# Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-a Trends

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

^{−1}[5,7,18,19,20]), were imposed on the time series. The range of trend values allow us to assess the interaction between the noise and trend magnitude/direction.

## 3. Results

^{−1}is shown in Figure 3. From this it can be seen that the peak of the distribution, i.e., the modal value, is ~−0.5%·yr

^{−1}, the same as the input trend for all the comparisons. Some variability can be seen around this peak; all distributions have a standard deviation of ~0.27%·yr

^{−1}. All distributions show some asymmetry, which can be attributed to the trends being estimated using log transformed chl-a data before being converted for plotting. It can be seen that for the three datasets OLCI-A-Aligned and OLCI-B are the most similar (expected from how OLCI-A-Aligned is created [14]) and that OLCI-A-Vicarious has a slightly, but negligibly, larger standard deviation of trend estimates (0.266%·yr

^{−1}for OLCI-A-Aligned, 0.272%·yr

^{−1}for OLCI-A-Vicarious, and 0.268%·yr

^{−1}for OLCI-B). These differences would not be expected to lead to any appreciable difference in trend detection when comparing the datasets. A realistic range of chl-a trends was tested, with very similar results in terms of consistency between datasets, although the standard deviation of trend estimates does increase with increasing values of input trend, and non-normal distributions indicate interactions with the underlying dataset (see Appendix A).

^{−1}). In Figure 4, for each grid cell the difference between (a) OLCI-A-Aligned and OLCI-A-Vicarious, (b) OLCI-A-Aligned and OLCI-B, and (c) OLCI-A-Vicarious and OLCI-B was calculated, with a positive value indicating that the latter had a greater standard deviation of chl-a trend estimates than the former. Appendix B shows similar figures at all input trend levels. The overall observation is the same as for the global histograms in that there is a relatively small difference between the datasets. OLCI-A-Vicarious shows more variance of chl-a trend estimates than OLCI-A-Aligned. OLCI-A-Aligned shows more similarity to OLCI-B than OLCI-A-Vicarious, which is expected from how the OLCI-A-Aligned dataset is produced. However, differences are typically small, with 90% of the differences in chl-a trend estimates between OLCI-A-Aligned and OLCI-A-Vicarious in the range ±0.11%·yr

^{−1}, ±0.027%·yr

^{−1}for OLCI-A-Aligned and OLCI-B, and ±0.11%·yr

^{−1}for OLCI-A-Vicarious and OLCI-B. By means of the radiometric alignment of OLCI-A to OLCI-B (OLCI-A-Aligned being understood as a “duplicate” of OLCI-B) it is completely consistent to see similar results between OLCI-A-Vicarious against either OLCI-A-Aligned or OLCI-B. Moreover, the differences are expected to be much smaller between OLCI-A-Aligned and OLCI-B.

## 4. Discussion

## 5. Conclusions

^{−1}for OLCI-B versus 0.266%·yr

^{−1}for OLCI-A-Aligned). OLCI-A-Vicarious has a slightly greater standard deviation, expected from how it is calculated (0.272%·yr

^{−1}), but this can still be considered a negligible difference from OLCI-B and OLCI-A-Aligned. There is some spatial variation in the differences, with open ocean regions being the most similar and high northern latitudes and coastal regions being the most different, albeit still with an almost negligible difference. This analysis shows that the effect of differences in sensor uncertainty will only have a negligible impact on future chl-a trend estimation using the Sentinel-3 platforms, where each satellite carries the same sensor design. This analysis does not, however, and cannot, estimate the differences from future drift in sensors, which may well have a significant impact and will need to be monitored. Likewise, differences between past, present, and future non-Sentinel sensors are not considered, even though these differences may have a significant impact on chl-a trend detection, which typically relies on records merged from multiple sensors.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Histograms of all chl-a trends estimated in all 1° grid cells and all bootstraps for OLCI-A-Aligned and OLCI-A-Vicarious. This shows that OLCI-A-Aligned and OLCI-A-Vicarious both show very similar distributions of chl-a trends, except that OLCI-A-Aligned has slightly smaller variance, which applies to all input trends.

**Figure A2.**Histograms of all chl-a trends estimated in all 1° grid cells and all bootstraps for OLCI-A-Aligned and OLCI-B. This shows that OLCI-A-Aligned and OLCI-B both show very similar distributions of chl-a trends, except that OLCI-A-Aligned has slightly smaller variance, which applies to all input trends.

**Figure A3.**Histogram of all chl-a trends estimated in all 1° grid cells and all bootstraps for OLCI-A-Vicarious and OLCI-B. This shows that OLCI-A-Vicarious and OLCI-B both show very similar distributions of chl-a trends, except that OLCI-A has slightly smaller variance, which applies to all input trends.

## Appendix B

**Figure A4.**Global 1° maps of the differences between the standard deviation of all chl-a trend estimates from all bootstraps for OLCI-A-Aligned and OLCI-A-Vicarious in each grid cell. The value expressed in percent per year above each subplot indicates the trend imposed on the synthetic time series. Red indicates that OLCI-A-Vicarious has higher standard deviation of chl-a trend estimates than OLCI-A-Aligned. Black grid cells indicate where missing data prevents analysis.

**Figure A5.**Global 1° maps of the differences between the standard deviation of all chl-a trend estimates from all bootstraps for OLCI-A-Aligned and OLCI-B in each grid cell. The value expressed in percent per year above each subplot indicates the trend imposed on the synthetic time series. Red indicates that OLCI-B has higher standard deviation of chl-a trend estimates than OLCI-A-Aligned. Black grid cells indicate where missing data prevents analysis.

**Figure A6.**Global 1° maps of the differences between the standard deviation of all chl-a trend estimates from all bootstraps for OLCI-A-Vicarious and OLCI-B in each grid cell. The value expressed in percent per year above each subplot indicates the trend imposed on the synthetic time series. Blue indicates that OLCI-A-Vicarious has higher standard deviation of chl-a trend estimates than OLCI-B. Black grid cells indicate where missing data prevents analysis.

## Appendix C

**Figure A7.**Global maps at 1° resolution of p-values from Levene’s test for significant difference in the variance of chl-a trend estimates from all bootstraps for an input trend of −0.5%·yr

^{−1}for (

**a**) OLCI-A-Aligned and OLCI-A-Vicarious, (

**b**) OLCI-A-Aligned and OLCI-B, and (

**c**) OLCI-A-Vicarious and OLCI-B in each grid cell. Values of less than 0.05 indicate evidence that the two datasets have a significant difference in their variance of chl-a trend estimates. Black grid cells indicate missing data.

## Appendix D

**Figure A8.**Global maps at 1° resolution of the OLCI-A-Aligned chl-a (

**a**) mean and (

**b**) standard deviation over the tandem period. Black grid cells indicate missing data.

**Figure A9.**Global maps at 1° resolution of the OLCI-A-Vicarious chl-a (

**a**) mean and (

**b**) standard deviation over the tandem period. Black grid cells indicate missing data.

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**Figure 1.**Comparison between Sentinel-3A Ocean and Land Colour Imager (OLCI-A) system vicarious calibration (SVC) gains and the mean inter-calibration coefficients used to align OLCI-A to Sentinel-3B OLCI (OLCI-B) per camera. Adapted from [15].

**Figure 2.**(

**a**) Diagrammatic example of a monthly chlorophyll-a (chl-a) time series of 10 years with seasonal and interannual variability but no trend. (

**b**) adds random observational noise to this series which induces an apparent small non-zero positive trend, a negative trend would be just as likely with another sampling of random noise.

**Figure 3.**Histograms of −0.5%·yr

^{−1}input chl-a trends estimated in all 1° grid cells and all bootstraps. From (

**a**) OLCI-A-Aligned and OLCI-A-Vicarious, (

**b**) OLCI-A-Aligned and OLCI-B, and (

**c**) OLCI-A-Vicarious and OLCI-B. All datasets show very similar distributions of chl-a trends. See Appendix A for corresponding histograms for all input trends.

**Figure 4.**Global maps at 1° resolution of the differences between the standard deviation of all chl-a trend estimates from all bootstraps for an input trend of −0.5%·yr

^{−1}for (

**a**) OLCI-A-Aligned and OLCI-A-Vicarious, (

**b**) OLCI-A-Aligned and OLCI-B, and (

**c**) OLCI-A-Vicarious and OLCI-B in each grid cell. Red indicates that the former dataset in each pair has higher standard deviation of chl-a trend estimates than the latter dataset in each pair. See Appendix B for similar maps for all input trends. If statistical testing determined that the difference in the variance of chl-a trend estimates between the two datasets was not significant (Appendix C) the cell is considered as having a difference of zero (i.e., white). Black grid cells indicate where missing data prevents analysis.

**Figure 5.**Global maps at 1° resolution of the OLCI-B chl-a (

**a**) mean and (

**b**) standard deviation over the tandem period. See Appendix D for similar maps produced for OLCI-A-Aligned and OLCI-A-Vicarious. Black grid cells indicate missing data.

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**MDPI and ACS Style**

Hammond, M.L.; Henson, S.A.; Lamquin, N.; Clerc, S.; Donlon, C. Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-*a* Trends. *Remote Sens.* **2020**, *12*, 2522.
https://doi.org/10.3390/rs12162522

**AMA Style**

Hammond ML, Henson SA, Lamquin N, Clerc S, Donlon C. Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-*a* Trends. *Remote Sensing*. 2020; 12(16):2522.
https://doi.org/10.3390/rs12162522

**Chicago/Turabian Style**

Hammond, Matthew L., Stephanie A. Henson, Nicolas Lamquin, Sébastien Clerc, and Craig Donlon. 2020. "Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-*a* Trends" *Remote Sensing* 12, no. 16: 2522.
https://doi.org/10.3390/rs12162522