基于陆面模式和遥感技术的地表温度比较
收稿日期: 2002-10-23
修回日期: 2003-01-06
网络出版日期: 2003-07-25
基金资助
国家自然科学基金项目 (90202002)； 973项目资助(2002CB4125)
The Comparison of Land Surface Temperature with CLM and Split Window Retrieving Method
Received date: 2002-10-23
Revised date: 2003-01-06
Online published: 2003-07-25
Supported by
National Natural Science Foundation of China, No.90202002; National 973 Program, No.2002CB4125
基于我国中部的样带基础上，采用陆面模式 (Common Land Surface Model CLM) 和遥感结合技术，对研究的样带区域的地表温度利用CLM模式进行模拟和利用遥感分裂窗技术进行反演；以观测地表温度为真值，分别比较模拟地表温度和反演地表温度空间分布特征和误差大小，分析模拟方法和反演方法对计算地表温度的可适性和应用范围，为进行大面的地表温度计算提供选择和参考依据。通过研究发现，模式模拟地表温度同实测地表温度分布的大的格局吻合非常的好， 但是因地貌类型及地表覆盖的影响，模拟地表温度对水域的模拟温度误差较大，大于3%以上，对耕地模拟误差偏小，在 (-3)% 以内，对裸地、草地和林地模拟温度吻合非常的好。反演地表温度同观测地表温度相差较大，分裂窗反演方法适合地表覆盖为草地和林地状态的地表温度的计算，裸露和农耕区域反演的地表温度误差特别的大。
高志强,刘纪远 . 基于陆面模式和遥感技术的地表温度比较[J]. 地理学报, 2003 , 58(4) : 494 -502 . DOI: 10.11821/xb200304002
In this paper, the Common Land Surface Model (CLM) and split window retrieving method were employed to compute and retrieve land surface temperature along transects, and then the spatial distribution and errors of two kinds of land surface temperatures from different ways were analyzed and compared based on the observed temperature, the applicability of two ways was decided by the comparison results. The conclusions drawn by this study are as follows: the land surface temperature simulated with CLM matches best with that from observed in distribution, but because of the impacts of different topographic types and land covers the errors of water surface temperature simulated with CLM to land surface temperature are greater (errors > 3%), the errors of cultivated land surface temperature are smaller (errors < -3%), the surface temperatures of barren land, grassland and forest match best with observed values. The land surface temperatures with split window method differ obviously from that observed. The split window method is applicable to retrieve the temperature of grassland and forest, but it has significant errors to retrieve the surface temperatures of barren land and cultivated land.
[1] IGBP SCIENCE NO.1. The terrestrial biosphere and global change: implications for natural and managed ecosystems. Synthesis of GCTE and Related Research.
[2] IGBP SCIENCE NO.3. Environmental Variablility and Climate Change.
[3] IGBP SCIENCE NO.4. Global Change and the Earth System: A Planet Under Pressure
[4] Xu Xingkui. The application of complementary relational model in remote sensing field. Journal of Remote Sensing, 1999, 3(1): 133-145.
[徐兴奎. 互补相关理论在卫星遥感领域的应用研究. 遥感学报, 1999, 3(1): 133-145.]
[5] K P Czajkowski, S N Goward, H Ouaidrari. Impact of AVHRR filter function on surface temperature estimation from the split window approach. Int. J. Remote Sensing, 1998, 19(10): 2007-2012.
[6] McMillin L M. Estimation of sea surface temperature from two infrared window measurements with different absorptions. Journal of Geophysical Research, 1975, 20: 5113-5117.
[7] Mcclain E P, Pichel W G, Wlton C C. Comparative performance of AVHRR-based multi-channel sea surface temperature. J. Geopys. Res., 1985, 20: 11587-11601.
[8] Bonan G B. A Land Surface Model For Ecological, Hydrological and Atmospheric Study. NCARE Technical Note NCAR/TN-417+STR. National Center for Atmospheric Reseach, Boulder, CO, 1996.
[9] Shanghai Normal University. Physical Geography of China. Beijing: People's Education Press, 1982. 1-102.
[上海师范大学. 中国自然地理. 北京: 人民教育出版社, 1982. 1-102.]
[10] Liu Jiyuan. Macro-scale Survey and Dynamic Study of Natural Resources and Environment of China by Remote Sensing. Beijing: China Science and Technology Press, 1996. 1-45.
[刘纪远. 中国资源环境遥感宏观调查与动态研究. 北京: 中国科学技术出版社, 1996. 1-45.]
[11] Dickinson R E. Biophere Atmosphere Transfer Scheme version 1e as coupled to the NCAR Community Climate Model. NCARE Technical Note NCAR/TN-387+STR. National Center for Atmospheric Reseach, Boulder, CO, 1993.
[12] Bonan G B. Do biophysics and physiology matter in ecosystem models? Climatic Change, 1993, 24: 281-285.
[13] Bonan G B. Comparison of two land surface process models using prescribed forcings. J. Geophys. Res., 1994, 99D: 25803-25818.
[14] Bonan G B. Land-atmosphere interactions for climate system models: coupling biophysical, biogeochemical, and ecosystem dynamical progress. Remote Sens. Environ., 1995, 51: 57-73.
[15] Qiang Gao, Mei Yu, Xiusheng Yang. An analysis of sensitivity of terrestrial ecosystems in China to climatic change using spatial simulation. Climate Change, 2000, 47: 373-400.
[16] Sellers P J. A global 1 degree by 1 degree NDVI data set for climate studies. Part 2: the generation of global fields of terrestrial biophysical parameters from the NDVI. Int. J. Remote Sensing, 1994, 15: 3519-3545.
[17] Steven W Running. Testing forest-BGC ecosystem process simulations across a climatic gradient in Oregon. Ecological Applications, 1994, 4(2): 238-247.
[18] V Caselles, C Coll, E Valor. Land surface emissivity and temperature determination in the whole HAPEX-Sahel area from AVHRR data. Int. J. Remote Sensing, 1997, 18(5): 1009-1027.
[19] J R Givri. The extension of the split window technique to passive microware surface temperature assessment. Int. J.Remote Sensing, 1997, 18(2): 335-353.
[20] R K Gupta, S Prasad, M V R Sesha Sai et al. The estimation of surface temperature over an agricultural area in the states of Haryana and Punjab, India, and its relationship with Normalized Difference Vegetation Index (NDVI), using NOAA-AVHRR data. Int. J. Remote Sensing, 1997, 18: 3729-3741.
[21] Inge Sandjholt, Kjekl Rasmuseen, Jens Andersen. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ., 2002, 79: 213-224.
/
| 〈 |
|
〉 |