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.ReferencesBarsi 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.