A remote sensing-based primary production model for grassland biomes

That data from polar orbiting satellites have detected a widespread increase in photosynthetic activity over the last 20 years in the grasslands of the Sahel is justifies investigating its role in the tropical carbon cycle. But this task is undermined because ground data that are generally used to support the use of primary production models elsewhere are lacking. In this paper, we profile a Light Use Efficiency (LUE) model of primary production parameterised with satellite information, and test it for the West African Sahel; solar radiation is absorbed by plants to provide energy for photosynthesis, while moisture shortfalls control the efficiency of light usage. In particular, we show how an economical use of existing, yet meagre data sets can be used to circumvent nominal, yet untenable approaches for achieving this for the region. Specifically, we use a cloudiness layer provided with the NOAA/NASA 8 km Pathfinder Land data archive (PAL) data set to derive solar radiation (and other energy balance terms) required to implement the model (monthly time-step). Of particular note, we index growth efficiency via transpiration by subsuming rangeland-yield formulations into our model. This is important for partially vegetated landscapes where the fate of rainfall is controlled by relative vegetation cover. We accomplish this by using PAL-derived Normalised Difference Vegetation Index (NDVI) to partition the landscape into fractional vegetation cover. A bare soil evaporation model that feeds into bucket model is then applied, thereafter deriving actual transpiration (quasi-daily time-step). We forgo a formal validation of the model due to problems of spatial scale and data limitations. Instead, we generate maps showing model robustness via Monte Carlo simulation. The precision of our Gross Primary Production (GPP) estimates is acceptable, but falls off rapidly for the northern fringes of the Sahel. We also map the locations where errors in the driving variables are mostly responsible for the bulk of uncertainty in predicted GPP, in this case the water stress factor and the NDVI. Comparisons with an independent model of primary production, CENTURY, are relatively poor, yet favourable comparisons are made with previous primary production estimates found for the region in the literature. A spatially exhaustive evaluation of our GPP map is carried out by regressing randomly sampled observations against integrated NDVI, a method traditionally used to quantify absolute amounts of primary production. Our model can be used to quantify stocks and flows of carbon in grasslands over the recent historical period.