APPLICATION OF LANDSAT THERMAL INFRARED DATA TO STUDY SOIL MOISTURE USING TEMPERATURE VEGETATION DRYNESS INDEX - Hung, Trinh Le (Vietnam Journal of Earth Sciences, 2014)
Drought is a natural phenomenon, which occurs in most regions in the world, caused immense damage in agricultural production and seriously affected on the environment. Application of remote sensing data in studying, monitoring and dealing with drought phenomenon has achieved positive results. Compared to traditional methods, remote sensing technology with advantages such as wide area coverage and short revisit interval has been used effectively in the study of soil moisture and monitoring vegetation health. This article presents results of soil moisture monitoring from LANDSAT multispectral images with average spatial resolution using temperature vegetation dryness index (TVDI) based analyzes a correlation between land surface temperature and land cover. Land surface temperature and soil moisture are the most important physical factors for water exchange processes and energy exchanges between land surfaces and the overlying atmosphere. Temperature can rise very quickly in the situation of drought on surface and vegetation. This study shows a program to use for calculation land surface temperature and temperature vegetation index by Visual C++ programming languages, which can help to reduce costs and save time - compared to using the image processing software such as ERDAS Imagine, ENVI,... The results obtained in this study can be used to create the soil moisture map, to monitor drought phenomenon and vegetation health. References Barsi J.A., Schott J.R., Palluconi F.D., Helder D.L., Hook S.J., Markham B.L., Chander G., O’Donnell E.M., 2003. LANDSAT TM and ETM+ thermal band calibration, Canada Journal of Remote sensing, Vol.29, No.2, pp. 141-153. Chavez P.S., 1996. Image-based atmospheric corrections-revisited and improved, Photogrammetric Engineering and Remote Sensing 62(9): pp.1025-1036. Chavez P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data, Remote Sensing of Environment 24: pp.459-479. Fei Yuan, Marvin E. Bauer, 2007: Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in LANDSAT imagery, Remote sensing of Environment, 106, pp. 375-386. Hyung Moo Kim, Beob Kyun Kim, Kang Soo You, 2005. A statistic correlation analysisalgorithm between land surface temperature and vegetation index, International journal of information processing systems, Vol. 1, No. 1, pp. 102-106. Trịnh Lê Hùng, 2014. Nghiên cứu sự phân bố nhiệt độ bề mặt bằng dữ liệu ảnh đa phổ LANDSAT. Tạp chí Các khoa học về Trái Đất, tập 36, số 01, 82-89. Lambin T.R., Ehrlich D., 1996. The surface temperature-vegetation index space for land cover and land cover change analysis, International journal of remote sensing, 17(3), 163-187. Lu Yuan, Tao Heping, Wu Hua, 2007. Dynamic drought monitoring in Guangxi using revised temperature vegetation dryness index, Wahan University journal of Natural sciences, Vol. 12, No.4, pp. 663-668. Sandholt I., Rasmussen K., Anderson J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status, Remote Sensing of Environment, Vol. 79, pp. 213-224. Sundara Kumar K., Udaya Bhaskar P., Padmakumari K., 2012. Estimation of land surface temperature to study urban heat island effect using LANDSAT ETM+ image. International journal of Engineering Science and technology, Vol. 4, No. 2, pp. 771-778. Trần Thị Vân, Hoàng Thái Lan, Lê Văn Trung, 2009. Phương pháp viễn thám nhiệt trong nghiên cứu phân bố nhiệt độ đô thị. Tạp chí Các Khoa học về Trái Đất, T.31, 2, 168-177. Tran H., Yasuoka Y., 2001. MODIS data acquisition, processing and scientific ultilization framework at the Institute of Industrial Science, University of Tokyo. In Proceeding of the 22nd Asian conference on Remote sensing, Singapore, 1, 488-492. Valor E., Caselles V., 1996. Mapping land surface emissivity from NDVI. Application to European African and South American areas. Remote sensing of Environment, 57, pp. 167-184. Van de Griend A.A., Owen M., 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface. International journal of remote sensing, 14, pp. 1119-1131. Yuhai Bao, Gang Gama, Bao Gang, Yongmei, Alatengtuya, Yinshan, Husiletu, 2013. “Monitoring of drought disaster in Xilin Guole grassland using TVDI model”, Taylor & Francis group, London, ISBN 978-1-138-00019-3, pp. 299-310. [National Aeronautics and Space Administration (NASA), LANDSAT Science data user’s Handbook. National Aeronautics and Space Administration (NASA), LANDSAT 8 Atmospheric correction. Inclides COST, DOS and TOA reflectance.