Removing the impact of wind direction on remote sensing of sea surface salinity

2006 ◽  
Vol 51 (11) ◽  
pp. 1368-1373 ◽  
Author(s):  
Xiaobin Yin ◽  
Yuguang Liu ◽  
Hande Zhang
Keyword(s):  
Remote Sensing ◽  
Wind Direction ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Impact

scholarly journals Singularity Power Spectra: A Method to Assess Geophysical Consistency of Gridded Products—Application to Sea-Surface Salinity Remote Sensing Maps

2018 ◽  
Vol 56 (9) ◽  
pp. 5525-5536 ◽  
Author(s):  
Nina Hoareau ◽  
Antonio Turiel ◽  
Marcos Portabella ◽  
Joaquim Ballabrera-Poy ◽  
Jur Vogelzang
Keyword(s):  
Remote Sensing ◽  
Power Spectra ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity

scholarly journals Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2975
Author(s):  
Huabing Xu ◽  
Rongzhen Yu ◽  
Danling Tang ◽  
Yupeng Liu ◽  
Sufen Wang ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Vertical Mixing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Ekman Pumping ◽  
Surface Salinity ◽  
Argo Data ◽  
Before And After ◽  
The Impact

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

SMOS-based estimation and validation of Total Alkalinity in the Mediterranean basin

2020 ◽  
Author(s):  
Roberto Sabia ◽  
Estrella Olmedo ◽  
Giampiero Cossarini ◽  
Aida Alvera-Azcárate ◽  
Veronica Gonzalez-Gambau ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mediterranean Sea ◽  
Mediterranean Basin ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Total Alkalinity ◽  
Surface Salinity ◽  
Carbonate System ◽  
The Mediterranean ◽  
The Mediterranean Basin

<p>ESA SMOS satellite [1] has been providing first-ever Sea Surface Salinity (SSS) measurements from space for over a decade now. Until recently, inherent algorithm limitations or external interferences hampered a reliable provision of satellite SSS data in semi-enclosed basin such as the Mediterranean Sea. This has been however overcome through different strategies in the retrieval scheme and data filtering approach [2, 3]. This recent capability has been in turn used to infer the spatial and temporal distribution of Total Alkalinity (TA - a crucial parameter of the marine carbonate system) in the Mediterranean, exploiting basin-specific direct relationships existing between salinity and TA.</p><p>Preliminary results [4] focused on the differences existing in several parameterizations [e.g, 5] relating these two variables, and how they vary over a seasonal to interannual timescale.</p><p>Currently, to verify the consistency and accuracy of the derived products, these data are being validated against a proper ensemble of in-situ, climatology and model outputs within the Mediterranean basin. An error propagation exercise is also being planned to assess how uncertainties in the satellite data would translate into the final products accuracy.</p><p>The resulting preliminary estimates of Alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of Ocean Acidification assessment and marine carbon cycle.</p><p>References:</p><p>[1] J. Font et al., "SMOS: The Challenging Sea Surface Salinity Measurement From Space," in Proceedings of the IEEE, vol. 98, no. 5, pp. 649-665, May 2010. doi: 10.1109/JPROC.2009.2033096</p><p>[2] Olmedo, E., J. Martinez, A. Turiel, J. Ballabrera-Poy, and M. Portabella,  “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity”. Remote Sensing of Environment 193, 103-126 (2017).</p><p>[3] Alvera-Azcárate, A., A. Barth, G. Parard, J.-M. Beckers, Analysis of SMOS sea surface salinity data using DINEOF, In Remote Sensing of Environment, Volume 180, 2016, Pages 137-145, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.02.044.</p><p>[4] Sabia, R., E. Olmedo, G. Cossarini, A. Turiel, A. Alvera-Azcárate, J. Martinez, D. Fernández-Prieto, Satellite-driven preliminary estimates of Total Alkalinity in the Mediterranean basin, Geophysical Research Abstracts, Vol. 21, EGU2019-17605, EGU General Assembly 2019, Vienna, Austria, April 7-12, 2019.</p><p>[5] Cossarini, G., Lazzari, P., and Solidoro, C.: Spatiotemporal variability of alkalinity in the Mediterranean Sea, Biogeosciences, 12, 1647-1658, https://doi.org/10.5194/bg-12-1647-2015, 2015.</p><p> </p><p> </p>

Preparing the potential and challenge of remote sensing-based sea surface salinity estimation: the CoSMOS airborne campaign

2008 ◽  
Author(s):  
Nicolas Reul ◽  
Joseph Tenerelli ◽  
Bertrand Chapron ◽  
Sebastien Guimbard ◽  
Stephane-S. Picard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity

scholarly journals Technical Note: Remote sensing of sea surface salinity using the propagation of low-frequency navigation signals

2014 ◽  
Vol 11 (6) ◽  
pp. 2971-2978
Author(s):  
I. Astin ◽  
Y. Feng
Keyword(s):  
Remote Sensing ◽  
Low Frequency ◽  
Potential Method ◽  
Propagation Delay ◽  
Technical Note ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Delay Variations ◽  
Navigation Signals

Abstract. This paper introduces a potential method for the remote sensing of sea surface salinity (SSS) using measured propagation delay of low-frequency Loran-C signals transmitted over an all-seawater path between the Sylt station in Germany and an integrated Loran-C/GPS receiver located in Harwich, UK. The overall delay variations in Loran-C surface waves along the path may be explained by changes in sea surface properties (especially the temperature and salinity), as well as atmospheric dynamics that determine the refractive index of the atmosphere. After removing the atmospheric and sea surface temperature (SST) effects, the residual delay revealed a temporal variation similar to that of SSS data obtained by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite.

scholarly journals Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

2019 ◽  
Vol 11 (17) ◽  
pp. 1964 ◽  
Author(s):  
Jorge Vazquez-Cuervo ◽  
Jose Gomez-Valdes ◽  
Marouan Bouali ◽  
Luis Miranda ◽  
Tom Van der Stocken ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Current System ◽  
California Current ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
California Current System ◽  
The Impact ◽  
Buoy Measurements

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

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