Landsat-8地表温度反演及其与MODIS温度产品的对比分析

Land surface temperature retrieved from Landsat-8 and comparison with MODIS temperature product

【目的】地表温度是区域与全球尺度地表过程分析与模拟的重要参数,在地表与大气能量交换的过程中扮演着重要的角色。本文使用3种算法对北京地区Landsat-8影像进行地表温度反演,并使用MODIS地表温度产品对反演结果进行交叉验证,评估Landsat-8用于地表温度反演的精度与适用性,为后续使用Landsat-8反演地表温度的研究提供参考。【方法】对反演地表温度所需的3个重要参数(大气平均作用温度、地表比辐射率、水汽含量)进行获取,得到3个算法的反演结果后,对各个算法的结果进行敏感性和差值分析,并将结果与同期MOD11A1地表温度产品进行对比分析。主要分析手段包括北京市不同行政区之间算法结果与温度产品的平均温度结果比较、不同地类之间算法结果与温度产品的平均温度结果比较,以及选取尺度效应较低、温度随时间变化较小的密云水库中心区域比较两者的温差。【结果】3个算法的反演结果总体平均温差不超过1 K,其中Cristóbal等提出的改进后单通道算法与其他两个算法的温差最小,Wang等提出的改进后单窗算法与其他两个算法的温差最大。Landsat-8地表温度反演结果普遍高于MODIS温度产品。通过选取密云水库中心区域对本研究反演结果和温度产品进行对比可以得出,3种算法结果与MODIS温度产品的总平均差值为1.373 K。【结论】反演结果总体上具有较理想的反演精度。Jiménez-Mu?oz提出的劈窗算法具有最好的敏感性分析结果,且与MODIS温度产品的结果最接近。Landsat-8反演结果与MODIS温度产品在总体地温分布规律上保持一致,但Landsat-8因具有更高的分辨率而能更好地分辨小地块不同地类的地温差异,在精确反演地表温度领域拥有更大的优势。

[Objective]Land surface temperature(LST)is a key parameter for analyzing and simulating land surface processes from local to global scales,which plays an important role in the processes of energy exchange between land surface and atmosphere.As the latest achievement of the series of Landsat missions,Landsat-8 has two thermal infrared channels compared with previous Landsat version,and has higher spatial resolution compared with other sensors such as AVHRR or MODIS,it has greater advantages when it comes to the use of land surface temperature retrieval.This paper uses three different algorithms including the improved mono-window algorithms proposed by Wang(2015),the improved single channel algorithm proposed by Cristóbal(2018)and the split window algorithm proposed by Jiménez-Mu?oz(2014),respectively to retrieve land surface temperature from Landsat-8 data in Beijing region,and chose MODIS LST product for cross validation in order to assess the accuracy and applicability of Landsat-8 used for LST retrieval,and provide a reference for the subsequent study of Landsat-8 LST retrieval.[Method]This paper determines and estimates 3 important parameters used for LST retrieval:atmospheric effective temperature,land surface emissivity,and water vapor contents.Sensitivity and differential analysis were conducted after getting the LST result by 3 algorithms.Comparative analysis methods between the retrieval result and MOD11 A1 product with the closest satellite passing time to Landsat-8 include the comparison of average temperature between retrieval result and MODIS temperature product by different administrative regions and different ground features,as well as choosing the center region of Miyun Reservoir,which has lower scale effect and little temperature variation to compare the temperature difference between the above two.[Result]The general average temperature difference between the three algorithms was less than 1 K,and the temperature difference between the improved single channel algorithm proposed by Cristóbal(2018)and the other two algorithms was the smallest,and the temperature difference between the improved mono-window algorithms proposed by Wang(2015)and the other two algorithms was the largest.The result of Landsat-8 LST retrieval was generally higher than MODIS temperature product,the mean temperature difference was1.3 K.By selecting the center area of Miyun Reservoir to compare the results between Landsat-8 LST retrieval and the temperature products,the total average difference between the three algorithm results and the MODIS temperature products was 1.373 K.[Conclusion]The retrieval results have an overall satisfying accuracy,among which,the split window algorithm proposed by Jiménez-Mu?oz(2014)has the best sensitivity analysis result,and has the closest result to MODIS temperature product.The results of Landsat-8 retrieval are consistent with the distribution of MODIS temperature products in the overall LST distribution,but Landsat-8 can better distinguish the temperature differences between different types of small feature and have a greater advantage when it comes to the use of accurate retrieval of land surface temperature because of the higher resolution.