北京城市下垫面大气CO2反演算法
Retrieval algorithm of atmospheric CO2 for urban underlying surface in Beijing
- 2019年23卷第6期 页码:1223-1231
纸质出版日期: 2019-11 ,
录用日期: 2018-5-31
DOI: 10.11834/jrs.20198107
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纸质出版日期: 2019-11 ,
录用日期: 2018-5-31
扫 描 看 全 文
吴浩, 王先华, 叶函函, 蒋芸, 段锋华, 吕松. 2019. 北京城市下垫面大气CO2反演算法. 遥感学报, 23(6): 1223–1231
Wu H, Wang X H, Ye H H, Jiang Y, Duan F H and Lyu S. 2019. Retrieval algorithm of atmospheric CO2 for urban underlying surface in Beijing. Journal of Remote Sensing, 23(6): 1223–1231
大气温室气体监测仪GMI(Greenhouse gases Monitor Instrument)是高分五号(GF-5)卫星载荷之一,主要用于全球温室气体含量监测和碳循环研究。高精度反演是卫星大气CO
2
遥感的基本需求。地表反射率影响卫星遥感辐射量及辐射传输过程中的地气耦合过程,严重制约着CO
2
的反演精度,针对GMI开发高精度的大气CO
2
反演算法,地表反射是一个需要重点考虑的因素。城市是CO
2
重要的发射源,且城市下垫面存在明显的二向反射特性,加上城市大气条件不良,复杂的地气耦合效应存在这都考验反演算法的准确性和鲁棒性。本文针对北京城市地区,利用2011年—2016年共5年的MODIS(MODerate-resolution Imaging Spectroradiometer)地表二向反射分布函数BRDF(Bidirectional Reflectance Distribution Function)数据,构建了适合利用单次观测数据反演的BRDF模型,并提出一种同时反演地表BRDF参数和大气CO
2
含量的算法。结果表明在550 nm波长处气溶胶光学厚度AOD(Aerosol Optical Depth)小于0.4时,大部分GMI模拟数据的反演误差控制在0.5%(~2 ppm)内。利用GOSAT (Greenhouse gases Observing SATellite)实测数据的反演结果与修正后的日本国立环境研究所NIES(National Institute for Environmental Studies)反演结果进行对比,其平均误差为1.25 ppm,相关性达到0.85。本算法满足GMI数据在北京城市区域高精度CO
2
反演的需求,并使得反演高值气溶胶区域数据成为可能,增加了GMI观测数据的利用率。
The greenhouse Gas Monitor Instrument (GMI) is a payload of GF-5 satellite. It is mainly used to ascertain the global distribution of carbon dioxide (CO
2
) and methane (CH
4
) and carbon cycling studies. Surface reflection is an important factor for developing a high-precision column-averaged carbon dioxide dry air mole fraction (XCO
2
) retrieval algorithm for the GMI short-wave infrared data. Urban areas are an important source of CO
2
emission
and an apparent directional reflection occurs in the urban underlying surface. However
the aerosol optical depth (AOD 550 nm) in Urban Beijing is relatively large
and combining atmospheric scattering and surface directional reflection makes the retrieval of XCO
2
in Beijing urban area difficult. In this study
we investigate the MODIS Bidirectional Reflectance Distribution Function (BRDF) data in Beijing urban area in 2011–2016. The three parameters (
f
iso
f
geo
and
f
vol
) of the BRDF model vary with season. The ratio of
f
iso
to
f
geo
and of
f
iso
to
f
vol
slightly change. Statistics show that the average value of
f
geo
/
f
iso
is 0.42±0.09 and
f
vol
/
f
iso
is 0.23±0.03. The simplification of the BRDF model allows us to invert the BRDF parameters simultaneously through one observation of the GMI. We present a new algorithm to improve the accuracy of XCO
2
in Beijing urban areas through simultaneous retrieval of BRDF parameters. An a priori value of AOD is provided by using a directional polarimetric camera
which is onboard the GF-5 satellite. The MODIS MCD43A1 product is used to build an a priori surface BRDF database after filtering and resampling. The a priori CO
2
profile is estimated on the basis of the analysis of CO
2
profile distribution in Beijing area. The monthly a priori covariance matrices of CO
2
at a grid cell (2°×3°) on the globe is precalculated as input parameters using the carbon tracker database. The measurement error covariance matrix is (y/SNR)
2
and the signal-to-noise ratio (SNR) used is the result of a laboratory test. This SNR is a tentative value and must be tuned after acquiring in-orbit data. To determine the effect of aerosols and surface BRDF reflection on the retrieval of XCO
2
we simulate GMI measurements in the spectral range of 6310–6380 cm
−1
. We use absolute radiance
ratio radiance
and BRDF methods to retrieve the simulation data separately. The error of the absolute radiance method exceeds 30% when the AOD is 0.05 considering the changes in surface albedo caused by surface direction reflection
and the error of the ratio radiance method is less than 1% in most observations. By contrast
the BRDF method has high accuracy
and its error is less than 0.2%. In the case of high AOD (0.4)
the maximum error of XCO
2
is 4.75% without the inversion of BRDF parameters. The evaluated precisions of the retrieved XCO
2
are less than 0.5% in most cases. To validate the correctness of our algorithm for the measured data
we retrieve the observations of the AOD less than 0.4 from Greenhouse Gases Observing Satellite (GOSAT) L2 data in Beijing urban area in 2016. We find that the correlation is 0.85
and the average error is 1.25 ppm by comparing our retrievals with GOSAT NIES XCO
2
. The retrievals are stable
except the results in summer. The AOD of 64.28% observations in summer is larger than 0.3
and the rest is close to 0.3. The difference in albedo caused by the BRDF characteristics of the urban underlying surface significantly influences the accuracy of XCO
2
retrievals
and the high-precision CO
2
concentration information cannot be obtained when it is not corrected. The aerosol conditions in urban areas are frequently complex. The proposed algorithm for the simultaneous retrieval of surface BRDF parameters and CO
2
content can accurately describe the surface direction reflection. The proposed algorithm improves the retrieval accuracy and data utilization of GMI data in the urban areas of Beijing.
高分五号GMICO2反演BRDF气溶胶
GF-5GMICO2 retrievalBRDFaerosol
Aben I, Hasekamp O and Hartmann W. 2007. Uncertainties in the space-based measurements of CO2 columns due to scattering in the Earth’s atmosphere. Journal of Quantitative Spectroscopy and Radiative Transfer, 104(3): 450–459
Dhakal S. 2009. Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy, 37(11): 4208–4219
Friedl M A, McIver D K, Hodges J C F, Zhang X Y, Muchoney D, Strahler A H, Woodcock C E, Gopal S, Schneider A, Cooper A, Baccini A, Gao F and Schaaf C. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1/2): 287–302
Hammerling D M, Michalak A M and Kawa S R. 2012. Mapping of CO2 at high spatiotemporal resolution using satellite observations: global distributions from OCO‐2. Journal of Geophysical Research: Atmospheres, 117(D6): D06306
Liu Y, Yang D X and Cai Z N. 2013. A retrieval algorithm for TanSat XCO2 observation: retrieval experiments using GOSAT data. Chinese Science Bulletin, 58(13): 1520–1523
Myhrvold N P and Caldeira K. 2012. Greenhouse gases, climate change and the transition from coal to low-carbon electricity. Environmental Research Letters, 7(1): 014019
O’Dell C W, Connor B, Bösch H, O’Brien D, Frankenberg C, Castano R, Christi M, Eldering D, Fisher B, Gunson M, McDuffie J, Miller C E, Natraj V, Oyafuso F, Polonsky I, Smyth M, Taylor T, Toon G C, Wennberg P O and Wunch D. 2012. The ACOS CO2 retrieval algorithm-Part 1: description and validation against synthetic observations. Atmospheric Measurement Techniques, 5(1): 99–121
Oishi Y, Ishida H, Nakajima T Y, Nakamura R and Matsunaga T. 2017. The impact of different support vectors on GOSAT-2 CAI-2 L2 cloud discrimination. Remote Sensing, 9(12): 1236
Schmidt M, Udelhoven T, Röder A and Gill T K. 2012. Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna. Journal of Applied Remote Sensing, 6(1): 063512
Shuai Y M, Schaaf C B, Strahler A H, Liu J C and Jiao Z T. 2008. Quality assessment of BRDF/albedo retrievals in MODIS operational system. Geophysical Research Letters, 35(5): L05407
王开存, 王建凯, 王普才, 陈洪滨. 2008. 用MODIS反演北京城市地区地表反照率精度以及算法改进. 大气科学, 32(1): 67–74
Wang K C, Wang J K, Wang P C and Chen H B. 2008. The accuracy of MODIS albedo over Beijing Urban area and its algorithm improvement. Chinese Journal of Atmospheric Sciences, 32(1): 67–74
Wu H, Wang X H, Ye H H, Jiang Y and Duan F H. 2018. Error analysis of the greenhouse-gases monitor instrument short wave infrared XCO2 retrieval algorithm. Journal of Applied Remote Sensing, 12(1): 016015
叶函函, 王先华, 吴军, 方勇华, 麻金继, 江新华, 韦秋叶. 2013. 大气CO2反演的地表反射率影响分析与比值反演方法. 光谱学与光谱分析, 33(8): 2182–2187
Ye H H, Wang X H, Wu J, Fang Y H, Ma J J, Jiang X H and Wei Q Y. 2013. Study of the effect of surface reflectance on atmospheric CO2 retrieval and ratio spectrometry. Spectroscopy and Spectral Analysis, 33(8): 2182–2187
Yokota T, Yoshida Y, Eguchi N, Ota Y, Tanaka T, Watanabe H and Maksyutov S. 2009. Global concentrations of CO2 and CH4 retrieved from GOSAT: first preliminary results. Sola, 5: 160–163
张虎, 焦子锑, 董亚东, 李佳悦, 李小文. 2015. 利用BRDF原型和单方向反射率数据估算地表反照率. 遥感学报, 19(3): 355–367
Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and surface directional reflectance. Journal of Remote Sensing, 19(3): 355–367
张晗, 余超, 苏林, 李令军, 范萌, 王雅鹏, 陈良富. 2017. MODIS和OMI数据评估阅兵期间北京市大气减排成效. 遥感学报, 21(4): 622–632
Zhang H, Yu C, Su L, Li L J, Fan M, Wang Y P and Chen L F. 2017. Emission control effects observed from space during the military parade 2015 in Beijing. Journal of Remote Sensing, 21(4): 622–632
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