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Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

Department of Biological & Ecological Engineering, Oregon State University, Corvallis, OR 97331, USA
Laboratory of Hydrology and Water Management, Ghent University, 9000 Ghent, Belgium
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
Estellus, 75020 Paris, France
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1601;
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)
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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MDPI and ACS Style

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.

AMA Style

Talsma CJ, Good SP, Miralles DG, Fisher JB, Martens B, Jimenez C, Purdy AJ. Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models. Remote Sensing. 2018; 10(10):1601.

Chicago/Turabian Style

Talsma, Carl J., Stephen P. Good, Diego G. Miralles, Joshua B. Fisher, Brecht Martens, Carlos Jimenez, and Adam J. Purdy. 2018. "Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models" Remote Sensing 10, no. 10: 1601.

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