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Remote Sens. 2018, 10(10), 1601; https://doi.org/10.3390/rs10101601

Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

1
Department of Biological & Ecological Engineering, Oregon State University, Corvallis, OR 97331, USA
2
Laboratory of Hydrology and Water Management, Ghent University, 9000 Ghent, Belgium
3
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
4
Estellus, 75020 Paris, France
*
Author to whom correspondence should be addressed.
Received: 9 September 2018 / Revised: 4 October 2018 / Accepted: 6 October 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)
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Abstract

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates. View Full-Text
Keywords: evapotranspiration; modelling; sensitivity; uncertainty; transpiration; soil evaporation; interception; partitioning evapotranspiration; modelling; sensitivity; uncertainty; transpiration; soil evaporation; interception; partitioning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Talsma, C.J.; Good, S.P.; Miralles, D.G.; Fisher, J.B.; Martens, B.; Jimenez, C.; Purdy, A.J. Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models. Remote Sens. 2018, 10, 1601.

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