Comparison of two models for decomposition of land surface temperature image using Landsat TM data
SONG Caiying1, QIN Zhihao1,2, WANG Fei1
1. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China; 2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract：Land surface temperature (LST) is a vital parameter controlling the energy and water balance between atmosphere and land surface. LST image with high spatial resolution compatible with visible bands of Landsat TM is very important for the application of the LST image to many studies such as environmental monitoring. This paper examines the accuracy and applicability of two widely-used models for decomposition of LST images: SUTM and E-Distrad. Landsat TM data acquired in Beijing were used for the study. LST retrieved by the mono-window algorithm (MWA) was used to compare the LST decomposition images by the two models. The results achieved by the authors indicate that SUTM is more applicable than E-Distrad in the regions with low vegetation cover and high LST such as downtown, while the latter is better than the former in the high vegetation cover and relatively cold areas such as water bodies. The RMSE and MAE are 1.522 K and 1.191 K respectively for SUTM and 1.768 K and 1.173 K for E-Distrad. It is thus concluded that both models are applicable for decomposition of LST images for high spatial resolution, but the results of decomposition are different in areas of different vegetation covers.
宋彩英, 覃志豪, 王斐. 基于Landsat TM的地表温度分解算法对比[J]. 国土资源遥感, 2015, 27(1): 172-177.
SONG Caiying, QIN Zhihao, WANG Fei. Comparison of two models for decomposition of land surface temperature image using Landsat TM data. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 172-177.
[1] Lu D S,Weng Q H.Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis,Indiana,USA[J].Remote Sensing of Environment,2006,104(2):157-167. [2] Merlin O,Duchemin B,Hagolle O,et al.Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images[J].Remote Sensing of Environment,2010,114(11):2500-2512. [3] Agam N,Kustas W P,Anderson M C,et al.A vegetation index based technique for spatial sharpening of thermal imagery[J].Remote Sensing of Environment,2007,107(4):545-558. [4] 刘东,李艳,孔繁花.中心城区地表温度空间分布及地物降温效应——以南京市为例[J].国土资源遥感,2013,25(1):117-122. Liu D,Li Y,Kong F H.Spatial distribution of land surface temperature in central city proper and the cooling of objects effect:A case study of Nanjing[J].Remote Sensing for Land and Resources,2013,25(1):117-122. [5] 王斐.基于地表类型的Landsat TM热红外波段遥感影像像元分解算法研究[D].南京:南京大学,2013. Wang F.An Approach to Increase Spatial Resolution of Landsat TM Thermal Band Images Through Pixel Decomposition on the Basis of Land Surface Patterns[D].Nanjing:Nanjing University,2013. [6] Kustas W P,Norman J M,Anderson M C,et al.Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship[J].Remote Sensing of Environment,2003,85(4):429-440. [7] Weng Q H,Lu D S,Schubring J.Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies[J].Remote Sensing of Environment,2004,89(4):467-483. [8] 历华,柳钦火,邹杰.基于MODIS数据的长株潭地区NDBI和NDVI与地表温度的关系研究[J].地理科学,2009,29(2):262-267. Li H,Liu Q H,Zou J,Relationships of LST to NDBI and NDVI in Changsha-Zhuzhou-Xiangtan area based on MODIS data[J].Scientia Geographica Sinica,2009,29(2):262-267. [9] Essa W,Verbeiren B,Van Der Kwast J,et al.Evaluation of the DisTrad thermal sharpening methodology for urban areas[J].International Journal of Applied Earth Observation and Geoinformation,2012,19:163-172. [10] Deng C B,Wu C S.Examining the impacts of urban biophysical compositions on surface urban heat island:A spectral unmixing and thermal mixing approach[J].Remote Sensing of Environment,2013,131:262-274. [11] Deng C B,Wu C S.A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution[J].Remote Sensing of Environment,2013,133:62-70. [12] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595. Xu H Q.A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J].Journal of Remote Sensing,2005,9(5):589-595. [13] 周纪,陈云浩,张锦水,等.北京城市不透水层覆盖度遥感估算[J].国土资源遥感,2007,19(3):13-17. Zhou J,Chen Y H,Zhang J S,et al.Urban impervious surface abundance estimation in Beijing based on remote sensing[J].Remote Sensing for Land and Resources,2007,19(3):13-17. [14] Qin Z H,Karnieli A,Berliner P.A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J].International Journal of Remote Sensing,2001,22(18):3719-3746. [15] 覃志豪,Zhang M H,Karnieli A,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466. Qin Z H,Zhang M H,Karnieli A,et al.Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J].Acta Geographica Sinica,2001,56(4):456-466. [16] 覃志豪,Li W J,Zhang M H,等.单窗算法的大气参数估计方法[J].国土资源遥感,2003,15(2):37-43. Qin Z H,Li W J,Zhang M H,et al.Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from Landsat TM6[J].Remote Sensing for Land and Resources,2003,15(2):37-43. [17] 覃志豪,李文娟,徐斌,等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,2004,16(3):28-32,36,41. Qin Z H,Li W J,Xu B,et al.The estimation of land surface emissivity for Landsat TM6[J].Remote Sensing for Land and Resources,2004,16(3):28-32,36,41. [18] Carlson T N,Gillies R R,Perry E M.A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover[J].Remote Sensing Reviews,1994,9(1-2):161-173.
2015, Vol. 27 
