NEW ADVANCE IN RETRIEVAL OF LAND SURFACE TEMPERATURE (LST)

doi:10.6046/gtzyyg.1999.03.10

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Abstract

Authors had analized some problems about the retrieval of LST using multi_band thermal infrared data and the possibility to retrieve component temperature of mixed pixel by multi_angle thermal infrared data based on the law of thermal radiant direction of non_isothermal mixed pixel.

Keywords Remote sensing Impervious surface Landscape pattern Object segmentation

Issue Date: 02 August 2011

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	LI Wei-Feng
	WANG Yi
	HU Shu-Qi
	MA Sheng-Ming
	LIU Chong-Min
	LI Bing
	XI Ming-Jie

Cite this article:

LI Wei-Feng,WANG Yi,HU Shu-Qi, et al. NEW ADVANCE IN RETRIEVAL OF LAND SURFACE TEMPERATURE (LST)[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(3): 47-50.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1999.03.10 OR https://www.gtzyyg.com/EN/Y1999/V11/I3/47

1 Labed J, Stoll M P. Spatial variability of land surface emissivity in the thermal infcaced band: Spectral signature and effective surface temperature. Remote Sens. Environ.,1991.38:1~17
2 Dozier J, Warren S G. Effect of viewing angle ar the infrared brightness temperature of snow. Water Resourocs Rag.,1982,18(5):1424~1434
3 Sutherland R A. I3roadband and spectral emnissavities (2~18pm) of sane natural soil and vegetation. American MeteorologicalSociety, 1986, 199~203
4 Salisbury J W, D. M. D'Aria. Infrared (8~14}rm) remote sensing of soil particle sire. Remote Sens. Enviton.,1992,42:157~165
5 Xu Xiru, Liu Qinhuo, Chen Jiay.D),retrieval of land surface temperature and emissivity. Seance in Chin SeriaK 1998,41(6):658~668
6 陈良富,徐希璐,张仁华.地表温度遥感反演的现状与发展趋势.地理科学进展,1998,增刊
7 Kimes D S, Smith J A, Link L E. Thermal IR extiance rrrodel of a plant canopy. Applied Optics, 1981, 20:623~632
8 Norman J, Backer F. Termirmlogy in thermal infrared remote sensing of natural surface. Agriculture and Frest Meteorobgy,1995,77:153~176
9 Wan Z, Dozier J. A generalized split window algorithm for retrieving land~surface temperature frorrr space. IEEE Traps.Geosci. and Remote Sens.,1996,34(4)892~905
10 Li X, Strahler A H. Effective emissivity for non~isothemrat surface and a conceptual model of its directiality. IEEE Traps. On Geoscience and Remote Sensing,1998
11 Tarantola A. Inverse problem theory mettnds for data fitting and model parameter estimation. Amsterdam-Oxford New York Tokyro, 1987
12 Zhao B L, Zhu Y G. et al. Meteorological satellite TIROSN TOVS xermte sensing of atmospheric property and ckrud, advance in atmospheric spheric xierxe. 1993,10(4):387~392

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