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高原气象  2018, Vol. 37 Issue (2): 305-316    DOI: 10.7522/j.issn.1000-0534.2017.00050
刘进军, 傅云飞, 李锐, 王雨, 符玉云, 胡继恒
中国科学技术大学地球和空间科学学院, 安徽 合肥 230026
The Influence of Atmosphere to Passive Microwave Retrieval of Snow Depth over Qinghai-Tibetan Plateau
LIU Jinjun, FU Yunfei, LI Rui, WANG Yu, FU Yuyun, HU Jiheng
School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, Anhui, China
 全文: PDF(10235 KB)  
摘要: 利用AMSR-E(Advanced Microwave Scanning Radiometer-EOS)观测的2002-2011年青藏高原上空大气顶上行微波亮温(TBTOA),经过辐射传输计算,对水汽和非降水云进行订正,推算出相应的高原地表上行亮温(TBSRF)。并用这两组亮温估算了青藏高原地区的雪深SDTOASDSRF。通过个例和近10年统计研究发现,低频18.7 GHz亮温几乎不受影响,而大气顶处36.5 GHz亮温明显高于相应的地表亮温。不考虑这一效应,忽略大气的影响将造成青藏高原雪深反演低估(SDTOA < SDSRF)。这种低估在多个个例中出现,在多年平均尺度上也很显著,不可忽略。直接用大气顶微波亮温反演雪深,将造成绝对误差2~3 cm。在青藏高原雪深较浅的区域,相对误差很大,为50%~80%。而在高原雪深较深的地区,相对误差较小为10%~20%。该误差(SDTOA-SDSRF)和云水路径呈较强的负相关(R=-0.45),敏感性为-0.047 cm·(g·m-2-1,该误差对冰云的敏感性较低,和水汽的相关性更弱。通过与MODIS雪盖产品比较发现,用地表出射亮温反演的雪深SDSRF与MODIS雪盖产品吻合得更好。
关键词: 青藏高原微波遥感雪深大气效应    
Abstract: The microwave at low and moderate frequencies (e. g., 18.7 and 36.5 GHz) has good transmittance through the atmosphere, therefor in most algorithms for satellite passive microwave (PMW) remote sensing snow parameters, ignoring the effect of atmosphere and the upwelling microwave brightness temperature at the top of atmosphere (TOA) were directly used to retrieve snow parameters on ground. There are few systematic analysis on the errors introduced by ignoring atmosphere effects. In this paper, we investigated the influences of atmosphere to the PMW retrieval of snow depth over Qinghai-Tibetan Plateau (QTP). We used the upwelling brightness temperature on TOA (TBTOA) measured by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) as the main input data. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product (MYD06_L2) and National Centers for Environmental Prediction (NCEP) FNL reanalysis data (including atmosphere water vapor, atmosphere component and land surface temperature) were used as the ancillary data. The effect of water vapor and non-rainy cloud were corrected and the upwelling brightness temperature at the plateau surface (TBSRF) were derived based on microwave radiation transfer model (MWRT) calculation. We then estimated the snow depths of SDTOA and SDSRF over QTP using these two TBs, respectively. By comparing the two TBs and two SDs, the effect of atmosphere and cloud on PMW remote sensing snow depth were investigated. Through case analysis and nearly 10 years of statistics, we found that:the atmosphere effect on TBs at low frequency microwave 18.7 GHz is weak while TBTOA at 36.5 GHz are significantly warmer than TBSRF. Without considering such effect, the snow depth over QTP would be underestimated (SDTOA<SDSRF). The underestimations are common in multiple cases and significant at multi-year mean scales, therefore should not be neglected. The absolute error (SDTOA-SDSRF) is approximately 2~3 cm. In the region with relative shallow snow, the relative error is up to 50%~80%. While in the region with relative deep snow, the relative error is 10%~20%. The error has strong negative correlation with liquid cloud water path (R=-0.45) with sensitivity of -0.047 cm·(g·m-2)-1. The error is not sensitive to ice cloud and even weaker to column water vapor. Snow extent retrieved from MODIS (MYD10CM product) has better correlation to SDSRF than that to SDTOA. This imply that corrections of the influence of atmosphere can improve the accuracy of satellite PMW retrieval of snow depth over QTP.
Key words: Qinghai-Tibetan Plateau    microwave remote sensing    snow depth    atmosphere effect
收稿日期: 2017-03-01 出版日期: 2018-04-28
ZTFLH:  P412.27  
基金资助: 国家自然科学基金项目(41675022,41375148,41661144007,41375030,91337213);公益性行业(气象)科研专项(GYHY201306077);中国科学院百人计划项目;江苏省气候变化协同创新中心项目;合肥物质研究院培育项目(2014FXZY007)
通讯作者: 傅云飞,;李锐,     E-mail:;
作者简介: 刘进军(1991),男,湖南益阳人,硕士研究生,主要从事卫星遥感应用研究
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刘进军, 傅云飞, 李锐, 王雨, 符玉云, 胡继恒. 青藏高原云和大气对被动微波遥感积雪雪深的影响[J]. 高原气象, 2018, 37(2): 305-316.

LIU Jinjun, FU Yunfei, LI Rui, WANG Yu, FU Yuyun, HU Jiheng. The Influence of Atmosphere to Passive Microwave Retrieval of Snow Depth over Qinghai-Tibetan Plateau. Plateau Meteorology, 2018, 37(2): 305-316.


Armstrong R L, Brodzik M J, 2001. Recent Northern Hemisphere snow extent:A comparison of data derived from visible and microwave satellite sensors[J]. Geophys Res Lett, 28(19):3673-3676. DOI:10.1029/2000GL012556.
Chang A T C, Foster J L, Hall D K, 1987. Nimbus-7 SMMR derived global snow cover parameters[J]. Annals of glaciology, 9(1):39-44.
Chang A T C, Gloersen P, Schmugge T, et al, 1976. Microwave emission from snow and glacier ice[J]. J Glaciol, 16(74):23-39.
Che T, Xin L, Jin R, et al, 2008. Snow depth derived from passive microwave remote-sensing data in China[J]. Annals of Glaciology, 49(1):145-154. DOI:10.3189/172756408787814690.
Dai L, Che T, 2009. Cross-platform calibration of SMMR, SSM/I and AMSR-E passive microwave brightness temperature[C]//Proc. SPIE 7841, Sixth International Symposium on Digital Earth:Data Processing and Applications, 7841:784103. DOI:10.1117/12.873150.
Derksen C, Walker A, Goodison B, 2005. Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada[J]. Remote Sens Environ, 96(3):315-327. DOI:10.1016/j. rse. 2005.02.014.
Dietz A J, Kuenzer C, Gessner U, et al, 2012. Remote sensing of snow-a review of available methods[J]. Int J Remote Sens, 33(13):4094-4134. DOI:10.1080/01431161.2011.640964.
Goodison B E, 1989. Determination of areal snow water equivalent on the Canadian prairies using passive microwave satellite data[C]//Geoscience and Remote Sensing Symposium, 1989. IGARSS'89.12th Canadian Symposium on Remote Sensing., 1989 International. IEEE, 3:1243-1246. DOI:10.1109/IGARSS. 1989.576061.
Grody N C, Basist A N, 1996. Global identification of snowcover using SSM/I measurements[J]. IEEE Trans Geosci Remote Sens, 34(1):237-249. DOI:10.1109/36.481908.
Jain S K, Goswami A, Saraf A K, 2008. Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions[J]. Int J Remote Sens, 29(20):5863-5878. DOI:10.1080/01431160801908129.
Li R, Min Q, Lin B, 2009. Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI)[J]. Remote Sens Environ, 113(9):2011-2018. DOI:10.1016/j. rse. 2009.05.007.
Li R, Min Q, 2013. Dynamic response of microwave land surface properties to precipitation in Amazon rainforest[J]. Remote Sens Environ, 133:183-192. DOI:10.1016/j. rse. 2013.02.001.
Lin B, Minnis P, 2000. Temporal variations of land surface microwave emissivities over the atmospheric radiation measurement program southern great plains site[J]. J Appl Meteor, 39(7):1103-1116. DOI:10.1175/1520-0450(2000)039<1103:Tvolsm>2.0. Co; 2.
Liu G S, 1998. A fast and accurate model for microwave radiance calculations[J]. J Meteor Soc Japan, 76, 335-343.
Min Q, Lin B, Li R, 2010. Remote sensing vegetation hydrological states using passive microwave measurements[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1):124-131. DOI:10.1109/JSTARS. 2009.2032557.
Min Q, Lin B, 2006a. Determination of spring onset and growing season leaf development using satellite measurements[J]. Remote Sens Environ, 104(1):96-102. DOI:10.1016/j. rse. 2006.05.006.
Min Q, Lin B, 2006b. Remote sensing of evapotranspiration and carbon uptake at Harvard Forest[J]. Remote Sens Environ, 100(3):379-387. DOI:10.1016/j. rse. 2005.10.020.
Neale C M U, Mcfarland M J, Chang K, 1990. Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager[J]. IEEE Trans Geosci Remote Sens, 28(5):829-838. DOI:10.1109/36.58970.
Pu Z, Xu L, 2009. MODIS/Terra observed snow cover over the Tibet Plateau:distribution, variation and possible connection with the East Asian Summer Monsoon (EASM)[J]. Theor Appl Climatol, 97(3-4):265-278. DOI:10.1007/s00704-008-0074-9.
Qiu Y, Shi J, Jiang L, et al, 2007. Study of atmospheric effects on AMSR-E microwave brightness temperature over Tibetan Plateau[C]//Geoscience and remote sensing symposium, 2007. IGARSS 2007. IEEE International. 1873-1876. DOI:10.1109/IGARSS. 2008.4779083.
Savoie M H, Armstrong R L, Brodzik M J, et al, 2009. Atmospheric corrections for improved satellite passive microwave snow cover retrievals over the Tibet Plateau[J]. Remote Sens Environ, 113(12):2661-2669. DOI:10.1016/j. rse. 2009.08.006.
Shi J C, Du Y, Du J Y, et al, 2012. Progresses on microwave remote sensing of land surface parameters[J]. Science China Earth Sciences, 55(7):1052-1078. DOI:10.1007/s11430-012-4444-x.
Tait A B, 1998. Estimation of snow water equivalent using passive microwave radiation data[J]. Remote Sens Environ, 64(3):286-291. DOI:10.1109/IGARSS. 1996.516870.
Tedesco M, Wang J R, 2006. Atmospheric correction of AMSR-E brightness temperatures for dry snow cover mapping[J]. IEEE Geosci Remote Sens Lett, 3(3):320-324. DOI:10.1109/LGRS. 2006.871744.
Ulaby F T, Moore R K, Fung A K, 1981. Microwave remote sensing:Active and passive. volume 1-microwave remote sensing fundamentals and radiometry[M]. Reading M A:Addison-Wesley.
Wang J R, Manning W, 2003. Near concurrent MIR, SSM/T-2, and SSM/I observations over snow-covered surfaces[J]. Remote Sens Environ, 84(3):457-470. DOI:10.1016/S0034-4257(02)00134-7.
Wang J R, Tedesco M, 2007. Identification of atmospheric influences on the estimation of snow water equivalent from AMSR-E measurements[J]. Remote Sens Environ, 111(2):398-408. DOI:10.1016/j. rse. 2006.10.024.
Wang X, Doherty S J, Huang J P, 2013. Black carbon and other light-absorbing impurities in snow across Northern China[J]. J Geophys Res Atmos, 118, 1471-1492. DOI:10.1029/2012jd018291.
Wang X, Xu B Q, Ming J, 2014. An Overview of the Studies on Black Carbon and Mineral Dust Deposition in Snow and Ice Cores in East Asia[J]. J Meteorol Res, 28, 354-370, DOI:10.1007/S13351-014-4005-7.
Wang X, Pu W, Zhang X Y, et al, 2015. Water-soluble ions and trace elements in surface snow and their potential source regions across northeastern China[J]. Atmos Environ, 114, 57-65. DOI:10.1016/J. Atmosenv. 2015.05.012.
Zhao H, Fernandes R, 2009. Daily snow cover estimation from advanced very high resolution radiometer polar pathfinder data over Northern Hemisphere land surfaces during 19822004[J]. J Geophys Res:Atmospheres, 114(D5). DOI:10.1029/2008JD011272.
车涛, 李新, 高峰, 2004. 青藏高原积雪深度和雪水当量的被动微波遥感反演[J]. 冰川冻土, 26(3):363-368. Che T, Li X, Gao F, 2004. Estimation of snow water equivalent in the tibetan plateau using passive microwave remote sensing data (SSM/I)[J]. Journal of Glaciology and Geocryology, 3:19.
冯璐, 仲雷, 马耀明, 等, 2016. 基于土壤温湿度观测资料估算藏北高原地区土壤热通量[J]. 高原气象, 35(2):297-308. Feng L, Zhong L, Ma Y M, et al, 2016. Estimation of soil heat flux over the northern Qinghai-Xizang Plateau based on insitu soil temperature and moisture data[J]. Plateau Meteor, 35(2):297-308. DOI:10.7522/j. issn. 1000-0534.2015.00006.
蒋玲梅, 王培, 张立新, 等, 2014. FY3B-MWRI 中国区域雪深反演算法改进[J]. 中国科学:地球科学, 44(3):531-547. Jiang L M, Wang P, Zhang L X, et al, 2014. Improvement of snow depth retrieval for FY3B-MWRI in China[J]. Science China:Earth Sciences, 44(3):531-547. DOI:10.1007/s11430-013-4798-8.
李丹华, 文莉娟, 隆霄, 等, 2017. 积雪对玛曲局地微气象特征影响的观测研究[J]. 高原气象, 36(2):330-339. Li D H, Wen L J, Long X, et al, 2017. Observation study on effects of snow cover on local micro meteorological characteristics in Maqu[J]. Plateau Meteor, 36(2):330-339. DOI:10.7522/j. issn. 1000-0534.2016.00074.
邱玉宝, 石利娟, 施建成, 等, 2016. 大气对星载被动微波影响分析研究[J]. 光谱学与光谱分析, 36(2):310-315. Qiu Y B, Shi L J, Shi J C, et al, 2016. Atmospheric influences analysis on the satellite passive microwave remote sensing[J]. Spectroscopy and Spectral Analysis, 36(2):310-315. DOI:10.1109/IGARSS. 2015.7326276.
王顺久, 2017. 青藏高原积雪变化及其对中国水资源系统影响研究进展[J]. 高原气象, 36(5):1153-1164. Wang S J, 2017. Progresses in variability of snow cover over the Qinghai-Tibetan Plateau and its impact on water resources in China[J]. Plateau Meteor, 36(5):1153-1164. DOI:10.7522/j. issn. 1000-0534.2016.00117.
王艺, 伯玥, 王澄海, 2016. 青藏高原中东部云量变化与气温的不对称升高[J]. 高原气象, 35(4):908-919. Wang Y, Bo Y, Wang C H, 2016. Relations of cloud amount to asymmetric diurnal temperature change in Central and Eastern Qinghai-Xizang Plateau[J]. Plateau Meteor, 35(4):908-919. DOI:10.7522/j. issn. 1000-0534.2015.00033.
周利敏, 陈海山, 彭丽霞, 等, 2016. 青藏高原冬春雪深年代际变化与南亚高压可能联系[J]. 高原气象, 35(1):13-23. Zhou L M, Chen H S, Peng L X, et al, 2016. Possible connection between interdecadal variations of snow depth in winter and spring over Qinghai-Xizang Plateau and South Asia High in summer[J]. Plateau Meteor, 35(1):13-23. DOI:10.7522/j. issn. 1000-0534.2014.00152.
张镱锂, 李炳元, 郑度, 2002. 论青藏高原范围与面积[J]. 地理研究, 21(1):1-8. Zhang Y L, Li B Y, Zheng D, 2002. A discussion on the boundary and area of the Tibetan Plateau in China[J]. Geographical Research, 21(1):1-8. DOI:10.11821/yj2002010001.
郑益群, 钱永甫, 苗曼倩, 等, 2000. 青藏高原积雪对中国夏季风气候的影响[J]. 大气科学, 24(6):761-774. Zheng Y Q, Qian Y P, Miao M Q, et al, 2000. Effect of the Tibetan Plateau snow cover on China summer monsoon climate[J]. Chinese J Atmos Sci, 24(6):761-774. DOI:10.3878/j. issn. 1006-9895.2000.06.04.
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