Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

Remote Sensing

Guillermo Murray-Tortarolo⁹  Alessandro Anav⁹  Pierre Friedlingstein⁹  Stephen Sitch¹  Shilong Piao⁶  Zaichun Zhu²  Benjamin Poulter⁴  Sönke Zaehle⁵  Anders Ahlström⁸  Mark Lomas³  Sam Levis⁷  Nicholas Viovy⁴ 

[1] College of Life and Environmental Sciences, University of Exeter, Amory Building, Rennes Drive, Exeter EX4 4RJ, UK; E-Mail:;Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA; E-Mail:;Department of Animal & Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK; E-Mail:;Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, Gif-sur-Yvette 91191, France; E-Mails:;Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64, D-07701 Jena, Germany; E-Mail:;Department of Ecology, Peking University, Beijing 100871, China; E-Mail:;National Center for Atmospheric Research, Boulder, CO80305, USA; E-Mail:;Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-22362, Lund; E-Mail:;College of Engineering, Mathematics & Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK; E-Mails:

关键词: LAI;    land surface models;    growing season;    trendy;    northern hemisphere;    phenology;   

DOI  :  10.3390/rs5104819

来源: mdpi

【 摘 要 】

Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland