Land surface temperature (LST) is a very important parameter to impact the energy and water exchange between the atmosphere and the territorial system. The technology of thermal infrared (TIR) remote sensing provides a chance to retrieve LST over large portions of the earth surface at different spatial resolutions and periodicities. In this study, the middle part of Province is chosen as the study area. It is the most important crop production area in Jilin Province, and a commodity grain base in China. The land surface temperature is retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) by split-window algorithm. The land use/cover is acquired from TM data by human-computer interactive interpretation. Combined with land use/cover and DEM data, the spatial distribution of LST is identified and the relation between NDVI and LST is analyzed in the middle part of Jilin Province. The result shows that: ① the temperature in the north and the west is higher than the south and the east respectively in the study area. It coincides with the local climate. The main reason to result in higher temperature is from landform in the west, and from difference of land use types in the north because the south with forest has much more transpiration than the north with cropland; ② With influence of human activities, difference of land use types result in differences of LST. The quantitative difference is 26.6K between the highest land surface temperature of bare rock and the lowest temperature of lake. There is a negative relation between altitude and LST. The rate of temperature change could be 23.6°C/km as elevation increases; ③ The LST has a negative relation with NDVI in vegetation covered region. The relation between LST and NDVI in urban areas is much more sensitive than that in forest and cropland. For the whole configuration of land use in the study area, the relation between LST and NDVI displays 'trapeziform’. The forest with higher NDVI and lower LST is located in right-down corner of the trapezoid, while the cropland with high NDVI and low LST is distributed in the middle of the trapezoid. Because of different heat capacities, cities covered by impermeable layers lay to the top of trapezoid with lower NDVI and higher LST. The NDVI-LST feature space contains a lot of geo-information, especially in soil moisture monitoring, and drought detection, and more research could be done in the future.
HOU Guang-lei, ZHANG Hong-yan, WANG Ye-qiao, QIAO Zhi-he, ZHANG Zheng-xiang
. Retrieval and Spatial Distribution of Land Surface Temperature in the Middle Part of Jilin Province Based on MODIS Data[J]. SCIENTIA GEOGRAPHICA SINICA, 2010
, 30(3)
: 421
-427
.
DOI: 10.13249/j.cnki.sgs.2010.03.421
[1] Li Z, Becker F. Feasibility of land surface temperature and emissivity determination from AVHRR data [J].Remote Sensing of Environment, 1993, 43 (1): 67- 85.
[2] 黄秉维,郑 度,赵名茶.现代自然地理[M].北京:科学出版社,1998.
[3] 黄初冬,邵 芸,李 静. 北京城市地表温度的遥感时空分析[J].国土资源遥感,2008,(3):64~68.
[4] 刘 宇,匡耀求,吴志峰,等. 不同土地利用类型对城市地表温度的影响 ——以广东东莞为例[J].地理科学,2006,26(5):597~601.
[5] 张心怡,刘敏,孟 飞. 基RS和GIS的地面温度和土地利用/覆被关系研究进展[J].遥感信息,2005,(3):66~71.
[6] 刘朝顺,高 炜,高志强,等.基于 ETM+遥感影像反演不同土地利用类型地表温度的研究[J].南京气象学院学报,2008, 31(4):503~510.
[7] 张春玲,余 华,宫 鹏,等.基于遥感的土地利用空间格局分布与地表温度的关系[J].遥感技术与应用,2008, 23(4):378~384.
[8] 覃志豪,Zhang Minghua, Arnon Karnieli,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.
[9] 毛克彪,覃志豪,施建成. 用MODIS影像和劈窗算法反演山东半岛的地表温度[J].中国矿业大学学报,2005,34(1): 46~50.
[10] 覃志豪,Zhang Minghua, Arnon Karnieli. 用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J].国土资源遥感,2001,(2):34~42.
[11] 毛克彪,唐华俊,陈仲新,等. 一个从 ASTER 数据中反演地表温度的劈窗算法[J].遥感信息,2006,(5):7~11.
[12] Schmugge T, Frencha A, Jerry C R. Temperature and emissivity separation from multispectral thermal infrared observations[J].Remote Sensing of Environmet,2002,79(2):189-198.
[13] 覃志豪,高懋芳,秦晓敏,等.农业旱灾监测中的地表温度遥感反演方法—以MODIS数据为例[J].自然灾害学报,2005,14(4):64~71.
[14] Qin Z, Dall’Olmo G, Karnieli A,et al. Derivation of split-window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA - AVHRR data[J]. Journal of Geophysical Research, 2001, 106 (D19): 22655-22670.
[15] 覃志豪, LI Wenjuan, ZHANG Minghua,等. 单窗算法的大气参数估计方法[J].国土资源遥感,2003,(2):37~43.
[16] 李红林,李万彪. MODIS近红外资料反演大气水汽总含量[J]. 北京大学学报(自然科学版),2008,44(1):121~128.
[17] Mao K, Qin Z, Shi J,et al. A practical split-window algorithm, for retrieving Land surface temperature from MODIS data[J] International Journal of Remote Sensing,2005, 15(26):3181-3204.
[18] 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,(3):28~36
[19] Jose' A Sobrino, Juan C Jime'nez-Mun'oz, Leonardo Paolini. Land surface temperature retrieval from LANDSAT TM5[J]. Remote Sensing of Environment, 2004, 90(4): 434 - 440.
[20] Kerr Y H, Lagouarde J P, Imbernon J. Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm[J]. Remote Sensing of Environment, 1992, 41(2): 197 - 209.
[21] Srivastava P K, Majumdar T J, Amit K. Bhattacharya. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data[J]. Advances in Space Research, 2009,43:1563-1574.
[22] Lo C P, Quattrochi D A, Luvall J C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect[J]. International Journal of Remote Sensing, 1997,18(2):287-304.
[23] Wilson J S, Clay M, Martin E, et al. Evaluating environmental influence of zoning in urban ecosystems with remote sensing[J].Remote Sensing of Environment, 2003, 86(3):303-321.
[24] 刘朝顺, 高 炜, 高志强,等. 基于ETM+遥感影像反演不同土地利用类型地表温度的研究[J].南京气象学院学报,2008,31(4):503~510.
