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An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)69-92
Number of pages24
JournalRemote Sensing of Environment
Volume229
Early online date9 May 2019
DOIs
DateSubmitted - 29 Jun 2018
DateAccepted/In press - 25 Apr 2019
DateE-pub ahead of print - 9 May 2019
DatePublished (current) - 1 Aug 2019

Abstract

Estimating regional evapotranspiration (ET)is challenging in data-limited regions where a lack of in situ observations constrain model calibration and implementation. Here we developed an ensemble mean surface energy balance (EnSEB)modeling framework that is independent of any ground calibration and applied it in India to understand the magnitude and variability of ET in this agriculturally important region. EnSEB uses daily land surface temperature (LST)and vegetation biophysical inputs from Moderate Resolution Imaging Spectroradiometer (MODIS)and climatic information from the National Aeronautics and Space Administration (NASA)Modern-Era Retrospective Analysis for Research and Applications, version 2 (Merra-2)products and runs seven surface energy balance (SEB)algorithms to estimate ensemble mean ET or latent heat (LE)fluxes at a spatial resolution of 1 km × 1 km. Due to limited access to observed flux data, we conducted three different types of model evaluation: i)instantaneous LE validation using observed SEB fluxes from Bowen ratio energy balance (BREB)measurements in four agroecosystems, ii)annual and seasonal ET comparison with six global products at the regional scale, and iii)by closing the basin-scale monthly water budget (WB)for five large river basins (87,900–312,812 km 2 )in India. Validation with the BREB measurements revealed hourly EnSEB LE estimates to be within 2% of the observed LE (R 2 = 0.57 and RMSE = 59 W m −2 )and EnSEB was more accurate than any of the individual SEB models. Annual ET from EnSEB was positively correlated with six widely-used global ET products (r = 0.52–0.83, p-value < 0.001), but EnSEB captured the magnitude of ET in intensively irrigated regions much better. Basin-scale monthly WB miscloures were found to be between −1 and 9 mm month −1 from EnSEB ET estimates, which were better than those from the six global ET products. The gap filling method based on the constant ET r F (ET/reference ET)approach introduced some uncertainties in EnSEB, which presents room for future improvements. Overall, our results suggest that the automated and calibration-free multi-model EnSEB framework, which uses only remote sensing and readily available reanalysis data, has the ability to estimate ET with reliable accuracy in Indian agroecosystems. Such a framework could help us better understand and monitor the water cycle in regions where ground data are limited or non-existent.

    Structured keywords

  • GlobalMass

    Research areas

  • Datalimited regions, Land surface temperature, Multi-model based ensemble mean, Regional evapotranspiration, Surface energy balance

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