Comparison of Cloud-Filling Algorithms for Marine Satellite Data
Comparison of Cloud-Filling Algorithms for Marine Satellite Data
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DOI：
10.3390/rs12203313
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发表时间：
2020-10
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影响因子：
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通讯作者：
A. Stock;A. Subramaniam;G. V. Dijken;Lisa M. Wedding;K. Arrigo;M. Mills;M. A. Cameron;F. Micheli
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作者：
A. Stock;A. Subramaniam;G. V. Dijken;Lisa M. Wedding;K. Arrigo;M. Mills;M. A. Cameron;F. Micheli
海洋遥感提供了跨越空间和时间的海洋表面的综合特征。然而，云层是海洋卫星监测的一个重大挑战。研究人员提出了各种算法来填补“云以下”的数据空白，但尚未对几个地理区域的算法性能进行比较。我们比较了10种基本算法，包括数据插值经验正交函数（DINEOF）、地统计插值和监督学习方法，在两个空白填充任务中：重建被云覆盖的像素的叶绿素a和校正区域平均叶绿素a浓度。为此，我们将四个研究区域的数十张无云图像与数百张云掩模相结合，创建了数千种测试算法的情况。最佳算法取决于研究领域和任务，最佳算法之间的差异很小。普通克里格、时空克里格和DINEOF在研究领域和任务中表现良好。随机森林最准确地重建了单个像素。我们还发现，高水平的云层覆盖导致估计区域平均叶绿素a浓度的相当大的误差。然而，如果事先进行云层填充，这些误差可以减少约50%至80%（取决于研究区域）。
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.