Evaluation of merged multi-sensor ocean-color chlorophyll products in the Northern Persian Gulf
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.
References (79)
- et al.
Source, spatial distribution, and toxicity potential of Polycyclic Aromatic Hydrocarbons in sediments from Iran's environmentally hot zones, the Persian Gulf
Ecotoxicol. Environ. Saf.
(2019) - et al.
An overview of historical harmful algae blooms outbreaks in the Arabian Seas
Mar. Pollut. Bull.
(2014) - et al.
Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment
ISPRS J. Photogrammetry Remote Sens.
(2017) - et al.
Chemical oceanography of the arabian gulf
Deep Sea Res. Part II Top. Stud. Oceanogr.
(2019) - et al.
A multi-sensor approach for the on-orbit validation of ocean color satellite data products
Rem. Sens. Environ.
(2006) - et al.
the Ocean colour climate change initiative: III. A round-robin comparison on in-water bio-optical algorithms
Rem. Sens. Environ.
(2015) - et al.
Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the east China coast (in the yellow sea and east China sea)
ISPRS J. Photogrammetry Remote Sens.
(2014) - et al.
A novel method for characterizing harmful algal blooms in the Persian Gulf using MODIS measurements
Adv. Space Res.
(2016) - et al.
Investigation and validation of MODIS SST in the northern Persian Gulf
Adv. Space Res.
(2016) - et al.
Spatial and temporal variation in chlorophyll a concentration in the Eastern China Seas based on a locally modified satellite dataset
Estuar. Coast Shelf Sci.
(2019)
SeaWiFS retrievals of chlorophyll in Chesapeake Bay and the mid-Atlantic bight
Estuar. Coast Shelf Sci.
Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment
J. Mar. Syst.
Landsat-8 imagery to estimate clarity in near-shore coastal waters: feasibility study-Chabahar Bay, Iran
Continent. Shelf Res.
Inherent and apparent optical properties of the complex estuarine waters of Tampa Bay: what controls light? Estuarine
Coastal and Shelf Science
Bio-optical water quality dynamics observed from MERIS in Pensacola Bay, Florida
Estuar. Coast Shelf Sci.
Regional chlorophyll a algorithms in the Arctic Ocean and their effect on satellite-derived primary production estimates
Deep Sea Res. Part II Top. Stud. Oceanogr.
Merged satellite ocean color data products using a bio-optical model: characteristics, benefits and issues
Rem. Sens. Environ.
Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: a protocol applied to OC-CCI chlorophyll-a data
Rem. Sens. Environ.
A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product
Rem. Sens. Environ.
Trend analysis and variations of sea surface temperature and chlorophyll-a in the Persian Gulf
Mar. Pollut. Bull.
Spatio-temporal variability of SST and chlorophyll-a from MODIS data in the Persian gulf
Mar. Pollut. Bull.
Spatio-temporal variability of red-green chlorophyll-a index from MODIS data–Case study: chabahar Bay, SE of Iran
Continent. Shelf Res.
Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf
Mar. Pollut. Bull.
Examining the consistency of products derived from various ocean color sensors in open ocean (case 1) waters in the perspective of a multi-sensor approach
Rem. Sens. Environ.
Determination of chlorophyll in marine waters: intercomparison of a rapid HPLC method with full HPLC, spectrophotometric and fluorometric methods
Mar. Chem.
Satellite monitoring of climatic factors regulating phytoplankton variability in the Arabian (Persian) Gulf
J. Mar. Syst.
Validation of standard and alternative satellite ocean-color chlorophyll products off Western Iberia
Rem. Sens. Environ.
Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: evaluation of three process-based approaches
Rem. Sens. Environ.
Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data
Rem. Sens. Environ.
Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China
Rem. Sens. Environ.
Evaluation of satellite retrievals of chlorophyll-a in the Arabian Gulf
Rem. Sens.
Nutritive effect of dust on microbial biodiversity and productivity of the Arabian Gulf
Aquat. Ecosys. Health Manag.
Climate-driven trends in contemporary ocean productivity
Nature
Inter-comparison of OC-CCI chlorophyll-a estimates with precursor data sets
Int. J. Rem. Sens.
Factors regulating the relationship between total and size-fractionated chlorophyll-a in coastal waters of the Red Sea
Front. Microbiol.
In-situ databases and comparison of ESA Ocean Colour Climate Change Initiative (OC-CCI) products with precursor data, towards an integrated approach for ocean colour validation and climate studies
EGU General Assembly Conference Abstracts
The lognormal distribution as a model for bio‐optical variability in the sea
J. Geophys. Res.: Oceans
Relationships between chlorophyll and ocean color constituents as they affect remote‐sensing reflectance models 1
Limnol. Oceanogr.
Root mean square error (RMSE) or mean absolute error Arguments against avoiding RMSE in the literature
Geosci. Model Dev. (GMD)
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