Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

ABSTRACT

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature () normalized difference vegetation index (NDVI) and daily maximum air temperature (). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the from Landsat relative to the from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Acknowledgements

This research was supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center (GSFC), administered by Universities Space Research Association under a contract, NNH15C048B. Additionally, funding support was provided by the U.S. Geological Survey (USGS) Land Remote Sensing and Land Change Science Programs. Landsat Surface Reflectance products courtesy of the U.S. Geological Survey Earth Resources Observation and Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center (GSFC), administered by Universities Space Research Association under a contract, NNH15C048B. Additionally, funding support was provided by the U.S. Geological Survey (USGS) Land Remote Sensing and Land Change Science Programs.