Abstract
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.
Highlights
The latent heat flux (LE) governs the associated heat flux of the interaction between land surface and its atmosphere [1], including vegetation transpiration, soil evaporation, and plant canopies interception evaporation [2]
R2 is the square of correlation coefficient R and it is a metric to assess the agreement between estimates and observations
We applied the Extremely Randomized Trees method to implement the fusion of five satellite-derived terrestrial LE products (RS-PM, SW, Priestley-Taylor of the Jet Propulsion Laboratory (PT-JPL), Modified Satellite-Based Priestley–Taylor (MS-PT), and Semi-Empirical Penman Algorithm (SEMI-PM)) based on flux tower observations
Summary
The latent heat flux (LE) governs the associated heat flux of the interaction between land surface and its atmosphere [1], including vegetation transpiration, soil evaporation, and plant canopies interception evaporation [2]. LE returns approximately 60% of rain back to the atmosphere and helps to cool the land surface by consuming an enormous amount of heat [3]. Europe makes up the western fifth of the Eurasian landmass. An accurate LE estimation over Europe plays a key role in many climatic, hydrologic, and agricultural applications [4]. As a confederation of regional observation networks, FLUXNET routinely provides long-term eddy covariance (EC) flux measurements of carbon, water vapor, and energy exchange over America, Europe, Asia, Africa, and Australia. As result of the spatial heterogeneity, point-based measurements of terrestrial LE cannot be applied for continuous monitoring on a large scale [5]
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