Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 345-352, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-345-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 345-352, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-345-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

ESTIMATION OF INSTANTANEOUS EVAPOTRANSPIRATION USING REMOTE SENSING BASED ENERGY BALANCE TECHNIQUE OVER PARTS OF NORTH INDIA

T. Sett1, B. R. Nikam2, S. Nandy3, A. Danodia4, R. Bhattacharjee5, and V. Dugesar6 T. Sett et al.
  • 1M. Tech, Forestry and Ecology Department, IIRS, Dehradun, India
  • 2Water Resource Department, IIRS, Dehradun, India
  • 3Forestry and Ecology Department, IIRS, Dehradun, India
  • 4Agricultural and Soils Department, IIRS, Dehradun, India
  • 5M. Tech, Geoscience Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India
  • 6PhD Scholar, Department of Geography, Institute of Science, Banaras Hindu University, Varanasi, India

Keywords: Evapotranspiration, Energy Balance Approach, Remote Sensing, Sensible heat flux

Abstract. Evapotranspiration (ET) is an essential element of the hydrological cycle and plays a significant role in regional and global climate through the hydrological circulation. Estimation and monitoring of actual crop evapotranspiration (ET) or consumptive water use over large-area holds the key for better water management and regional drought preparedness. In the present study, the remote sensing based energy balance (RS-EB) approach has been used to estimate the spatial variation of instantaneous evapotranspiration (ETinst). The (ETinst) is evaluated as the residual value after computing net radiation, soil heat flux and sensible heat flux using multispectral remote sensing data from Landsat-8 for the post-monsoon and summer season of 2016–2017 over the parts of North India. Cloud free temporal remote sensing data of October 12, 2016; November, 13, 2016; March 05, 2017 and May 24, 2017 were used as primary data for this study. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of (ETinst).