A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes

Yao Yunjun, Liang Shunlin, Li Xianglan, Chen Jiquan, Wang Kaicum, Jia Kun, Cheng Jie, Jiang Bo, Fisher Joshua B., Mu Qiaozhen, Grünwald Thomas, Bernhofer Christian, Roupsard Olivier. 2015. A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes. Remote Sensing of Environment, 165 : pp. 216-233.

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Quartile : Outlier, Sujet : REMOTE SENSING / Quartile : Outlier, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q1, Sujet : ENVIRONMENTAL SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Abstract : Accurate estimation of the terrestrial latent heat flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FLUXNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations. (Résumé d'auteur)

Mots-clés Agrovoc : Écosystème, Physiologie végétale, Température du sol, Télédétection, Évapotranspiration, Photosynthèse, Cycle du carbone, Surface foliaire, Couverture végétale, Modèle mathématique, Satellite, Méthode statistique, Spectrométrie, Observation météorologique, Cartographie

Mots-clés géographiques Agrovoc : Monde

Mots-clés libres : Global latent heat flux, Hybrid algorithm, Plant functional type, Priestley-Taylor parameter, Ecophysiological constraints

Classification Agris : P40 - Meteorology and climatology
F40 - Plant ecology
U30 - Research methods
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

  • Yao Yunjun, Beijing Normal University (CHN)
  • Liang Shunlin, Beijing Normal University (CHN)
  • Li Xianglan, Beijing Normal University (CHN)
  • Chen Jiquan, MSU (USA)
  • Wang Kaicum, Beijing Normal University (CHN)
  • Jia Kun, Beijing Normal University (CHN)
  • Cheng Jie, Beijing Normal University (CHN)
  • Jiang Bo, Beijing Normal University (CHN)
  • Fisher Joshua B., OUCE (GBR)
  • Mu Qiaozhen, University of Montana (USA)
  • Grünwald Thomas, Technische Universität Dresden (DEU)
  • Bernhofer Christian, Technische Universität Dresden (DEU)
  • Roupsard Olivier, CIRAD-PERSYST-UMR Eco&Sols (CRI)

Source : Cirad-Agritrop (

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