Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model.

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPARps) and (ii) using direct quantum yield by classifying the vegetation into C3 or C4 classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm-2day-1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm-2day-1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr-1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.

[1]  Jadunandan Dash,et al.  Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data , 2013, Global change biology.

[2]  G. Farquhar,et al.  Effect of temperature on the CO2/O2 specificity of ribulose-1,5-bisphosphate carboxylase/oxygenase and the rate of respiration in the light , 1985, Planta.

[3]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[4]  Hongliang Fang,et al.  Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data , 2006 .

[5]  Nadine Gobron,et al.  The interannual variability of Africa's ecosystem productivity: a multi-model analysis , 2008 .

[6]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[7]  B. Choudhury,et al.  Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study , 1992 .

[8]  Andrew E. Suyker,et al.  Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems , 2005 .

[9]  W. Oechel,et al.  Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements , 2001 .

[10]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light‐use efficiency , 1999 .

[11]  Dario Papale,et al.  A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities , 2014 .

[12]  Song S Qian,et al.  Response to CO2 enrichment of understory vegetation in the shade of forests , 2016, Global change biology.

[13]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[14]  P. Ciais,et al.  The carbon balance of Africa: synthesis of recent research studies , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  W. Oechel,et al.  On the use of MODIS EVI to assess gross primary productivity of North American ecosystems , 2006 .

[16]  Steffen Fritz,et al.  Improved light and temperature responses for light-use-efficiency-based GPP models , 2013 .

[17]  Bo-Cai Gao,et al.  Using EO-1 Hyperion to Simulate HyspIRI Products for a Coniferous Forest: The Fraction of PAR Absorbed by Chlorophyll $(\hbox{fAPAR}_{\rm chl})$ and Leaf Water Content (LWC) , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[18]  W. Kustas,et al.  Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production , 2016 .

[19]  F. Gao,et al.  Estimation of Crop Gross Primary Production (GPP): Fapar(sub Chl) Versus MOD15A2 FPAR , 2014 .

[20]  Jonas Ardö,et al.  Precipitation as driver of carbon fluxes in 11 African ecosystems , 2008 .

[21]  Christopher O. Justice,et al.  The Miombo Network: Framework for a Terrestrial Transect Study of Land-Use and Land-Cover Change in the Miombo Ecosystems of Central Africa , 1997 .

[22]  Niall P. Hanan,et al.  Photosynthesis and carbon balance of a Sahelian fallow savanna , 1998 .

[23]  Markus Reichstein,et al.  The role of trace gas flux networks in the biogeosciences , 2012 .

[24]  Hans Papen,et al.  Diurnal, seasonal, and interannual variation in carbon dioxide and energy exchange in shrub savanna in Burkina Faso (West Africa) , 2008 .

[25]  Andrew E. Suyker,et al.  Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity , 2010 .

[26]  F. Veroustraete,et al.  Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data , 2002 .

[27]  Jonas Ardö,et al.  Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan , 2008, Carbon balance and management.

[28]  Jonas Ardö,et al.  Evaluation of MODIS gross primary productivity for Africa using eddy covariance data , 2013 .

[29]  D. M. Hamby.  A numerical comparison of sensitivity analysis techniques , 1993 .

[30]  Jadunandan Dash,et al.  The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation , 2010 .

[31]  R. Valentini,et al.  The role of soil in storing carbon in tropical rainforests: the case of Ankasa Park, Ghana , 2010, Plant and Soil.

[32]  E. Singsaas,et al.  Variation in measured values of photosynthetic quantum yield in ecophysiological studies , 2001, Oecologia.

[33]  P. Ciais,et al.  Spatiotemporal patterns of terrestrial gross primary production: A review , 2015 .

[34]  P. G. Jarvis,et al.  Productivity of temperate de-ciduous and evergreen forests , 1983 .

[35]  J. Ehleringer,et al.  Variation in Quantum Yield for CO(2) Uptake among C(3) and C(4) Plants. , 1983, Plant physiology.

[36]  Wenjiang Huang,et al.  Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices , 2009 .

[37]  S. Verma,et al.  Gross primary production and ecosystem respiration of irrigated and rainfed maize–soybean cropping systems over 8 years , 2012 .

[38]  Nicholas C. Coops,et al.  Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States , 2009 .

[39]  Robert J. Scholes,et al.  Canopy structure in savannas along a moisture gradient on Kalahari sands , 2004 .

[40]  Gail Taylor,et al.  Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution , 2010 .

[41]  D. Potts.  Viewpoint: What do we know about urbanisation in sub-Saharan Africa and does it matter? , 2012 .

[42]  T. A. Black,et al.  The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. , 2008, The Science of the total environment.

[43]  Jadunandan Dash,et al.  A new approach for estimating northern peatland gross primary productivity using a satellite‐sensor‐derived chlorophyll index , 2011 .

[44]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[45]  Frédéric Baret,et al.  A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements , 2015, Remote. Sens..

[46]  Rainfall, topography and primary production relationships in a semiarid ecosystem , 2011 .

[47]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[48]  T. Vesala,et al.  Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation , 2006 .

[49]  Jadunandan Dash,et al.  An algorithm to derive the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (FAPAR(ps)) from eddy covariance flux tower data. , 2013, The New phytologist.

[50]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[51]  M. Huston,et al.  The global distribution of net primary production: resolving the paradox , 2009 .

[52]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): global pattern and differentiation by major biomes , 1999 .

[53]  D. Kuang,et al.  Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest , 2010 .

[54]  D. Unwin.  Microclimate measurement for ecologists. , 1980 .

[55]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[56]  K. Tu,et al.  Modeling plant-soil-atmosphere carbon dioxide exchange using optimality principles , 2000 .

[57]  Y. Ran,et al.  Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale , 2016 .

[58]  Jonas Ardö,et al.  Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems , 2011 .

[59]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[60]  S. Jackson,et al.  Novel climates, no‐analog communities, and ecological surprises , 2007 .

[61]  E. Medina,et al.  Determinants of Tropical Savannas , 1996 .

[62]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[63]  Jonathan M. Adams,et al.  Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate , 2009 .

[64]  M. G. Ryan,et al.  First direct landscape-scale measurement of tropical rain forest Leaf Area Index, a key driver of global primary productivity. , 2007, Ecology letters.

[65]  R. Valentini,et al.  Seasonal trends of dry and bulk concentration of nitrogen compounds over a rain forest in Ghana , 2013 .

[66]  E. DeLucia,et al.  Photosynthetic responses to CO2 enrichment of four hardwood species in a forest understory , 2000, Oecologia.

[67]  James R. Ehleringer,et al.  Quantum Yields for CO2 Uptake in C3 and C4 Plants: Dependence on Temperature, CO2, and O2 Concentration , 1977 .

[68]  Christopher A Williams,et al.  Africa and the global carbon cycle , 2007, Carbon balance and management.

[69]  Heiko Balzter,et al.  Land Degradation Assessment Using Residual Trend Analysis of GIMMS NDVI3g, Soil Moisture and Rainfall in Sub-Saharan West Africa from 1982 to 2012 , 2015, Remote. Sens..