ESTIMATION OF LAND SURFACE TEMPERATURE USING EMISSIVITY CALCULATED FROM NORMALIZED DIFFERENCE VEGETATION INDEX

Abstract

Using thermal infrared remote sensing for calculation of land surface temperature recently has been applied increasingly in the environmental sciences. This paper presents a method of determination of land surface temperature using Normalized Difference Vegetation Index (NDVI) for calculating emissivity. In this study, an attempt has been made to replace the use of an in-situ emissivity as a standard coefficient for the whole study area in traditional methods. The emissivity per pixel retrieved directly from satellite data has been estimated for retrieval of land surface temperature at the better accuracy level. The results show that the satellite derived land surface temperature values are in acceptable agreement with the values which were calculated from in-situ emissivity. It is also found that a strong correlation between emissivity and normalized difference vegetation index (NDVI) over different landcover classes is taken.

References

ARSC, 2002. Arizona Remote Sensing Center: Landsat 5 Atmospheric and Radiometric Correction. Information on website adapted from Skirvin, S (2000). Cited at: http://arsc.arid.arizona.edu/resources/image_process ing/landsat/ls5-atmo.html. Last accessed: July, 2011.

Chavez, P. S., Jr, 1996. Image-based atmospheric corrections - Revisited and Improved. Photogrammetric Engineering and Remote Sensing 62 (9), pp. 1025-1036.

Cổng thông tin điện tử thành phố Hải Phònghttp://haiphong.gov.vn/. Last accessed: July, 2013.

France, G.B. & Cracknell, A.P., 1994. Retrieval of land and sea surface temperature using NOAA-11 AVHRR data in North-Eastern Brazil, International Journal of Remote Sensing, 15, 1695-1712.

French, A.N., Norman, J.M., Anderson. M.C., 2003. A simple and fast atmospheric correction for spaceborne remote sensing of surface temperature. Remote Sensing of Environment 87 (2003), 326-333.

Javed Mallick, Yogesh Kant & B.D. Bharath, 2008. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union. Vol. 12, No.3, pp.131-140.

Matthew, M. W. et al., 2003. Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data, Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery. IX, SPIE, Orlando, FL, USA, 474-482.

Ou, S. C., Chen, Y., Liou, K. N., Cosh, M. & Brutsaert, W., 2002. Satellite remote sensing of land surface temperatures: Application of the atmospheric correction method and split-window technique to data of ARM-SGP site. International Journal of Remote Sensing, Vol. 23 (24), pp. 5177- 5192.

Pham Van Cu, Hiroshi Watanabe, 2004. Use of Thermal Infrared Channels of ASTER to Evaluate the Land Surface Temperature Changes of an Urban Area in Hanoi, Vietnam. Proceedings of the International Symposium GIS-IDEAS 2004, 85-90, Hanoi, Vietnam, 16-18 September.

Pu, R., Gong, P., Michishita, R., Sasagawa, T., 2006. Assessment of multiresolution and multi-sensor data for urban surface temperature retrieval. Remote Sensing of Environment, 104 (2006), pp. 211-225.

Richter, R., 2003. Status of Model ATCOR4 on Atmospheric/Topographic Correction for Airborne Hyperspectral Imagery. 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003.

Salisbury, J.W. & D'Aria, D.M., 1994. Emissivity of Terrestrial Materials in the 2 to 5micro meter Atmospheric Window, Remote Sensing Environment, 47 (3), 345-361.

Tran Thi Van, 2005. Investigating Feature of Urban Surface Temperature with Distribution of Land Cover Types in HoChiMinh ity using Thermal Infrared Remote Sensing. Proceedings of the 26th Asian Conference on Remote Sensing (ACRS 2005), Hanoi, Vietnam, 7-11 November. 192.

Trần Thị Vân, Hoàng Thái Lan, Lê Văn Trung, 2009. Phương pháp viễn thám nhiệt trong nghiên cứu phân bố nhiệt độ bề mặt đô thị. Tc. Các KH về TĐ, T.31, 2, 168-177.

Valor, E. & Caselles, V., 1996. Mapping land surface emissivity from NDVI. Application to European, African and South American areas. Remote Sensing of Environment, 57, 167-184.