Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology

Abstract

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full dataset and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5% for CHL and 97.2% for TSM. For the MODIS TSM dataset, 97.5% of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98% of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006). Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithm artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, and cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.

Comparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea

“…A Data Interpolation Empirical Orthogonal Functions (DINEOF, Beckers and Rixen (2003); AlveraAzcárate et al (2005)) analysis of the data was realized to the AVHRR data to identify and remove outliers from the original data set, following Alvera-Azcárate et al (2011), which proposes an improvement from the methodology used in Sirjacobs et al (2011). Outliers are defined as data that present anomalous values with respect to the surrounding pixels.…”

A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

“…The latter method has been favorably compared to OI and has been exploited in a series of applications (e.g., Sheng et al, 2009;Ganzedo et al, 2011;Nikolaidis et al, 2014;Wang and Liu, 2014), including operational setups (e.g., Volpe et al, 2012). In some situations, however, the truncation of the EOFs series can reject some interesting small-scale features that only give a small contribution to the total variance, and that can often be split into several modes (Sirjacobs et al, 2008). This is due, on one hand, to the fact EOF truncation is related to the percentage of variance that would be associated with noise and, on the other hand, to the limits of EOF decomposition itself in identifying evolving mesoscale features in a single mode (actually, any feature propagating across the spatial domain is split into several modes) and under clouds.…”

Sea Surface Temperature Retrieval from MODIS Radiances Using Truncated Total Least Squares with Multiple Channels and Parameters

“…The advantages of satellite-derived SST are vast coverage at high resolution compared to any other form of collected SST data from relatively few sources. Among the satellite derived SST products, MODIS (MODerate resolution Imaging Spectroradiometers) SSTs has an impressive long-term data record (~15 years) from a single sensor, and is used for a wide variety of oceanographic and atmospheric applications, e.g., ocean circulation modeling, numerical weather prediction, boundary currents, air-sea interactions, upwelling regions, studies of planetary boundary layer divergence, ocean biology, including algae blooms, and coral reefs (e.g., [13][14][15][16][17][18][19][20]). The performance of these studies always depends on the accuracy of SST retrieval and data coverage of MODIS measurement.…”