Chlorophyll Animation Methods
Time Series Animations have been developed showing temporal fluxuations of chlorophyll within individual LMEs
Phytoplankton chlorophyll time-series animations (movies) were constructed for 62 large marine ecosystems (LMEs) for the 15-year period from September 1997 to June 2012 using NASA ocean color satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectrometer (MODIS-Aqua) sensor. These sensors carried on satellites in daily orbit have provided comprehensive coverage of the world's oceans (McClain, 2004). Ocean color is a key indicator of chlorophyll, phytoplankton abundance and biomass and provides important data about conditions at the base of the oceans' food chain.
Sherman and Hempel (2009) defined 64 LMEs over the global oceans (Figure 1). These LMEs produce 80% of the world's annual marine fisheries yields (Sherman et al. 2011). The boundaries of these coastal LMEs are based on distributions and trends in several trophic components, including phytoplankton, zooplankton, fish and shellfish, as well as political and economic boundaries, oceanic currents, and bathymetric gradients.
Figure 1. Large Marine Ecosystems of the World
The construction of time series animations focusing on 62 LMEs is a new product. Movies of surface chlorophyll are not feasible for two (Antarctic, Arctic Ocean) of the 64 LMEs due to persistent ice and cloud cover. Time- series movies of remotely sensed satellite data are a very effective way for revealing event, seasonal, and other scales of variability that are not always evident in either synoptic or temporally-composited images. These movies allow rapid assessment and comparison of trends and conditions of phytoplankton biomass and provide insight into major ecosystem trends and processes. They also elucidate the timing of key events such as blooms.
The time series data were extracted from 8,120 global daily, nine kilometer-resolution chlorophyll estimates from SeaWifs and AQUA. Greater coverage of the LMEs was achieved by combining data from both sensors. Chlorophyll data were processed by NASA using Version 6 of the maximum band ratio algorithms (O’Reilly et. al. 1998; O’Reilly et. al. 2000). Standard mapped image data (SMI) from SeaWiFS and MODIS AQUA Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite were obtained from NASA's OceanColor Web. The dates for acquired imagery are as follows: SeaWiFS - Sep 04,1997 to Dec 11,2010; MODIS - Jul 04,2002 to Jun 04,2012.
I thank K. Sherman and M. Fogarty for their support of this project; C. McClain, G. Feldman, P. Bontempi and the NASA GSFC Ocean Biology Processing Group for their sustained efforts over several decades to provide the calibrated, quality-controlled, comprehensive data sets from the SeaWiFS and MODIS missions; T. Ducas and K. Hyde for their IDL-programming assistance; C. Damon for help with LME boundary files; M. Caracena for help with AVI-Constructor software; and J. Landry for editorial assistance.
McClain, C., G. Feldman, and S. Hooker ,2004, An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series, Deep-Sea Res. II, 51, 5-42.
O’Reilly, J.E., S. Maritorena, B.G Mitchell, D. Siegel, K Carder, S. Garver, M. Kahru, and C. McClain, 1998, Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research 103, 24937–24953.
O’Reilly, J., et. al., 2000. Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. NASA Technical Memorandum, 2000-206892, 11, 9–23.
Sherman K., J. O’Reilly, I. Belkin, C. Melrose, and K. Friedland, 2011, The application of satellite remote sensing for assessing productivity in relation to fisheries yields of the world’s large marine ecosystems, 2011, ICES Journal.
Sherman, K. and Hempel, G. (Editors) 2009. The UNEP Large Marine Ecosystem Report: A perspective on changing conditions in LMEs of the world’s Regional Seas. UNEP Regional Seas Report and Studies No. 182. United Nations Environment Programme. Nairobi, Kenya.
J.E. O'Reilly, 2013