全球分光地表反照率的长期变化

大气与环境光学学报 ›› 2022, Vol. 17 ›› Issue (3): 279-293.

• 大气光学 • 下一篇

全球分光地表反照率的长期变化

何娟1, 张华1;2∗, 苏红娟1;3, 周喜讯1;3, 陈琪1, 谢冰4, 游婷5

1 中国气象科学研究院灾害天气国家重点实验室, 北京 100081; 2 南京信息工程大学气象灾害预报预警与评估协同创新中心, 气象灾害教育部重点实验室, 江苏南京 210000; 3 中国科学院大学, 北京 100049; 4 中国气象局气候研究开放实验室/中国气象局气候中心, 北京 100081; 5 重庆市气象科学研究所, 重庆 401147

收稿日期:2021-01-22 修回日期:2022-03-08 出版日期:2022-05-28 发布日期:2022-05-28
通讯作者: E-mail: huazhang@cma.gov.cn E-mail:huazhang@cma.gov.cn
作者简介:何娟 (1994 - ), 女, 四川广安人, 硕士研究生, 主要从事土地利用变化的辐射效应研究。 E-mail: hejuan181@mails.ucas.edu.cn
基金资助:
Supported by National Key R&D Program of China (国家重点研发计划项目, 2017YFA0603502), Key National Natural Science Foundation of China (国家自然科学基金重点项目, 91644211)

Study on long-term change of global spectral surface albedo

HE Juan1, ZHANG Hua1;2∗, SU Hongjuan1;3, ZHOU Xixun1;3, CHEN Qi1, XIE Bing4, YOU Ting5

1 State Key Laboratory of Disaster Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China; 2 Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory of Meteorological Disaster (KLME), Nanjing University of Information Science & Technology, Nanjing 210000, China; 3 University of Chinese Academy of Sciences, Beijing 100049, China; 4 Laboratory for Climate Studies, China Meteorological Administration, Beijing 100081, China; 5 Chongqing Institute of Meteorological Science, Chongqing 401147, China

Received:2021-01-22 Revised:2022-03-08 Published:2022-05-28 Online:2022-05-28
Contact: ZHANG Hua E-mail:huazhang@cma.gov.cn

摘要/Abstract

摘要： 为了精确地定量研究土地利用变化对地表反照率的影响, 利用公元 850–2100 年期间土地利用数据集获取了一套长时期的全球分光 (近红外、可见光、短波) 地表反照率数据集, 并进行了详细的分析和对比验证。验证结果表明, 所得的分光地表反照率与 MODIS 数据对应结果的空间分布特征基本一致; 在非冰雪覆盖区的可见光反照率差别在 −0.0081∼0.0029 之间, 精度较高; 差别最大的区域为南、北半球中高纬度的冰雪覆盖区。研究表明, 自 1860 年以来, 所有典型区域 (包括中国东部、欧洲东南部、美国中东部和巴西南部) 的城市建成区逐年增加, 特别是巴西南部的自然植被逐年减少; 在 1860–1980 年间, 各区域主要表现为自然植被向城市建成区和耕地转化; 在 1980–2015 年, 巴西南部耕地类型持续增加, 其余区域均表现出自然植被逐步恢复的变化趋势。此外, 不同土地利用类型的分光反照率存在很大差别, 例如, 春季可见光反照率在冰雪表面为 0.5069, 混交林为 0.0444, 而城市建成区为 0.0870; 且受不同纬度和下垫面性质的影响, 同一土地利用类型的分光地表反照率也存在明显的空间差异, 例如, 春季草原的可见光反照率最大为 0.4915, 最小为 1.127×10−4。这套长时期全球分光地表反照率数据集可以为土地利用变化驱动气候变化的相关定量研究奠定数据基础。

关键词: 地表反照率, 土地利用, 土地利用数据集, 中分辨率成像光谱仪, 人类活动

Abstract: In order to study the impact of land use change on the surface albedo accurately and quantitatively, a long-term global spectral surface albedo dataset (near-infrared, visible, shortwave) is obtained by using land use types dataset from 850 A.D. to 2100 A.D. provided by land-use harmonization (LUH2) project, and then detailed analysis and comparative verification are carried out. The results show that, the spatial distribution characteristics of spectral surface albedo obtained in this work are consistent with those of MODIS. The visible albedo in non-snow and non-ice covered area varies from −0.0081 to 0.0029 with high precision, while the largest difference is found in snow and ice covered area in the middle and high latitudes of the southern and northern hemisphere. It is also shown that, since 1860, the urban built-up area has increased year by year in all typical regions (including Eastern China, Southeastern Europe, Mid-east Unite States and Southern Brazil), while the natural vegetations have decreased in Southern Brazil. From 1860 to 1980, natural vegetations were mainly transformed into urban and croplands in each region. In 1980∼2015, croplands continued to increase in Southern Brazil, while the other regions showed a trend of gradual restoration of natural vegetations. In addition, the spectral albedo of different land cover types varies greatly. For example, the visible albedo in spring is 0.5069 on the surface of ice/snow, while it is 0.0444 in mixed forest and 0.0870 in urban. Under the influence of different latitudes and underlying surface properties, even the spectral albedo of the same land use type also has obvious spatial differences. For example, the maximum visible albedo of grassland in spring is 0.4915, and the minimum is 1.127×10−4. It is believed that the long-term global spectral albedo dataset can lay a data foundation for the quantitative research on the climate change driven by land use change.

Key words: surface albedo, land use, land-use harmonization project, moderate-resolution imaging spectroradiometer, human activities

中图分类号:

P422

何娟, 张华, ∗, 苏红娟, 周喜讯, 陈琪, 谢冰, 游婷. 全球分光地表反照率的长期变化[J]. 大气与环境光学学报, 2022, 17(3): 279-293.

HE Juan, ZHANG Hua, ∗, SU Hongjuan, ZHOU Xixun, CHEN Qi, XIE Bing, YOU Ting. Study on long-term change of global spectral surface albedo[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(3): 279-293.

参考文献 45

[1]	Collins W D, Rasch P J, Boville B A, et al. Description of the NCAR community atmosphere model (CAM 3.0) [R]. (No.
	NCAR/TN-464+STR), University Corporation for Atmospheric Research, 2004.
[2]	Sellers P J, Meeson B W, Hall F G, et al. Remote sensing of the land surface for studies of global change: Models-algorithmsexperiments [J]. Remote Sensing of Environment, 1995, 51(1): 3-26.
[3]	Dickinson R E. Land processes in climate models [J]. Remote Sensing of Environment, 1995, 51(1): 27-38.
[4]	He J. Changes of Surface Albedo and Its Radiation Effects Caused by Land Use Change [D]. Beijing: Chinese Academy of
	Meteorological Sciences, 2021.
	何娟. 土地利用变化导致的地表反照率变化及其辐射效应研究 [D]. 北京: 中国气象科学研究院, 2021.
[5]	Shen Z B, Zuo H C. The study on the variation of the surface albedo over the Qinghai-Xizang plateau [J]. Plateau Meteorology,
19	93, 12(3): 294-301.
	沈志宝, 左洪超. 青藏高原地面反射率变化的研究 [J]. 高原气象, 1993, 12(3): 294-301.
[6]	Zhai J, Liu R Go, Liu J Y, et al. Radiative forcing over China due to albedo change caused by land cover change during
19	90–2010 [J]. Acta Geographica Sinica, 2013, 68(7): 875-885.
	翟俊, 刘荣高, 刘纪远, 等. 1990–2010 年中国土地覆被变化引起反照率改变的辐射强迫 [J]. 地理学报, 2013, 68(7):
87	5-885.
[7]	Barnes C A, Roy D P. Radiative forcing over the conterminous United States due to contemporary land cover land use albedo
	change [J]. Geophysical Research Letters, 2008, 35(9): L09706.
[8]	Bala G, Caldeira K, Mirin A, et al. Biogeophysical effects of CO2 fertilization on global climate [J]. Tellus B: Chemical and
	Physical Meteorology, 2006, 58(5): 620-627.
[9]	Lee X, Goulden M L, Hollinger D Y, et al. Observed increase in local cooling effect of deforestation at higher latitudes [J].
	Nature, 2011, 479(7373): 384-387.
[10]	Rotenberg E, Yakir D. Contribution of semi-arid forests to the climate system [J]. Science, 2010, 327(5964): 451-454.
[11]	Xu Z F, Zhang W Y, Guo W D. The impacts of large scale land use change on regional air temperature and its variability [J].
	China Basic Science, 2015, 17(5): 34-39.
	徐忠峰, 张炜月, 郭维栋. 大尺度土地利用变化对区域气温及其变率的影响 [J]. 中国基础科学, 2015, 17(5): 34-39.
[12]	Andrews T, Betts R A, Booth B B B, et al. Effective radiative forcing from historical land use change [J]. Climate Dynamics,
20	17, 48(11/12): 3489-3505.
[13]	Collins W J, Bellouin N, Doutriaux-Boucher M, et al. Development and evaluation of an Earth-System model-HadGEM2 [J].
	Geoscientific Model Development, 2011, 4(4): 1051-1075.
[14]	Tang R Y, Zhao X, Tang B J, et al. Influence of urbanization on radiative forcing in Beijing [J]. Journal of Beijing Normal
	University (Natural Science), 2017, 53(4): 443-450.
	唐荣云, 赵祥, 唐碧剑, 等. 北京地区城市化对辐射强迫的影响估算 [J]. 北京师范大学学报 (自然科学版), 2017, 53(4):
44	3-450.
[15]	Chen H S, Li X, Hua W J. Numerical simulation of the impact of land use/land cover change over China on regional climates
	during the last 20 years [J]. Chinese Journal of Atmospheric Sciences, 2015, 39(2): 357-369.
	陈海山, 李兴, 华文剑. 近 20 年中国土地利用变化影响区域气候的数值模拟 [J]. 大气科学, 2015, 39(2): 357-369.
[16]	Li Z Q, Garand L. Estimation of surface albedo from space: A parameterization for global application [J]. Journal of Geophysical Research Atmospheres, 1994, 99(D4): 8335.
[17]	Liang S L. Mapping daily snow/ice shortwave broadband albedo from moderate resolution imaging spectroradiometer
	(MODIS): The improved direct retrieval algorithm and validation with Greenland in situ measurement [J]. Journal of Geophysical Research Atmospheres, 2005, 110(D10): D10109.
[18]	Liang S L. Narrowband to broadband conversions of land surface albedo I: Algorithms [J]. Remote Sensing of Environment,
20	01, 76(2): 213-238.
[19]	Liang S L, Shuey C J, Russ A L, et al. Narrowband to broadband conversions of land surface albedo: II. Validation [J]. Remote
	Sensing of Environment, 2003, 84(1): 25-41.
[20]	Liang S L, Strahler A H, Walthall C. Retrieval of land surface albedo from satellite observations: a simulation study [J]. Journal
	of Applied Meteorology, 1999, 38(6): 712-725.
[21]	Liang S L. A direct algorithm for estimating land surface broadband albedos from MODIS imagery [J]. IEEE Transactions on
	Geoscience and Remote Sensing, 2003, 41(1): 136-145.
[22]	Dickinson R E. Land processes in climate models [J]. Remote Sensing of Environment, 1995, 51(1): 27-38.
[23]	Wang K C, Liu J M, Zhou X J, et al. Retrieval of the surface albedo under clear sky over China and its characteristics analysis
	by using MODIS satellite date [J]. Chinese Journal of Atmospheric Sciences, 2004, 28(6): 941-949.
	王开存, 刘晶淼, 周秀骥, 等. 利用 MODIS 卫星资料反演中国地区晴空地表短波反照率及其特征分析 [J]. 大气科学,
20	04, 28(6): 941-949.
[24]	Xu X K, Liu S H. Deriving monthly means surface aibedo of China [J]. Acta Meteorologica Sinica, 2002, 60(2): 215-220.
	徐兴奎, 刘素红. 中国地表月平均反照率的遥感反演 [J]. 气象学报, 2002, 60(2): 215-220.
[25]	Ghimire B, Williams C A, Masek J, et al. Global albedo change and radiative cooling from anthropogenic land cover change,
17	00 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis [J]. Geophysical Research Letters,
20	14, 41(24): 9087-9096.
[26]	Gao F, He T, Wang Z S, et al. Multiscale climatological albedo look-up maps derived from moderate resolution imaging
	spectroradiometer BRDF/albedo products [J]. Journal of Applied Remote Sensing, 2014, 8: 083532.
[27]	Chen A J, Bian L G, Liu Y J, et al. Using MODIS data to retrieve albedo over the Qinghai-Tibet plateau [J]. Journal of Nanjing
	Institute of Meteorology, 2009, 32(2): 222-229.
	陈爱军, 卞林根, 刘玉洁, 等. 应用 MODIS 数据反演青藏高原地区地表反照率 [J]. 南京气象学院学报, 2009, 32(2):
22	2-229.
[28]	Kalnay E, Kanamitsu M, Kistler R, et al. The NCEP/NCAR 40-year reanalysis project [J]. Bulletin of the American Meteorological Society, 1996, 77(3): 437-471.
[29]	Kistler R, Collins W, Saha S, et al. The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation [J].
	Bulletin of the American Meteorological Society, 2001, 82(2): 247-267.
[30]	Hall D K, Riggs G A, Salomonson V V, et al. MODIS snow-cover products [J]. Remote Sensing of Environment, 2002, 83(1/2):
18	1-194.
[31]	Schaaf C B, Gao F, Strahler A H, et al. First operational BRDF, albedo nadir reflectance products from MODIS [J]. Remote
	Sensing of Environment, 2002, 83(1/2): 135-148.
[32]	Schaaf C B, Liu J C, Gao F, et al. Aqua and Terra MODIS Albedo and Reflectance Anisotropy Products [M]. Ramachandran
	B, Justice C O, Abrams M J. Land Remote Sensing and Global Environmental Change. New York: Springer, 2011: 549-561.
[33]	Hurtt G C, Frolking S, Fearon M G, et al. The underpinnings of land-use history: Three centuries of global gridded land-use
	transitions, wood-harvest activity, and resulting secondary lands [J]. Global Change Biology, 2006, 12(7): 1208-1229.
[34]	Hurtt G C, Chini L P, Frolking S, et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global
	gridded annual land-use transitions, wood harvest, and resulting secondary lands [J]. Climatic Change, 2011, 109(1/2): 117-
	161.
[35]	Jiang X, Wang N L, Yang S P. Analysis of global radiation and surface albedo features in summer and autumn in a permafrost
	region of Tanggula range, Tibetan Plateau [J]. Journal of Glaciology and Geocryology, 2007, 29(6): 889-899.
	蒋熹, 王宁练, 杨胜朋. 青藏高原唐古拉山多年冻土区夏、秋季节总辐射和地表反照率特征分析 [J]. 冰川冻土, 2007,
29	(6): 889-899.
[36]	Liu Q Q, Cui Y P, Liu S J, et al. Study on surface albedo of spectral radiation of different land use types in China [J]. Remote
	Sensing Technology and Application, 2019, 34(1): 46-56.
	刘亲亲, 崔耀平, 刘素洁, 等. 中国不同土地利用类型分光辐射地表反照率研究 [J]. 遥感技术与应用, 2019, 34(1): 46-56.
[37]	Chen A J, Cao X Y, Han C H, et al. Spatial-temporal distribution and variation of land surface albedo over the Tibetan Plateau
	during 2000–2016 [J]. Climatic and Environmental Research, 2018, 23(3): 355-365.
	陈爱军, 曹晓云, 韩琛惠, 等. 2000–2016 年青藏高原地表反照率时空分布及动态变化 [J]. 气候与环境研究, 2018, 23(3):
35	5-365.
[38]	Yang J X, Li Z C, Wei Z G, et al. Characteristics of solar spectral radiation and corresponding albedo in sparse vegetation
	region [J]. Acta Energiae Solaris Sinica, 2017, 38(3): 852-859.
	杨佳希, 李振朝, 韦志刚, 等. 稀疏植被地表分光辐射及其反照率特征研究 [J]. 太阳能学报, 2017, 38(3): 852-859.
[39]	Zheng Z Y, Wei Z G, Li Z C, et al. Study of parameterization of surface albedo of bare soil over the Gobi desert in the
	Dunhuang region [J]. Chinese Journal of Atmospheric Sciences, 2014, 38(2): 297-308.
	郑志远, 韦志刚, 李振朝, 等. 敦煌荒漠戈壁地区裸土地表反照率参数化研究 [J]. 大气科学, 2014, 38(2): 297-308.
[40]	盛裴轩, 毛节泰, 李建国, 等. 大气物理学 (第 2 版) [M]. 北京: 北京大学出版社, 2013: 88-89.
[41]	Li X W, Strahler A, Zhu Q J, et al. Geometric-optical bidirectional reflectance modelling of ground objects and its progress in
	measurement [J]. Remote Sensing for Land & Resources, 1991, 3(1): 9-19.
	李小文, Strahler A, 朱启疆, 等. 地物二向性反射几何光学模型和观测的进展 [J]. 国土资源遥感, 1991, 3(1): 9-19.
[42]	Gannon P T. Influences of earth surface and cloud properties on the South Florida sea breeze [R]. NOAA Technical Report
	ERL402-NHELM2, 1978.
[43]	Wu H Y, Tong L. Modeling visible and near-infrared snow surface reflectance-simulation and validation [J]. Chinese Optics
	Letters, 2011, 9(10): 102901-102903.
[44]	Zhang Y L, Dang H S, Xia X L, et al. Retrieval of dynamic surface albedo in Taibai Mountain, Qinling [J]. Resources and
	Environment in the Yangtze Basin, 2010, 19 (S2): 135-140.
	张玉龙, 党海山, 夏小玲, 等. 秦岭太白山地表反照率动态反演 [J]. 长江流域资源与环境, 2010, 19(S2): 135-140.
[45]	Wu H Y. Research of Snow Surface Relfectance in Pixel Scale [D]. Chengdu: University of Electronic Science and Technology
	of China, 2012.
	吴宏伊. 遥感像元尺度上的雪地表反射率研究 [D]. 成都: 电子科技大学, 2012.

全球分光地表反照率的长期变化

Study on long-term change of global spectral surface albedo

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