zooplankton, fish, and other animals in the “food chain.” Because of its rapid growth and many consumers, phytoplankton biomass or chlorophyll concentration varies on short timescales, yet the extent of a “patch” of accumulated biomass is on the order of 10-100 km.

Satellites have allowed scientists to routinely estimate phytoplankton productivity on an annual basis for the first time, enabling them to detect a trend in decreasing phytoplankton productivity associated with warming of the surface ocean at mid- to low latitudes. Because a phytoplankton bloom and its associated productivity are such large-scale yet short-lived phenomena, there is simply no way to survey large enough areas of the ocean to capture their dynamics using ships to map phytoplankton biomass and productivity.

Prior to the introduction of satellite observations, estimates of oceanic primary production depended on relatively few labor-intensive ship-based incubations using the ¹⁴C technique that had become the standard method for measuring primary productivity in the ocean (Steeman-Nielsen and Jensen 1957). To estimate global annual oceanic production (gigatons of carbon per year), the mean integral productivity was first estimated for the different oceans and depth ranges using relatively few measurements made in each domain. These were then multiplied by the area of the ocean domain and 365 days per year to derive annual oceanic primary production. Due to the vastness of the ocean and high spatial and temporal variability, ship-based global mapping was infrequently attempted and could not realistically capture the interannual variability. Even with the development of fluorescence-based estimates of marine primary productivity, which could be obtained from instruments towed behind ships, obtaining global coverage would still require years. It has long been recognized that ship-based sampling methods suffer from significant undersampling in both space and time (McCarthy 1999). Consequently, the best quantitative global estimates of both biomass and productivity are derived with the use of satellite observations that provide the necessary frequency of global coverage.

To estimate primary productivity from satellite measurements, it is assumed that the productivity is proportional to the phytoplankton biomass. Consequently, measuring biomass is the first critical step in estimating marine primary productivity from space. Chlorophyll a, the ubiquitous light-harvesting pigment found in all green plants, has long been a standard measure of phytoplankton biomass (Box 9.3, Figure 9.2, Table 9.1). This is largely because chlorophyll can be measured rapidly and easily owing to its fluorescent and absorption properties.

Early estimates of oceanic primary productivity derived using satellite data provided a relative static picture in that they represented average annual productivity (Platt and Sathyendranath 1988, Antoine et al. 1996, Behrenfeld and Falkowski 1997, Field et al. 1998). One of the most thorough estimates was that of Longhurst el al. (1995), who estimated global ocean net primary production using Coastal Zone Color Scanner (CZCS) data and models of the subsurface chlorophyll distribution and Photosynthesis-irradiance (P-I) relationships defined for 57 biogeochemical provinces.

Net primary productivity (NPP) is influenced by climate and biotic controls that interact with each other. Field et al. (1995) predicted global terrestrial NPP on a monthly time step using the Carnegie-Ames-Stanford Assimilation (CASA) model, incorporating a set of ecological principles and satellite and surface data. Several authors have used satellite data to estimate global net primary production, combining both terrestrial and oceanic models. Within a few years they used a linked ocean-terrestrial model that combined an 8-year Advanced Very High Resolution Radiometer (AVHRR) record and a 6-year CZCS data record with a biogeochemistry model to estimate global land and ocean NPP (Field et al. 1998, Figure 9.3). This study found that the contribution of land and ocean to NPP was nearly equal but that there was striking variability in NPP at a local level. Based on the spatial variability in the satellite data, their model predicted strong differential resource limitations for terrestrial and ocean habitats.

Behrenfeld et al. (2001) used the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) data to estimate terrestrial and ocean primary production during the transition between El Niño and La Niña conditions in 1997 to 1999. They found that the ocean exhibited the greatest effect, particularly in tropical regions where El Niño-Southern Oscillation (ENSO) impacts on upwelling and nutrient availability were greatest. Terrestrial ecosystems did not exhibit a clear ENSO response, although regional changes were substantial. These studies clearly demonstrate the invaluable contribution satellite observation of NPP make to the fundamental understanding of climate change impacts on the biosphere.

Satellite observations afford the only means of estimating and monitoring the role of ocean biomass as a sink for carbon. In particular, the fundamental question of whether the biological carbon uptake is changing in response to climate change can only be addressed with satellite measurements. It requires not only ocean color measurements (phytoplankton biomass and productivity) but also coincident space-based observations of the physical ocean environment (circulation and mixing) and land-ocean exchanges through rivers and tidal wetlands, as well as winds, tides, and solar energy input to the upper ocean. Observing linkages between the physical and chemical environment and the biology of the ocean is a significant achievement of observations from space. Continuity of this record is critical. Understanding the consequences of the CO₂ increase and its effect on terrestrial and marine