Evaluation of merged multi-sensor ocean-color chlorophyll products in the Northern Persian Gulf

https://doi.org/10.1016/j.csr.2021.104415Get rights and content

Highlights

  • Performance of OC-CCI ocean color product was evaluated in the Persian Gulf.

  • The GlobColour merged CHL2 and OC5 single-sensor datasets were compared with OC-CCI.

  • Statistical comparison between in situ and satellite-derived Chl-a were made.

  • Time-series datasets of merged and single-sensor satellite data were compared.

  • Statistical calibration was performed on matchup paired and time-series datasets.

Abstract

Persian Gulf is a highly turbid and optically complex marginal sea. Satellite remotely sensed chlorophyll-a (Chl-a) products have been widely used in this marine area, despite uncertainties due to complex oceanic and atmospheric optical properties. In this study, accuracy of daily merged multi-sensor Ocean Color Climate Change Initiative (OC–CCI), Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour, and OC5 single-sensor products of SeaWiFS, MERIS, MODIS, and VIIRS datasets were evaluated using in situ chlorophyll concentrations collected from 2008 to 2018 in the Iranian territorial waters. The results showed that the MAPE, RMSE, bias(δ) and R2 values between in situ and satellite-4 derived Chl-a vary in the range of 131–273%, 0.38–0.69, 0.27–0.43, and 0.27–0.44, respectively. Satellite-derived Chl-a concentrations overestimated the field observations by 131–232% in the northern parts of the middle deeper areas and up to 173–273% in Iranian coastal areas. The OC-CCI and GlobColour merged datasets overestimate the Chl-a concentrations by 19% more than the average of OC5 single-sensor products. Systematic errors were observed in the log-normal distributions of difference between in situ and satellite-derived Chl-a. A bootstrapping-like assessment was performed to eliminate the systematic errors, and to reduce the bias from satellite datasets. The results of statistical adjustment were applied on daily matchup data-pairs and time-series datasets. Furthermore, an inter-comparison was made between merged multi-sensor (OC–CCI, GlobColour CHL2) and OC5 single-sensor (SeaWiFS, MERIS, MODIS, and VIIRS) Chl-a products using 8-day time-series datasets during the years 2000–2020. The results showed that the OC5 single-sensor Chl-a datasets were more consistent with OC-CCI than GlobColour merged CHL2 in the deep and shallow regions of the study area. In contrast, the merged multi-sensor products were more similar to each other than the OC5 single-sensor datasets in the river plume zone. After performing statistical adjustment of time-series datasets, the bias values between OC-CCI, GlobColour CHL2, and OC5 single-sensor datasets decreased by 14–22%, and the single-sensor datasets showed more similarity to OC-CCI datasets.

Introduction

The oceanic chlorophyll-a is an important factor for studying climate variability, as well as it is an ecological indicator of marine environment which plays a crucial role in photosynthesis, and knowledge of carbon cycle (Behrenfeld et al., 2006; Brewin et al., 2015; Couto et al., 2013). The Global Climate Observing System (GCOS) has introduced the near-surface chlorophyll-a concentration (Chl-a) derived from ocean color radiometry as a valuable factor which provides useful information about the state of the oceans (GCOS, 2011). Ocean color satellite sensors have provided Chl-a datasets at global to regional scales science the lunch of the Coastal Zone Color Scanner (CZCS) in the late 1970s (Moore et al., 2009; O'Reilly et al., 1998; Zhang et al., 2006). During the past three decades, the Ocean Biology Processing Group (OBPG) of NASA provided the greatest collection of ocean color datasets from different satellite sensors (http://oceancolor.gsfc.nasa.gov). To increase the spatial and temporal coverage of Chl-a datasets that provided by single satellite sensors, the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (http://www.esa-oceancolour-cci.org), and the Globcolour (http://www.globcolour.info) project of the Copernicus Marine Environment Monitoring Service (CMEMS) have generated merged multi-sensor ocean color products. Both programs aim to produce and validate the most complete and consistent possible time-series of multi-sensor Chl-a datasets over Case-I and Case-II waters in global and regional scales. The CCI ocean color products are created based on reflectance merging before Chl-a derivation and then perform the constrained flagging approach. Conversely, the GlobColour performs a specific flagging approach to merge the mono-sensor Chl-a products which have been computed using specific spectral bands (Mangin and d'Andon, 2017; Jackson et al., 2020). The OC5 lookup table approach (Gohin et al., 2002) is used to guarantee the continuity of both algorithms for mesotrophic and complex waters. Furthermore, a linear interpolation of OC5 and Chlorophyll Index (CI) (Hu et al., 2012) is used when Chl-a concentration is in the range of 0.15–0.2 mg m−3, to provide the continuity between the two algorithms (Garnesson et al., 2019). As a result, the GlobColour and CCI ocean color products use the same Chl-a algorithms (CI and OC5) in complex waters, and the differences between them mainly result from the merging and flagging schemas. Both algorithms are very sensitive to the water suspended particles such as coloured dissolved organic matters (CDOM) and non-algal particles (NAP), thus the regionalization approaches have been adopted by CMEMS and CCI programs. However, additional works needed to improve the performance of GlobColour and CCI ocean color products in optically complex coastal waters (Sathyendranath et al., 2019). In fact, knowledge of regional optical and biogeochemical properties of water bodies are required to evaluate the performance of Chl-a products in coastal complex water bodies (Shang et al., 2014; Wang et al., 2019).

Persian Gulf is a shallow marginal sea located at the north-west of the Arabian sea. It connects to the Gulf of Oman through the narrow Strait of Hormuz, which leads to the Indian Ocean (Fig. 1). To date, ocean color products from different satellite sensors has been used to study Chl-a spatial-temporal, and phytoplankton dynamics in the Persian Gulf area (Moradi and Kabiri, 2015; Nezlin et al, 2007, 2010). It has been shown that the climatic and oceanographic parameters play an important role in the variability of Chl-a in this area (Al Shehhi et al., 2017; Moradi and Moradi, 2020). Further, dust fertilization is the most important factor in regulating phytoplankton growth and leads to algal blooms over the whole Persian Gulf (Al-Najjar et al., 2020; Moradi and Moradi, 2020; Nezlin et al., 2010). In practice, the performance of ocean color retrieval algorithms is influenced by these climatic and environmental factors, which leads to uncertainties in the satellite-derived Chl-a concentrations. The effect of these factors on optical properties and Chl-a retrieval algorithms has not been studied carefully in the Persian Gulf. To our knowledge, the only attempts in this regard have been carried out by Al-Naimi et al. (2017), and Al-Shehhi et al. (2017). They have evaluated the performance of atmospheric correction models, and ocean color products in the southern parts of the study area. However, the accuracy of merged and single sensor Chl-a products over the Persian Gulf is still lacking, despite the fact that this is required for exploring the long-term dynamics of phytoplankton. Furthermore, comparing the merged ocean color products to single-sensor datasets in a complex water body such as the Persian Gulf provides information for quality control programs of the merged Chl-a datasets, and it aims to obtain some insight on the uncertainties affecting these products. The main goal of this article is to evaluate the performance of OC-CCI and GlobColour merged and OC5 single-sensor Chl-a products using in-situ data in the northern part of the Persian Gulf. In the following, we aim to assess the similarity of long-term merged Chl-a datasets with the single-sensor products to highlights the instabilities of the merged products that can rise from merging strategies.

Section snippets

Study area

Natural and anthropogenic activities have significant effects on the biological and chemical characteristics of the study area water bodies. The results of these activities lead to transfer of huge volumes of sediments and nutrients (iron, nitrate, and phosphate) to the Gulf, which increase the productivity and blooms of algae (Al Shehhi et al., 2014; Al-Yamani and Naqvi, 2019). The major sources of sediment transport to the Gulf are Tigris, Euphrates and Karun rivers, located at the

Spatial comparisons

Fig. 2 shows the spatial comparisons between in situ and satellite derived Chl-a. High concentrations of satellite derived Chl-a values in the eastern part of Persian Gulf (zone OS) along the northern coasts were observed in December 2008 (Fig. 2a). The values of Chl-a >10 mg m−3 patches were concurrent to a developed red tide event, where in situ Chl-a concentrations values were in range of 8.23–17.86 mg m−3. In situ bio-optical, Chl-a, and MODIS fluorescence data have been used to detect the

Matchups constraints

The pattern of Chl-a variations in the Persian Gulf is dominantly seasonal (Al-Naimi et al., 2017; Moradi and Kabiri, 2015; Nezlin et al., 2007), and it is controlled mainly by climate regimes, aerial dust deposition, water circulation, and rivers outflow (Al-Najjar et al., 2020; Moradi and Moradi, 2020; Nezlin et al., 2010; Reynolds, 1993). It has been shown that the maximum Chl-a concentrations are observed in late summer-early autumn in deeper zones, and in winter at shallow and river plume

Conclusion

This study addresses the evaluation and inter-comparison of merged multi-sensor products of OC-CCI and GlobColour CHL2, and OC5 single-sensor datasets of SeaWiFS, MERIS, MODIS, and VIIRS in the Persian Gulf, a semi-enclosed complex water body. Due to the spatial resolution of the selected satellite sensors (4 km × 4 km), in situ data were selected using strict criteria to match the most homogenous satellite pixels. A systematic error is observed in the log-normal distribution of differences

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was fully supported by Iranian National Institute of Oceanography and Atmospheric Science Grant No. INIOAS-398-023-01. I would like to thank the captains and crew of R/V KAVOSHGAR-E-KHALIJEFARS who made the data collection possible. I express my gratitude to S. Sanjani, M. Ghaneh, V. Aghadadashi, and S. Rahmanpour for their cooperation in field and laboratory measurements. Thanks to NASA, CMEMS GlobColour project, and ESA OC-CCI program for providing the ocean color datasets.

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      Seasonal and interannual variations of Chl-a differ significantly in each of these regions; deep regions (ON, OS) are characterized by winter maximum and summer minimum, northwest region (CN, river plume zone) display the highest values and decreased in summer, shallow regions of southern area (CW, CS) show minimum values in February–April and maximum in August–October. We recently showed that the Ocean Color Climate Change Initiative (OC–CCI) products have greater advantages than the other merged multi-sensor and single-sensor ocean color products in the study area (Moradi, 2021a). The 8-day average of OC-CCI version 5.0 Chl-a concentrations at 4km × 4 km spatial resolution during 1998–2020 downloaded from the Climate Change Initiative program of the European Space Agency (ESA-CCI) (http://www.esa-oceancolour-cci.org).

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