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Open AccessArticle

Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe

1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Jincheng Meteorological Administration, Jincheng 048026, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 687; https://doi.org/10.3390/rs12040687
Received: 24 December 2019 / Revised: 14 February 2020 / Accepted: 18 February 2020 / Published: 19 February 2020
(This article belongs to the Special Issue Hydrometeorological Prediction and Mapping)
An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe. View Full-Text
Keywords: terrestrial latent heat flux; machine learning method; Europe terrestrial latent heat flux; machine learning method; Europe
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MDPI and ACS Style

Shang, K.; Yao, Y.; Li, Y.; Yang, J.; Jia, K.; Zhang, X.; Chen, X.; Bei, X.; Guo, X. Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe. Remote Sens. 2020, 12, 687.

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