Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay

Abstract

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean-Atmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given.Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the *Corresponding author. Email: unai.ganzedo@ehu.es International Journal of Remote Sensing Vol. 32, No. 4, 20 February 2011, 933-950 experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

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

“…Another approach for analyzing a set of satellite images (data interpolating empirical orthogonal functions -DINEOF) uses the data to create a truncated empirical orthogonal function (EOF) representation of the data set to fill in missing data (e.g., Beckers and Rixen, 2003;AlveraAzcárate et al, 2005AlveraAzcárate et al, , 2007. 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).…”

“…Optimal interpolation (in the following noted OI) is well established (e.g., Gandin, 1965;Delhomme, 1978;Bretherton et al, 1976) and a reference tool when analyzing satellite images. The method has therefore been applied in a large number of scientific studies (e.g., Kawai et al, 2006) and operational setups (e.g., Stark et al, 2007;Donlon et al, 2012;Nardelli et al, 2013).…”

Southward re‐distribution of tropical tuna fisheries activity can be explained by technological and management change

“…However, the variance of effortA or SSTA is not equal at all gridpoints; therefore, the “local” explained variance (LC) is also calculated using Equation (2). which is equivalent to the fraction of variance expressed by the i th EOF at each j th grid point over the total variability reconstructed by the leading k max EOF (Ganzedo, Alvera‐Azcárate, Esnaola, Ezcurra, & Sáenz, ). λ i represents the eigenvalue of the covariance matrix associated with the i th eigenvector e i .…”

Modeling environmental influence on Atlantic bluefin tuna bycatch by Mexican longliners in the Gulf of Mexico

“…Although the combination of data from different sensors allows the reduction of cloud cover pixels, data were interpolated using the Data Interpolating Empirical Orthogonal Function (DINEOF) to prevent data gaps (Alvera‐Azcárate, Barth, Rixen, & Beckers, ; Beckers, Barth, & Alvera‐Azcarate, ). This approach has been successfully applied to reconstruct oceanographic remote sensing data sets (Ganzedo, Alvera‐Azcárate, Esnaola, Ezcurra, & Sáenz, ; Hilborn & Costa, ; Sirjacobs et al, ; Wang & Liu, ) and even fishery spatiotemporal data (Ganzedo, Erdaide, Trujillo‐Santana, Alvera‐Azcárate, & Castro, ). The resulting set comprises daily data from 1998 to 2012 on a regular 0.25 degree grid for the study area.…”