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| 土壤发射率光谱提取算法的对比研究 | |
| 引用本文： | 程洁,肖青,李小文,柳钦火,杜永明.土壤发射率光谱提取算法的对比研究[J].遥感学报,2008,12(5). |
| 作者姓名： | 程洁 肖青 李小文 柳钦火 杜永明 |
| 作者单位： | 1. 中国科学院,遥感应用研究所,北京师范大学,遥感科学国家重点实验室,北京,100101;中国科学院研究生院,北京,100039 2. 中国科学院,遥感应用研究所,北京师范大学,遥感科学国家重点实验室,北京,100101 3. 中国科学院,遥感应用研究所,北京师范大学,遥感科学国家重点实验室,北京,100101;北京师范大学,遥感与GIS研究中心,北京,100875 |
| 基金项目： | 国家自然科学基金项目 ， 中国科学院知识创新工程重要方向性项目 ， 国家重点基础研究发展项目 |
| 摘 要： | 土壤的发射率具有较大的不确定性,为了准确提取土壤的发射率,利用ASTER光谱库中的58条土壤光谱,模拟产生了热红外高光谱数据集,利用这些数据进行了土壤的发射率提取试验,分析了较为典型的几种温度发射率分离方法,如NEM、ISSTES、α剩余法、MMD、TES在土壤发射率提取中的适用性、稳定性和精度,并根据分析的结果对各种算法在土壤发射率反演中的应用进行了相应改进.对于NEM方法,给出了最优的最大发射率;对于MMD方法,提出了一种比原平均-最小最大发射率之差更好的经验关系;在TES方法中,使用ISSTES代替原先的NEM方法,获得了精确的发射率初始值.基于模拟数据的算法分析结果表明,对于地面测量高光谱数据的土壤发射率信息提取,ISSTES准确度最高.最后给出了使用这5种方法由地面实测高光谱数据提取的土壤发射率光谱实例,提取的发射率光谱的分布情况很好印证了基于模拟数据的算法分析结果. |
| 关 键 词： | 温度发射率分离 反演 土壤发射率 高光谱数据 遥感 |
Algorithm Research of Soil Emissivity Extraction |
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| CHENG Jie,XIAO Qing,LIXiao-wen,LIUQin-huo and DU Yong-ming.Algorithm Research of Soil Emissivity Extraction[J].Journal of Remote Sensing,2008,12(5). | |
| Authors: | CHENG Jie XIAO Qing LIXiao-wen LIUQin-huo and DU Yong-ming |
| Institution: | State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing100101,China;Graduate schoo lof Chinese Academy of Sciences, Beijing 100039,China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing100101,China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing100101,China;Center for Remote Sensing and GIS, Beijing Normal University, Beijing 100875,China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing100101,China;Graduate schoo lof Chinese Academy of Sciences, Beijing 100039,China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing100101,China;Graduate schoo lof Chinese Academy of Sciences, Beijing 100039,China |
| Abstract: | Temperature and emissivity are two mi portantparameters of thermal infrared remote sensing. Surface emitted radiance is a function of both its kinetic temperature and its spectral emissivity. Temperature and emissivity separation from radiometricmeasurements relates to the problems ofsolvingN+1 parameterswithN equations. Some approxmi ations or assumptionsmust be taken to reduce the number of unknown parameters and make the equation complete. Many temperature and emissivity separation algorithms have been put forward according to the different strategies. Mostof these temperature and emissivity separation algorithms are designed for processingmulti-spectral data. As far as hyperspectral FTIR data is concerned, their applicability needs to be evaluated. Moreover, we exploreswhether there is an optmi al algorithm for retrieving soil emissivity from hyperspectralFTIR data. Five typical temperature emissivitymethods (e. g. NEM, ISSTES, alpha residualmethod, MMD and TES) were investigated in this study by smi ulated dataset. The smi ulated dataset is composed of two parts, smi ulated ground-leaving radiance and smi ulated atmospheric downward radiance. Totally58 soildirectionalhemispherical reflectancewere obtained from theASTER spectral library, and were converted to emissivities based on Kirchhoffs' law. The soil temperaturewas assigned as300K. The atmospheric downward radiancewas smi ulated byMODTRAN4.0 inwhich the1976US atmosphere modelwas used. The smi ulated datawas added a random Gaussnoisewith zeromean and standard deviation of3.14e-9W / cm2/sr/cm-1, whichwas theNoiseEquivalentSpectralRadiance (NESR) ofourspectrometerBOMENMR 304measured in laboratory. In order to evaluate these algorithms sensitivity of response to the instrument random noise, the smi ulated data added with zero mean and standard deviation of 2, 4, 6, 8, 10, 15, 20 tmi es of instrumentNESR were also considered. On the basis of the result, we draw some valuable conclusions. ForNEM, an optmi almaxmi um emissivity of0.985 is suggested, the RMSE of derived soil emissivities and mean absolute temperature is minmi um with this maxmi um emissivity. A better empirical relationship has been discovered to substitute the original mean-minmi um maxmi um difference relationship in MMD method. The alpha residual method is not suitable to retrieve soil emissivity from hyperspectralFTIR data. By comparing the accuracy of NEM and ISSTES, we find that the RMSE of derived soil emissivities suingNEM under ideal condition is two tmi es than ISSTES, so the originalNEMmodule hasbeen replaced by ISSTES to acquire the accurate initial value of emissivity inTES, the original power relationship inMMD module ofTES has been replaced by a linear relationship forhigher fitprecision. As a conclusion, we find the ISSTES is the bestmethod with the true instrument noise leve,l the RMSE of derived soil emissivities is only 0. 0007 and the mean absolute temperature bias is only 0. 02K. The RMSE of derived soil emissivities and the mean absolute temperature bias monotonically growwith the increase of instrumentnoise leve.l Finally, we presentan example ofsoilemissivity extraction using fivemethodsmentioned abovewith ground-basedmeasured hyperspectraldata, whichweremeasured atour field test sitewith BOMENMR 304 spectrometer on the afternoon of September 26, 2005. The distribution of derived emissivity spectra verifies the results of algorithm analysis. |
| Keywords: | temperature emissivity separation inversion soi l emissivity hyperspectral data remote sensing |
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