cochin station implies variability in coarse mode aerosols with AOD near 0.3 in summer and near 0.2 in winter. The seas surrounding south India have significant impact in the aerosol loading as they are the sources of marine aerosols such as sea-salt, sulphates etc. Exchange of aerosols takes place from the marine environment and land regions over south India and surroundings. The coastal region of south India experiences high aerosol loading during June to August period. Different climate pattern of the region and the presence of adjoining seas lead to a mixed aerosol content, which includes aerosols of marine and continental origin. In north India, anthropogenic and natural aerosols such as dust, black carbon etc. are dominant in the atmosphere.     

Remote sensing of aerosols optical thickness over various sites using SeaWiFS or VEGETATION and ground measurements

A key problem in aerosol retrieval is to distinguish between surface and atmospheric contributions to the variability in the satellite signal. A major contribution in the surface-related variability is caused by the non-Lambertian nature of the Earth surface reflectance and the fact that the illumination/observation geometry varies considerably between successive observations of the same area (with a polar orbiting sensor). In principle, if the surface boundary condition can be specified with sufficient accuracy by means of a bidirectional reflectance distribution function (BRDF), the two contributions can be unfolded and aerosol information retrieved. This approach has been tested using combined datasets made of satellite measured “top of atmosphere” (TOA) radiance and corresponding ground estimation of the aerosol optical thickness. Studying a time series of data, taking into account geometrical conditions and assuming the ground BRDF to be constant during the time period, a coupled surface/atmosphere model was used to investigate the retrieval of aerosol optical thickness (AOT) over several sites. By fitting a subset of satellite observations associated with ground photometer data, a best fit of BRDF model parameters could be determined. This surface characterization is then used to reduce the model unknowns to AOT only and thereby to permit its retrieval from the satellite data alone, by means of a simple inversion process. The study was conducted on three European AERONET sites and using satellite data from both the VEGETATION and Sea viewing Wide Field of view (SeaWiFS) sensors. In all cases, the AOT retrieved from satellite was in good agreement with the measurements.