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。     

2005—2018年北京市地基气溶胶光学厚度趋势分析北大核心CSCD

针对基于精细尺度的长时序及区域地基气溶胶光学厚度趋势的分析较少,且在原因分析过程中较少考虑环保政策及措施的问题,利用北京地区近14年AERONET站点数据对北京气溶胶光学厚度(aerosol optical depth,AOD)的类型和变化趋势进行研究,并分析了数据的完整性以及相关环保政策及措施对气溶胶的影响。结果表明:北京AOD月均值季节变化明显,最大值一般出现在夏季的6、7月份,最小值在秋冬季节;沙尘型气溶胶(desert dust aerosol,DD)、城市污染型气溶胶(urban industry aerosol,UI)、烟煤型气溶胶(bituminous coal aerosol,BC)分别为春、夏、冬季的主导型气溶胶,混合型气溶胶(mixed type aerosol,MT)在四季的占比相差不大;奥运会前后的环保工作、《大气污染防治行动计划》的颁布及削减散煤等环保政策和措施使北京气溶胶厚度明显下降,北京空气质量得到改善。     

Himawari-8气溶胶产品的验证及应用

为准确分析中国地区气溶胶空间分布与时间变化特征规律,首先利用中国地区9个AERONET（Aerosol Robotic Network）地基站点观测资料对新一代静止气象卫星Himawari-8气溶胶光学厚度（Aerosol Optical Depth, AOD）产品数据进行一致性验证,并在此基础上选取2015年7月至2018年4月Himawari-8逐小时AOD数据分析了中国地区气溶胶光学厚度时空变化特征。结果表明：①Himawari-8 AOD与AERONET AOD之间相关性很高,9个站点的相关系数R在0.64 ~ 0.91之间,拟合曲线斜率k的范围为0.57 ~ 0.68。②Himawari AOD产品与AERONET AOD的相关性在中午时段较其他时段相对较低;北方地区Himawari-8 AOD冬季反演效果与夏季相比较差,南方地区则相反。③中国地区年平均AOD呈东高西低分布,春、夏两季AOD明显高于秋、冬两季,其中夏季最高,春季次之;地区间AOD月变化差异也较大;大部分地区AOD日变化呈现先下降后上升再下降的趋势,AOD最高值出现在午后14 ~ 16时,最低值出现在18时。研究结果为了解中国地区大气气溶胶的时空变化规律和全天时的大气污染监测方法提供新的参考。     

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。     

Himawari-8气溶胶产品的验证及应用

为准确分析中国地区气溶胶空间分布与时间变化特征规律,首先利用中国地区9个AERONET(Aerosol Robotic Network)地基站点观测资料对新一代静止气象卫星Himawari-8气溶胶光学厚度(Aerosol Optical Depth,AOD)产品数据进行一致性验证,并在此基础上选取2015年7月至2018年4月Himawari-8逐小时AOD数据分析了中国地区气溶胶光学厚度时空变化特征。结果表明:①Himawari-8 AOD与AERONET AOD之间相关性很高,9个站点的相关系数R在0.64～0.91之间,拟合曲线斜率k的范围为0.57～0.68。②Himawari AOD产品与AERONET AOD的相关性在中午时段较其他时段相对较低;北方地区Himawari-8 AOD冬季反演效果与夏季相比较差,南方地区则相反。③中国地区年平均AOD呈东高西低分布,春、夏两季AOD明显高于秋、冬两季,其中夏季最高,春季次之;地区间AOD月变化差异也较大;大部分地区AOD日变化呈现先下降后上升再下降的趋势,AOD最高值出现在午后14～16时,最低值出现在18时。研究结果为了解中国地区大气气溶胶的时空变化规律和全天时的大气污染监测方法提供新的参考。     

Himawari-8气溶胶产品的验证及应用

为准确分析中国地区气溶胶空间分布与时间变化特征规律，首先利用中国地区9个AERONET（Aerosol Robotic Network）地基站点观测资料对新一代静止气象卫星Himawari-8气溶胶光学厚度（Aerosol Optical Depth, AOD）产品数据进行一致性验证，并在此基础上选取2015年7月至2018年4月Himawari-8逐小时AOD数据分析了中国地区气溶胶光学厚度时空变化特征。结果表明：①Himawari-8 AOD与AERONET AOD之间相关性很高，9个站点的相关系数R在0.64 ~ 0.91之间，拟合曲线斜率k的范围为0.57 ~ 0.68。②Himawari AOD产品与AERONET AOD的相关性在中午时段较其他时段相对较低；北方地区Himawari-8 AOD冬季反演效果与夏季相比较差，南方地区则相反。③中国地区年平均AOD呈东高西低分布，春、夏两季AOD明显高于秋、冬两季，其中夏季最高，春季次之；地区间AOD月变化差异也较大；大部分地区AOD日变化呈现先下降后上升再下降的趋势，AOD最高值出现在午后14 ~ 16时，最低值出现在18时。研究结果为了解中国地区大气气溶胶的时空变化规律和全天时的大气污染监测方法提供新的参考。     

MODIS气溶胶光学厚度产品和激光雷达数据在大气颗粒物监测中的应用

遥感反演是区域尺度上近地面颗粒物数据获取的有效手段。利用激光雷达观测的消光系数垂直分布、地面相对湿度、风速等数据,对无锡市MODIS(中分辨率成像光谱仪)气溶胶光学厚度(AOD)产品进行垂直、湿度和风速订正,并用研究区域中7个地面站点的PM_(10)和PM_(2.5)浓度监测数据对订正结果进行评估。结果表明:经过订正的MODIS AOD产品与地面监测数据具有良好的相关性,其中与PM_(10)的决定系数达到0.452,与PM_(2.5)的决定系数达到0.449,说明MODIS AOD产品经相关订正后,可用于无锡及其附近地区地面空气污染的监测。在MODIS AOD产品的垂直订正方面,利用激光雷达数据的订正效果好于利用能见度数据的订正效果。在遥感与实测数据的相关性季节变化方面,夏季相关性最高,秋季次高,春季较低,冬季最低。     

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。     

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。     

暗目标法从风云三号数据反演陆地气溶胶

针对2013年发射升空的FY-3C星的中分辨率光谱成像仪(MERSI)应用气溶胶反演较少的不足,开展了暗目标法反演陆地气溶胶的应用研究,为研究气候变化、大气环境监测等提供数据支撑。在MODIS暗目标法基础上,针对FY-3C/MERSI数据反演陆地气溶胶,使用6SV完成辐射传输计算建立大气参数查找表,采用IDL的HDF5读写接口完成数据提取与辐射定标,利用蓝光波段(470nm)与短波红外波段(2 130nm)的线性关系分离出大气信息,插值大气参数查找表得到气溶胶光学厚度(aerosol optical depth,AOD)。2014年5月15日华北地区的算法应用表明,该算法能较好地监测空气污染的分布。2014年5月的MERSI数据反演结果与同期AERONET香河站的气溶胶产品比对表明,该算法与地面观测结果有着较好的一致性,相关系数优于0.8。     

山区可见光—近红外遥感影像浓密植被暗像元自动识别方法研究

暗目标法是目前气溶胶光学厚度遥感反演中应用最为广泛的方法,浓密植被暗像元的识别是暗目标法的基础。针对可见光—近红外影像缺少中红外波段难以有效识别浓密植被暗像元的问题,引入红波段直方图阈值法识别山区可见光—近红外影像的浓密植被暗像元。该方法利用浓密森林像元在可见光波段反射率低的特点,通过搜索红波段直方图的最小峰值自动识别浓密植被暗像元。试验中选取Landsat TM影像前4个波段利用红波段直方图阈值法识别可见光—近红外影像的浓密植被暗像元,并与在中红外波段影像和可见光—近红外影像中广泛应用的两种暗像元识别方法进行对比分析,探讨红波段直方图阈值法的有效性,最后将该方法应用于环境减灾卫星(HJ-1)CCD影像的暗像元识别和气溶胶反演。实验结果表明:红波段直方图阈值法明显优于常用的可见光—近红外影像暗像元识别方法,识别精度接近传统的中红外波段影像识别方法,相似度指数小于2和小于3的暗像元分别为83.12%和93.48%。该方法为山区可见光—近红外影像浓密植被暗像元自动识别提供了一种新的适用方法,识别结果能够满足暗目标法反演气溶胶光学厚度的要求。     

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.     

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。     

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.     

Evaluation of PARASOL aerosol retrieval over North East Asia

The third POLDER (POLarization and Directionality of the Earth Reflectance) instrument, PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was launched in December 2004 and started its operational life at the early beginning of March 2005. This study is devoted to the regional validation of PARASOL aerosol retrieval scheme over land surfaces against independent automatic sun-photometers located in the northeast part of China at the Beijing and Xianghe sites both included in AERONET (Aerosol Robotic Network). PARASOL Level 2 Aerosol Optical Thickness (AOT) is shown, thanks to the high quality sun-photometer dataset, to be quite consistent with the AERONET AOT of the fine fractional part of the size distribution (radius ≤ 0.3 μm). In other words, PARASOL retrieval over land is mainly sensitive to the anthropogenic aerosols which are known for influencing the climate, environment as well as human health. Moreover, analysis of polarization in the 490 nm band (Level 1 data) shows the possibility of dust type aerosol identification thus yielding to a potential algorithm improvement in the future.     

Toward aerosol optical depth retrievals over land from GOES visible radiances: determining surface reflectance

Frequent observations of aerosol over land are desirable for aviation, air pollution and health applications. Thus, a method is proposed here to correct surface effects and retrieve aerosol optical depth using visible reflectance measurements from the Geostationary Operational Environmental Satellite (GOES). The surface contribution is determined from temporal compositing of visible imagery, where darker pixels correspond to less atmospheric attenuation and surface reflectance is deduced from the composite using radiative transfer. The method is applied to GOES‐8 imagery over the eastern US. Retrieved surface reflectance is compared with separate retrievals using a priori ground‐based observations of aerosol optical depth. The results suggest that surface reflectances can be determined to within ±0.04. The composite‐derived surface reflectance is further analysed by retrieving aerosol optical depth and validating retrievals with Aerosol Robotic Network (AERONET) observations. This analysis indicates that the retrieved optical depth is least biased, hence the surface reflectance is most accurate, when the composite time period varies seasonally. Aerosol optical depth retrievals from this validation are within ±0.13 of AERONET observations and have a correlation coefficient of 0.72. While aerosol optical depth retrieval noise at low optical depths may be limiting, the retrieval accuracy is adequate for monitoring large outbreaks of aerosol events.     

Aerosol optical depth spatio-temporal characterization over the Canadian BOREAS domain

A complete set of Advanced Very High Resolution Radiometer (AVHRR) data (75 images) is used to retrieve aerosol optical depth (AOD) over dense vegetation and over lake water in the visible AVHRR channel. The present approach for remote sensing of aerosols from the National Oceanic and Atmospheric Administration (NOAA)-11 AVHRR sensor is based on the detection of atmospherically dominated signals over dark surface covers such as dense dark vegetation (DDV). Such targets were identified using the reflective portion of the middle-wave AVHRR channel 3 signal. When a fixed DDV surface reflectance is subtracted from the observed reflectance after correction for all other atmospheric effects, the remaining part, which is due to aerosols, is inverted to derive aerosol optical thickness using a look-up table (LUT) similar to that used in water surface inversion. The algorithm was applied to the daily afternoon NOAA-11 AVHRR (1?km×1?km) data acquired from the end of May to mid-August 1994 over the Canadian 1000?km×1000?km Boreal Ecosystem Atmosphere Study (BOREAS) domain. A validation analysis using five ground-based Sun photometers within the studied area shows the good performance of the retrieval algorithm. The approach allows detailed analysis of the AOD spatio-temporal behaviour at the regional scale useful for climate and transport model validation.     

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.     

Ozone Monitoring Instrument aerosol products: a comparison study with ground-based airborne sun photometer measurements over Europe

Airborne sun photometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD). These data are extracted from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite taken during 2005. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD using two different retrieval techniques: the Aura OMI Near-Ultraviolet Aerosol Data Product, OMAERUV, and the multi-wavelength Aura OMI Aerosol Data Product, OMAERO. In this work, we propose a study that specifically compares the instantaneous aerosol optical thicknesses retrieved from OMI at several locations containing sites and those of the Aerosol Robotic Network (AERONET). The result of the comparison shows that, just over Europe, OMI aerosol optical thicknesses are better retrieved in the multi-wavelength retrieval than in the near-ultraviolet. Correlations have been improved by applying a simple criterion to avoid scenes probably contaminated by thin clouds, and surface scattering. The ultraviolet irradiance positive bias in the OMI data is corrected using a procedure based on global climatological fields of aerosol absorption optical depth. The results generally show a bias significantly reduced by 5–20%, a lower variability and an unchanged, high correlation coefficient.