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遥感技术与应用  2009, Vol. 24 Issue (5): 617-621    DOI: 10.11873/j.issn.1004-0323.2009.5.617
技术研究与图像处理     
SMMR与SSM/I被动微波亮度温度数据交叉定标
戴礼云1,2,车 涛1
1.中国科学院寒区旱区环境与工程研究所,甘肃 兰州 730000 2.中国科学院研究生院,北京 100039
Cross-platform Calibration of Passive Microwave Brightness Temperature
DAI Li-yun 1,2,CHE Tao1
1.Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou 730000,China 2.GraduateUniversity of the Chinese Academy of Sciences,Beijing 100039,China
 全文: PDF(527 KB)  
摘要:

过去30 a星载微波辐射计(SMMR和SSM/I)长时间序列的被动微波亮度温度数据,在陆地表层系统科学以及气候变化研究中起到了非常重要的作用。由于卫星及其携带的微波辐射计的更新,不同传感器所测得的同一地物在同一时间的亮温存在不同程度的偏差,通过分析相邻传感器重复观测时期同一地表18/19GHz和37GHz水平和垂直极化的亮度温度,并以DMSP的F13卫星上的SSM/I传感器为标准,建立了4个通道的交叉定标系数。

关键词: 亮度温度 被动微波 交叉定标 SMMR SSM/I    
Abstract:

The long time series of passive microwave satellite data (SMMR and SSM/I) have provided important information on the earth surface science and climate research in the past decades.Due to update of satellite-based radiometers and their platforms,there are biases among brightness temperature from different sensors,at the same place.In order to obtain consistent brightness temperature datasets,the relationship of the microwave brightness temperatures from similar sensors on successive satellite platforms must be understood.The brightness temperature data at 18 and 37GHz channels of Nimbus-7 and 19GHz,37GHz channels of DMSP were analyzed.The cross cabibration coefficiemts are estimated based on F13.

Key words:  Brightness temperature    Passive microwave    Cross-platform calibration    SMMR    SSM/I
收稿日期: 2009-02-09 出版日期: 2010-08-24
基金资助:

国家重点基础研究发展计划(2007CB411506),国家自然科学基金项目(40601065)和冰冻圈科学国家重点实验室开放基金项目(SKLCS 08-01)资助

作者简介: 戴礼云(1980-),女,硕士研究生,主要研究方向为被动微波数据的定标和应用。E-mail:dlydai@163.com
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引用本文:

戴礼云, 车 涛. SMMR与SSM/I被动微波亮度温度数据交叉定标[J]. 遥感技术与应用, 2009, 24(5): 617-621.

DAI Li-Yun, CHE Tao. Cross-platform Calibration of Passive Microwave Brightness Temperature. Remote Sensing Technology and Application, 2009, 24(5): 617-621.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2009.5.617        http://www.rsta.ac.cn/CN/Y2009/V24/I5/617

 [1] Van der Veen C J,Jezek K C.Seasonal Variations in Brightness Temperature for Central Antarctica[J].Annals of Glaciology,1993,17:300-306.
 [2] Derksen C,LeDrew E,Walker A,et al.The Influence of Sensor Overpass Time on Passive Microwave Retrieval of Snow Cover Parameters[J].Remote Sensing Environment,2000,71(3):297-308.
 [3] Che Tao,Li Xin,Gao Feng.Estimation of Snow Water Equivalent in the Tibetan Plateau Using Passive Microwave Remote Sensing Data (SSM/I)[J].Journal of Glaciology and Geocryology,2004,26(3):363-368.[车涛,李新,高峰.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,2004,26(3):343-368.]
 [4] Torinesi O,Fily M,Genthon C.Interannual Variability and Trend of the Antarctic Summer Melting Period from 20 Years of Spaceborne Microwave Data[J].Journal of Climate,2003,16(7):1047-1060.
 [5] Picard G,Fily M.Surface Melting Observations in Antarctica by Microwave Radiometers:Correcting 26-year Time Series from Changes in Acquisition Hours[J].Remote Sensing of Environment,2006,104:325-336.
 [6] Jezek K,Merry C,Cavalieri D.Comparison of SMMR and SSM/I Passive Microwave Data Collected Over Antarctica[J].Ann.Glaciol,1993,17:131-136.
 [7] Abdalati W,Steffen K,Otto C,et al.Comparison of Brightness Temperatures from SSMI Instruments on the DMSP F8 and F11 Satellites for Antarctica and the Greenland Ice Sheet[J].International Journal Remote Sensing,1995,16(7):1223-1229.
 [8] Stroeve J,Maslanik J,Li X.An Intercomparison of DMSP F11 and F13-derived Sea Ice Products[J].Remote Sensing Environ,1998,64(2):132-152.
 [9] Foster J L,Hall D K,Kelly R E J,et al.Seasonal Snow Extent and Snow Mass in South American Using SMMR and SSM/I Passive Microwave Data(1979-2006)[J].Remote Sensing of Environment,2009,113:291-305.

10 Derksen C,Walker A E,Member.Identification of Systematic Bias in the Cross-Platform(SMMR and SSM/I)EASE-Grid Brightness Temperature Time SeriesJ.IEEE Transactions on Geoscience and Remote Sensing,2003,41(4):910-915.

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