Éditeur(s) : IEEE
Résumé : This paper aims at studying the quality of the sea surface salinity (SSS) retrieved from soil moisture and ocean salinity (SMOS) data in coastal areas. These areas are characterized by strong and variable SSS gradients [several practical salinity units (psu) on relatively small scales: the extent of river plumes is highly variable, typically at kilometric and daily scales. Monitoring this variability from SMOS measurements is particularly challenging because of their resolution (typically 30-100 km) and because of the contamination by the nearby land. A set of academic tests was conducted with a linear coastline and constant geophysical parameters, and more realistic tests were conducted over the Bay of Biscay. The bias of the retrieved SSS has been analyzed, as well as the root mean square (rms) of the bias, and the retrieved SSS compared to a numerical hydrodynamic model in the semirealistic case. The academic study showed that the Blackman apodization window provides the best compromise in terms of magnitude and fluctuations of the bias of the retrieved SSS. Whatever the type of vegetation cover, a strong negative bias, greater than 1 psu, was found when nearer than 36 km from the coast. Between 44 and 80 km, the type of vegetation cover has an impact of less than a factor 2 on the bias, and no influence further than 80 km from the coast. The semirealistic study conducted in the Bay of Biscay showed a bias over ten days lower than 0.2 psu for distances greater than 47 km, due to an averaging over various geometries (coastline orientation, swath orientation, etc.). The bias showed a weak dependence on the location of the grid point within the swath. Despite the noise on the retrieved SSS, contrasts due to the plume of the Loire River and the Gironde estuary remained detectable on ten-day averaged maps with an rms of 0.57 psu. Finally, imposing thresholds on the major axis of the measurements brought little improvement to the bias, whereas it increased the rms and- could lead to strong swath restriction: a 49-km threshold on the major axis resulted in an effective swath of 800-900 km instead of 1200 km. NOT CONTROLLED OCR
Transactions on geoscience an remote sensing IEEE (0196-2892) (IEEE), 2007-07 , Vol. 45 , N. 7 , P. 2061-2072

Droits : 2007 IEEE
http://archimer.ifremer.fr/doc/2007/publication-3643.pdf
DOI:10.1109/TGRS.2007.894934
http://archimer.ifremer.fr/doc/00000/3643/

Éditeur(s) : HAL CCSD Wiley Open Access
Résumé : Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
ISSN: 2045-7758

hal-01204216
https://hal.archives-ouvertes.fr/hal-01204216
DOI : 10.1002/ece3.1303
PRODINRA : 280886

Éditeur(s) : Amer Geophysical Union
Résumé : Sea surface salinity (SSS) measured from space by the Soil Moisture and Ocean Salinity (SMOS) mission is validated in the subtropical North Atlantic Ocean. 39 transects of ships of opportunity equipped with thermosalinographs (TSG) crossed that region from 2010 to 2012, providing a large database of ground truth SSS. SMOS SSS is also compared to Aquarius SSS. Large seasonal biases remain in SMOS and Aquarius SSS. In order to look at the capability of satellite SSS to monitor spatial variability, especially at scales less than 300 km (not monitored with the Argo network), we first apply a monthly bias correction derived from satellite SSS and In Situ Analysis System (ISAS) SSS differences averaged over the studied region. Ship SSS averaged over 25 km is compared with satellite and ISAS SSS. Similar statistics are obtained for SMOS, Aquarius and ISAS products (root mean square error of about 0.15 and global correlation coefficient r of about 0.92). However, in the above statistics, SSS varies due to both large scale and mesoscale (here, for scales around 100 km) variability. In order to focus on mesoscale variability, we consider SSS anomalies with respect to a monthly climatology. SMOS SSS and Aquarius SSS anomalies are more significantly correlated (r > 0.5) to TSG SSS anomaly than ISAS. We show the effective gain of resolution and coverage provided by the satellite products over the interpolated in situ data. We also show the advantage of SMOS (r=0.57) over Aquarius (r=0.52) to reproduce SSS mesoscale features.
Journal Of Geophysical Research-oceans (0148-0027) (Amer Geophysical Union), 2014-12 , Vol. 119 , N. 12 , P. 8878-8896

Droits : 2014. American Geophysical Union. All Rights Reserved.
http://archimer.ifremer.fr/doc/00210/32150/34082.pdf
DOI:10.1002/2013JC009610
http://archimer.ifremer.fr/doc/00210/32150/

Éditeur(s) : Amer Meteorological Soc
Résumé : Salinity measurements from 119 surface drifters in 2007-12 were assessed; 80% [Surface Velocity Program with a barometer with a salinity sensor (SVP-BS)] and 75% [SVP with salinity (SVP-S)] of the salinity data were found to be usable, after editing out some spikes. Sudden salinity jumps are found in drifter salinity records that are not always associated with temperature jumps, in particular in the wet tropics. A method is proposed to decide whether and how to correct those jumps, and the uncertainty in the correction applied. Northeast of South America, in a region influenced by the Amazon plume and fresh coastal water, drifter salinity is very variable, but a comparison with data from the Soil Moisture and Ocean Salinity satellite suggests that this variability is usually reasonable. The drifter salinity accuracy is then explored based on comparisons with data from Argo floats and from thermosalinographs (TSGs) of ships of opportunity. SVP-S/SVP-BS drifter records do not usually present significant biases within the first 6 months, but afterward biases sometimes need to be corrected (altogether, 16% of the SVP-BS records). Biases start earlier after 3 months for drifters not protected by antifouling paint. For the few drifters for which large corrections were applied to portions of the record, the accuracy cannot be proven to be better than 0.1 psu, and it cannot be proven to be better than 0.5 psu for data in the largest variability area off northeast South America. Elsewhere, after excluding portions of the records with suspicious salinity jumps or when large corrections were applied, the comparisons rule out average biases in individual drifter salinity record larger than 0.02 psu (midlatitudes) and 0.05 psu (tropics).
Journal Of Atmospheric And Oceanic Technology (0739-0572) (Amer Meteorological Soc), 2014-04 , Vol. 31 , N. 4 , P. 967-983

Droits : 2014 American Meteorological Society
http://archimer.ifremer.fr/doc/00190/30095/28748.pdf
DOI:10.1175/JTECH-D-13-00158.1
http://archimer.ifremer.fr/doc/00190/30095/