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

Abstract

a b s t r a c tOcean-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(CZCS, -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 |bias| ≤ 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 |bias| ≤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.

Modeling of extreme freshwater outflow from the north-eastern Japanese river basins to western Pacific Ocean

“…To reduce missing data due to cloud cover, firstly, we applied a linear interpolation procedure (interpolating spatially-adjacent values) such that gaps were filled with the average value of the surrounding grid points. The averaging window had a width of five points and the surrounding points were weighted equally (Racault et al, 2014). Secondly, we produced 15-Day composites for pre-typhoon and 5-Day composites for post-typhoon conditions for the Typhoon Chataan case and 5-Day composites for pre-and post-typhoon conditions for the Typhoon Roke case, which had better data coverage.…”

Diatom Phenology in the Southern Ocean: Mean Patterns, Trends and the Role of Climate Oscillations

“…Nevertheless, the deep Chla maximum may also result from photoacclimation to reduced light levels and nutrient availability and may not represent an increase in the organic carbon content [40,41]. A second limitation is that due to gaps in satellite data the right timing of the bloom may be missed [1,42]. Racault et al [42] have shown that if 40% of data are missing in an annual time-series, the RMSE and bias in estimates of timing of peak are ∼30 and 10 days respectively.…”

“…A second limitation is that due to gaps in satellite data the right timing of the bloom may be missed [1,42]. Racault et al [42] have shown that if 40% of data are missing in an annual time-series, the RMSE and bias in estimates of timing of peak are ∼30 and 10 days respectively. Data gaps due to clouds, high solar zenith angle and sea ice for example also reduce the length of the time series and the significance of the statistical tests.…”

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

“…Blending in-situ and satellite observations of TCHLa (e.g. AMT and SeaWiFS) may lead to more robust estimates of trends (Aiken et al, 2009;Gregg & Casey, 2010;Gregg & Rousseaux, 2014) and minimise the impact of periods of missing data (Beaulieu et al, 2013;Racault et al, 2014a).…”

“…However, long-term changes in these two ecological indicators remain non-trivial to evaluate (e.g. Antoine et al, 2005;Boyce et al, 2010;Brewin et al, 2012;Gregg and Conkright, 2002;Kostadinov et al, 2010;Mackas, 2011;McQuatters-Gollop et al, 2011;Racault et al, 2014a;Vantrepotte & Mélin, 2009;Vantrepotte & Mélin, 2011).…”