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。     

基于MODIS数据的城市气溶胶光学厚度反演方法

遥感的方法为大面积获取气溶胶光学厚度提供了手段。目前使用MODIS(中分辨率成像光谱仪)数据反演气溶胶光学厚度常用的是暗像素方法以及对比法。由于城市地区符合暗像素标准的像素较少,而且城市地区的气溶胶成分复杂,难以确定其具体成分,所以暗像素方法会产生较大误差。根据MODIS第7、3、1波段对气溶胶的不同响应,以三者的反射率为自变量,结合地面实地观测数据,给出了计算北京地区气溶胶光学厚度的计算公式,误差分析表明该方法更适合城市地区。     

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。     

京津冀冬季气溶胶光学厚度遥感反演

针对传统的暗像元算法难以满足植被稀疏陆表气溶胶遥感监测需求的问题,提出了冬季植被稀疏的京津冀地区气溶胶光学厚度的遥感反演方法。以2016—2018年连续3年1—2月的AQUA/MODIS L1B数据为数据源,采用暗像元算法与深蓝算法结合的方法对冬季京津冀地区的气溶胶光学厚度进行了遥感监测。使用AERONET数据对结果进行了验证,并与MODIS MYD04_L2暗像元-深蓝气溶胶产品进行了对比。结果表明,该算法在冬季京津冀地区的气溶胶监测效果远好于暗像元算法,并与MODIS气溶胶产品表现出了显著的相关性,且有效监测范围更大、空间分辨率更高。根据连续监测结果,分析了京津冀地区冬季气溶胶光学厚度空间分布特征及其影响因素。     

模拟配准误差对结构函数法气溶胶反演的影响

为了确定清晰图像和待反演图像间的配准误差对结构函数法气溶胶光学厚度反演结果的影响程度,该文基于辐射传输模型反演了不同配准误差条件下的气溶胶光学厚度。在已知地表反射率的基础上,利用辐射传输模型得到一定气溶胶光学厚度下的表观反射率,线性插值得到不同配准误差下的表观反射率,利用这些表观反射率和地表反射率反演气溶胶光学厚度;通过不同配准误差下的反演结果分析配准误差的影响,并通过统计反演误差小且不随配准误差改变的稳定像元地表反射率结构函数值和均值等特征得到稳定像元的地表特征。结果表明,配准误差结构函数法的反演结果显著影响,当配准误差小于0.2个像元时,反演误差小于0.2的像元占总像元的比例达63%。此外,研究发现,地表反射率结构函数值与相应计算窗口地表反射率的标准差相关性强,当标准差大于0.02时,反演结果受配准误差的影响相对较小,相对误差最大不超过40%;反之,受配准误差的影响较大。     

利用PARASOL数据反演陆地气溶胶光学厚度

研究了利用PARASOL多角度偏振数据反演我国陆地气溶胶,假设单次散射在扣除大气分子和地表的偏振辐亮度后,根据气溶胶光学性质查找表,采用最佳匹配的方法,选择最适合的气溶胶模式,得到气溶胶光学厚度。并用AERONET的北京站和香河站的地基观测数据对结果进行了验证。结果表明,多角度偏振方法反演陆地气溶胶精度稳定,受季节和地表类型的影响很小,但精度较低还需作进一步的改进。     

利用地物光谱仪测算大气气溶胶光学厚度方法

气溶胶光学厚度是进行大气校正的重要参数，本文提出了一种通过地物光谱仪测量太阳辐射来测算气溶胶光学厚度的方法。并将测算结果与6S大气辐射传输模型的模拟结果进行比较，详细分析了误差来源。研究表明该方法是一种实用有效的方法，可用于遥感数据大气校正及大气气溶胶光学厚度的估算。     

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。     

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。     

Estimation of atmospheric particulate matter based on MODIS haze optimized transformation

This research is an attempt to simulate the relationship between haze optimized transformation (HOT) and aerosol optical thickness (AOT), and explore the influence of typical ground covers on this relationship using the 6S atmospheric radiative transfer model for the Chinese city of Nanjing. The HOT data were derived from moderate resolution imaging spectroradiometer (MODIS) satellite images recorded in the winter and spring seasons of December 2007–May 2009. They were analysed in conjunction with ground observed atmospheric particulate matter (PM) data so as to establish their quantitative relationship. Such a relationship may open a new avenue for remotely estimating atmospheric PM based on HOT. The results obtained indicate that HOT is related positively to AOT. This relationship is most accurately depicted by a second-order polynomial equation. Although built-up areas, waterbodies, and vegetation have differing HOT values, all of them bear a close and consistent correlation with AOT. HOT of built-up areas, waterbodies, and vegetative surfaces derived from MODIS images is also positively correlated with PM₁₀ (PM with diameter <10 μm), which was measured near the surface. The second-order polynomial equation has a coefficient of determination (R²) value of 0.375 (built-up), 0.344 (water), and 0.362 (vegetation) and a root mean squared error (RMSE) of 0.0258, 0.0264, and 0.0261, respectively. The closeness in R² value and RMSE for different ground covers suggests that correlation is marginally affected by the ground cover. It is thus concluded that HOT can be used as a reliable alternative for estimating PM₁₀ from MODIS data.     

Aerosol optical thickness over Pearl River Delta region,China

We validated moderate resolution imaging spectroradiometer (MODIS) Level 2 aerosol products with ground-based sun photometer (CE-318) measurements over the Pearl River Delta (PRD) region. MODIS aerosol products are also used to investigate the temporal and spatial variations of aerosol optical thickness (AOT). The results show that MODIS AOT is validated quantitatively with a higher correlation coefficient (r = 0.88, 0.80 at Guangzhou and r = 0.95, 0.92 at Hong Kong) and lower root mean square errors (RMSE = 0.15, 0.16 at Guangzhou and RMSE = 0.07, 0.08 at Hong Kong), while the Ångström exponent (α) is still in doubt (r = 0.09). The MODIS AOT values are generally higher than those of the CE-318 values in Guangzhou and smaller than those in Hong Kong. The regional multi-year monthly (July 2002–December 2012) mean AOT values are 0.66 ± 0.20 and 0.64 ± 0.18 for Terra- and Aqua-MODIS, respectively. From month to month, the values of Terra-MODIS AOT are larger than those of Aqua-MODIS during most of the month. This implies that AOT in the morning is generally larger than that in the afternoon. The largest monthly AOT occurred in April at 0.85 ± 0.16 and 0.88 ± 0.17 for Terra-MODIS and Aqua-MODIS, respectively, and the smallest occurred in November for both Terra- and Aqua-MODIS at 0.47 ± 0.13 and 0.47 ± 0.10, respectively. The spatial distribution of AOT in spring and summer shows more variation than in autumn and winter. This can be partially attributed to the cleansing effect of precipitation which clears aerosol particles over the whole region in spring and summer and results in a lower AOT outside urban areas, while AOT in urban areas is higher where anthropogenic aerosols build up quickly despite the cleansing effect of the rain.     

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.     

Technical Note: Multi‐sensor data fusion of aerosol optical thickness

This paper investigates the applicability and limitations of combining multi‐sensor data through data fusion, to increase the usefulness of the datasets. This study focuses on merging daily mean aerosol optical thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood (ML) technique to merge the pixel values where available, and then the optimal interpolation method to fill the remaining gaps. The algorithm was applied to a regional AOT subset. The results illustrate that the fusion algorithm can produce complete AOT fields with reasonably good data values and acceptable errors. The cumulative semivariogram (CSV) was found to be sensitive to the spatial distribution and fraction of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.     

Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex

This paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.     

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.     

Indirect method for estimation of the errors in measurements of sky radiance by Sun–sky radiometers of CIMEL type: calibration by molecular scattering

The results of measurements of the aerosol optical thickness (AOT) of the atmosphere by CIMEL Sun–sky radiometers are analysed. In arid zones, days with values of AOT not greater than 0.02 are common. In these cases, the sky radiance in backward hemisphere in the visible wavelength range is practically fully determined by the molecular scattering, which is well studied up to date. This fact opens up possibilities to estimate the errors in measuring sky radiance comparing observed and calculated data, as well as to separate them into random and systematic.     

Retrieval of aerosol optical thickness from HJ-1 CCD data based on MODIS-derived surface reflectance

The successfully launched Huanjing-1 (HJ-1) satellite by China in 2008 provides a new source of data for monitoring the environment. In this article, we develop a new algorithm for retrieving the aerosol optical thickness (AOT) using HJ-1 charge-coupled device (CCD) data with the assistance of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the bidirectional reflectance distribution function (BRDF) data products. This algorithm is then used to retrieve AOT in a delta region of the Yangtze River. The retrieved results are assessed for their accuracy by comparison with ground-measured data using sun photometers. Comparison of such derived AOT with in situ AOT measured using sun photometers indicates a root mean squared error (RMSE) of 0.123, and their regression relation has a correlation coefficient of 0.896 that is statistically significant at the 0.01 level. Such a relatively high level of retrieval accuracy suggests that HJ-1 CCD data can be used competently and effectively to retrieve AOT with the assistance of MODIS products that are used to construct the surface reflectance model. This study successfully demonstrates the feasibility of synergistically retrieving AOT from data acquired by different sensors. The lower dependence on data from a sole source means that the retrieval is less restrictive by data availability.     

Spatial variability of the atmosphere over southern England,and its effect on scene-based atmospheric corrections

Earth observation data acquired in the optical region require atmospheric correction before they can be used quantitatively. Most operational methods of atmospheric correction assume that the atmospheric properties are uniform across the image, but this assumption is unlikely to be valid for large images. This study aims to characterize the spatial variation in atmospheric properties over a typical mid-latitude area (southern England), and to assess the errors that would result from applying a scene-based atmospheric correction to data collected under this variable atmosphere. Two key atmospheric properties – aerosol optical thickness (AOT) and precipitable water content (PWC) – are assessed over two clear days in June 2006, and results show an AOT range of approximately 0.1–0.5 and a PWC range of 1.5–3.0 cm. Radiative transfer modelling shows that errors in reflectance of up to 1.7 percentage points, and up to a 5% change in normalized difference vegetation index, can be caused by AOT variability, but that PWC variability has minimal effects. Sensitivity analyses also show that the high uncertainty of many data sources used to provide AOT values for atmospheric correction may also lead to significant errors in the resulting products. The spatial variability of the atmosphere cannot be ignored, and we are in need of operational, generic methods to perform a spatially variable atmospheric correction.