陆表比辐射率对遥感陆表温度反演结果的影响分析  

The Impact of Land Surface Emissivity on the Retrieval of Land Surface Temperature from Thermal Remote Sensing Data

作　　者：曹广真 闵敏 侯鹏 CAO Guangzhen;MIN Min;HOU Peng(Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,FengYun Meteorological Satellite Innovation Center(FY-MSIC),China Meteorological Administration(LRCVES/CMA),Beijing 100081,China;School of Atmospheric Sciences,Sun Yat-Sen University,Zhuhai 519082,China;State Environmental Protection Key Laboratory of Satellite Remote Sensing,Satellite Environment Center,Ministry of Environmental Protection of People’s Republic of China,Beijing 100094,China)

机构地区：[1]中国气象局中国遥感卫星辐射测量和定标重点开放实验室许健民气象卫星创新中心国家卫星气象中心,北京100081 [2]中山大学大气科学学院,广东珠江519082 [3]环境保护部卫星环境应用中心国家环境保护卫星遥感重点实验室,北京100094

出　　处：《遥感技术与应用》2023年第4期835-841,共7页Remote Sensing Technology and Application

基　　金：国家重点研发计划项目(2018YFF0300101,2018YFB0504900);国家自然科学基金项目(42071393);风云四号地面应用系统工程建设项目等共同资助。

摘　　要：陆表比辐射率(Land Surface Emissivity,LSE)是衡量物体表面以热辐射形式释放能量相对强弱能力的物理量,是影响遥感陆表温度(Land Surface Temperature,LST)的重要基本参数之一。为了分析不同条件下LSE对遥感LST反演结果的影响,基于Himawari8AHI(Advanced Himawari Imager,AHI)的分裂窗通道热红外遥感数据,采用Wan及Dozier的分裂窗算法,研究陆表温度反演结果对LSE的敏感性。首先,对LSE产品分别加入不同均值和标准差的高斯噪声,以此代表LSE不同的误差水平;然后,在其他条件不变的情况下,将带不同噪声的LSE和不带噪声的LSE分别输入分裂窗算法进行LST的反演;最后,分析不同时间、不同水汽、不同观测角度和不同下垫面条件下,LSE对LST的影响。结果表明:(1)无论白天、夜间还是日均LST,输入添加噪声的LSE较输入原始的LSE反演所得的LST数值总体略低,而且随着所添加噪声标准差的增大,所得LST差值的标准差增大。当LSE的噪声标准差为0.01时,所得白天、夜间和日均LST差值的标准差分别为0.48 K、0.52 K和0.34 K;而当LSE的噪声标准差为0.03时,所得LST差值的标准差则分别升高到1.46 K、1.57 K和0.88 K。(2)不管水汽和观测角度条件如何,总体上,输入添加噪声的与输入原始LSE反演所得的LST相关系数随着所加噪声标准差的增大而减小,均方根误差和标准差则随着所加噪声标准差的增大而增大,偏差为负值,其绝对值随所加噪声标准差的增大而减小。(3)对于不同的下垫面,随着所加噪声标准差的增大,LSE添加噪声与不添加噪声反演所得的LST差值的标准差增大,当所加噪声标准差为0.01时,多树草原、开放灌木和草原LST差值的标准差分别为0.52 K、0.51 K和0.53 K。而当所加噪声标准差为0.03时,三者LST差值的标准差分别升高到1.58 K、1.53 K和1.6 K。Land Surface Emissivity(LSE)is a key parameter that measures the ability of the object surface to release energy in the thermal radiation.And it plays an important role in Land Surface Temperature(LST)re⁃trieval from the thermal remote sensing data.To evaluate the effect of Land Surface Emissivity(LSE)on the retrieval of Land Surface Temperature(LST),firstly three groups of Gaussian distribution randoms with differ⁃ent mean and standard deviation values are generated to present the noises of the LSE products.Secondly the well-known Split Window Algorithm(SWA)is selected to retrieve LST with the Advanced Himawari Imager(AHI)data and LSE products added the Gaussian distribution noises.Finally LST difference between retrieved by inputting LSE with noises and that without noise under different conditions(single temporal LST,multi-temporal LST,averaged LST,LST of different water vapor contents and different sensor zenith angles,LST of different land covers)are analyzed.Our study shows that the retrieved LST will be smaller when LSE with noises is input into the SWA;The bigger the noise’s standard deviation is,the bigger the LST difference’s standard deviation will be;When the noise’s standard deviation is 0.01,the standard deviation of the LST dif⁃ference in day,night and daily average is 0.48 K、0.52 K and 0.34 K relatively.While when the noise’s standard deviation is 0.03,the standard deviation of the LST difference in the three different time is 1.46 K、1.57 K and 0.88 K.At conditions of different water vapor contents and different sensor zenith angles,the results show that the correlation coefficient between the LST retrieved with LSE added noise and that without noise will be small⁃er with the bigger of the added noise,while the root mean squared error and standard deviation will be bigger with the bigger of standard deviation of the added noise.The bias volue is less than 0,and its absolute will be smaller with the bigger of standard deviation of the added noise.As for different land covers,whe

关 键 词：陆表比辐射率 陆表温度 高斯分布噪声 分裂窗算法 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]