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Article

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

by
Tumelo Mathe
1 and
Hamisai Hamandawana
2,*
1
Department of Geography School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Braamfontein 2000, South Africa
2
Afromontane Research Unit, Risk and Vulnerability Science Centre, University of Free State, Private Bag X13, Phuthaditjhaba 9866, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 12699; https://doi.org/10.3390/su151712699
Submission received: 26 June 2023 / Revised: 8 August 2023 / Accepted: 19 August 2023 / Published: 22 August 2023
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Abstract

:
Eleven level-2 Sentinel 3A OLCI images that were acquired between 29 March 2017 and 11 December 2017 were used to assess their ability to retrieve oceanic Chl-a concentrations in South Africa’s Algoa and St Francis Bays. This was done by applying a 7-colour gradient pallet in the SNAP software to produce oceanic Chl-a concentration maps on a scale ranging from 0.1–30 mg/m3. The validation of Sentinel’s Chl-a’s retrieval potentials was based on temporally corresponding in-situ data from eight stations. Comparative analysis of the image-based and in-situ Chl-a concentrations revealed statistically significant correlations (r 0.609–r 0.899, ᾶ 0.05) at five stations out of the eight that were used as sources of reference data. This finding is helpful as an objectively premised source of insights on how to sustainably utilise the oceanic resources at our disposal. It is also useful because it verifiably demonstrates that Sentinel images can be reliably used to retrieve usable information on Chl-a concentrations in lieu of the costly sea-surface-based in-situ measurements at appropriate temporal and spatial scales.

1. Introduction

Chlorophyll (Chl) is the green molecule in plant cells that performs the light-induced fixation of carbon in the process of photosynthesis [1,2,3,4] by synthesizing carbohydrates and oxygen from atmospheric CO2 and water [5,6]. There are six types of chlorophylls (Chl-a–Chl-f) that have been identified [7,8,9,10,11,12]. Of these six types, Chl-a is the most abundant [1] and most important because it is the primary molecule that is responsible for oxygenic photosynthesis [13] in all photosynthesising organisms [14]. In marine ecosystems, Chl-a plays a vital role in their productivity by regulating the abundance of phytoplankton, which determines the reproduction and availability of commercially important fish and other societally beneficial goods and services by sustaining the oceanic food chain [15,16,17]. This role explains why sustainable development goal (SDG) 14 is dedicated to the conservation and environmentally friendly use of the oceans and their resources [18,19]. SDG 14 is specifically premised on 10 targets that are directly related to marine ecosystems. Of these 10 targets, only SDG 14.1.1a is devoted to how Chl-a deviations, coastal eutrophication, [19] and the persistent increase in land-based nutrient effluents from agricultural runoff, domestic and industrial wastewater discharge [20] impact the sustainability of our marine ecosystems.
A better understanding of these deviations is therefore critical for the sustainable use of these ecosystems because it provides informative insights into their trophic and health status [21,22]. Chl-a concentration measurements are also useful because they are some of the most frequently measured oceanographic biochemical pigments [23] that support all life forms by regulating the fixation of atmospheric CO2 in different terrestrial and marine life form types [24,25]. This natural fixation process produces ~5 × 1010 tons of organic carbon by sequestering ~20 × 1011 tons of CO2 and ~13 × 1010 tons of atmospheric oxygen yr−1 from oceans and the atmosphere [26]. Chl-b and Chl-c are mainly found in freshwater plants that include dinoflagellates and green and brown algae, while Chl-d mainly occurs in marine red algae, with Chl-e and Chl-f predominantly occurring in yellow-green algae and cyanobacteria [27].
Although Chls b–f are largely accessory pigments, they also play important roles in the appropriation of carbon by making photosynthesis more efficient through the absorption of different wavelengths and infrared light, which are not absorbed by Chl-a [28,29,30]. These Chl-s enhance the photo-receptive capabilities of Chl-a by transferring the energy they absorb to the primary Chl-a instead of directly participating in the carbon sequestration process [31]. They can do this because they are acclimatised to the blue–green light which penetrates deep depths of ocean water [32,33]. Since Chl-a is more abundant than all the other Chls, it plays a more dominant role in all photosynthetic processes both on land and in oceans. In marine ecosystems, this sequestration accounts for ~50% of global photosynthetic productivity [34], with the abundance of Chl-a being crucial for informative analysis of these ecosystem’s functional dynamics because it can be used as a proxy for phytoplankton biomass [35,36].
In general, more Chl indicates more phytoplankton and more algae (eutrophication), which often causes harmful algal blooms (HABs) and oxygen dead zones [37]. HABs are environmentally unfriendly because they devastate marine fisheries and disrupt the proper functioning of ecosystems by (1) depleting dissolved oxygen supplies through nocturnal respiration, (2) inducing imbalances in nutrient compositions [38,39,40], and (3) amplifying and accelerating changes in seawater through the acidification, changes in water chemistry, loss of biodiversity and the mass mortality of impacted marine organisms [41,42,43]. The depletion of dissolved oxygen induces additional adverse effects by creating oxygen dead zones (hypoxic zones) that further enhance the production of greenhouse gases (e.g., methane and nitrous oxide), which adversely impact our climate system [44,45,46]. These adverse impacts are being accelerated and amplified by excessive nutrient effluence from terrestrially driven unsustainable human-related resource use activities [47]. Examples of this include nutrient overloading from agriculture, septic systems, sewage treatment plants and urban run-off [37,48,49].
Although there is indisputable evidence that marine ecosystems are being systematically degraded and subjected to increasing natural and non-natural stressors [50,51], effective monitoring of what is happening continues to be problematic due to the lack of cost-effective techniques to do so at appropriate spatial and temporal scales. What is needed to address these challenges is an overdue and objectively informed actionable intervention to do so, with the logic behind this reasoning being premised and informed by the fact that the time to act is now before it becomes too late to contain and reverse deteriorating environmental conditions. This challenge must be addressed because it has snowballing adverse impacts that continue to undermine our abilities to assimilate global-change-friendly resource use practices that are guided by the limits of what nature allows.
The above captioned stressors reinforce each other to undermine the ability of marine ecosystem to sustainably provide the critical goods and services that are indispensably essential for the livelihoods of millions of people and a habitable planet [52,53,54]. Although evidence strongly suggests that oceanic oxygen-dead zones have been increasing due to nutrient overloading by human-driven unsustainable resource use practices [47,55], this trend continues to persist due to increasing reliance on exploitive resource use practices that undermine our abilities to assimilate and implement sustainable utilisation of the oceanic resources at our disposal [37,56,57].
This trend has snowballing impacts that limit our abilities to monitor how the health of our coastal oceanic ecosystems is evolving and being impacted by multiple stressors that are largely driven by human-driven changes in the abundance of oceanic Chl-a. Some of the methods that have been traditionally used to monitor this pigment’s spatial and temporal variations include (1) fluorometry, (2) spectrophotometry and (3) high-performance liquid chromatography [58].
  • Fluorometry involves the measurement of energy that is emitted by Chl molecules after incoming energy has been absorbed and used in photosynthesis [59,60], with the intensity of fluoresced energy indicating discrete levels of Chl concentration [61].
  • Spectrophotometry involves measuring the amount of light that is absorbed by Chl at a particular wavelength [62].
  • High-performance liquid chromatography involves the extraction of Chl molecules from water by absorption, partition, and size exclusion [63].
Unfortunately, however, these techniques are expensive, time-consuming and incapable of providing Chl-a concentration measurements at optimum temporal and spatial scales. Although attempts have been made to overcome some of these limitations by using Automated in situ sensors, such as Autonomous Underwater Vehicles (AUV) that are equipped with bio-optical sensors [64,65], this advancement still renders in-situ monitoring techniques incapable of timely providing adequate information.
The inability of these techniques to provide spatiotemporal representative Chl-a concentration estimates is mainly because the water samples on which they are based often only represent a small percentage of the total water body extent [66]. The challenge emerging from this persevering situation is that it continues to be extremely difficult to provide representative assessments of temporal and spatial variations in the abundances of phytoplankton without exploring cost-effective techniques to provide usable information on oceanic Chl-a concentrations. To address this challenge, researchers have been using satellite-image-based datasets and different algorithms to retrieve oceanic Chl-a concentrations. Most of these algorithms have, however, been specifically designed to retrieve Chl-a concentrations in open oceans by using reflectance in the blue-green spectral regions [67,68,69,70]. Unfortunately, the reflectance in these spectral bands cannot be used for estimating Chl-a concentrations in the turbid waters of coastal areas due to overlapping and uncorrelated absorption, scattering and absorption of downwelling and upwelling energy by detritus and other contaminants in these waters [70,71,72].
Equally challenging is the fact that the use of most of these NIR-based algorithms continues to be undermined by our limited abilities to routinely provide reliable atmospheric corrections that can accurately detect temporal and spatial variations in oceanic Chl-a concentrations [73]. Blondeau-Patissie et al. [65] provide an informative review of the strengths and limitations of some of the commonly used Chl-a estimation algorithms. Although a detailed review of these algorithms’ performance is beyond the scope of this paper, what is clear is that a lot still needs to be done by formulating robust techniques to reliably estimate oceanic Chl-a concentrations.
The above-captioned challenges prompted us to consider ascertaining the usefulness of multi-band Sentinel images to cost-effectively provide this information by tapping on an improvised pilot, user-friendly, affordable, and adaptable Chl-a concentration measurement technique to objectively monitor this pigment’s abundance in coastal areas.

1.1. Study Area

Our study area comprises Algoa Bay and St Francis Bay, which are situated along the coast of South Africa’s Eastern Cape Province (Figure 1).
Straddling the area between latitudes 33° S and 35° S and longitudes 24° E and 28° E, these two bays are collectively called the Agulhas Bank, which covers three hydrological zones comprising the western, central, and eastern segments in a broad continental shelf that extends 250 km eastward from the coast [74]. This region is dominated by the south-flowing Agulhas Current [75,76,77,78], which brings nutrient-poor tropical water from the equatorial region of the western Indian Ocean [79] before it retroflects into the South Indian Ocean as the Agulhas Return Current [80,81]. The area is commonly called the Agulhas bioregion because of its high chlorophyll concentrations in the range of 2–5 mg m−3 [82] and productively favourable sea-surface temperatures that range between 17 °C in August and 21.6 °C in February [82,83].
The area harbors one of South Africa’s most productive fisheries with a total of 327 marine protected areas (MPAs) that consist of; (1) 197 category 1 MPAs in which the extraction of living marine resources is prohibited, (2) 72 category 2 MPAs in which fishing and other forms of extractive exploitation from the shore are permitted, and (3) 52 closed coastal MPAs in which exploitation is prohibited [84]. This area is ecologically important because its wide oceanic shelf provides a wide range of habitats that account for high levels of biodiversity [83]. It is also important because it is home to several endemic fish species [85,86] that include Sparidae clinidae, Sparidae kyphosidae, Sparidae soleidae Clinus superciliosus, Engraulis encrasicolus, Heteromycteris capensis, Diplodus capensis, Engraulis encrasicolus, Heteromycteris capensis, Neoscorpis lithophilus, Rhabdosargus globiceps, Petrus rupestris [87,88] and a destination for recreational shore angling [89] which, creates a lot of employment by supporting other commercial establishments that include bait and tackle outlets [90].
These ecosystem goods and services justify why it is essential to closely monitor the ecological condition and health of this important marine environment by tracking temporal and spatial variations in Chl-a concentrations [91], which aids the identification of fish-rich areas that need to be conserved [92].

1.2. Observational Datasets

The datasets that were used in this study include (1) 11 (eleven) level-2 Sentinel 3A OLCI in the near-infrared (NIR) bands 2 and 3 that were acquired between 29 March 2017 and 11 December 2017, and (2) in-situ Chl-a concentration measurements at eight stations in the Algoa and Francis Bays, South Africa. NIR bands were preferred because of their established ability to better detect Chl-a concentrations in coastal waters than the blue and green bands of the electronic, magnetic spectrum [93]. The Sentinel images were downloaded free of charge from the Eumetsat file transfer protocol site [94], and in-situ Chl-a measurements were acquired from the South Africa Institute of Aquatic Biodiversity (SAIAB) [95]. Although the initial intention was to use near-anniversary summer-season satellite and in-situ datasets of March and June, during which strong upwelling events [75] increase the abundance of Chl-a to levels that can be detected by Sentinel’s NIR bands [96,97], this was not possible due to lack of cloud-free images. We addressed this limitation by using near-coincident images that were acquired 1–2 days before or after the days on which the available in-situ measurements were recorded. Table 1 shows the acquisition dates, identities and cloud cover percentages of the images that were used. Although six of the eleven images had cloud cover percentages of 5% and one a cloud cover percentage of 10%, the target areas were still visible. Table 2 shows the acquisition dates and levels of in-situ Chl-a concentrations by date and their station IDs (P1–P8), with P denoting a contraction we coined from the first letter in the South African Institute of Biodiversity’s Pelagic Ecosystem Long-term Ecological Research Programme (PELTER) which runs these stations.

1.3. Image Pre-Processing and Classification

The images were atmospherically corrected to Top Of Atmosphere (TOA) reflectances by using the Sen2Cor plug-in tool provided in the Sentinel Application Platform (SNAP) toolbox [98]. Thereafter, they were classified in the SNAP software by applying a 7-colour gradient pallet to produce gradient Chl-a concentration maps on a scale ranging from 0.1–30 mg/m3 (Figure 2).
After classification, the image-based Chl-a estimates were extracted from the maps by averaging observed values for 4 pixels that were selected by using a 2 × 2-pixel window, which was closely centred on each station as much as possible to enhance the extraction of spatial correspondence with their in-situ equivalents. Figure 3 illustrates how this extraction was interactively performed.

2. Results

The results are presented in the form of tables (Table 3, Table 4 and Table 5). Table 3 shows in-situ measurements that were used to validate the ability of Sentinel images to estimate Chl-a concentrations and their corresponding image-based Chl-a concentrations at each station in the near-infrared (NIR) bands 2 and 3 of these images. Table 4 shows standard deviations, coefficients of variation, and Pearson’s correlations between image-based Chl-a concentration estimates and in-situ measurements and the mean values of these measurements. Table 5 presents the results of the analysis of variance (ANOVA) between image-based Chl-a concentrations and in-situ measurements. The statistical significance of correlations was determined by reference to critical values of the Pearson product-moment correlation coefficient, and the statistical significance of ANOVA was determined by reference to procedures provided at [98], with these tests being performed following recommendations provided by Tóth et al. [99].
There was close temporal correspondence between in-situ data and the image-based Chl-a estimates that were retrieved from Sentinel images. Out of the eleven paired datasets, eight were acquired on the same dates, and three were acquired 1–2 days apart (Table 3). Image-based Chl-a concentration estimates were temporally and spatially variable, with this variability pointing to the influence of dynamic oceanic processes that include episodic variations in sea surface temperatures and nutrient enrichment. Stations P-2 and P-6 had the highest and lowest in-situ measurements of 30.09 mg/m3 on 30 March 2017 and 3.650 mg/m3 on 6 October 2017, respectively. The highest and lowest image-based Chl-a measurements were 12.066 mg/m3 at station P-2 on 6 October 2017 and 9.910 mg/m3 at station 8 on 28 November 2017 in NIR-3. Overall, both NIR-2 and NIR-3 produced estimates that closely matched in-situ observations. These results concur with recent findings by many researchers [100,101,102,103] who report that NIR bands produce satisfactory Chl-a estimates for turbid/highly turbid oceanic waters, with this discovery being based on observations from extensive studies that covered wide-ranging coastal environments.
The high standard deviations in in-situ measurements at Stations P2, P6, and P8 can be explained by isolated extreme highs and lows on isolated days at these stations, which point to the dynamic nature of Chl-a concentrations at these stations. Possible factors accounting for these factors, as observed in other coastal areas worldwide, include changes in surface winds and sea surface temperatures [50,104] and wave direction and wave height [105]. The correspondence we obtained between NIR bands and in-situ observations was cross-checked for accuracy/reliability by calculating standard deviations, coefficients of variation and correlation coefficients (Table 4).
As shown in Table 4, all standard deviations (St-dev) and coefficients of variation (CV) between image-based Chl-a measurements were low. NIR-2 produced lower/better mean, standard deviations, and coefficients of variation (St-Dev 0.53, CV 0.005) than NIR-3 (St-Dev = 0.278, CV = 0.024), which suggests that this band is better able to estimate Chl-a concentrations than its NIR-3 equivalent. Station P-2 produced the lowest and highest St-Devs of 0.028 and 0.754 in the NIR-1 and NIR-3 bands, respectively.
NIR-2 also produced better correlations, with measurements from four of the eight stations (P-1, P-3, P-4 and P-7) being significantly correlated with in-situ data, while NIR-3 only produced three significantly correlated measurements at stations P-1, P-3 and P-7. The absence of correlation between image-based and in-situ measurements at three of these stations (stations 2, 6 and 8) suggests the possible influence of wide-ranging factors that include but are not limited to; (1) the earlier stated unavoidable lack of real-time correspondence in the acquisition of in-situ and image-based measurements we used, (2) the use of averaged daily in-situ measurements which may deviate from time-specific image-based measurements, and (3) temporal variations in atmospheric conditions when the images we used were acquired. These and other factors may have reduced the ability of Sentinel images to capture representative signals that compared well with in-situ measurements. Basing analysis of the correlations between in-situ and the image-based measurements we obtained in this study (Table 4) on a two-tier high and low classification in which r values ≥ 0.538 show good correspondence [106], it is evident that at most stations (five out of eight) Sentinel images yielded good Chl-a estimations that can be reliably used to objectively inform the identification of environmentally friendly of the oceanic resources at our disposal. The results shown in Table 4 closely mimic what ANOVA revealed (Table 5).
A close examination of the results of ANOVA shows that only station 5 produced Chl-a concentrations in both bands that were significantly different from in-situ measurements. What this implies is that although NIR-2 marginally outperformed NIR-3, both bands were able to reliably retrieve oceanic Chl-a concentrations.

3. Discussion

The objective of this investigation was to assess the Chlorophyll-a retrieval capabilities of Sentinel 3A OLCI images for the monitoring of coastal waters in Algoa and Francis Bays, South Africa. Overall, the results of this initiative show high enough positive correlations between in-situ and image-based Chl-a measurements, which justify the use of Sentinel images in the monitoring of oceanic Chl-a concentrations. This deduction is based on recommendations by Moutzouris-Sidiris and Topouzelis [107], who report that correlations ≥ 0.538 are indicative of good correspondence between in-situ and image-based Chl-a concentration measurements. In this study, the marginal variation between the means of image-based and in-situ measurements (Table 3) and the ability of NIR-2 and NIR-3 to yield correlations that were above this threshold at five out of the eight stations (Table 4) points to the ability of Sentinel images to provide usable oceanic Chl-a concentration information.
This observation is consistent with findings by other researchers [106,107,108,109] who observed good correlations between Sentinel-2-based Chl-a concentration measurements and in-situ data. This finding was further validated through ANOVA, which revealed statistically insignificant differences at seven of the eight stations that were used as sources of validation reference data (Table 5). Since these statistical tests revealed good correspondence between image-based and in-situ measurements, it can be concluded that Sentinel images were able to reliably estimate Chl-a concentrations. Presentation of the performance of Sentinel images in the form of visually intelligible graphs shows that on most of the days on which the measurements we used were obtainable, the majority of image-based and in-situ Chl-a measurements at each of the eight stations were very close (Figure 4).
The isolated outliers in which Sentinel images underestimated and overestimated in-situ observations can be explained by the earlier stated use of datasets that were 1–2 days apart, mismatch in the time on which images and in-situ data were acquired on each day, unavoidable use of averaged daily in-situ measurements instead of real-time data and other factors that need to be investigated.
The results of this investigation suggest that Sentinel images are reliably capable of providing usable information on oceanic Chl-a concentrations at appropriate temporal and spatial scales in lieu of the costly and time-consuming conventional techniques. This finding is consistent with observations by other researchers in different coastal areas worldwide. In Vietnam’s coastal waters, for example, Bihn et al. [109] successfully retrieved Chl-a concentrations from Sentinel 3A images (R2 = 0.58, RMSE = 1.018 mg/m3). Moses et al. [110] report similar results from a study of the Sea of Azov (a shallow north of the Black Sea) in which they obtained accurate Chl-a concentrations with accuracies in the order of 90%. Interestingly, however, other researchers [111] have found Sentinel-3 OLCI data to be of very limited use for assessing Chl-a concentration in coastal waters where productivity is low, and turbidity is moderate because the upwelling signals are too low to contain enough information for the reliable Chl-a concentration estimates irrespective of the retrieval algorithm that is used. This should, however, not be taken to suggest that Sentinel-3 OLCI images are not helpful because they have been demonstrated to be reliable in areas where productivity is high and turbidity substantial.

4. Conclusions

Basing insightful conclusive remarks and take-home points on the results of this study and those by others from different areas worldwide, our terminal submission is that Sentinel-3 OLCI images have immense potential to provide usable Chl-a information for oceanic coastal areas whose sustainability is being threatened by wide-ranging natural and non-natural stressors. Although the non-natural stressors may be difficult to handle, the onus is on us to harmoniously synchronise our livelihood strategizes in tandem with the limits of what nature is able to provide. The value of this discursive narrative goes beyond enhanced monitoring of coastal waters because information on the temporal and spatial variations in the distributions of oceanic Chl-a can concentrations can also be used as a proxy of what is happening in terrestrial ecosystems, with this information further providing used and managed for the benefit of present and future generations useful insights on how the same coastal waters from which is derived can be sustainably managed. Given the fact that the scientific community is societally obliged to provide this information, there is an urgent need for the scientific community to be exploring methodologies that can help tap into the rich and freely accessible remote-sensed data at our disposal. Accomplishing this requires collective efforts. We conclude by inviting those interested in enhancing responsible stewardship of the oceanic resources at our disposal to complement our efforts by exploring work-around strategies and techniques that can be used to provide the badly needed but difficult to retrieve information on spatial and temporal variations in oceanic Chl-a concentrations.

Author Contributions

Conceptualization, T.M.; methodology, T.M.; software, T.M.; validation, T.M. and H.H.; formal analysis, T.M. and H.H.; investigation, T.M.; resources, T.M. and H.H.; data curation, T.M.; writing—original draft preparation, T.M. and H.H.; writing—review and editing, H.H.; visualization, T.M. and H.H.; supervision, H.H.; project administration, T.M. and H.H. All authors have read and agreed to the published version of the manuscript.

Funding

No funding was obtained for this study.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data for this project can be provided upon request by contacting Tumelo Mathe.

Acknowledgments

The authors thank the South Africa’s Aquatic Institute of Aquatic Biodiversity (SAIAB) for generously providing in-situ data free of charge. We also thank the four reviewers whose comments helped us to improve our paper.

Conflicts of Interest

The authors declare that there are no competing interests.

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Figure 1. Location of Algoa Bay and St Francis Bay.
Figure 1. Location of Algoa Bay and St Francis Bay.
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Figure 2. Chl- concentration maps were produced by applying a 7-colour gradient pallet on a gradient scale ranging from 0–30 mg/m3.
Figure 2. Chl- concentration maps were produced by applying a 7-colour gradient pallet on a gradient scale ranging from 0–30 mg/m3.
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Figure 3. Illustration of how Chl-a concentrations were extracted from the classified map outputs.
Figure 3. Illustration of how Chl-a concentrations were extracted from the classified map outputs.
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Figure 4. Graphical depiction of correspondences between image-based and in-situ Chl-a measurements.
Figure 4. Graphical depiction of correspondences between image-based and in-situ Chl-a measurements.
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Table 1. Acquisition dates and characteristics of Sentinel 3A OLCI images that were used.
Table 1. Acquisition dates and characteristics of Sentinel 3A OLCI images that were used.
Acquisition DateScene ID% Cloud Cover
30 March 201720170330T064420_20170330T072824_20170330T092717_2644_016_063_MAR_O_NR_0020
10 May 201720170510T072056_20170510T080201_20170510T095854_2464_017_263_MAR_O_NR_0025
2 June 201720170602T072003_20170602T080429_20170602T100331_2666_018_206_MAR_O_NR_0020
30 June 20170170630T065327_20170630T073751_20170630T093231_2664_019_220_MAR_O_NR_0020
26 July 201720170726T072040_20170726T080458_20170726T100300_2658_020_206_MAR_O_NR_0025
8 August 201720170808T064416_20170808T072831_20170808T092435_2655_021_006_MAR_O_NR_0025
4 September 201720170904T064705_20170904T073117_20170904T092051_2652_022_006_MAR_O_NR_0020
7 October 201720171007T073608_20171007T082023_20171007T100427_2655_023_092_MAR_O_NR_0025
31 October 201720171031T071626_20171031T080043_20171031T094658_2657_024_049_MAR_O_NR_0025
28 November 201720171128T065229_20171128T073643_20171128T091641_2654_025_063_MAR_O_NR_0025
12 December 201720171212T073029_20171212T081438_20171212T094917_2649_025_263_MAR_O_NR_00210
Table 2. Acquisition dates and levels of in-situ Chl-a concentrations by station ID.
Table 2. Acquisition dates and levels of in-situ Chl-a concentrations by station ID.
Acquisition DateObserved In-Situ Chl-a (mg/m3) Concentrations by Station ID:
(P1–P8)
P1P2P3P4P5P6P7P8
29 March 201710.9730.094.7413.8312.0125.729.089.76
10 May 201711.0021.0011.006.0013.005.0015.0020.00
1 June 201711.0611.1015.457.0010.506.7616.7928.74
30 June 201716.3719.2211.3110.259.1512.2310.9312.29
27 July 201710.4611.367.0813.466.4710.6915.1013.07
8 August 201712.988.7911.818.6311.338.3510.938.84
5 September 201713.008.0012.008.008.006.0012.008.00
6 October 201712.176.6811.508.093.853.6513.497.93
31 October 201712.338.259.868.555.428.6313.298.44
28 November 201712.489.828.239.026.9813.067.3612.83
11 December 201712.1415.4112.3613.6510.1613.088.5616.59
Table 3. Chl-a concentration measurements that were obtained from NIR bands 2 and 3 of Sentinel 3A images and their corresponding in-situ measurements, means and standard deviations (St-dev) by date and by station.
Table 3. Chl-a concentration measurements that were obtained from NIR bands 2 and 3 of Sentinel 3A images and their corresponding in-situ measurements, means and standard deviations (St-dev) by date and by station.
Temporal Sequencing of Sentinel 3A
and In-Situ Datasets by Station		In-Situ and Sentinel Image-Based Chl-a Concentration Measurements (mg/m3)
Station IDSentinelIn-situ dataIn-situ(NIR-2)(NIR-3)
P130 March 201730 March 201710.97* 11.26211.339
10May 201710 May 201711.0011.32011.317
2 June 20174 June 201711.0611.32011.287
30 June 201730 June 201716.3711.42011.273
26 July 201726 July 201710.4611.30111.319
8 August 20178 August 201712.9811.31111.321
4 September 20174 September 201713.0011.30911.332
6 October 20177 October 201712.1711.30011.316
31 October 201731 October 201712.3311.41111.158
28 November 201728 November 201712.4811.31911.333
11 December 201712 December 201712.1411.32211.267
Mean--12.2711.32711.297
St-dev--1.6110.0470.052
P-230 March 201730 March 2017** 30.0911.26211.405
10 May 201710 May 2017** 21.0011.34011.255
2 June 20174 June 201711.1011.32711.272
30 June 201730 June 2017** 19.2211.25013.908
26 July 201726 July 2017** 11.3611.29911.321
8 August 20178 August 20178.79011.29711.293
4 September 20174 September 20178.00011.29811.317
6 October 20177 October 20176.65011.25712.066
31 October 201731 October 20178.25011.26311.413
28 November 201728 November 20179.82011.29811.324
11 December 201712 December 201715.4111.31111.301
Mean--13.6111.29111.625
St-dev--7.2010.0300.791
P-330 March 201730 March 20174.74011.27011.283
10 May 201710 May 201711.0011.34511.248
2 June 20174 June 201715.4511.32611.295
30 June 201730 June 201711.3111.29811.305
26 July 201726 July 20177.08011.30111.320
8 August 20178 August 201711.8111.29911.317
4 September 20174 September 201712.0011.30111.320
6 October 20177 October 201711.5011.25512.286
31 October 201731 October 20179.86011.24911.251
28 November 201728 November 20178.23011.30511.308
11 December 201712 December 201712.3611.35811.228
Mean--10.4911.30111.378
St-dev--2.9030.0340.303
P-430 March 201730 March 201713.8311.20510.507
10 May 201710 May 20176.00011.32211.276
2 June 20174 June 20177.00011.29611.320
30 June 201730 June 201710.2511.29111.321
26 July 201726 July 201713.4611.30111.319
8 August 20178 August 20178.63011.29611.316
4 September 20174 September 20178.00011.29911.307
6 October 20177 October 20178.09011.27911.403
31 October 201731 October 20178.55011.37311.234
28 November 201728 November 20179.02011.25811.413
11 December 201712 December 201713.6511.33011.460
Mean--9.68011.29511.261
St-dev--2.7660.0420.258
P-530 March 201730 March 201712.0111.32011.342
10 May 201710 May 201713.0011.33911.252
2 June 20174 June 201710.5011.40611.164
30 June 201730 June 20179.15011.30511.303
26 July 201726 July 20176.47011.30811.302
8 August 20178 August 201711.3311.33011.269
4 September 20174 September 20178.00011.30211.306
6 October 20177 October 20173.85011.24911.251
31 October 201731 October 20175.42011.37611.249
28 November 201728 November 20176.98011.32411.295
11 December 201712 December 201710.1611.37911.196
Mean--8.80611.33111.266
St-dev--2.9070.0440.052
P-630 March 201730 March 2017** 25.7211.25111.146
10 May 201710 May 20175.00011.33711.259
2 June 20174 June 20176.76011.33211.268
30 June 201730 June 2017** 12.2311.30511.287
26 July 201726 July 2017** 10.6911.81610.489
8 August 20178 August 20178.35011.32211.276
4 September 20174 September 20176.00011.30111.315
6 October 20177 October 20173.65011.24911.251
31 October 201731 October 20178.63011.24911.251
28 November 201728 November 2017** 13.0611.32811.275
11 December 201712 December 2017** 13.0811.49811.032
Mean--10.28811.36311.168
St-dev--6.0570.1650.239
P-730 March 201730 March 20179.0511.30011.309
10 May 201710 May 201715.0011.33311.403
2 June 20174 June 201716.7911.31511.293
30 June 201730 June 201710.9311.30011.306
26 July 201726 July 201715.1011.30411.313
8 August 20178 August 201710.9311.31611.289
4 September 20174 September 201712.0011.30611.314
6 October 20177 October 201713.4911.24911.251
31 October 201731 October 201713.2911.24911.251
28 November 201728 November 20177.30011.22510.887
11 December 201712 December 20178.56011.40511.159
Mean--12.04011.30011.252
St-dev--3.0060.0480.135
P-830 March 201730 March 20179.76011.24711.526
10 May 201710 May 2017** 20.0011.32311.277
2 June 20174 June 2017** 28.7411.30411.295
30 June 201730 June 201712.2911.28811.313
26 July 201726 July 201713.0711.29911.330
8 August 20178 August 20178.94011.30911.315
4 September 20174 September 20178.00011.30011.314
6 October 20177 October 20177.93011.25412.016
31 October 201731 October 20178.44011.24911.251
28 November 201728 November 201712.8311.2659.910
11 December 201712 December 201716.5911.33411.261
Mean--13.32611.28811.255
St-dev--6.3830.0300.498
* Image-based Chl-a measurement that was used to illustrate the calculation procedure shown in Figure 3. ** Extreme outliers accounted for high standard deviations.
Table 4. Standard deviations (St-dev), coefficients of variation (CV) for and correlation coefficients (r) between image-based (I-b) and in-situ measurements (I-m).
Table 4. Standard deviations (St-dev), coefficients of variation (CV) for and correlation coefficients (r) between image-based (I-b) and in-situ measurements (I-m).
Station
ID		Standard Deviations for I-b MeasurementsCoefficients of Variation for I-b EstimatesCorrelations (r) between
I-b Estimates and I-m
NIR-2NIR-3NIR-2NIR-3NIR-2NIR-3
P-10.0470.0520.0040.005* 0.899* 0.894
P-20.0280.7540.0030.065₵ 0.273₵ 0.305
P-30.0320.2890.0030.025* 0.631*0.633
P-40.0400.2460.0040.022* 0.609⁑ 0.587
P-50.0420.0490.0040.004⁑ 0.538⁑ 0.533
P-60.1580.2280.0140.020₵ 0.221₵ 0.207
P-70.0460.1280.0040.011* 0.680* 0.698
P-80.0290.4750.0030.042₵ 0.330₵ 0.303
Mean0.0530.2780.0050.0240.5230.520
Correlation is statistically significant at ᾶ 0.05 if observed r exceeds critical r. n = 11 (Table 3). Degrees of freedom (df) = n − 2 = 11 − 2 = 9. Critical r = 0.602. CV < 1 = low, > 1 = high. * Statistically significant, ⁑ High but statistically insignificant, ₵ = No correlation.
Table 5. Results of analysis of variance (ANOVA) between image-based Chl-a concentrations and in-situ measurements on the 11 days between 29 March 2017 and 11 December 2017.
Table 5. Results of analysis of variance (ANOVA) between image-based Chl-a concentrations and in-situ measurements on the 11 days between 29 March 2017 and 11 December 2017.
ANOVA
Station IDBand DesignationObserved Fp Value at ᾱ 0.05F Crit
P-1.S3A (NIR-2 red band)* 3.75832* 0.066794.35124
P-2.S3A (NIR-2 red band)* 1.13896* 0.298594.35124
P-3.S3A (NIR-2 red band)* 0.86731* 0.362814.35124
P-4.S3A (NIR-2 red band)* 3.75226* 0.066994.35124
P-5.S3A (NIR-2 red band)⁑ 8.2921⁑ 0.009274.35124
P-6.S3A (NIR-2 red band)* 0.34578* 0.563104.35124
P-7.S3A (NIR-2 red band)* 0.66629* 0.423964.35124
P-8.S3A (NIR-2 red band)* 1.12120* 0.302284.35124
P-1.S3A (NIR-3 red band)* 4.00293* 0.059184.35124
P-2.S3A (NIR-3 red band)* 0.82442* 0.374704.35124
P-3.S3A (NIR-3 red band)* 1.02933* 0.322434.35124
P-4.S3A (NIR-3 red band)* 3.56568* 0.073584.35124
P-5.S3A (NIR-3 red band)⁑ 7.87340⁑ 0.010914.35124
P-6.S3A (NIR-3 red band)* 0.23175* 0.635464.35124
P-7.S3A (NIR-3 red band)* 0.75407* 0.395494.35124
P-8.S3A (NIR-3 red band)* 1.15093* 0.296134.35124
df = n − 1 [98], n = 11 (Table 3), df is ∴ = 10. Observed F is statistically significant if it exceeds F critical (F crit) and vice versa. P ≤ 0.05 = statistically significant difference. * = statistically insignificant difference. ⁑ = statistically significant difference.
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Mathe, T.; Hamandawana, H. Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa. Sustainability 2023, 15, 12699. https://doi.org/10.3390/su151712699

AMA Style

Mathe T, Hamandawana H. Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa. Sustainability. 2023; 15(17):12699. https://doi.org/10.3390/su151712699

Chicago/Turabian Style

Mathe, Tumelo, and Hamisai Hamandawana. 2023. "Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa" Sustainability 15, no. 17: 12699. https://doi.org/10.3390/su151712699

APA Style

Mathe, T., & Hamandawana, H. (2023). Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa. Sustainability, 15(17), 12699. https://doi.org/10.3390/su151712699

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