被动微波遥感具有较强的穿透云、雾、雨、雪获取地表辐射信息的能力，在有云情况下的地表温度反演中明显优于热红外遥感。但云层和除去云层之外的大气，也会对微波信号有影响。以理论分析和模型模拟相结合的方式研究有云大气对AMSR-E（the advanced microwave scanning radiometer-earth observing system）传感器各通道微波信号的影响。结果表明：6.925和10.65 GHz 2个通道可以忽略有云大气的影响；18.7、23.8、36.5和89 GHz 4个通道必须考虑其影响，且它们可以表示成大气可降水量和云中液态水含量的函数。在此基础上，通过进一步分析，选出18.7、23.8和36.5 GHz 3个通道作为将来构建较高精度的被动微波地表温度反演算法的预选通道。
Passive microwave remote sensing has the ability to obtain surface radiation information through clouds, fog, rain, and snow. Therefore, in cloudy weather, it is obviously better than thermal infrared remote sensing in land surface temperature retrieval. However, the clouds and the atmospheric molecules affect the microwave signals to some extent. The effects of cloudy atmosphere on microwave signals in the advanced microwave scanning radiometer-earth observing system (AMSR-E) channels were studied by the way of combining theoretical analysis and model simulation. Results show that the effects can be ignored in 6.925 and 10.65 GHz channels, but cannot be ignored in 18.7, 23.8, 36.5, and 89 GHz channels. The effects in the last four channels can be expressed as functions of precipitable water vapor and cloud liquid water. Based on the above study and further analysis, three channels, 18.7, 23.8, and 36.5 GHz, are selected to build a land surface temperature inversion algorithm with higher accuracy.
[1] 徐希孺, 陈良富, 庄家礼. 基于多角度热红外遥感的混合像元组分温度演化反演方法[J]. 中国科学:地球科学, 2001, 31(1):81-88.
[2] Wan Z, Zhang Y, Zhang Q, et al. Quality assessment and validation of the MODIS global land surface temperature[J]. International Journal of Remote Sensing, 2004, 25(1):261-274.
[3] 范熙伟. 考虑气溶胶和高空薄云影响的地表温度反演方法研究[D]. 北京:中国科学院地理科学与资源研究所, 2015.
[4] 刘奇, 傅云飞, 冯沙. 基于ISCCP观测的云量全球分布及其在NCEP再分析场中的指示[J]. 气象学报, 2010, 68(5):689-704.
[5] 贾媛媛. 被动微波遥感数据反演地表温度算法研究[D]. 北京:中国科学院地理科学与资源研究所, 2007.
[6] 刘曾林. 被动微波遥感地表温度算法研究[D]. 北京:中国科学院地理科学与资源研究所, 2013.
[7] 周芳成, 宋小宁, 李召良,等. 微波遥感定量反演研究中大气参数的估算模型[J]. 中国科学院大学学报, 2015, 32(1):63-69.
[8] 姚志刚, 陈洪滨. 七通道微波辐射计遥感大气温度廓线的性能分析[J]. 气象科学, 2005, 25(2):133-141.
[9] 黄兴友, 张曦, 冷亮,等. 基于MonoRTM模型的微波辐射计反演方法研究[J]. 气象科学, 2013, 33(2):138-145.
[10] Qiu Y, Shi J, Jiang L, et al. Study of atmospheric effects on AMSR-E microwave brightness temperature over Tibetan Plateau[C]//Geoscience and remote sensing symposium, 2007. IGARSS 2007. IEEE International. IEEE, 2007:1873-1876.
[11] Qiu Y, Shi J, Lemmetyinen J, et al. The Atmosphere influence to AMSR-E measurements over snow-covered areas:simulation and experiments[C]//Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009. IEEE, 2009, 2:Ⅱ-610-Ⅱ-613.
[12] 邱玉宝. 陆面被动微波遥感大气影响分析与校正研究[D]. 北京:中国科学院遥感与数字地球研究所, 2008.
[13] 王伟民, 刘华强, 王桂玲,等. 大气科学基础[M]. 北京:气象出版社, 2011.
[14] 周秀骥. 大气微波辐射及遥感原理[M]. 北京:科学出版社, 1982.
[15] 邱玉宝, 石利娟, 施建成,等. 大气对星载被动微波影响分析研究[J]. 光谱学与光谱分析, 2016, 36(2):310-315.
[16] 刘本培, 蔡运龙. 地球科学导论[M]. 北京:高等教育出版社, 2000.
[17] Galve J M, Coll C, Caselles V, et al. An atmospheric radiosounding database for generating land surface temperature algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5):1547-1557.
[18] Wu H, Ni L, Wang N, et al. Estimation of atmospheric profiles from hyperspectral infrared IASI sensor[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3):1485-1494.
[19] 车云飞, 马舒庆, 杨玲,等. 云对地基微波辐射计反演湿度廓线的影响[J]. 应用气象学报, 2015(2):193-202.
[20] 白翎, 师春香, 刘冰,等. CRT M微波亮温模拟对地表和云参数的敏感性分析[J]. 气象, 2014, 40(11):1363-1371.
[21] 董佩明, 王海军, 韩威,等. 水物质对云雨区卫星微波观测模拟影响[J]. 应用气象学报, 2009, 20(6):682-691.
[22] 顾成明, 王云峰, 张晓辉,等. 云参数对微波亮温模拟计算的影响试验[J]. 应用气象学报, 2016, 27(3):380-384.
[23] Kerr Y H, Njoku E G. A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(3):384-393.
[24] Ulaby F T, Moore R K, and Fung A K. Microwave remote sensing Ⅰ:fundamentals and radiometry[M]. Addison-Wesley Publishing Company, 1981.
[25] 赵英时. 遥感应用分析原理与方法[M]. 北京:科学出版社, 2013.
[26] 盛裴轩, 毛节泰, 李建国, 等. 大气物理学[M]. 北京:北京大学出版社,2003.
[27] 王明康. 云和降水物理学[M]. 北京:科学出版社, 1991.
[28] 顾震潮. 云雾降水物理基础[M]. 北京:科学出版社, 1980.
[29] Li L, Njoku E G, Im E, et al. A preliminary survey of radio-frequency interference over the US in Aqua AMSR-E data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2):380-390.
[30] Njoku E G, Ashcroft P, Chan T K, et al. Global survey and statistics of radio-frequency interference in AMSR-E land observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(5):938-947.
