A key step in assessing the global carbon budget is the determination of the partial pressure of CO2 in seawater (pCO(2(sw))). Spatially complete observational fields of pCO(2(sw)) are routinely produced for regional and global ocean carbon budget assessments by extrapolating sparse in situ measurements of pCO(2(sw)) using satellite observations. As part of this process, satellite chlorophyll a (Chl a) is often used as a proxy for the biological drawdown or release of CO2. Chl a does not, however, quantify carbon fixed through photosynthesis and then respired, which is determined by net community production (NCP). In this study, pCO(2(sw)) over the South Atlantic Ocean is estimated using a feed forward neural network (FNN) scheme and either satellite-derived NCP, net primary production (NPP) or Chl a to compare which biological proxy produces the most accurate fields of pCO(2(sw)). Estimates of pCO(2(sw)) using NCP, NPP or Chl a were similar, but NCP was more accurate for the Amazon Plume and upwelling regions, which were not fully reproduced when using Chl a or NPP. A perturbation analysis assessed the potential maximum reduction in pCO(2(sw)) uncertainties that could be achieved by reducing the uncertainties in the satellite biological parameters. This illustrated further improvement using NCP compared to NPP or Chl a. Using NCP to estimate pCO(2(sw)) showed that the South Atlantic Ocean is a CO2 source, whereas if no biological parameters are used in the FNN (following existing annual carbon assessments), this region appears to be a sink for CO2. These results highlight that using NCP improved the accuracy of estimating pCO(2(sw)) and changes the South Atlantic Ocean from a CO2 sink to a source. Reducing the uncertainties in NCP derived from satellite parameters will ultimately improve our understanding and confidence in quantification of the global ocean as a CO2 sink.