scholarly journals Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

2021 ◽  
Vol 13 (16) ◽  
pp. 3224
Author(s):  
Joan Francesc Munoz-Martin ◽  
Adriano Camps
Keyword(s):  
Wind Speed ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Third Party ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Weekly Schedule ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
L Band

The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass.

scholarly journals Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent

2021 ◽  
Vol 13 (8) ◽  
pp. 1570
Author(s):  
Sarah B. Hall ◽  
Bulusu Subrahmanyam ◽  
Ebenezer S. Nyadjro ◽  
Annette Samuelsen
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Spatial And Temporal Variability ◽  
Polar Regions ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate.

scholarly journals New insights into SMOS Sea Surface Salinity retrievals in the Arctic Ocean.

2020 ◽  
Author(s):  
Alexandre Supply ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Nicolas Kolodziejczyk ◽  
Gilles Reverdin ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Near Surface ◽  
The Arctic Ocean ◽  
L Band

<p>Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band, providing the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, retrieving SSS at high latitudes with a reasonable accuracy remains challenging, in particular due to the low sensitivity of L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents and sea ice as well as the presence of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval are significant sources of uncertainty. We develop a novel correction methodology accordingly.</p><p>SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.26 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD = 1.86 pss), particularly in river plumes fresher by about 10 pss than surrounding waters. Furthermore, comparisons with in-situ measurements ranging from 1 to 11 m depths identify huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.</p>

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multi-year estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll-a concentrations

2017 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014, after mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll-a concentration (Chl-a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. The overall relationship between pCO2w and Chl-a is negative in most regions when Chl-a ≤ 1 mg m−3, whereas there is no significant relationship when Chl-a > 1 mg m−3. In the Kara Sea and the East Siberian Sea and the Bering Strait, however, the relationship is typically positive in summer. The addition of Chl-a as a parameter in the SOM process enabled us to improve the estimate of pCO2w via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. Mainly as a result of the inclusion of Chl-a, the uncertainty in the CO2 flux estimate was reduced, and a net annual Arctic Ocean CO2 uptake of 180 ± 130 TgC y−1 was determined to be significant.

scholarly journals Analysis of the Antenna Array Orientation Performance of the Interferometric Microwave Radiometer (IMR) Onboard the Chinese Ocean Salinity Satellite

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5396
Author(s):  
Yan Li ◽  
Mingsen Lin ◽  
Xiaobin Yin ◽  
Wu Zhou
Keyword(s):  
Antenna Array ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Field Of View ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Earth ◽  
Simulation Based ◽  
Ocean Salinity ◽  
L Band

The Chinese Ocean Salinity Satellite is designed to monitor global sea-surface salinity (SSS). One of the main payloads onboard the Chinese Ocean Salinity Satellite, named the Interferometric Microwave Radiometer (IMR), is a two-dimensional interferometric radiometer system with an L-band, Y-shaped antenna array. The comparison of two different array orientations is analyzed by an end-to-end simulation based on the configuration of the IMR. Simulation results of the different array orientations are presented and analyzed, including the brightness temperature (TB) images, the distribution of the incidence angles in the field of view, the TB radiometric resolutions, the spatial resolutions, the number of measurements in the Earth grid and the expected SSS accuracy. From the simulations we conclude that one of the array orientations has better performance for SSS inversion than the other one. The advantages mainly result in wider swath and better SSS accuracy at the edge of the swath, which then improve the accuracy of the monthly SSS after averaging. The differences of the Sun’s effects for two different array orientations are also presented. The analysis in this paper provides the guidance and reference for the in-orbit design of the array orientation for the IMR.

scholarly journals Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes

2020 ◽  
Author(s):  
Nicolas Kolodziejczyk ◽  
Mathieu Hamon ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Gilles Reverdin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Latitude ◽  
Large Scale ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Radiometric Measurements ◽  
L Band

AbstractTen years of L-Band radiometric measurements have proven the capability of satellite Sea Surface Salinity (SSS) to resolve large scale to mesoscale SSS features in tropical to subtropical ocean. In mid to high latitude, L-Band measurements still suffer from large scale and time systematic errors. Here, a simple method is proposed to mitigate the large scale and seasonal varying biases. First, an Optimal Interpolation (OI) using a large correlation scale (~500 km) is used to map independently Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) Level 3 data. The mapping is compared to the equivalent mapping of in situ observations to estimate the large scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products, and increases the signal to noise ratio of the absolute SSS estimates, reducing the RMSD of in situ-satellite products by about 26-32% from mid to high latitude, respectively, in comparison to the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to e.g. radio frequency interferences, land-sea contamination, ice-sea contamination remain challenging to reduce given the low sensitivity of L-Band radiometric measurements to SSS in cold water. Using the thermodynamic equation of state (TEOS-10), the resulting L4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful are these satellite derived parameters to fully characterize the surface ocean water masses at large mesoscale.

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multiyear estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll <i>a</i> concentrations

2018 ◽  
Vol 15 (6) ◽  
pp. 1643-1661 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Co2 Flux ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60∘ N from 1997 to 2014. This was done by mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll a concentration (Chl a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. We applied new algorithms for extracting Chl a from satellite remote sensing reflectance with close examination of uncertainty of the obtained Chl a values. The overall relationship between pCO2w and Chl a was negative, whereas the relationship varied among seasons and regions. The addition of Chl a as a parameter in the SOM process enabled us to improve the estimate of pCO2w, particularly via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net annual Arctic Ocean CO2 uptake of 180 ± 130 Tg C yr−1. Seasonal to interannual variation in the CO2 influx was also calculated.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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