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Evaluation of MODIS gross primary productivity for Africa using eddy covariance data

Sjöström, Martin LU ; Zhao, M. ; Archibald, S. ; Arneth, Almut LU ; Cappelaere, B. ; Falk, U. ; de Grandcourt, A. ; Hanan, N. ; Kergoat, L. and Kutsch, W. , et al. (2013) In Remote Sensing of Environment 131. p.275-286
Abstract
MOD17A2 provides operational gross primary production (GPP) data globally at 1 km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (epsilon(max)). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate epsilon(max) and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP... (More)
MOD17A2 provides operational gross primary production (GPP) data globally at 1 km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (epsilon(max)). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate epsilon(max) and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred epsilon(max) calculated from tower data was higher than the epsilon(max) prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2. (C) 2013 Elsevier Inc. All rights reserved. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Remote sensing, Africa, Gross primary production (GPP), Moderate, Resolution Imaging Spectroradiometer (MODIS), MOD17A2, CarboAfrica, AMMA
in
Remote Sensing of Environment
volume
131
pages
275 - 286
publisher
Elsevier
external identifiers
  • wos:000315546900021
  • scopus:84873840441
ISSN
0034-4257
DOI
10.1016/j.rse.2012.12.023
language
English
LU publication?
yes
id
0d12af50-6a8c-4543-9192-a8c2824c10da (old id 3651204)
date added to LUP
2016-04-01 10:44:35
date last changed
2020-10-04 03:47:51
@article{0d12af50-6a8c-4543-9192-a8c2824c10da,
  abstract     = {MOD17A2 provides operational gross primary production (GPP) data globally at 1 km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (epsilon(max)). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate epsilon(max) and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred epsilon(max) calculated from tower data was higher than the epsilon(max) prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2. (C) 2013 Elsevier Inc. All rights reserved.},
  author       = {Sjöström, Martin and Zhao, M. and Archibald, S. and Arneth, Almut and Cappelaere, B. and Falk, U. and de Grandcourt, A. and Hanan, N. and Kergoat, L. and Kutsch, W. and Merbold, L. and Mougin, E. and Nickless, A. and Nouvellon, Y. and Scholes, R. J. and Veenendaal, E. M. and Ardö, Jonas},
  issn         = {0034-4257},
  language     = {eng},
  pages        = {275--286},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Evaluation of MODIS gross primary productivity for Africa using eddy covariance data},
  url          = {http://dx.doi.org/10.1016/j.rse.2012.12.023},
  doi          = {10.1016/j.rse.2012.12.023},
  volume       = {131},
  year         = {2013},
}