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Water 2018, 10(4), 474; https://doi.org/10.3390/w10040474

Improvement of Two Evapotranspiration Estimation Models Using a Linear Spectral Mixture Model over a Small Agricultural Watershed

1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing 210044, China
2
Tianjing Climate Center, Tianjing 300074, China
3
Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
4
IMSG at NCEP/EMC, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Received: 27 February 2018 / Revised: 1 April 2018 / Accepted: 9 April 2018 / Published: 12 April 2018
(This article belongs to the Section Water Use and Scarcity)
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Abstract

Accurately measuring regional evapotranspiration (ET) is of great significance for studying global climate change, regional hydrological cycles, and surface energy balance. However, estimating regional ET from mixed vegetation types is still challenging. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and the Surface Energy Balance System (SEBS) models were applied to estimate surface ET in a small agricultural watershed. Landsat8 satellite images were used as input data to the single-source models. The two models were validated at single point and ecosystem scales. The results showed that both models overestimated ET observations in paddy fields and orange groves but underestimated them in dry farmland. The error was mainly caused by the heterogeneity of the mixed pixels. The linear spectral mixture model and a set of equations were introduced to reduce the simulation error. The revised results showed that the relative precision of SEBAL was improved by 9.87% and 10.06%, respectively. This research is expected to provide new ideas for future development of accurate remote-sensing ET estimations on heterogeneous surfaces. View Full-Text
Keywords: evapotranspiration; SEBAL; SEBS; linear spectral mixture model; heterogeneous evapotranspiration; SEBAL; SEBS; linear spectral mixture model; heterogeneous
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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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Li, G.; Jing, Y.; Wu, Y.; Zhang, F. Improvement of Two Evapotranspiration Estimation Models Using a Linear Spectral Mixture Model over a Small Agricultural Watershed. Water 2018, 10, 474.

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