%0 Journal Article %1 Machwitz2015 %A Machwitz, Miriam %A Gessner, Ursula %A Conrad, Christopher %A Falk, Ulrike %A Richters, Jochen %A Dech, Stefan %D 2015 %J International Journal of Applied Earth Observation and Geoinformation %K article Gessner LSFE %R http://dx.doi.org/10.1016/j.jag.2015.04.007 %T 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 %U http://www.sciencedirect.com/science/article/pii/S0303243415000860 %X Abstract 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.

@article{Machwitz2015, abstract = {Abstract 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. }, added-at = {2020-09-11T12:04:50.000+0200}, author = {Machwitz, Miriam and Gessner, Ursula and Conrad, Christopher and Falk, Ulrike and Richters, Jochen and Dech, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2c8dcdb8777007ecf03adb8f855cc3b04/earthobs_uniwue}, description = {Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250m MODIS FPAR and fractional vegetation cover information}, doi = {http://dx.doi.org/10.1016/j.jag.2015.04.007}, interhash = {144696d09c6196bf33275701f24b8b5a}, intrahash = {c8dcdb8777007ecf03adb8f855cc3b04}, issn = {0303-2434}, journal = {International Journal of Applied Earth Observation and Geoinformation }, keywords = {article Gessner LSFE}, timestamp = {2020-11-18T22:08:33.000+0100}, 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 }, url = {http://www.sciencedirect.com/science/article/pii/S0303243415000860}, year = 2015 }