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Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information

Machwitz, Miriam and Gessner, Ursula and Conrad, Christopher and Falk, Ulrike and Richters, Jochen and Dech, Stefan (2015) Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information. International Journal of Applied Earth Observation and Geoinformation, 43, pp. 177-194. Elsevier. doi: 10.1016/j.jag.2015.04.007. ISSN 0303-2434.

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Official URL: http://www.sciencedirect.com/science/article/pii/S0303243415000860


"Global warming associated with climate change is one of the greatest challenges of today’s world. Increasing emissions of the greenhouse gas CO2 are considered as a major contributing factor to global warming. One regulating factor of CO2 exchange between atmosphere and land surface is vegetation. Measurements of land cover changes in combination with modelling the Gross Primary Productivity (GPP) can contribute to determine important sources and sinks of CO2. The aim of this study is to accurately model the GPP for a region in West Africa with a spatial resolution of 250 m, and the differentiation of GPP based on woody and herbaceous vegetation. For this purpose, the Regional Biomass Model (RBM) was applied, which is based on a Light Use Efficiency (LUE) approach. The focus was on the spatial enhancement of the RBM from the original 1000–250 m spatial resolution (RBM+). The adaptation to the 250 m scale included the modification of two main input parameters: (1) the fraction of absorbed Photosynthetically Active Radiation (FPAR) based on the 1000 m MODIS MOD15A2 FPAR product which was downscaled to 250 m using MODIS NDVI time series; (2) the fractional cover of woody and herbaceous vegetation, which was improved by using a multi-scale approach. For validation and regional adjustments of GPP and the input parameters, in situ data from a climate station and eddy covariance measurements were integrated. The results of this approach show that the input parameters could be improved significantly: downscaling considerably reduces data gaps of the original FPAR product and the improved dataset differed less than 5.0% from the original data for cloud free regions. The RMSE of the fractional vegetation cover varied between 5.1 and 12.7%. Modelled GPP showed a slight overestimation in comparison to eddy covariance measurements. The in situ data was exceeded by 8.8% for 2005 and by 2.0% for 2006. The model results were converted to NPP and also agreed well with previous NPP measurements reported from different studies. Altogether a high accuracy and suitability of the regionally adjusted and downscaled model RBM+ can be concluded. The differentiation between vegetation growth forms allows a separation of long-term and short-term carbon storage based on woody and herbaceous vegetation, respectively."

Item URL in elib:https://elib.dlr.de/96835/
Document Type:Article
Title:Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Richters, JochenCenter for Remote Sensing of Land Surfaces, University of Bonn,UNSPECIFIEDUNSPECIFIED
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 177-194
Keywords:Gross Primary Productivity , West Africa , Regional Biomass Model, Light Use Efficiency, FPAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Wöhrl, Monika
Deposited On:24 Jun 2015 13:43
Last Modified:26 Aug 2022 11:54

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