H
Hongbo Su
Researcher at Florida Atlantic University
Publications - 151
Citations - 2720
Hongbo Su is an academic researcher from Florida Atlantic University. The author has contributed to research in topics: Evapotranspiration & Emissivity. The author has an hindex of 24, co-authored 138 publications receiving 2246 citations. Previous affiliations of Hongbo Su include Beijing Normal University & Texas A&M University–Kingsville.
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Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction
TL;DR: In this article, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture-Atmosphere Coupling Experiment (SMACEX).
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Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA)
Andrew N. French,Frédéric Jacob,Martha C. Anderson,William P. Kustas,W.J. Timmermans,Ambro Gieske,Zhongbo Su,Hongbo Su,Matthew F. McCabe,Fuqin Li,John H. Prueger,Nathaniel A. Brunsell +11 more
TL;DR: In this article, the surface energy flux retrieval from ASTER data is demonstrated using data collected over an experimental site in central Iowa, USA, in the framework of the Soil Moisture Atmosphere Coupling Experiment (SMACEX).
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Comparison of leaf angle distribution functions: Effects on extinction coefficient and fraction of sunlit foliage
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Two Improvements of an Operational Two-Layer Model for Terrestrial Surface Heat Flux Retrieval
TL;DR: The improved method was successfully used to predict the component surface heat fluxes from the soil and vegetation and it provides a promising approach to study the canopy transpiration and the soil evaporation quantitatively during the rapid growing season of winter wheat in northern China.
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Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies
Matthew F. McCabe,Matthew F. McCabe,Eric F. Wood,R. Wójcik,Ming Pan,Justin Sheffield,Huilin Gao,Hongbo Su +7 more
TL;DR: In this article, a multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales.