Impact of missing data on the estimation of ecological indicators from satellite ocean-colour time-series

Ocean-colour remote sensing provides high-resolution and global-coverage of chlorophyll concentration, which can be used to estimate ecological indicators and to study inter-annual and long-term trends in the state of the marine ecosystem. To date, the record of ocean-colour observations is a rich one, including data from a number of sensors spanning more than three decades. The ESA Ocean-Colour Climate Change Initiative has advanced seamless merging of ocean-colour observations from missions during the period 1990s to 2010s. However, comparison of these more recent observations with records from 1970s to 1980s remains a complex undertaking, particularly for absolute values of chlorophyll concentration, primarily due to differences in the sensors. A further impediment to the analysis of the past records is the non-uniform distribution of gaps in the observations, in both time and space dimensions, when data from two or more sensors are compared. Here, we use the CZCS gap distribution from the Coastal Zone Color Scanner (CZCS, 1978-1986) as a mask to evaluate the impact that missing data may have on the estimation of six ecological indicators, when using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data set. Specifically, we evaluate the precision and accuracy of indicators by computing the root-mean-square-error (RMSE) and the bias arising purely from missing data. We develop an original resampling method allowing comparison of indicator estimates between SeaWiFS reference time-series and SeaWiFS time-series with CZCS-like gaps. We reduce some of the sampling gaps by applying a linear interpolation procedure, and compute multi-year averages of the indicators for every one-by-one degree pixel where sufficient data are available. Indicators from SeaWiFS reference and SeaWiFS with CZCS-like gaps are compared. Lowest uncertainty arising from missing data is observed in the indicators of annual mean and median chlorophyll concentration (global mean RMSE of 8% and vertical bar bias vertical bar >= 1%), whilst higher uncertainty is recorded for the peak chlorophyll values and the duration of the phytoplankton growing period (global mean RMSE of 33 and 47% respectively and vertical bar bias vertical bar >= 20%). Timing of initiation of the increasing phase of chlorophyll concentration in the seasonal cycle and timing of peak chlorophyll are subject to a global mean RMSE of nearly two months and a bias of two weeks or less. The present quantitative evaluation of uncertainty due to missing data demonstrates that, when pooled to create a nine-year climatology at 8-day temporal resolution, the coverage of CZCS is adequate for many climate-related studies on the marine ecosystem. Phytoplankton annual mean biomass can be estimated with low error in approximately 95% of the global oceans (i.e. regions where the indicators can be estimated with RMSE values of less than 30% and bias within 10%), and the phenological patterns can be estimated with low error in approximately 25% of the global oceans. (C) 2014 Elsevier Inc. All rights reserved.