High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data
Abstract
:1. Introduction
2. Methodology
2.1. In Situ Data
2.2. Satellite Data
2.3. Matching Process
- In situ data containing salinity and temperature from the 23 June 2015 onwards (start of Sentinel-2 mission) is downloaded from the Copernicus Marine In Situ data portal, [4]. Data worldwide is obtained, with more than files and about 160GB of data downloaded.
- For each in situ measurement point, the Sentinel-2 image collection is filtered to the tiles that contain the point on the day and time when the measurement was taken. The image is only considered if the in situ measurement was taken within 1 h of Sentinel-2 pass time. Previous research considered wider time windows, but with this approach more accurate results are expected.
- If there is any valid tiles for that point, they are clipped in boxes of m m area centred in the point location to obtain high-resolution estimators of SST and SSS. This box size is taken to average the effect of wave reflectance.
- The time difference between the in situ measurement and the satellite image is recorded. In case of multiple tiles covering the point of interest, the matched data is sorted by time difference, and the one with the smallest difference is selected.
- A table containing satellite data (band information and other properties) and equivalent in situ information (salinity and temperature) for each valid point is composed.
- Band QA60 containing a cloud mask has been used to select points only with a clear sky, i.e., points were clouds are persistent have not been considered: no opaque clouds or cirrus clouds present.
2.4. Data Usage and Coverage
2.5. Neural Network
2.5.1. Data Processing, File Loading, Filtering and Outliers Removal
2.5.2. Normalisation
2.5.3. Training and Test Data Split
2.5.4. Trainer
2.5.5. Running Predictions
2.5.6. Architecture of the Neural Network
- -
- ReLu:
- -
- Sigmoid:
- -
- Tanh:
- -
- Mean Absolute Error:
- -
- Mean Squared Error:
- -
- Logcosh
3. Results and Discussion
3.1. Temperature Interpolation
3.2. Salinity Interpolation
Dependence on Temperature Ranges
3.3. Sensitivity Analysis
3.4. Temperature Extrapolation
3.5. Salinity Extrapolation
3.6. Evaluation: Mapping Sea Surface Salinity and Temperature at the Guadalquivir River Mouth and the Bay of CáDiz
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Mission/Project | Measured Property | Sensor Type | Spatial Resolution | Revisit Time | Organisation |
---|---|---|---|---|---|
SMOS | Soil moisture, ocean salinity | MIRAS | 35 km | 3 days | ESA |
Aquarius | Ocean salinity | Radiometer | 150 km | 7 days | NASA |
MODIS–Aqua | Ocean colour and temperature | Spectro–radiometer | 1 km | 2 days | NASA |
SeaWiFs | Ocean salinity | VNIR | 1.1 km | 2 days | NASA |
Sentinel-2 | Coastal areas and water bodies monitoring | Multi-spectral | 60 m | 5 days | ESA |
Sentinel-3 | Ocean colour | Radiometer and spectrometer | 300 m | 2 days | ESA |
CoastWatch | Ocean temperature | VIIRS, AVHRR, ABI, ... | 1 km | - | NOAA |
Estimated Property | Method | Notes | Location |
---|---|---|---|
SST [10,11] | Retrieval algorithm for MODIS–Aqua | Very low resolution (50 km). Noise and geophysical errors more representative in higher radiometric frequencies. | Worldwide |
SST [12,13] | NN from previous days info/from relevant meteorological components | Low resolution (4 km). Long-term data needed (45 years). Localised results. | Indian Ocean/Western Mediterranean Sea |
SST [14] | NN from numerical model | Very low resolution (20 km). Combines numerical and neural techniques. Focuses on error time-series. | Indian Ocean |
SST [15] | AVHRR-derived compared with local information | Low resolution (1 km). Dependent on limited in situ data sources. Innovative approach to mitigate drawbacks from remote sensed information. | North-East Atlantic Ocean |
SSS [16] | Algorithm from MODIS data | Low resolution (1 km). High accuracy of algorithm tested. | South China Sea |
SSS [17] | MODIS–Aqua, neural network | Low resolution (1 km). Relevant for estuarine processes. Relatively high errors. Relationships between SST and SSS. | Mid-Atlantic Ocean |
SSS [3] | NN MODIS and SeWiFs. | Low resolution (1 km). Match in situ-satellite range ±6 h. Site-specific. | Gulf of Mexico |
SSS [18] | SMOS-derived (6 years), data interpolation. | Improved resolution (SMOS), but still low for coastal applications (5 km). Error-reducing. High accuracy compared to in situ data. | Western Mediterranean Sea and North Atlantic Ocean. |
In Situ Observation | (Lat, Lon) () | Location | z (m) | d (km) | # |
---|---|---|---|---|---|
Off-shore buoys | |||||
MO_TS_MO_61141 | (, ) | Ibiza | 800 | 40 | |
(, ) | Cabo de Gata (Almeria) | 536 | 188 | ||
(, ) | Tarragona | 688 | 178 | ||
(, ) | Valencia | 260 | 143 | ||
(, ) | Cabo de Palos (Cartagena) | 230 | 210 | ||
Near-shore buoys | |||||
(, ) | Mallorca | 30 | 77 | ||
MO_TS_MO_LA-MOLA | (, ) | Menorca (East) | 10 | 13 | |
MO_TS_MO_SON-BLANC | (, ) | Menorca (West) | 6 | 29 | |
(, ) | Gerona | 192 | 38 | ||
(, ) | Barcelona | 20 | 318 | ||
Total | 1234 |
Data | Resolution (m) | Wavelength (nm) | Description |
---|---|---|---|
B1 | 60 | 443.9 | Aerosols |
B2 | 10 | 496.6 | Blue |
B3 | 10 | 560 | Green |
B4 | 10 | 664.5 | Red |
B5 | 20 | 703.9 | Red Edge 1 |
B6 | 20 | 740.2 | Red Edge 2 |
B7 | 20 | 782.5 | Red Edge 3 |
B8 | 10 | 835.1 | NIR |
B8a | 20 | 864.8 | Red Edge 4 |
B9 | 60 | 945 | Water vapour |
B10 | 60 | 1373.5 | Cirrus |
B11 | 20 | 1613.7 | SWIR1 |
B12 | 20 | 2202.4 | SWIR2 |
QA60 (*) | 60 | - | Cloud mask |
Data | Description |
---|---|
Cloud pixel percentage | Granule-specific cloudy pixel percentage. |
Cloud coverage assessment | Cloudy pixel percentage for the whole archive. |
Mean Incident Azimuth angle for every band () | Mean value containing viewing incidence azimuth angle average for each band. |
Mean Incident Zenith angle for every band () | Mean value containing viewing incidence zenith angle average for each band. |
Mean Solar Azimuth angle | Mean value containing sun zenith angle average for all bands. |
Reflectance conversion correction | Earth-Sun distance correction factor. |
SST Range (C) | SSS Correl. (R) |
---|---|
0–5 | |
5–10 | |
10–15 | |
15–20 | |
20–25 | |
25–30 | |
30–35 |
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Medina-Lopez, E.; Ureña-Fuentes, L. High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data. Remote Sens. 2019, 11, 2191. https://doi.org/10.3390/rs11192191
Medina-Lopez E, Ureña-Fuentes L. High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data. Remote Sensing. 2019; 11(19):2191. https://doi.org/10.3390/rs11192191
Chicago/Turabian StyleMedina-Lopez, Encarni, and Leonardo Ureña-Fuentes. 2019. "High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data" Remote Sensing 11, no. 19: 2191. https://doi.org/10.3390/rs11192191
APA StyleMedina-Lopez, E., & Ureña-Fuentes, L. (2019). High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data. Remote Sensing, 11(19), 2191. https://doi.org/10.3390/rs11192191