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.

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 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 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 A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity

2012 ◽  
Vol 29 (6) ◽  
pp. 867-879 ◽  
Author(s):  
Bruno Buongiorno Nardelli
Keyword(s):  
High Resolution ◽  
Root Mean Square Error ◽  
Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Mean Square ◽  
Surface Salinity ◽  
Signal Ratio ◽  
In Situ Observations

Abstract A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air–sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.75°C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of ~0.11 g kg−1 and a mean error of ~0.01 g kg−1, that is, reducing the errors by ~25% and ~60%, respectively, with respect to the first guess.

scholarly journals Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

2021 ◽  
Vol 13 (15) ◽  
pp. 2995
Author(s):  
Frederick M. Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
High Resolution ◽  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

scholarly journals Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys

2014 ◽  
Vol 119 (9) ◽  
pp. 6171-6189 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Alexander G. Fore ◽  
Akiko Hayashi
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats

Sea surface salinity reemergence in an updated North Atlantic in-situ salinity data set

2021 ◽  
pp. 1-49
Author(s):  
Claude Frankignoul ◽  
Elodie Kestenare ◽  
Gilles Reverdin
Keyword(s):  
Large Scale ◽  
Cold Season ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Data Set ◽  
Salinity Data ◽  
Deep Mixed Layer ◽  
Geostrophic Advection

AbstractMonthly sea surface salinity (SSS) fields are constructed from observations, using objective mapping on a 1°x1° grid in the Atlantic between 30°S and 50°N in the 1970-2016 period in an update of the data set of Reverdin et al. (2007). Data coverage is heterogeneous, with increased density in 2002 when Argo floats become available, high density along Voluntary Observing Ship lines, and low density south of 10°S. Using lag correlation, the seasonal reemergence of SSS anomalies is investigated between 20°N and 50°N in 5°x5° boxes during the 1993-2016 period, both locally and remotely following the displacements of the deep mixed-layer waters estimated from virtual float trajectories derived from the daily AVISO surface geostrophic currents. Although SSS data are noisy, local SSS reemergence is detected in about half of the boxes, notably in the northeast and southeast, while little reemergence is seen in the central and part of the eastern subtropical gyre. In the same period, sea surface temperature (SST) reemergence is found only slightly more frequently, reflecting the short data duration. However, taking geostrophic advection into account degrades the detection of remote SSS and even SST reemergence. When anomalies are averaged over broader areas, robust evidence of a second and third SSS reemergence peak is found in the northeastern and southeastern parts of the domain, indicating long cold-season persistence of large-scale SSS anomalies, while only a first SST reemergence is seen. An oceanic reanalysis is used to confirm that the correlation analysis indeed reflects the reemergence of subsurface salinity anomalies.

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.

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