Editorial Type:
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Article Type:
Research Article

A New Characterization of the Land Surface Heterogeneity over Africa for Use in Land Surface Models

Armel Thibaut Kaptué Tchuenté CNRM/GAME URA 1357, Météo France, Toulouse, France

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Jean-Louis Roujean CNRM/GAME URA 1357, Météo France, Toulouse, France

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Agnès Bégué UMR TETIS, CIRAD, Montpellier, France

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Sietse O. Los Department of Geography, Swansea University, Swansea, United Kingdom

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Aaron A. Boone CNRM/GAME URA 1357, Météo France, Toulouse, France

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Jean-François Mahfouf CNRM/GAME URA 1357, Météo France, Toulouse, France

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Dominique Carrer CNRM/GAME URA 1357, Météo France, Toulouse, France

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Badiane Daouda Ecole Supérieure Polytechnique, UCAD, Dakar, Sénégal

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/JHM-D-11-020.1
Page(s):
1321–1336
Restricted access

Abstract

Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database—constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993—was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000–07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34% instead of 64% in comparison with the original version of ECOCLIMAP.

Corresponding author address: Armel Kaptué, CNRM, 42 Av. G. Coriolis, 31057 Toulouse CEDEX 01, France. E-mail: armel.kaptue@cnrm.meteo.fr

Abstract

Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database—constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993—was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000–07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34% instead of 64% in comparison with the original version of ECOCLIMAP.

Corresponding author address: Armel Kaptué, CNRM, 42 Av. G. Coriolis, 31057 Toulouse CEDEX 01, France. E-mail: armel.kaptue@cnrm.meteo.fr
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