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。     

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。     

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GF-4 PMS数据反演城市地区气溶胶光学厚度

高分四号是我国发射的第一颗地球同步轨道卫星,具有较高的空间分辨率及快速重复成像能力,在城市大气环境的高动态监测方面拥有较大的应用潜力。针对城市地区地表反射率高、地表类型复杂,传统的单一算法较难实现大气气溶胶光学厚度有效反演等问题,以北京市为研究区,使用了一种暗目标法和地表反射率数据库相结合的方法,分别对2017年5月25日和10月5日两个时相的气溶胶光学厚度进行了估算。结果表明,该方法能够有效实现城市暗目标区和亮目标区的气溶胶反演,且与MODIS标准陆地气溶胶产品及AERONET站点地面观测数值较为一致,相关系数超过了0.9。     

利用MODIS数据监测北京地区气溶胶

采用暗像元法对北京地区的气溶胶进行了监测：以2005~2007年的MODIS 1B数据为数据源,使用6S进行辐射传输计算构建查找表,进行气溶胶光学厚度的反演,并使用AERONET数据对结果进行验证,对冬季和夏季的监测结果进行了比较。结果表明,该算法能够较好地监测气溶胶,反映城市气溶胶的区域变化;但冬季时的监测结果要远远差于夏季,很难满足气溶胶监测需求。     

高分一号数据的气溶胶光学厚度反演和验证

针对高分辨率卫星遥感反演气溶胶光学厚度地表噪声难以分离的问题,利用国产"高分一号"(GF-1)的数据特点,提出了一种气溶胶光学厚度反演方法和处理流程。该方法分别基于暗像元和深蓝算法去除了浓密植被和城市亮目标地区的地表贡献,并应用于我国污染较为严重的京津冀、长三角、珠三角等示范区域。利用北京、杭州、香港AERONET地基观测数据,对GF-1反演得到的气溶胶光学厚度进行验证,结果表明:气溶胶高值均集中在三大区域工业排放大和人类活动密集的核心城市,年均光学厚度值在1左右。卫星和地基的相关性总体较好,三大区域的相关系数分别达到了0.71、0.55、0.54。受云识别、亮地表覆盖和气溶胶模式假设等影响,GF-1反演的气溶胶光学厚度存在一定程度的偏差。     

中国东南地区及近海海域气溶胶反演遥感研究

大气气溶胶在很多生物地球化学循环中具有重要作用,但是由于它的来源广泛并且具有很大的时空变化性,难以在全球范围内精确、实时确定气溶胶的性质、组成及时空分布,因而对大气气溶胶的研究依赖于监测手段的发展。地基试验能获取点源的大气气溶胶光学厚度(AOD)的地面测量数据,得到的气溶胶光学厚度用于卫星数据的预处理以及气溶胶光学厚度反演的精度验证。而经过地基校验后的卫星遥感数据,可以反映大范围内实时动态的气溶胶信息。利用MODIS资料和地基探测的太阳光度计资料,对中国东南地区及近海海域的大气气溶胶光学特性进行了分析,讨论了适用于中国东南地区的大气气溶胶模型;利用连续的太阳光度计数据对MODIS资料的反演结果进行校验,结果表明：改进气溶胶模型和采用连续波段太阳光度计探测数据,可以提高MODIS AOD的校验结果。     

基于随机森林的高分辨率 PM2.5 遥感反演 ——以广东省为例

细颗粒物(PM2.5)监测是大气污染治理的重要手段,受限于地面观测点的数量,从遥感反演 PM2.5 是常规地面观测的有效补充,是当前的研究热点。通常遥感反演 PM2.5 的思路是先反演大气气溶胶光学厚度,然后基于统计关系由大气气溶胶光学厚度反演 PM2.5。该方法容易造成误差传递,从而 导致反演模型的不稳定。该文提出了一种基于随机森林算法(一种机器学习算法)的 PM2.5 遥感反演方法,直接建立中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)影像与地 面实测 PM2.5 的关系,可以避免传统反演 PM2.5 时先反演大气气溶胶光学厚度带来的误差,最终得到精度更高的 PM2.5 反演结果。该方法先用随机森林算法对 MODIS 影像和经过克里金插值后的地面监测站PM2.5 数据进行训练和测试;然后,根据测试的均方根误差从多个模型中选取最优(均方根误差最小)的模型;最后,将此模型用于整幅 MODIS 影像,得到整个区域的 PM2.5 反演结果。实验选取了广东省 四个季节多幅 MODIS 影像数据进行验证,并通过决定系数和均方根误差两个表现指标进行对比和分析,验证了所提算法的优越性。     

基于分区暗像元和Spline插值方法估算太湖气溶胶光学厚度

传统暗像元大气校正算法认为研究区域上空的气溶胶光学厚度呈均匀分布状态。对于Ⅱ类水体,尤其是气溶胶类型复杂的内陆湖区,暗像元算法的均匀性假设将不再适用。针对传统暗像元算法的不合理性,本研究将太湖湖区划分为9个子区域,每个子区域利用传统暗像元算法估算其气溶胶光学厚度,然后结合Spline插值算法获取整个太湖的气溶胶光学厚度信息,并以传统暗像元大气校正算法作为参照,探讨与分析分区暗像元算法的精度状况。通过本文的研究可知：气溶胶光学厚度是遥感大气校正的关键参数;在2003年10月28日,受西北风的影响,太湖上空的气溶胶光学厚度呈湖南低,湖北高的分布模式;分区暗像元大气校正算法获取的气溶胶光学厚度平均值为0.79,标准偏差为0.099,标准偏差与平均值的比值为12.58%,与传统暗像元算法相比,分区暗像元算法综合考虑了水体上空气溶胶光学厚度空间分布的不均匀性,进而有利于改善大气校正的精度。     

武汉地区环境卫星气溶胶光学厚度反演

针对难以及时、准确掌握中部地区气溶胶污染状况的问题,选用适用于城市等亮地表区域的结构函数法,结合环境一号A/B卫星CCD影像数据,开展武汉及周边地区气溶胶光学厚度反演研究。首先对卫星遥感数据进行预处理,然后根据结构函数法的原理和模型,选择了合适的结构函数公式、窗口范围和距离值。通过选取研究时间范围内的"清洁日",在原始影像预处理的基础上使用归一化植被指数剔除水体影响,实现环境一号卫星CCD影像的武汉地区气溶胶光学厚度反演。通过CE318实测数据、湖北省环保厅的大气污染数据、MODIS产品检验结果,对反演结果进行了对比验证,从精度、准确度、空间分布和时间分布趋势等方面验证了模型反演的可靠性。     

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.     

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.     

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.     

Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China

A new algorithm, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance and aerosol single scattering properties simulated from a chemistry transport model (GEOS-Chem), is developed to retrieve aerosol optical thickness (AOT) over land in China during the spring dust season. The algorithm first uses a “dynamic lower envelope” approach to sample the MODIS dark-pixel reflectance data in low AOT conditions, to derive the local surface visible (0.65 μm)/near infrared (NIR, 2.1 μm) reflectance ratio. Joint retrievals of AOT at 0.65 μm and surface reflectance at 2.1 μm are then performed, based on the time, location, and spectral-dependent single scattering properties of the dusty atmosphere as simulated by the GEOS-Chem. A linearized vector radiative transfer model (VLIDORT) that simultaneously computes the top-of-atmosphere reflectance and its Jacobian with respect to AOT, is used in the forward component of the inversion of MODIS reflectance to AOT. Comparison of retrieved AOT results in April and May of 2008 with AERONET observations shows a strong correlation (R = 0.83), with small bias (0.01), and small RMSE (0.17); the figures are a substantial improvement over corresponding values obtained with the MODIS Collection 5 AOT algorithm for the same study region and time period. The small bias is partially due to the consideration of dust effect at 2.1 μm channel, without which the bias is − 0.05. The surface PM₁₀ (particulate matter with diameter less than 10 μm) concentrations derived using this improved AOT retrieval show better agreement with ground observations than those derived from GEOS-Chem simulations alone, or those inferred from the MODIS Collection 5 AOT. This study underscores the value of using satellite reflectance to improve the air quality modeling and monitoring.     

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.     

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.     

Estimation of particulate matter from satellite- and ground-based observations over Hyderabad,India

Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm (PM₂) in regard to air quality observations over Greater Hyderabad Region (GHR), India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 (satellite) and July 2009–Dec 2013 (ground-based). Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical thickness (AOT) (MODIS-AOTs) was validated against that measured from Microtops-II Sunphotometer (MTS) AOTs (MTS-AOTs) and then utilized to estimate surface-level PM₂ concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM₂. In general, the MODIS-estimated PM₂ concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM₂ from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM₂ estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM₂ estimates from MODIS. The estimates of PM₂ using MTS or MODIS-AOT exhibit a root mean square deference (RMSD) of about 8–16% against measured PM₂ on a seasonal basis. Furthermore, a tendency of increasing PM₂ concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM₂ estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM₂ from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.     

Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method

Theoretical analysis based on the atmospheric radiative transfer indicated a positive correlation between the aerosol optical thickness (AOT) and the surface-level particulate matter (PM) concentrations, and this correlation is improved significantly using vertical-and-RH correcting method. The correlative analysis of the ground-based measurement indicates that, (a) the correlation between AOT and the aerosol extinction coefficient at surface level (k_a,0) is improved as a result of the vertical correction, with the coefficient of determination R² increasing from 0.35 to 0.56; (b) the correlation between k_a,0 and PM concentrations can be significantly improved by the RH correction with the R² increasing from 0.43 to 0.77 for PM10, and from 0.35 to 0.66 for PM2.5. Based on the in-situ measurements in Beijing, two linear correlative models between the ground-based AOT and PMs (e.g. PM10 and PM2.5) concentrations were developed. These models are used to estimate the regional distribution of PM10 and PM2.5 using the satellite-retrieved AOT in Beijing area. Validation against the in-situ measurements in Beijing shows that both of the correlations of the satellite-estimated PM10 and PM2.5 with the measurements are R² = 0.47, and the biases are 26.33% and 6.49% respectively. When averaged in the urban area of Beijing, the R² between the estimated PM10 and the measurements increased to 0.66. These results suggest that by using the vertical-and-RH correcting method we can use the MODIS data to monitor the regional air pollution.