the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Nov 2021
New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea
Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.
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RC1: 'Comment on essd-2021-364', Anonymous Referee #1, 21 Nov 2021
ESSD-2021-364: New SMOS SSS maps in the framework of the Earth Observation data for science and innovation in the Black Sea.
By Olmedo et al.
General considerations
1) The paper is part of series published by the authors in 2020 and 2021 (papers published in IEEE Journal and ESSD in my knowledge). Authors are developing salinity products at ‘high’ resolution starting from low resolution SMOS data. For sure, such products need to be developed at regional scales. This justifies this and previous publications.
On the base of these considerations, I read the paper looking for the originality and quality of data.
2) Many concepts/ideas have been presented in previous papers and are repeated here. The authors must look at the similarity report showing that many sentences are pasted and copied. Contamination by land, radio frequency interference, effects on temperature brightness are well discussed in other papers by the same authors or many of them (e.g., DOI 10.1109/JSTARS.2020.3034432) and are again presented here. I would expect a short summary with references instead of repetition, eventually synthesizing the approaches in tables.
3) A crucial aspect in all publication is the use of ‘empirical corrections’ to ‘mitigate’ the land sea contamination (as written in the paper DOI 10.1109/JSTARS.2020.3034432), This seems to be a normal practice in ‘SMOS community’ (e.g., https://www.sciencedirect.com/science/article/pii/S0034425720303977). But the approach is not convincing me. In the Black Sea the authors are using a linear extrapolation. May be the authors will use another approximation in future papers on Mediterranean or Baltic Seas.
4) Uncertainties on various data sources are discussed, but their combined effects /error propagation is not. This happens also for residual errors in the correction of parameters used to estimate radio-brightness contrast.
5) Here a robust statistical analysis valid for all semi enclosed seas should be analyzed, compared, criticized, and (in case) adopted. The relationship SST and SSS should be more carefully assessed, as well as the role of stratification in SSS retrieval. I expect a significant difference between in situ S and SSS in areas strongly influenced by river runoff.
It seems to me that authors apply a simple ‘cut and try’ method, while could be better to say that it is impossible to retrieve salinity data near the coast from SMOS or that the error is relatively high.
Other considerations
Line 71: salinity retrieval depends from … sea ice cover – This is never discussed in the paper. See comment 2 above
Lines 171-172: both the Baltic and the Black Seas are characterized – nothing is said of vertical stratification. See comment 5 above
Line 191-192: None of the existing dielectric models are well characterized to be used in this low (and negative) regime of salinity values. – See comment 3 above
Lines 219 … : Seasonality of SSS biases. There is a strong seasonal bias with respect to the global ocean, it would be interesting to know if this is happening in all semi enclosed seas and how the authors manage this in a general way.
Para 3.2: The paper is weak in the selection of data for absolute calibration and validation. This is a compromise between the need for a set of data representative of the Black Sea and the need for a set of data representative of SMOS estimates. In the paper this compromise is confusing.
Conclusions
I would see a ‘unique’ methodology applied to seas having similar characteristics (e.g., semi-enclosed seas) although lying in different latitudes This approach would assess strength / weakness of the methodology and provide suggestions for general vs specific solutions.
From the reading of previous publications, the authors are finding partial solutions in specific geographical area (e.g., Arctic, Black Sea) avoiding a general common approach.
On the base of these considerations, my opinion is that the paper is not publishable.
Citation: https://doi.org/10.5194/essd-2021-364-RC1 - AC1: 'Reply on RC1', Estrella Olmedo, 26 Nov 2021
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RC2: 'Comment on essd-2021-364', Anonymous Referee #2, 27 Dec 2021
This work presents the new regional SSS product for the Black Sea as part of the EO4SIBS project. It is an interesting attempt to fill the gap in the knowledge of sea surface salinity in the basin from satellite observations.
This paper requires additional work before the publication. In the following, some comments/suggestions.
- The introduction on Black Sea oceanography is a bit weak and almost taken from Stanev et al. 2005. There are many references that can be taken into account, starting from the most recent Stanev et al. 2019, Lima et al. 2021 and associated bibliography to keep into account for describing the peculiarities of the estuarine basin. Figure 1, in particular, shows the catchment area and it is not strictly connected to the concept exposed in ln. 25-28.
Ln. 30-35 is from Stanev et al. 2005. The importance of salinity in the Black Sea is not well focused with respect to its connection with the Mediterranean Sea and it seems to be not adequately connected to the main objective of the work. - In Section 2, an introduction to the overall method used for the generation of EO4SIBS SMOS SSS product would help.
- Ln.80, 83: CMEMS products have a dedicated DOI and references to product identifier than can be easily used (for example, ln. 80 can be simply SST_BS_SST_L4_REP_OBSERVATIONS_010_022 and not the cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022 which refers to dataset name).
- Ln. 83 about OISST_HR_NRT-GOS-L4-BLK-v1.0: in the paper, the reference to CMEMS is issues. However, the near real time SST observations are provided by SST_EUR_PHY_L4_NRT_010_031 product: is this the product effectively used? The product OISST_HR_NRT-GOS-L4-BLK-v1.0 is then not part of CMEMS.
- Section 2.2 “Algorithm description” is missing on a general description of the overall adopted methodology, so it results quite hard to understand the overall process just referring to single subsections.
- Figures 2 and 3, as well as Figure 5, report data also in the Marmara Sea and a portion of the Mediterranean Sea, which is not the scope of the paper: would it be possible to mask them? Otherwise, could you please explain how you applied the methodology in both additional sub-basins?
- Would it be possible to improve the quality of figures? Fonts are small. Would it be possible to refer subplots also inside the text? (Suggestion: use index by letters to identify each subplot. It applies to all 2D maps plots).
- Brightness Temperature is referred as TB inside the text: would it be more coherent to use BT?
- Section 3 “Quality assessment” would benefit of an introduction that prepares the reader to the incoming list of data and methods the authors adopted for the validation exercise. It is not immediately clear for how many years the validation is accounting for.
- In Section 3.1.3 would it be possible to plot a map of the spatial distribution of available ARGO floats as used in the validation exercise? And for SeaDataNet insitu data?
- Have you performed also validation of SMOS SSS against climatological fields from SeaDataNet?
- Section 3.2.5: the conclusion is not clear. In particular, in which sense “the EO4SIBS SMOS SSS is consistent in describing the dynamics in the Black Sea when comparing with other geophysical variables”?
- Ln. 494: the model is able to reconstruct the salinity fields, not to observe it. Futhermore, if you refer to Black Sea Physics Reanalysis, please keep into account that ARGO from CMEMS and SeaDataNet have been assimilated.
- Figure 15: authors refer to model generically, which it is assumed to be the BLKSEA_MULTIYEAR_PHY_007_004? Could you please clarify?
- Is there any reason why the product is provided at 0.25 degree resolution? Could you please comment?
Citation: https://doi.org/10.5194/essd-2021-364-RC2 - The introduction on Black Sea oceanography is a bit weak and almost taken from Stanev et al. 2005. There are many references that can be taken into account, starting from the most recent Stanev et al. 2019, Lima et al. 2021 and associated bibliography to keep into account for describing the peculiarities of the estuarine basin. Figure 1, in particular, shows the catchment area and it is not strictly connected to the concept exposed in ln. 25-28.
Status: closed
-
RC1: 'Comment on essd-2021-364', Anonymous Referee #1, 21 Nov 2021
ESSD-2021-364: New SMOS SSS maps in the framework of the Earth Observation data for science and innovation in the Black Sea.
By Olmedo et al.
General considerations
1) The paper is part of series published by the authors in 2020 and 2021 (papers published in IEEE Journal and ESSD in my knowledge). Authors are developing salinity products at ‘high’ resolution starting from low resolution SMOS data. For sure, such products need to be developed at regional scales. This justifies this and previous publications.
On the base of these considerations, I read the paper looking for the originality and quality of data.
2) Many concepts/ideas have been presented in previous papers and are repeated here. The authors must look at the similarity report showing that many sentences are pasted and copied. Contamination by land, radio frequency interference, effects on temperature brightness are well discussed in other papers by the same authors or many of them (e.g., DOI 10.1109/JSTARS.2020.3034432) and are again presented here. I would expect a short summary with references instead of repetition, eventually synthesizing the approaches in tables.
3) A crucial aspect in all publication is the use of ‘empirical corrections’ to ‘mitigate’ the land sea contamination (as written in the paper DOI 10.1109/JSTARS.2020.3034432), This seems to be a normal practice in ‘SMOS community’ (e.g., https://www.sciencedirect.com/science/article/pii/S0034425720303977). But the approach is not convincing me. In the Black Sea the authors are using a linear extrapolation. May be the authors will use another approximation in future papers on Mediterranean or Baltic Seas.
4) Uncertainties on various data sources are discussed, but their combined effects /error propagation is not. This happens also for residual errors in the correction of parameters used to estimate radio-brightness contrast.
5) Here a robust statistical analysis valid for all semi enclosed seas should be analyzed, compared, criticized, and (in case) adopted. The relationship SST and SSS should be more carefully assessed, as well as the role of stratification in SSS retrieval. I expect a significant difference between in situ S and SSS in areas strongly influenced by river runoff.
It seems to me that authors apply a simple ‘cut and try’ method, while could be better to say that it is impossible to retrieve salinity data near the coast from SMOS or that the error is relatively high.
Other considerations
Line 71: salinity retrieval depends from … sea ice cover – This is never discussed in the paper. See comment 2 above
Lines 171-172: both the Baltic and the Black Seas are characterized – nothing is said of vertical stratification. See comment 5 above
Line 191-192: None of the existing dielectric models are well characterized to be used in this low (and negative) regime of salinity values. – See comment 3 above
Lines 219 … : Seasonality of SSS biases. There is a strong seasonal bias with respect to the global ocean, it would be interesting to know if this is happening in all semi enclosed seas and how the authors manage this in a general way.
Para 3.2: The paper is weak in the selection of data for absolute calibration and validation. This is a compromise between the need for a set of data representative of the Black Sea and the need for a set of data representative of SMOS estimates. In the paper this compromise is confusing.
Conclusions
I would see a ‘unique’ methodology applied to seas having similar characteristics (e.g., semi-enclosed seas) although lying in different latitudes This approach would assess strength / weakness of the methodology and provide suggestions for general vs specific solutions.
From the reading of previous publications, the authors are finding partial solutions in specific geographical area (e.g., Arctic, Black Sea) avoiding a general common approach.
On the base of these considerations, my opinion is that the paper is not publishable.
Citation: https://doi.org/10.5194/essd-2021-364-RC1 - AC1: 'Reply on RC1', Estrella Olmedo, 26 Nov 2021
-
RC2: 'Comment on essd-2021-364', Anonymous Referee #2, 27 Dec 2021
This work presents the new regional SSS product for the Black Sea as part of the EO4SIBS project. It is an interesting attempt to fill the gap in the knowledge of sea surface salinity in the basin from satellite observations.
This paper requires additional work before the publication. In the following, some comments/suggestions.
- The introduction on Black Sea oceanography is a bit weak and almost taken from Stanev et al. 2005. There are many references that can be taken into account, starting from the most recent Stanev et al. 2019, Lima et al. 2021 and associated bibliography to keep into account for describing the peculiarities of the estuarine basin. Figure 1, in particular, shows the catchment area and it is not strictly connected to the concept exposed in ln. 25-28.
Ln. 30-35 is from Stanev et al. 2005. The importance of salinity in the Black Sea is not well focused with respect to its connection with the Mediterranean Sea and it seems to be not adequately connected to the main objective of the work. - In Section 2, an introduction to the overall method used for the generation of EO4SIBS SMOS SSS product would help.
- Ln.80, 83: CMEMS products have a dedicated DOI and references to product identifier than can be easily used (for example, ln. 80 can be simply SST_BS_SST_L4_REP_OBSERVATIONS_010_022 and not the cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022 which refers to dataset name).
- Ln. 83 about OISST_HR_NRT-GOS-L4-BLK-v1.0: in the paper, the reference to CMEMS is issues. However, the near real time SST observations are provided by SST_EUR_PHY_L4_NRT_010_031 product: is this the product effectively used? The product OISST_HR_NRT-GOS-L4-BLK-v1.0 is then not part of CMEMS.
- Section 2.2 “Algorithm description” is missing on a general description of the overall adopted methodology, so it results quite hard to understand the overall process just referring to single subsections.
- Figures 2 and 3, as well as Figure 5, report data also in the Marmara Sea and a portion of the Mediterranean Sea, which is not the scope of the paper: would it be possible to mask them? Otherwise, could you please explain how you applied the methodology in both additional sub-basins?
- Would it be possible to improve the quality of figures? Fonts are small. Would it be possible to refer subplots also inside the text? (Suggestion: use index by letters to identify each subplot. It applies to all 2D maps plots).
- Brightness Temperature is referred as TB inside the text: would it be more coherent to use BT?
- Section 3 “Quality assessment” would benefit of an introduction that prepares the reader to the incoming list of data and methods the authors adopted for the validation exercise. It is not immediately clear for how many years the validation is accounting for.
- In Section 3.1.3 would it be possible to plot a map of the spatial distribution of available ARGO floats as used in the validation exercise? And for SeaDataNet insitu data?
- Have you performed also validation of SMOS SSS against climatological fields from SeaDataNet?
- Section 3.2.5: the conclusion is not clear. In particular, in which sense “the EO4SIBS SMOS SSS is consistent in describing the dynamics in the Black Sea when comparing with other geophysical variables”?
- Ln. 494: the model is able to reconstruct the salinity fields, not to observe it. Futhermore, if you refer to Black Sea Physics Reanalysis, please keep into account that ARGO from CMEMS and SeaDataNet have been assimilated.
- Figure 15: authors refer to model generically, which it is assumed to be the BLKSEA_MULTIYEAR_PHY_007_004? Could you please clarify?
- Is there any reason why the product is provided at 0.25 degree resolution? Could you please comment?
Citation: https://doi.org/10.5194/essd-2021-364-RC2 - The introduction on Black Sea oceanography is a bit weak and almost taken from Stanev et al. 2005. There are many references that can be taken into account, starting from the most recent Stanev et al. 2019, Lima et al. 2021 and associated bibliography to keep into account for describing the peculiarities of the estuarine basin. Figure 1, in particular, shows the catchment area and it is not strictly connected to the concept exposed in ln. 25-28.
Data sets
Black Sea SMOS Sea Surface Salinity Level 4 Olmedo, E., González-Gambau, V., González-Haro, C., García-Espriu, A., and Turiel, A. https://doi.org/10.20350/digitalCSIC/13997
Black Sea SMOS Sea Surface Salinity Level 3 Olmedo, E., González-Gambau, V., González-Haro, C., García-Espriu, A., and Turiel, A. https://doi.org/10.20350/digitalCSIC/13996
Black Sea SMOS Sea Surface Salinity Level 2 (descending) Olmedo, E., González-Gambau, V., González-Haro, C., García-Espriu, A., and Turiel, A. https://doi.org/10.20350/digitalCSIC/13995
Black Sea SMOS Sea Surface Salinity Level 2 (ascending) Olmedo, E., González-Gambau, V., González-Haro, C., García-Espriu, A., and Turiel, A. https://doi.org/10.20350/digitalCSIC/13993
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Cited
3 citations as recorded by crossref.
- First SMOS Sea Surface Salinity dedicated products over the Baltic Sea V. González-Gambau et al. 10.5194/essd-14-2343-2022
- STUDYING OF THE SATELLITE BASED MODELS FOR LOCAL SPATIO-TEMPORAL MONITORING OF OCEAN ACIDIFICATION IN COSTAL SEA WATER IN BLACK SEA N. Drumeva & M. Chanev 10.32006/eeep.2023.2.3441
- Monitoring Black Sea environmental changes from space: New products for altimetry, ocean colour and salinity. Potentialities and requirements for a dedicated in-situ observing system M. Grégoire et al. 10.3389/fmars.2022.998970