浏览全部资源
扫码关注微信
纸质出版日期： 2018-1 ，
录用日期： 2017-5-15
扫 描 看 全 文
田信鹏, 孙林, 刘强, 李秀红. 2018. 北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演. 遥感学报, 22(1): 51–63
Tian X P, Sun L, Liu Q and Li X H. 2018. Retrieval of high-resolution aerosol optical depth using Landsat 8 OLI data over Beijing. Journal of Remote Sensing, 22(1): 51–63
卫星气溶胶光学厚度(AOD)反演中，传统暗目标方法在反射率较低的水体、浓密植被覆盖区域取得了较好效果，在反射率较高且结构复杂的高反射地表上空目前多采用深蓝算法，但存在空间分辨率较低，对细节分布描述性较差等问题。为解决这一问题，本文首先以5年(2008年—2012年)长时间序列MODIS地表反射率产品为基础，采用最小值合成法建立500 m分辨率逐月地表反射率产品数据集，然后利用地物波谱库中典型地物波谱数据，分析建立MODIS与Landsat 8 OLI传感器蓝光波段反射率转换模型，最后北京地区AERONET地基观测数据确定了气溶胶光学物理参数，并反演获取了北京地区上空500 m分辨率的AOD分布。为验证反演算法的精度，分别将反演结果同AERONET及MODIS/Terra气溶胶产品(MOD04)进行交叉对比，同时利用相关系数
R
，均方根误差RMSE，平均绝对误差MAE以及MODIS AOD产品预期误差EE共4个指标进行衡量。结果表明：算法反演获取的AOD与AERONET观测值具有较高的一致性，各指标分别为
R
=0.963，RMSE=0.156，MAE=0.097，EE=85.3%，稍优于MOD04产品(
R
=0.962，RMSE=0.158，MAE=0.101，EE=75.8%)，并且有效的对比点数也高于MOD04。通过与地基观测相比，卫星遥感获取的高分辨率城市地区AOD精度可作为定量评估城市空气质量的有效依据。
Satellite remote sensing has been widely used to retrieve Aerosol Optical Depth (AOD)
which is an indicator of air quality and radiative forcing. The Dark Target (DT) algorithm is applied to low reflectance areas
such as vegetated or water areas
and the Deep Blue (DB) algorithm is adopted over bright-reflecting source regions. However
the spatial resolutions of the AOD products obtained with the DT and DB algorithms are relatively low
and the distribution details for urban areas are poor. In this study
a modified retrieval algorithm is proposed for the retrieval of AOD at a spatial resolution of 500 m over Beijing
China
on the basis of Landsat 8 OLI data. The key points are the accurate estimation of surface reflectance and the reasonable assumptions of the aerosol model. We developed a new algorithm to improve the accuracy of land surface reflectance for urban areas. A monthly Minimum Land Surface Reflectance (MLSR) database for China was established using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products. The construction of this database was based on the principle of minimum synthesis technique. The conversion model from the blue band reflectance of MODIS to Landsat 8 OLI was established using the ASTER spectrum database to compensate for the difference between the spectral settings of the two sensors. AErosol RObotic NETwork (AERONET) data were adopted to determine aerosol optical properties
such as Ångström exponent
complex refractive index
Single Scattering Albedo (SSA)
and asymmetric factor (
g
). The AODs retrieved from the 36 OLI images were validated with the AERONET data and the NASA/MODIS Collection 6 aerosol products at a 10 km resolution. For this purpose
the DT and DB (DT/DB) algorithms were combined. Results indicated that the proposed algorithm accurately retrieved AODs over the Beijing area and that the retrieved aerosol distribution contained more spatial details and variability than the DT/DB AOD products. Ground-based AERONET observations from four sites (Beijing
Xianghe
Beijing_CAMS
and Beijing_RADI) were used to validate the retrieved AODs. The results from the proposed algorithm demonstrated the highest accuracy
with an average correlation coefficient (
R
) and Root-Mean-Square Error (RMSE) of approximately 0.963 and 0.156
respectively. Approximately 85.3% of the points fell within the expected error (EE) ± (0.05+0.15 AOD) envelope defined by NASA. The DT/DB AODs showed a small R (0.962) and slightly large RMSE (0.158)
with 75.8% of the collocations falling within the EE. An aerosol retrieval algorithm for Landsat 8 OLI 500 m data was proposed in this study. The assumptions in this algorithm are as follows: the variation of surface reflectance needs to be small for a month
and the SSA and g are regionally constant for a particular day. The method can also be used to achieve AOD inversion of other terrestrial observation satellite data. However
according to the uncertainty analysis
the proposed algorithm has some limitations that must be addressed. (1) Errors may arise from the use of constant SSA and g values for the day of retrieval. (2) The MLSR database approach was sometimes unsuccessful over snow surfaces
particularly when seasonal changes such as the snow melting in March and the accumulation of snow in Novembers are significant. These factors will be explored in our future studies.
气溶胶光学厚度光谱转化地表反射率数据库Landsat 8 OLIAERONET
aerosol optical depthspectrum conversionsurface reflectance databaseLandsat 8 OLIAERONET
Ångström A. 1964. The parameters of atmospheric turbidity. , 16(1): 64–75
Baldridge A M, Hook S J, Grove C I and Rivera G. 2009. The ASTER spectral library version 2.0. Remote Sensing of Environment, 113(4): 711–715
Bodhaine B A, Wood N B, Dutton E G and Slusser J R. 1999. On Rayleigh optical depth calculations. Journal of Atmospheric and Oceanic Technology, 16(11): 1854–1861
Bucholtz A. 1995. Rayleigh-scattering calculations for the terrestrial atmosphere. Applied Optics, 34(15): 2765–2773
Gent J F, Koutrakis P, Belanger K, Triche E, Holford T R, Bracken M B and Leaderer B P. 2009. Symptoms and medication use in children with asthma and traffic-related sources of fine particle pollution. Environmental Health Perspectives, 117(7): 1168–1174
Hauser A, Oesch D, Foppa N and Wunderle S. 2005. NOAA AVHRR derived aerosol optical depth over land. Journal of Geophysical Research: Atmospheres, 110(D8): D08204
Herman M, Deuzé J L, Devaux C, Goloub P, Bréon F M and Tanré D. 1997. Remote sensing of aerosols over land surfaces including polarization measurements and application to POLDER measurements. Journal of Geophysical Research: Atmospheres, 102(D14): 17039–17049
Holben B N, Tanré D, Smirnov A, Eck T F, Slutsker I, Abuhassan N, Newcomb W W, Schafer J S, Chatenet B, Lavenu F, Kaufman Y J, Castle J V, Setzer A, Markham B, Clark D, Frouin R, Halthore R, Karneli A, O’Neill N T, Pietras C, Pinker R T, Voss K and Zibordi G. 2001. An emerging ground-based aerosol climatology: aerosol optical depth from AERONET. Journal of Geophysical Research: Atmospheres, 106(D11): 12067–12097
Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C. 2013. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296–9315
Hsu N C, Tsay S C, King M D and Herman J R. 2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557–569
Hyer E J, Reid J S and Zhang J. 2011. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals. Atmospheric Measurement Techniques, 4(3): 379–408
IPCC. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the intergovernmental panel on climate change. New York: Cambridge University Press.
Jackson J M, Liu H Q, Laszlo I, Kondragunta S, Remer L A, Huang J F and Huang H C. 2013. Suomi-NPP VIIRS aerosol algorithms and data products. Journal of Geophysical Research: Atmospheres, 118(22): 12673–12689
Kahn R A, Gaitley B J, Garay M J, Diner D J, Eck T F, Smirnov A and Holben B N. 2010. Multiangle Imaging Spectro Radiometer global aerosol product assessment by comparison with the Aerosol Robotic Network. Journal of Geophysical Research: Atmospheres, 115(D23
Kaufman Y J, Wald A E, Remer L A, Gao B C, Li R R and Flynn L. 1997. The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1286–1298
Levy R C, Mattoo S, Munchak L A, Remer L A, Sayer A M, Patadia F and Hsu N C. 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques: Discussions, 6(1): 159–259
Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399–10420
李剑东, 毛江玉, 王维强. 2015. 大气模式估算的东亚区域人为硫酸盐和黑碳气溶胶辐射强迫及其时间变化特征. 地球物理学报, 58(4): 1103–1120
Li J D, Mao J Y and Wang W C. 2015. Anthropogenic Eastern Asian radiative forcing due to sulfate and black carbon aerosols and their time evolution estimated by an AGCM. Chinese Journal of Geophysics, 58(4): 1103–1120 (
Li S S, Chen L F, Tao J H, Han D, Wang Z T, Su L, Fan M and Yu C. 2012. Retrieval of aerosol optical depth over bright targets in the urban areas of North China during winter. Science China Earth Sciences, 55(9): 1545–1553
Li Z, Zhao X, Kahn R, Mishchenko M, Remer L, Lee K H, Wang M, Laszlo I, Nakajima T and Maring H. 2009. Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective. Annales Geophysicae, 27(7): 2755–2770
North P R J. 2002. Estimation of aerosol opacity and land surface bidirectional reflectance from ATSR-2 dual-angle imagery: operational method and validation. Journal of Geophysical Research: Atmospheres, 107(D12): 4149
Penner J E, Dong X Q and Chen Y. 2004. Observational evidence of a change in radiative forcing due to the indirect aerosol effect. Nature, 427(6971): 231–234
Pope C A, Burnett R T, Thun M J, Calle E E, Krewski D, Ito K and Thurston G D. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Association, 287(9): 1132–1141
Prados A I, Kondragunta S, Ciren P and Knapp K R. 2007. GOES aerosol/smoke product (GASP) over North America: comparisons to AERONET and MODIS observations. Journal of Geophysical Research: Atmospheres, 112(D15): D15201
Remer L A, Kleidman R G, Levy R C, Kaufman Y J, Tanré D, Mattoo S, Martins J V, Ichoku C, Koren I, Yu H B and Holben B N. 2008. Global aerosol climatology from the MODIS satellite sensors. Journal of Geophysical Research: Atmospheres, 113(D14): D14S07
Remer L A, Mattoo S, Levy R C and Munchak L A. 2013. MODIS 3 km aerosol product: algorithm and global perspective. Atmospheric Measurement Techniques, 6(7): 1829–1844
Sayer A M, Hsu N C, Bettenhausen C and Jeong M J. 2013. Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research: Atmospheres, 118(14): 7864–7872
Sayer A M, Hsu N C, Bettenhausen C, Jeong M J, Holben B N and Zhang J. 2012. Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS. Atmospheric Measurement Techniques, 5(7): 1761–1778
Sun L, Sun C K, Liu Q H and Zhong B. 2010. Aerosol optical depth retrieval by HJ-1/CCD supported by MODIS surface reflectance data. Science China Earth Sciences, 53(S1): 74–80
Sun L, Wei J, Bilal M, Tian X P, Jia C, Guo Y M and Mi X T. 2015. Aerosol optical depth retrieval over bright areas using Landsat 8 OLI images. Remote Sensing, 8(1): 23
Tollefson J. 2010. Asian pollution delays inevitable warming. Nature, 463(7283): 860–861
Torres O, Bhartia P K, Herman J R, Sinyuk A, Ginoux P and Holben B. 2002. A long-term record of aerosol optical depth from TOMS observations and comparison to AERONET measurements. Journal of Atmospheric Sciences, 59(3): 398–413
Torres O, Tanskanen A, Veihelmann B, Ahn C, Braak R, Bhartia P K, Veefkind P and Levelt P. 2007. Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview. Journal of Geophysical Research: Atmospheres, 112(D24): D24S47
Vermote E F and Kotchenova S. 2008. Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Research: Atmospheres, 113(D3): D23S90
Vidot J, Santer R and Aznay O. 2008. Evaluation of the MERIS aerosol product over land with AERONET. Atmospheric Chemistry and Physics, 8(24): 7603–7617
王中挺, 厉青, 王桥, 李莘莘, 陈良富, 周春艳, 张丽娟, 徐拥军. 2012. 利用深蓝算法从HJ-1数据反演陆地气溶胶. 遥感学报, 16(3): 596–610
Wang Z T, Li Q, Wang Q, Li S S, Chen L F, Zhou C Y, Zhang L J and Xu Y J. 2012. HJ-1 terrestrial aerosol data retrieval using deep blue algorithm. Journal of Remote Sensing, 16(3): 596–610 (
相关作者
相关机构
京公网安备11010802024621