Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product
- 290 Downloads
Water vapour is the most variable constituent in the atmosphere which is responsible for serious noise in the optical satellite images. This research is focused on the vertical distribution of water vapour and deducing its possible effects on the atmospheric correction process. The vertical distribution of precipitable water vapour, water vapour mixing ratio with geopotential height and pressure were estimated through the weather research and forecasting (WRF) model by downscaling the National Center for Environmental Prediction (NCEP) global reanalysis product. In addition, the most widely used LANDSAT TM satellite image has been used for this assessment. The WRF model was applied with three domains centred on a LANDSAT captured image over the area. The 6S atmospheric correction code was utilised for viewing the effect of precipitable water vapour on satellite image correction. The analysis was conducted on two pressure levels (1,000 and 100 hPa) representing the troposphere and stratosphere, respectively. The validation of the atmospheric correction has been performed by estimating the land surface temperature (LST) over the Walnut Creek region and its comparison with the Soil Moisture Experiments in 2002 (SMEX02) LST field validation datasets. The overall analyses indicate a higher accuracy of LST repossession with 100 hPa corrected image.
KeywordsPrecipitable water vapour Agricultural landscape Weather research and forecasting model (WRF) Land surface temperature (LST) 6S correction
The authors would like to thank the Commonwealth Scholarship Commission, United Kingdom and Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. The authors are also thankful to SMEX team for all the validation data generated during the experiments and Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR) for the NCEP data. The authors also acknowledge the Advanced Computing Research Centre at University of Bristol for providing the access to supercomputer facility (The Blue Crystal). The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA/NASA or the authors’ affiliated institutions.
- Anderson GP, Chetwynd JH, Kneizys FX et al (1994) MODTRAN 3: suitability as a flux-divergence code. In: Proceedings of the 4th ARM-Science Team Meeting, pp 75–80Google Scholar
- Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note NCAR/TN-398STRGoogle Scholar
- Srivastava PK (2013) Soil moisture estimation from SMOS satellite and mesoscale model for hydrological applications. Ph.D. thesis, University of Bristol, Bristol. doi: 10.1002/asl.427
- Srivastava PK, Mukherjee S, Gupta M (2010) Impact of urbanization on land use/land cover change using remote sensing and GIS: a case study. Int J Ecol Econ Stat 18(S10):106–117Google Scholar
- Srivastava PK, Han D, Rico-Ramirez MA, Al-Shrafany D, Islam T (2013b) Data Fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resour Manage 27(15):5069–5087Google Scholar