Evapotranspiration estimation using remote sensing data

Authors

  • D. SINGH
  • I. HERLIN
  • J. P. BERROIR
  • S. BOUZIDI
  • F. LAHOCHE

DOI:

https://doi.org/10.54302/mausam.v54i1.1509

Keywords:

Evapotranspiration, Surface temperature, Normalized difference vegetation index, Radiometer, Remote sensing

Abstract

Evapotranspiration (ET) is a critical hydrological link between the earth surface and the atmosphere. It is therefore important point of issues involving many aspects of climate, climate change, and ecosystem response. It is well known that ET is the process responsible for the transfer of the moisture from soil and vegetated surface to the atmosphere. Changes in ET are likely to have large impacts on terrestrial vegetation. Since the distribution and abundance of plant communities are controlled to a large extent by the quantity and seasonality of moisture. If the changes in water balance are significant, major shifts in vegetative patterns and condition are a likely result of climate change. Equally changes in ET are likely to impact atmospheric composition of green house gases, and climate, as the hydrological cycle increases in intensity with warming. Therefore, in this paper, it is attempted to estimate the ET over vegetative and bare field using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km as a function of Normalized Difference Vegetation Index (NDVI) and with semi-empirical approach.  For this purpose, a model has been proposed to estimate the ET over vegetative and bare field. The dependence of ET on NDVI-Surface temperature has been checked by multiple regression analysis and quite good percentage of dependence of ET on NDVI-Surface temperature has been observed. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planer.

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Published

01-01-2003

How to Cite

[1]
D. SINGH, I. HERLIN, J. P. BERROIR, S. BOUZIDI, and F. LAHOCHE, “Evapotranspiration estimation using remote sensing data”, MAUSAM, vol. 54, no. 1, pp. 247–252, Jan. 2003.

Issue

Section

Research Papers

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