FN Archimer Export Format
PT J
TI Merging SeaWiFS and MODIS/Aqua Ocean Color Data in North and Equatorial Atlantic Using Weighted Averaging and Objective Analysis
BT 
AF POTTIER, C
   GARCON, V
   LARNICOL, G
   SUDRE, J
   SCHAEFFER, P
   LE TRAON, Pierre-Yves
AS 1:1;2:2;3:1;4:2;5:1;6:3;
FF 1:;2:;3:;4:;5:;6:PDG-DPS-LOS;
C1 CLS, Space Oceanog Div, F-31520 Ramonville St Agne, France.
   CNRS, LEGOS, F-31401 Toulouse 9, France.
   IFREMER, F-29280 Plouzane, France.
C2 CLS, FRANCE
   CNES, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-DPS-LOS
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 1.752
TC 51
UR https://archimer.ifremer.fr/doc/2006/publication-3631.pdf
LA English
DT Article
DE ;signal analysis;sea surface;marine vegetation;Biology
AB 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 logtransformed 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. OCR NOT CONTROLLED
PY 2006
PD NOV
SO IEEE
SN 0196-2892
PU IEEE
VL 44
IS 11
UT 000241933600022
BP 3436
EP 3451
DI 10.1109/TGRS.2006.878441
ID 3631
ER

EF