Merging SeaWiFS and MODIS/Aqua Ocean Color Data in North and Equatorial Atlantic Using Weighted Averaging and Objective Analysis

• Published in: IEEE transactions on geoscience and remote sensing Vol. 44; no. 11; pp. 3436 - 3451
• Main Authors: Pottier, C., Garcon, V., Larnicol, G., Sudre, J., Schaeffer, P., Le Traon, P.-Y.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Biology; Biosensors; Chlorophyll; Chlorophylls; Earth sciences; Earth, ocean, space; Exact sciences and technology; Image color analysis; Image sensors; Imaging; Internal geophysics; Marine geology; marine vegetation; Merging; MODIS; Ocean color; Oceans; Satellites; Sea measurements; sea surface; Sea-viewing Wide Field-of-view Sensor; Sensor phenomena and characterization; Signal analysis; Spectroradiometers; Storage systems; Equatorial Atlantic; signal analysis; maps; color; Atlantic Ocean; Space remote sensing; chlorophyll; vegetation; covariance; Biology; marine vegetation; in situ; quality; sea surface; surface water; marine environment; ocean; noise; errors
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2006.878441

Two approaches of ocean color data merging were tested and compared in the North and Equatorial Atlantic Basin: the weighted averaging and the objective analysis. The datasets used were the daily level-3 binned data of chlorophyll-a from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer on the Aqua satellite over the year 2003, which is the first common full year of operation. Since they represent input for both approaches, matchups between the satellite and the in situ data from the SeaWiFS Bio-optical Archive and Storage System and the Atlantic Meridional Transect were first studied to compute a spatial map of the root mean-square error and of the bias. Because of the log distribution of the chlorophyll fields, each approach was applied to untransformed and log-transformed values. The application of the weighted averaging to log-transformed values does not show significant differences in comparison to its application to untransformed values. This is not the case, however, for the objective analysis that gives better results when applied to log-transformed values. Both approaches give combined chlorophyll data of equivalent quality, although the objective analysis could be improved with a better statistical characterization of noise and signal covariance. The main advantage of the objective analysis is its ability to interpolate in space (and time) by taking into account the characteristic scales of chlorophyll-a. As a result, the spatial coverage of the combined data is at least twice as large in the case of objective analysis than weighted averaging