dataesr/scientific-paragraphs-categorization huggingface dataset:
1-0. 2 pss 1 at monthly and 100 × 100 km 2 , or 10-day and 200 × 200 km 2 scales. Tremendous efforts to reach this goal using in situ sensor networks have been carried out by the ocean science community since the early 2000s. At global scale, the large increase in salinity data sampling (Fig. 1 ) is dominantly associated with the invention and deployment of the Argo profiler network. Since reaching its full planned capacity in 2007, the network includes ~3000 Argo floats in the global Ocean providing at least one salinity cast every 10 days in a 3°x3°cell (see  Argo, 2000). In addition to Argo floats which profile to the top 2 km of the global ocean column, near surface salinity is monitored by thermosalinographs (TSG) on board numerous ships of opportunity and research vessels. These TSGs are installed and maintained on board ships via international programs such as the Global Ocean Surface Underway Data (GOSUD;  Alory et al.
5 to -1 K/pss for incidence angles from 0°to 60°at characteristic ocean conditions (SSS = 35pss and Sea Surface Temperature (SST) = 15 °C). This sensitivity to SSS remains relatively small compared to (i) noise characteristics of available radiometers (~0. 3 to 2 K) and (ii) the small range of natural variability of SSS in the open ocean (32 to 38 pss). Finally, as illustrated in Fig. 3b , ∂T B /∂SSS also decreases with decreasing SST from ~-0. 7 K/pss at 30 °C to ~-0. 2 K/pss at 0 °C making SSS estimation in the high latitude cold waters even more challenging than in the tropics. Given the sensitivity of T B to salinity at   Bingham et al. , 2002). Note that only few WOCE lines were available into the NODC database used to build the density map (personal exchanges with F. Bingham). (Right) Number of historical in situ SSS observations per 1°s quare during the first 9 years of the satellite salinity era from 2010 to 2018.
Interest in salinity remote sensing was renewed in the late 1980s with the development of an airborne demonstration instrument called the Electrically Scanning Thinned Array Radiometer (ESTAR), which was operated at 1. 4 GHz and used the concept of aperture synthesis (Le  Vine et al. , 1990)  allowing an increase in cross-track spatial resolution. Small salinity variations typical of the open ocean were retrieved from the data acquired by the ESTAR interferometer during flights across a coastal current in Delaware and the Gulf Stream (Le  Vine et al. , 1998). Such a system demonstrated that it was feasible to achieve high spatial and radiometric resolution at a reasonable cost for a potential 1. 4 GHz satellite radiometer design. By the mid-1990s, the Scanning Low Frequency Microwave Radiometer (SLFMR) was built  (Goodberlet and Swift, 1993)  based on the similar concept as the ESTAR but with an improved radiometric resolution and adapted for light aircraft.
, 2001). The PALS design and data provided the basis for the NASA Aquarius and SMAP instrument designs. In 1995, during the "Soil Moisture and Ocean Salinity" Workshop organized at the ESA ESTEC (European Space Research and Technology Centre, Noordwijk, the Netherlands), possible techniques to remotely measure SSS from space were discussed. The two most suitable approaches were identified to be L-band microwave radiometry using either real or synthetic, aperture antenna design. The use of a real aperture antenna with moderate size (diameter < 3 m) eases sensor integration into a launch vehicle and better fulfills satellite weight constraints but results in lower spatial resolution on the ground. This option was realized for the Aquarius mission with a moderately large antenna (reflector diameter ~2.
16 K. The NEDT slightly increased to 0. 22 K for the third Stokes parameter. The equatorial crossings of Aquarius' orbit were at 6 PM (ascending) and 6 AM (descending) local time, the opposite of SMOS. A hardware failure occurred onboard the Aquarius/SAC-D on June 7, 2015, which unfortunately resulted in the mission operation end. The full Aquarius dataset record spans the period from 8/25/2011 to 6/7/2015 (i. e. , a full 3 years and 9 months period). The SMAP instrument architecture  (Entekhabi et al. , 2014 ) is a conically scanning, wide swath single channel L-band radiometer. SMAP includes a radar and radiometer, both operating in the L-band at a center frequency of 1. 26 GHz and 1. 41 GHz, respectively. Both instruments share a single feedhorn and a large parabolic mesh reflector (Fig.
The SMAP radiometer provides T B data within ~1000-km wide swath with a spatial resolution of about 40 km (see Fig. 5 ), and the instrument almost fully covers the globe within 3 days. The radar, unfortunately, stopped transmitting mid 2015 due to an anomaly in the high-power amplifier. The primary requirement of SMAP is to provide estimates of soil moisture in the top 5 cm of soil with an accuracy of 0. 04 cm 3 /cm 3 volumetric soil moisture. This requires a relatively low radiometric accuracy for the radiometer of about1 Kelvin. Note that Aquarius antenna emissivity was negligibly small while the SMAP mesh reflector emissivity is about 0. 2%, which is small enough and thus only a relatively minor correction is needed for salinity retrieval. The first 10 years of SSS measurements from space have successfully explored different technological approaches to sensor design (pushbroom, interferometer, and conical scanner).
However, Aquarius offers some advantages over SMOS and SMAP because of significantly lower radiometric noise (~0. 12 K) in comparison with the SMOS (from ~2. 6 K to 5 K) and the SMAP (~1 K). In addition, the simultaneous Aquarius scatterometer and radiometer acquisition significantly improves the sea surface roughness correction and provides more accurate individual SSS measurements. For this correction (see Section 4), SMOS and SMAP retrieval algorithms rely on external auxiliary wind speed data to indirectly characterize the sea surface roughness state in the radiometer footprint. Finally, the L-band window used is a frequency range protected for passive observations since the 1960s under International Telecommunication Union (ITU) Radio Regulations. However, SMOS measurements first revealed that man-made sources emit above legal thresholds in this part of the spectrum, thus interfering with the satellite observations.
Systematic comparisons between SMOS L2 SSS data and available co-located in situ salinity were performed for 2010-2018 data by LOCEAN and more recently using the ESA SMOS Pilot Mission Exploitation Platform (PI-MEP, see also Section 5. 5). Concerning the latter, the complete in situ salinity database is first filtered based on quality flags provided by in situ data centers (e. g. , Coriolis Data Assembly Centre) so that only valid ground truth data remain. Highspatial resolution in situ measurements, such as those from TSG or surface drifters, are also spatially filtered (using a running median window) to match with the ~43 km SMOS ground footprint. For each in situ data sample collected in the Pi-MEP database, the platform searches for all SMOS L2 satellite SSS data at grid nodes located within 22 km of the in situ data location and acquired with a time-lag ≤6 h.
The SMOS Pilot Mission Exploitation Platform The SMOS Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (see the dedicated web platform) is an ESA initiative with twofold motivation: i) To serve as a generic satellite salinity validation and algorithm evolution platform, ii) To offer a testbed to enable oceanographic process studies using SMOS SSS in synergy with other satellite data (e. g. , SST, winds, SSH, precipitation). The Pi-MEP Platform is fully operational as of the middle of 2019 and transitioning into a reference hub for satellite SSS missions by providing access to all satellite (SMOS, Aquarius, SMAP) SSS product validation and additional thematic datasets, including precipitation, evaporation, currents, sea level anomalies, winds, and SST. Collocated databases of SMOS, Aquarius, and SMAP products with in situ SSS from Argo, TSG, moorings, and drifters, and inter-comparison reports including quality assessments at different spatio-temporal scales are systematically generated by the platform.
Remaining uncertainties for these second order effects are still the subject of on-going research efforts, and are also being considered within the ESA Climate Change Initiative-SSS project. Corrections associated with ionospheric, atmospheric, solar, and sky radiation contributions to the antenna measured brightness commonly involve the use of the ratio of the third Stokes (U) parameter to the difference between the vertical and horizontal polarization channels. This helps to infer the rotation angle (geometry plus Faraday) across the ionosphere. Again, accurate calibration of the third Stokes parameter is still challenging, particularly for the SMOS interferometer. The ionospheric rotation estimates also rely on the first guess data, including the total electronic content and geomagnetic field data  (Vergely et al.
g. ,  Meissner et al. , 2018). Remaining uncertainties from the rough ocean surface scattering models in the L-band, the T B map of the celestial sky, and the antenna gain pattern appear as sources of remaining ascendingdescending biases in all the satellite data. Empirical corrections to best mitigate these remaining signals are currently under investigation. They are based on either alternative antenna gain-weighted sky maps obtained from the satellite data or rely on the multiple view capabilities of the instrument (e. g. , use of the fore and aft-look for SMAP). Antenna gain-weighted sky maps can be derived from the accumulated radiometer direct observation of the sky as recorded during the regular coldsky calibration maneuvers. Being significantly warmer than the ocean water at L-band, land surfaces can significantly impact the ocean T B measured, and this over a 400 to 1000 km-wide band off the coasts of the major land masses.
These reduced accuracies are related to the loss of sensitivity, remaining uncertainties on the dielectric constant model, the correction for roughness in the stormy high latitude conditions, and the presence of land (Arctic) and sea-ice, and to the presence of RFI (Northern latitudes). All three sensors also show residual seasonal and latitudinal largescale biases, due to instrument thermal drifts and solar effects which are tentatively corrected empirically using different reference fields (HYCOM, Argo Optimal Interpolation). Finally, SMOS and Aquarius data are less capable of detecting Radio Frequency Interference (RFI) than SMAP (e. g. , Asia coastlines, the Bay of Bengal, Arabian and Mediterranean Seas). The SMAP sensor is equipped with a dedicated on-board RFI filtering device, hence providing unprecedented monitoring of main river plumes in the vicinity of continents (e.
g. Fournier et al. , 2016a Fournier et al. , , 2016b Fournier et al. , , 2017a Fournier et al. , , 2017b)). On the other hand, the calibration of the SMAP instrument is more difficult than for Aquarius  (Fore et al. , 2016; Meissner et al. , 2018)  and requires empirical adjustments in the SMAP salinity retrieval algorithms. As such, the absolute value of SMAP SSS may remain imprecise to about 0. 2 pss in low to mid-latitudes of the open ocean  (Fore et al. , 2018). While challenges still exist in the calibration, retrieval algorithms, and validation process, largely due to in situ measurement sparsity, satellite measurements now routinely provide essential means to help major scientific achievements (as reviewed in Section 6). Thanks to their global coverage, frequent revisit time, upper ocean first centimeter-depth sampling, the unique data from SMOS, Aquarius, and SMAP missions largely transform and improve our knowledge of the global SSS field.
). The SMAP and SMOS salinity products are reaching a relatively high skill level with temporal consistency to help identify anomalous mooring data and inadequate temporal sampling in the Argo-monthlygridded and ocean circulation model products. Satellite SSS gives more uniform spatio-temporal sampling and improves the ability to estimate horizontal gradients which is important for frontal genesis, eddy-mean flow interaction, and biogeochemistry. Despite being less sensitive to SSS in the cold waters than in the warm tropical seas, the data from L-band spaceborne radiometers have also been uniquely exploited to monitor fresh water plumes in the Arctic region, as well as large changes in fresh water content associated with seasonal changes in sea ice extent, providing an unprecedented monitoring in regions very poorly covered by in situ measurements.
SMAP CAP SSS is produced at Jet Propulsion Laboratory. Thomas Meissner and Frank Wentz acknowledge funding from NASA (contracts no. NNG04HZ29C, NNH15CM44C, 80HQTR18C0015, and JPL subcontract 1602331). SMAP RSS/NASA SSS is produced at Remote Sensing Systems. It is available on https://podaac. jpl. nasa. gov/SMAP. Aquarius SSS was produced at NASA GSFC Aquarius Data Processing System. It is available on https://podaac. jpl. nasa. gov/aquarius. Appendix A. List of satellite SSS products available in January 2020 SSSA SSS Anomalies SST Sea Surface Temperature STD STandard Deviation SWH Significant Wave Height TA Total Alkalinity T B Sea surface brightness temperature TC Tropical Cyclones TEC Total Electron Content TIW Tropical Instability Wave TSG ThermoSalinoGraphs VTEC Vertical ionospheric Total Electron Content VV Vertically-polarized emitted Vertically-polarized received WOCE World Ocean Circulation Experiment WOD World Ocean Database Dataset name Level Spatial resolution Temporal coverage Variables Version Provider Temporal resolution Update frequency SMOS SSS L2 v662 (ESA) L2 30-80 km From 2010-05-01 to present SSS1 v662 ESA 50-minute half-orbit (granule Daily duration) SMAP SSS L2 v4 (RSS) L2 ~70 km (grid 0.
In other words, seawater from open shelves and epeiric domains could record Sr isotope anomalies just through cumulative effects of sporadic terrestrial Sr inputs in coastal sub-reservoirs not sufficiently mixed with the global oceanic reservoir. Here, we test how submarine groundwater discharges (SGD) and water mass restriction can influence the Sr isotope composition of shelf water devoid of river influences by measuring and comparing the 87 Sr/ 86 Sr ratios of waters and shells from four coastal domains with different levels of connection to the open ocean. These include an open shelf Mediterranean transect, two coastal lagoons influenced by groundwater discharges in the Atlantic and Mediterranean contexts, and a Pacific hemipelagic platform. Through data compilation, our aim is also to better document the worldwide variability of seawater 87 Sr/ 86 Sr ratios in the coastal ocean and to better constrain the environmental processes acting on seawater 87 Sr/ 86 Sr patterns in these transition zones.
000018, respectively  (Neymark et al. , 2014). For the bivalve shells, we analyzed at repeated times the Sr isotopic composition of the JCt-1 international standard (a giant clam Tridacna gigas) treated in the clean room as for our shell samples. The measurements provide an external reproducibility of 0. 709176 ± 0. 000015 (2SD; n = 11) for a certified value of 0. 709150 ± 0. 000050  (Ohno and Hirata, 2007). For comparison purposes between the 87 Sr/ 86 Sr ratios reported in this study and the literature data (Tables  1  and 2 ), the 87 Sr/ 86 Sr ratios were corrected by adjusting the measured NBS 987 standard reference value to a certified value of 0. 710250.
Geochemical and Sr isotopic database To better depict potential influences of groundwater in the Oualidia and Salses-Leucate lagoons, we compiled a dataset of published major and trace elements concentrations (Ca, Mg, Na, K, SO 4 , Cl, Br and Sr) and 87 Sr/ 86 Sr ratios corresponding to groundwaters representative of Moroccan aquifers (n = 112) and the Corbières aquifer in France (n = 141) (see Supplementary data). In order to analyze the geochemical patterns at a worldwide scale, we also compiled a dataset gathering 522 87 Sr/ 86 Sr ratios and 571 Sr concentrations representative of OOW and shelf waters (SW) (see Supplementary data). As for our samples, all 87 Sr/ 86 Sr ratios reported in the literature were corrected by adjusting their respective NBS 987 standard reference value to a certified value of 0.
, 2014; Jeandel and Oelkers, 2015). Moreover, the shell data from the BSM transect indicate that the Sr isotope composition of local seawater did not significantly change over life spans of 2 to 3 years. However, these results do not preclude caution in paleoenvironmental interpretation of shell 87 Sr/ 86 Sr ratios from the BSM coast and more generally nearshore contexts. This is because SGD influence may strongly vary in time and space along continental margins  (Burnett et al. , 2006) , and especially in the Gulf of Lion where SGD are very important  (Rodellas et al. , 2015). In the foreshore zone, additional factors can impact the seawater 87 Sr/ 86 Sr ratios. This is illustrated by the BA9 water sample collected on the BSM sandy beach, characterized by a more radiogenic value and a much lower salinity (i.
Acknowledgement This study is a contribution of the  MALACO  project financed by the  French national program LEFE/INSU. We thank the crew of the  RV Nereis II and 'Service at Sea' from Banyuls Oceanological Observatory (OOB)  for their assistance in fieldwork as well as  C. Destrigneville  for analytical advices. We thank  M. Böttcher  for editorial handling and the four reviewers for their valuable remarks which considerably improved the manuscript.
6. running under MATLAB 7. 10. 0 (R2010a). The datasets of freshwaters (rivers, groundwater) and marine waters (back reef, fore reef, and oceanic waters) were processed separately due to their differences in salinity values and fluorescence intensities  (Stedmon and Bro, 2008; Ferretto et al. , 2017). Thirty samples (EEMs) were used for the freshwater PARAFAC model, while 91 samples were used for the marine water PARAFAC model. In this study, we validated two PARAFAC components for each modeled dataset (freshwater and marine samples): one humic-like component (λ Ex1, λ Ex2 /λ Em of 230, 315/440 nm for freshwater samples, and λ Ex1, λ Ex2 /λ Em of 235, 315/436 nm for marine samples) and one tryptophan-like component (λ Ex1, λ Ex2 /λ Em of 230, 290/348 nm for freshwater samples, and λ Ex1, λ Ex2 /λ Em of 225, 280/340 nm for marine samples).
Statistical Treatments In most cases non-parametric tests were preferred to parametric tests  (Joliffe, 1986). Principal component analyses (PCA) were performed based on Spearman's rank order correlation matrices. In some cases, Pearson correlations were applied when the slope and y-intercept of the linear regression were investigated. Hierarchical ascendant classification (HAC) was used to find homogenous groups of samples within the dataset, with respect to selected variables. The dissimilarity measurement between groups was based on Ward's method, which uses squared Euclidean distance. Variable values were centered and reduced beforehand. Non-parametric tests of Mann-Whitney (U-test) and Kruskal-Wallis (H-test) were used to compare the distributions of two (U-test) or more than two (H-test) groups.
Normality tests, PCA, HAC, correlations, box-andwhisker plots, U-and H-tests were carried out on salinity and all biogeochemical parameters (nutrients, DOC, DON, urea, FDOM, TChl a, and Pheo a) using XLSTAT 2013. 5. 01. Moreover, canonical correspondence analysis (CCA) was performed with PAST 3. 25 software, following the eigen analysis algorithm given in  Legendre and Legendre (1998)  to investigate the potential influence of biogeochemical parameters on the distribution of benthic communities. CCA was applied on salinity, nutrients, FDOM, and benthic communities (abundances of mixed algal assemblages, coralline algae, Acropora corals, non-Acropora corals, soft corals, abiotic substratum). Color maps of CTD data were carried out using Ocean Data View (ODV) software version 4. 6. 5  (Schlitzer, 2014).
Interestingly, we found similar distributions of TChl a and Pheo a concentrations (data not shown for Pheo a), with a strong positive correlation between both (r = 0. 92, n = 56, p < 0. 05). This emphasizes the strong coupling between phytoplankton biomass and grazing or photo-oxidation processes in fore reef waters. In the present work, benthic community structure was not determined in the back reef zone. Nonetheless, previous studies showed that all back reef sites, except TB (unaffected by freshwaters), exhibit a decrease in living coral cover, and specifically Acropora communities, to the benefit of fleshy algae communities  (Mioche and Cuet, 1999; Chazottes et al. , 2002; Bruggemann et al. , 2008; Naïm et al. , 2013; Tourrand et al. , 2013; Bigot et al. , 2016; Zubia et al. , 2018; Nicet et al.
ACKNOWLEDGMENTS We are grateful to  P. Durville  and its crew from the  Aquarium de La Réunion  for their help and assistance in the sampling aboard the  R/V Explorer. We thank  B. Charrière  ( MIO ) for DOC analyses,  C. Bachet  ( MIO ) for FDOM analyses, as well as  P. Raimbault  and  N. Garcia  (the MIO-PAPB platform) for TChl a and Pheo a analyses. We acknowledge  J. L. Join  and  J. Leze  for their help in sampling groundwater. We are also grateful to two reviewers for their relevant comments and corrections.
FUNDING This work was funded by the  Ministère des Outre-Mer (MOM) -La Réunion  and received the support of the  Réserve Naturelle Marine de La Réunion (RNMR). It was also supported by the " IBISCUS" research project , which was funded by the  Agence Nationale de la Recherche  ( ANR) -ECOTECH program  (project  ANR-09-ECOT-009-01 ),  Aix-Marseille University (AMU)  and  CNRS.
, 2013; Black et al. , 2012). The frequency and intensity of equatorial precipitations, which control the formation and the evolution of these rivers, and therefore the age and the current activity of these landforms, are still largely unconstrained. A way to better understand the ongoing processes in this region is thus to build an accurate digital terrain model (DTM) of the Huygens probe landing site where there are the most resolved images of Titan surface. These images have been acquired by the DISR panchromatic cameras (with a spectral range going from 660 nm to 1000 nm) during the descent of the Huygens probe. They were not intended for photogrammetric reconstruction but, thanks to the wind, the probe slightly swung allowing to acquire a few pairs of images with sufficient overlap. From these images, the United States Geological Survey (USGS) produced a DTM of the bright region incised by rivers near the landing site of Huygens using SOCET SET c , a commercial photogrammetry software.
A first version of this DTM was presented in  Tomasko et al. (2005)  and the process of reconstruction was partly explained by L. A. Soderblom et al. (2007b)  and  Archinal et al. (2006). For clarity, this DTM will be referred as USGS-DTM in the following. As it was the only one showing Titan's rivers at a decameter resolution, it has been used in many studies  (Tomasko et al. , 2005; L. A. Soderblom et al. , 2007a; Jaumann et al. , 2008; Black et al. , 2012; Tewelde et al. , 2013). Although the USGS-DTM is the best to--3-©2020 American Geophysical Union. All rights reserved. manuscript submitted to Earth and Space Science pographic model of the Huygens landing area produced to date, it is limited by the quality of the data and the mapping technology. Their authors tried to improve the accuracy by revising the geometric calibration of the DISR cameras and they attempted to quantify the precision of the measured elevations  (Archinal et al.