Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies
1. General Introduction and Background
- Section 3 presents the main methodologies and data used for our investigations;
SST Gradients’ Retrieval: Context and Motivation of the Proposed Approach
2. Insights on Co-Registration Related Effects
- An ideal case, in the absence of inter-channel co-registration issues, leading to the estimation of SST;
- A realistic case, introducing the co-registration error, yielding the SST field.
3. Methods and Data
- Demonstrate that horizontal gradients of atmospheric radiatively active variables (vertical distribution of water vapor, ozone and temperature) are mostly characterized by scales of variability larger than the ones of interest for the sea surface, so that gradients in the TOA BTs can be considered to be locally determined, at a first-order approximation, by the SST ones;
- Quantify the attenuation of atmospheric gases (e.g., water vapor) on the magnitude of the SST gradients from TOA BT observations;
- Test different approaches for computing SST gradients from gridded 2D fields in order to minimize the effect of radiometric noise, whose expected extent for the Harmony mission has been presented in Table 1.
- The Sentinel-3 non-time-critical (NTC) observations distributed as L2P SST products by EUMETSAT. These products follow the Group for High Resolution Sea Surface Temperature data format specification (GHRSST, https://www.ghrsst.org/, accessed on 28 November 2022), which means they include both single-channel top-of-the-atmosphere brightness temperatures (L1) and skin SST (L2) data in the same file, together with specific L2P and user-defined SST quality flags. The quality flag is an indicator of the SST accuracy and ranges from 0 to 5: (i) 0 = missing data; (ii) 1 = cloud; (iii) 2 = worst quality, (iv) 3 = low quality; (v) 4 = acceptable quality; (vi) 5 = best quality. The L2P SST and related flags are actually those computed from dual-view data, but nadir-view SST can be recovered adding the “dual minus nadir SST difference” provided as an experimental variable in the L2P file. Here, we focused only on nadir-view data to maximize the spatial correspondence between L1 and L2 data. We selected only the highest quality flag (keeping only best quality “flag 5” data), to remove cloud-contaminated pixels, which might alter SST gradient estimates. This results in some additional artefact clouds being removed from BT/SST data. In our case study, the percentage of best quality data was estimated to be around 97%, thus guaranteeing a large availability of observations for our analyses. The L2P GHRSST data are provided in sensor coordinates. The spatial resolution of these data is around 1 km.
- The level 1.5 Meteosat Second Generation (MSG)-3 SEVIRI data. The data are provided as high-rate transmissions in 12 spectral channels spanning the 0.6 to 13.4 m range. The images consist of geolocated, radiometrically pre-processed data, including radiometric and geometric quality control information. The data contain TOA radiances and are expressed in . The spatial resolution of these data is around 3 km at the subsatellite point . For our purposes, we extracted information from the following IR channels: 8.7, 9.7, 10.8 and 12.0 m.
4.1. SST Gradients from L1 Observations: A Test Case
4.2. Test Case Based on SEVIRI Data
- The BT gradients shown in Figure 6b–e (given in K) differ in the description of the SST-related gradients in the selected area. Channel #9, compared to #7 and 10, exhibits the sharpest gradients. This channel is indeed characterized by the lowest NWF from 700 hPa to TOA levels and is thus chosen as a reference for the description of the sea surface ;
- As expected, channel #8 (9.7 m) yields a highly smoothed description of the sea surface thermal conditions. The signal contains small-scale noise although still capturing some of the sharpest gradients seen by channel #9.
4.3. SST Gradients from BT Observations: Testing Water Vapor Effects
4.4. Optimizing the Gradient Numerical Scheme
- In ideal conditions (i.e., no noise), the finite central differences numerical scheme yields the best gradient estimates. The bias and root mean square error (RMSE) with respect to the analytical case (aGI) are, respectively, −0.0011 and 0.085 K. The wider stencil numerical schemes show comparable averaged performances but exhibit a slight degradation with respect to the central approach. Indeed, going from the Sobel to Pavel11 scheme, the bias and RMSE progressively increase, respectively, reaching −0.0065 and 0.087 K in the Pavel11 case. This behavior is mostly due to an enhanced smoothing of the gradient field for wider stencil width estimators. The Roberts estimator constitutes an exception, as it tends to misplace the gradient structure yielding inaccurate intensities (Figure 11).
- In the presence of noise, the results of the ideal scenario are reversed. The inaccuracies on the SST introduced via the Gaussian noise are highly detrimental for the gradient field. The bias and RMSE, respectively, reach 0.15 and 0.21 K for the central estimator and decrease monotonically down to 0.10 and 0.028 K for the Pavel11 case. The visual inspection of Figure 12 also enables the assessment that the Pavel11, although slightly smoothing the highest values, enables the correct representation of the gradient feature and a more refined description of the marginal area (the transition from the uniform background SST to the SST values related to our synthetic warm-core eddy). As for the previous case, the Roberts estimator constitutes an exception, exhibiting the largest bias (0.23 K), and highly degrades the gradient field as depicted by the 2D maps in Figure 12.
- Quantify the atmospheric effects in the space-based (TOA) PAN BT-derived SST gradients. Two extreme cases were treated, i.e., the retrieval in the presence of tropical and subarctic conditions. Very large concentrations of water vapor (typical of tropical conditions) can degrade the signal in TOA observation and allow the recovery of 30% of the gradient features found at the sea surface. However, the amount of the recovered signal can rise up to 80% if one progressively switches from tropical to typical subarctic conditions. For the specific purposes of the Harmony mission, this would favor the applicability of the proposed approach to mid-high latitude areas;
- Quantify the effect of inter-channel co-registration on the SST-based gradient retrieval, i.e., considering a co-registration issue between the TIR-1 and TIR-2 narrow-band channels by about 10% of the pixel length. This turned out to be critical for the accuracy of the gradient features extracted from SST geophysical retrievals. Not only are co-registration issues responsible for generating spurious features but they can also generate degradations of the overall gradient estimates by about 10%. This emphasizes the advantages of retrieving SST gradients from PAN-derived BTs, which enables us to get rid of any inter-channel co-registration issues;
- Assess which is the optimal numerical scheme to compute gradients from 2D BT (or SST scenes) in the presence of radiometric noise. In general, wide-stencil (i.e., at least 5 × 5) noise-robust differentiators, such as the Pavel kernels, are recommended. They indeed enable the preservation of the main gradient features even when the original 2D field is corrupted due to random noise. The chosen numerical scheme should, however, account for the number of available observations, which may vary according to the atmospheric conditions (cloud cover) or according to the distance between the study area and the coastline.
Data Availability Statement
Conflicts of Interest
Appendix A. The Atmospheric Radiative Transfer Simulator (ARTS)
- The Harmony TIR payload spectral characteristics and viewing geometry were extracted from . The TIR payload will enable observations with five different off-nadir viewing angles (51, 45, 39, 33, 27)° and, in this study, only results based on the 27° angle are presented;
- The simulations were performed with a spectral resolution of 0.5 GHz;
- A collection of atmospheric profiles was extracted from the Garand dataset . The dataset considers 42 atmospheric profiles representative of different pressure, temperature and gas concentration (HO, O, CO, CH, NO and CO) conditions. For this study, we used the tropical summer and subarctic winter profiles (see also Section 4);
- Regarding the seawater emissivity, in the presented numerical simulations, referring to a single geometry, priority was given to the detailed description of spectral characteristics (http://www.icess.ucsb.edu/modis/EMIS/html/em.html, accessed on 23 July 2022) neglecting the dependence from surface roughness and seawater temperature;
- The bottom boundary condition (BBC) (i.e., sea surface temperature) is approximated as the same temperature of the lowest atmospheric level. Additional synthetic BBCs were also obtained by varying the SST in the range [SST-5 K, SST+5 K] and keeping only results for which 271 K ≤ SST ≤ 308 K.
Appendix B. Numerical Estimates of Gradient Fields
Appendix B.1. Sobel
Appendix B.2. Roberts
Appendix B.3. Prewitt
Appendix B.4. Central Differences Methods
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|HARMONY||Central WL||Width||SSD||NET @ 280 K||Rad. Accuracy @ 280 K|
|S3-SLSTR||Central WL||Width||SSD||NET @ 266 K||Rad. Accuracy @265-310K|
|∇ BT Gradients||Bias|
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Ciani, D.; Sabatini, M.; Buongiorno Nardelli, B.; Lopez Dekker, P.; Rommen, B.; Wethey, D.S.; Yang, C.; Liberti, G.L. Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies. Remote Sens. 2023, 15, 1163. https://doi.org/10.3390/rs15041163
Ciani D, Sabatini M, Buongiorno Nardelli B, Lopez Dekker P, Rommen B, Wethey DS, Yang C, Liberti GL. Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies. Remote Sensing. 2023; 15(4):1163. https://doi.org/10.3390/rs15041163Chicago/Turabian Style
Ciani, Daniele, Mattia Sabatini, Bruno Buongiorno Nardelli, Paco Lopez Dekker, Björn Rommen, David S. Wethey, Chunxue Yang, and Gian Luigi Liberti. 2023. "Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies" Remote Sensing 15, no. 4: 1163. https://doi.org/10.3390/rs15041163