High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals Machine Learning and the End of Atmospheric Corrections: A Comparison between High-Resolution Sea Surface Salinity in Coastal Areas from Top and Bottom of Atmosphere Sentinel-2 Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 2924
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Coastal Areas ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Common Error ◽  
Source Of Error ◽  
Sentinel 2

This paper introduces a discussion about the need for atmospheric corrections by comparing data-driven sea surface salinity (SSS) derived from Top- and Bottom-of-Atmosphere imagery. Atmospheric corrections are used to remove the effect of the atmosphere in reflectances acquired by satellite sensors. The Sentinel-2 Level-2A product provides atmospherically corrected Bottom-of-Atmosphere (BOA) imagery, derived from Level-1C Top-of-Atmosphere (TOA) tiles using the Sen2Cor processor. SSS at high resolution in coastal areas (100m) is derived from multispectral signatures using artificial neural networks. These obtain relationships between satellite band information and in situ SSS data. Four scenarios with different input variables are tested for both TOA and BOA imagery, for interpolation (previous information on all platforms is available in the training dataset) and extrapolation (certain platforms are isolated and the network does not have any previous information on these) problems. Results show that TOA always outperforms BOA in terms of higher coefficient of determination (R2), lower mean absolute error (MAE) and lower most common error (μe). The best TOA results are R2=0.99, MAE=0.4PSU and μe=0.2PSU. Moreover, the evaluation of the neural network in all the pixels of Sentinel-2 tiles shows that BOA results are accurate only far away from the coast, while TOA data provides useful information on nearshore mixing patterns, estuarine processes and is able to estimate freshwater salinity values. This suggests that land adjacency corrections could be a relevant source of error. Sun glint corrections appear to be another source of error. TOA imagery is more accurate than BOA imagery when using machine learning algorithms and big data, as there is a clear loss of information in the atmospheric correction process that affects the multispectral–in situ relationships. Finally, the time and computational resources gained by avoiding atmospheric corrections can make the use of TOA imagery interesting in future studies, such as the estimation of chlorophyll or coloured dissolved organic matter.

scholarly journals Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

2019 ◽  
Vol 11 (15) ◽  
pp. 1818 ◽  
Author(s):  
Daniele Ciani ◽  
Rosalia Santoleri ◽  
Gian Luigi Liberti ◽  
Catherine Prigent ◽  
Craig Donlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Microwave Radiometer ◽  
Ocean Surface ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Optimal Interpolation ◽  
Surface Salinity ◽  
Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

scholarly journals The MULTI OBSERVATIONS Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service

2021 ◽  
Author(s):  
Stephanie Guinehut ◽  
Bruno Buongiorno Nardelli ◽  
Trang Chau ◽  
Frederic Chevallier ◽  
Daniele Ciani ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Environment Monitoring ◽  
Surface Salinity ◽  
In Situ Observations ◽  
Marine Environment Monitoring ◽  
Monitoring Service

<p>Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is available through the MULTI OSERVATIONS (MULTIOBS) Thematic Assembly Center (TAC) of the European Copernicus Marine Environment Monitoring Service (CMEMS).</p><p>CMEMS MULTIOBS TAC proposes products based on satellite & in situ observations and state-of-the-art data fusion techniques. These products are fully qualified and documented and, are distributed through the CMEMS catalogue (http://marine.copernicus.eu/services-portfolio). They cover the global ocean for physical and biogeochemical (BGC) variables. They are available in Near-Real-Time (NRT) or as Multi-Year Products (MYP) for the past 28 to 36 years.</p><p>Satellite input observations include altimetry but also sea surface temperature, sea surface salinity as well as ocean color. In situ observations of physical and BGC variables are from autonomous platform such as Argo, moorings and ship-based measurements. Data fusion techniques are based on multiple linear regression method, multidimensional optimal interpolation method or neural networks.</p><p>MULTIOBS TAC provides the following products at global scale:</p><ul><li>3D temperature, salinity and geostrophic current fields, both in NRT and as MYP;</li> <li>2D sea surface salinity and sea surface density fields, both in NRT and as MYP;</li> <li>2D total surface and near-surface currents, both in NRT and as MYP;</li> <li>3D vertical current as MYP;</li> <li>2D surface carbon fields of CO<sub>2</sub> flux (fgCO<sub>2</sub>), pCO<sub>2</sub> and pH as MYP;</li> <li>Nutrient vertical distribution (including nitrate, phosphate and silicate) profiles as MYP;</li> <li>3D Particulate Organic Carbon (POC) and Chlorophyll-a (Chl-a) fields as MYP.</li> </ul><p>Furthermore, MULTIOBS TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor the global ocean 3D hydrographic variability patterns (water masses) and the global ocean carbon sink.</p>

Pi-MEP Salinity – an ESA-NASA Platform for sustained satellite surface salinity validation 

2021 ◽  
Author(s):  
Roberto Sabia ◽  
Sebastien Guimbard ◽  
Nicolas Reul ◽  
Tony Lee ◽  
Julian Schanze ◽  
...  
Keyword(s):  
Program Planning ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Data ◽  
Sea Level Anomalies ◽  
Ocean Salinity ◽  
Salinity Data ◽  
Planning Group

<p>The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.</p><p>Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates.</p><p> </p><p>Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA Joint Program Planning Group (JPPG).</p><p> </p><p>The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature). </p><p>Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.</p><p> </p><p>The Platform is also meant to monitor salinity in selected oceanographic “case studies”, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins.</p><p> </p><p>The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform.</p><p> </p><p>A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.</p>

CATDS CEC-LOCEAN debiased version 4 Sea Surface Salinity

2020 ◽  
Author(s):  
Clovis Thouvenin-Masson ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Dimitry Khvorostyanov ◽  
Stéphane Tarot
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
The Mean ◽  
Analysis System ◽  
Areas Of Interest

<p>The Centre Aval de Traitement des Données SMOS (CATDS), developped by the CNES in collaboration with the CESBIO and IFREMER, produces and continuously improves SMOS sea surface salinity (SSS) products.</p><p>The aim of this poster is to present the last version of CATDS L3 products developed by the LOCEAN CATDS Expertise Center (CEC-LOCEAN debiased v4, https://www.catds.fr/Products/Available-products-from-CEC-OS/CEC-Locean-L3-Debiased-v4), and to highlight its main improvements with respect to previous version 3.</p><p>The L3 products are available for 9-day and 18-day Gaussian averaging. Both versions 3 and 4 contain a bias correction based on internal consistency of SMOS SSS retrieved in various locations across swath, and on seasonal variability of salinity. The main evolutions of version 4 consist in refining the absolute correction methodology, limiting wind speed to 16m/s, add a refined filtering for sea ice and radio frequency contamination based on SMOS retrieved pseudo dielectric constant, the so-called ACARD (Waldteufel et al. 2004) and an improved sea surface temperature (SST) correction in cold waters based on Dinnat et al. (2019) observed dependency.</p><p>Improvements with respect to version 3 are assessed through systematic validation that consists in two main stages: (1) Comparison with respect to in-situ measurements (repetitive ship transects across Atlantic and Arctic regions, and Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) moorings); (2) Comparison with the In-Situ Analysis System (ISAS) monthly fields (Kolodziejczyk, 2017), in terms of both mean spatial maps and time series of key statistics parameters. The key statistics parameters are computed both over the global ocean and for individual areas of interest. Thus, both the mean spatial patterns and temporal variability in various regions are evaluated.</p><p>Comparisons between the two last versions exposed in this poster are based on relevant examples from this systematic validation: main improvements are observed in high latitudes (over 45° latitude).In the Southern Ocean modification of wind speed filtering and SST correction lead to a decrease in the mean difference between SMOS  and ISAS SSS south of 45S from 0.16+/-0.07 to 0.02+/-0.05pss. Std of the differences and r2 are also improved over global ocean. Statistics obtained with this new version are close to the ones obtained with SMAP RemSS v4 SSS.</p><p> </p><p>Dinnat, E.P.; Le Vine, D.M.; Boutin, J.; Meissner, T.; Lagerloef, G. Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote Sens. 2019, 11, 750.</p><p>Kolodziejczyk Nicolas, Prigent-Mazella Annaig, Gaillard Fabienne (2017). ISAS-15 temperature and salinity gridded fields. SEANOE. https://doi.org/10.17882/52367</p><p>Waldteufel, P., J. L. Vergely, and C. Cot, A modified cardioid model for Processing multiangular radiometric observations, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.5, pp.1059-1063, 2004. DOI : 10.1109/TGRS.2003.821698.</p>

scholarly journals In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

2019 ◽  
Vol 11 (11) ◽  
pp. 1361 ◽  
Author(s):  
Giuseppe Aulicino ◽  
Yuri Cotroneo ◽  
Estrella Olmedo ◽  
Cinzia Cesarano ◽  
Giannetta Fusco ◽  
...  
Keyword(s):  
High Resolution ◽  
General Circulation ◽  
Spatial Scales ◽  
Autonomous Underwater Vehicles ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Basin Scale

The Algerian Basin is a key area for the general circulation in the western Mediterranean Sea. The basin has an intense inflow/outflow regime with complex circulation patterns, involving both fresh Atlantic water and more saline Mediterranean water. Several studies have demonstrated the advantages of the combined use of autonomous underwater vehicles, such as gliders, with remotely sensed products (e.g., altimetry, MUR SST) to observe meso- and submesoscale structures and their properties. An important contribution could come from a new generation of enhanced satellite sea surface salinity (SSS) products, e.g., those provided by the Soil Moisture and Ocean Salinity (SMOS) mission. In this paper, we assess the advantages of using Barcelona Expert Center (BEC) SMOS SSS products, obtained through a combination of debiased non-Bayesian retrieval, DINEOF (data interpolating empirical orthogonal functions) and multifractal fusion with high resolution sea surface temperature (OSTIA SST) maps. Such an aim was reached by comparing SMOS Level-3 (L3) and Level-4 (L4) SSS products with in situ high resolution glider measurements collected in the framework of the Algerian Basin Circulation Unmanned Survey (ABACUS) observational program conducted in the Algerian Basin during falls 2014–2016. Results show that different levels of confidence between in situ and satellite measurements can be achieved according to the spatial scales of variability. Although SMOS values slightly underestimate in situ observations (mean difference is −0.14 (−0.11)), with a standard deviation of 0.25 (0.26) for L3 (L4) products), at basin scale, the enhanced SMOS products well represent the salinity patterns described by the ABACUS data.

scholarly journals Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

2020 ◽  
Vol 12 (23) ◽  
pp. 3996
Author(s):  
Frederick M. Bingham ◽  
Zhijin Li
Keyword(s):  
North Atlantic ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Regional Ocean Modeling System ◽  
Surface Salinity ◽  
In Situ Data

Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall.

scholarly journals Improved Multifractal Fusion Method to Blend SMOS Sea Surface Salinity Based on Semiparametric Weight Function

2019 ◽  
Vol 36 (8) ◽  
pp. 1501-1520 ◽  
Author(s):  
Hengqian Yan ◽  
Ren Zhang ◽  
Gongjie Wang ◽  
Huizan Wang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Weight Function ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Weight Functions ◽  
Fusion Method ◽  
Surface Salinity ◽  
In Situ Data ◽  
Ocean Salinity ◽  
Rossby Radius Of Deformation

AbstractThe multifractal fusion method has proved to be an effective algorithm to mitigate the noise of the sea surface salinity (SSS) of Soil Moisture Ocean Salinity (SMOS) mission. However, the traditional nonparametric weight function used in this method is unable to fully capture the dynamic evolution of the oceanic environment. Considering the multiscale, nonuniform, anisotropic, and flow-dependent nature of the ocean, a prototype with the so-called flexible circle (FLC) weight function or flexible ellipse (FLE) weight function with a set of predefined parameters is proposed in this paper. The improved weight functions could draw dynamic information from the sea surface temperature, Rossby radius of deformation, and surface geostrophic flow to improve the quality of the remotely sensed SSS. The validation against the in situ data indicates that the improved weight functions perform better than the traditional one with a reduced root-mean-square (RMS) and standard deviation (STD) of the differences with respect to EN 4.2.0 profiles (from 0.50 and 0.46 to 0.42 and 0.38 for FLC and 0.39 and 0.36 for FLE in the global ocean). In particular, the FLE scheme could highlight the variation of the strong currents without affecting the computational efficiency. Furthermore, this paper discusses the influences of the error distribution on the fusion results and underlines the importance of error-based adaptions for further improvements.

Sign in / Sign up

Export Citation Format

Share Document