Abstract
Marine remote sensing provides comprehensive characterizations of the ocean surface across space and time. However, cloud cover is a significant challenge in marine satellite monitoring. Researchers have proposed various algorithms to fill data gaps “below the clouds”, but a comparison of algorithm performance across several geographic regions has not yet been conducted. We compared ten basic algorithms, including data-interpolating empirical orthogonal functions (DINEOF), geostatistical interpolation, and supervised learning methods, in two gap-filling tasks: the reconstruction of chlorophyll a in pixels covered by clouds, and the correction of regional mean chlorophyll a concentrations. For this purpose, we combined tens of cloud-free images with hundreds of cloud masks in four study areas, creating thousands of situations in which to test the algorithms. The best algorithm depended on the study area and task, and differences between the best algorithms were small. Ordinary Kriging, spatiotemporal Kriging, and DINEOF worked well across study areas and tasks. Random forests reconstructed individual pixels most accurately. We also found that high levels of cloud cover led to considerable errors in estimated regional mean chlorophyll a concentration. These errors could, however, be reduced by about 50% to 80% (depending on the study area) with prior cloud-filling.
Highlights
The world’s marine ecosystems are exposed to many anthropogenic pressures from climate change, fishing, pollution, and habitat destruction [1,2,3,4]
We found that applying even simple cloud-filling algorithms before calculating spatial means of Chlorophyll a (Chl a) substantially reduced the means’ errors
Prior cloud-filling can be a straightforward way to improve regional time series derived from marine satellite data
Summary
The world’s marine ecosystems are exposed to many anthropogenic pressures from climate change, fishing, pollution, and habitat destruction [1,2,3,4]. Since the launch of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in 1997, the continuous availability of moderate-resolution, multi-spectral ocean color and thermal satellite imagery has expanded our ability to study and monitor marine phenomena at broad spatial scales [5]. Because electromagnetic radiation at visible, near- and thermal-infrared wavelengths is absorbed and scattered by clouds, essential marine satellite data products have significant gaps, limiting the ability to observe phenomena with high spatial and temporal variability. At regional scales, receding sea ice has led to increased primary production [12,13] and the development of fall blooms in the Arctic Ocean [14], with the potential for fundamental changes of the marine food web [15]. Remote sensing of phytoplankton biomass is essential for monitoring marine ecosystems and the services they provide in a changing climate and amid other anthropogenic stressors
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