Optical Classification of the Coastal Waters of the Northern Indian Ocean

Monolisha, S; Platt, Trevor; Sathyendranath, Shubha; Jayasankar, J; George, Grinson; Jackson, T. J.

doi:10.3389/fmars.2018.00087

Cited by 19 publications

(13 citation statements)

References 41 publications

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“…We mapped and analyzed the seasonal and spatial trends in the pre-monsoon monthly data during 2019 and 2020 (Figures 2 and 5). Although the river discharge and nutrient input to coastal waters remains low during the pre-monsoon dry season, the wind-driven coastal upwelling and vertical mixing helps increase the algal biomass and chlorophyll-a during the pre-monsoon months [12,13]. We analyzed the time-averaged mean monthly surface runoff for pre-monsoon months in 2019 and 2020 to examine the fluctuations in surface runoff, which could alter N loading to the coastal waters (Figure 3).…”

Section: Figurementioning

confidence: 99%

“…runoff, a proxy for nutrient loading as indirect evidence of the fluctuations in coastal N-fluxes during the study period (Figures 3 and 4). Algal blooms along the Indian coast are known to exhibit a strong seasonal trend, with a majority of the blooms occurring during the pre-monsoon dry season (April-May) and the withdrawal of the southwest monsoon (September-October) [10][11][12]. We mapped and analyzed the seasonal and spatial trends in the pre-monsoon monthly data during 2019 and 2020 (Figures 2 and 5).…”

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confidence: 99%

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Decline in Phytoplankton Biomass along Indian Coastal Waters due to COVID-19 Lockdown

et al. 2020

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The SARS-CoV-2 (or COVID-19) lockdown in India, which started at an early stage of its infection curve, has been one of the strictest in the world. Air quality has improved in all urban centers in India, a major emitter of greenhouse gases (GHG). This study is based on the hypothesis that an abrupt halt in all urban activities resulted in a massive decline in NO2 emissions and has also altered coastal nitrogen (N) inputs; in-turn, this affected the trophic status of coastal waters across the country. We present the first evidence of an overall decline in pre-monsoon chlorophyll-a, a proxy for phytoplankton biomass, in coastal waters off urban centers during the peak of the lockdown in April. The preliminary field data and indirect evidence suggests the reduction in coastal chlorophyll-a could be linked to a net decline in nutrient loading, particularly of bioavailable N through watershed fluxes and atmospheric deposition. The preliminary results stress the importance of a further understanding of the relationship between fluctuations in anthropogenic N, due to lockdown measures and coastal ecosystem responses, as countries open-up to a business-as-usual scenario.

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Section: Figurementioning

confidence: 99%

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confidence: 99%

Decline in Phytoplankton Biomass along Indian Coastal Waters due to COVID-19 Lockdown

et al. 2020

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“…The OC-CCI ocean color dataset is created by band-shifting and bias-correcting different ocean color satellite sensor data from MERIS and MODIS and match these to SeaWiFS data, merging the datasets and computing per-pixel uncertainty estimates. The OC-CCI surface chlorophyll-a (Chl_a) concentration data from version 3.1 (Sathyendranath et al, 2018), for the period 1998 to 2014 has been used in this study. OC-CCI Chl_a data has already been verified and used to estimate the surface Chl_a concentration in the Arabian Sea by several authors (eg: Shafeeque et al, 2017;Monolisha et al, 2018;Smitha et al, 2019;Menon et al, 2019).…”

Section: Datamentioning

confidence: 99%

“…The OC-CCI surface chlorophyll-a (Chl_a) concentration data from version 3.1 (Sathyendranath et al, 2018), for the period 1998 to 2014 has been used in this study. OC-CCI Chl_a data has already been verified and used to estimate the surface Chl_a concentration in the Arabian Sea by several authors (eg: Shafeeque et al, 2017;Monolisha et al, 2018;Smitha et al, 2019;Menon et al, 2019). SST data are based on extended reconstructed SST (ERSST v4) (Huang et al, 2015;Liu et al, 2015), produced on a 2°x2° grid derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS).…”

Section: Datamentioning

confidence: 99%

The influence of tropical Indian Ocean warming and Indian Ocean Dipole on the surface chlorophyll concentration in the eastern Arabian Sea

Sankar

Ramachandran

Ghomsi

et al. 2019

Preprint

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<p><strong>Abstract.</strong> This study examines the role of increased Indian Ocean warming and positive Indian Ocean Dipole (IOD) events on the surface chlorophyll concentration in the Eastern Arabian Sea (EAS) during the period 1998 to 2014. Remotely sensed surface chlorophyll concentration &ndash; during the month of October when IOD strength is maximum &ndash; at three selected areas in the EAS, viz., the central eastern Arabian Sea (CEAS, 73°&thinsp;E&ndash;76°&thinsp;E, 13°&thinsp;N&ndash;18°&thinsp;N), south eastern Arabian Sea (SEAS, 74°&thinsp;E&ndash;77°&thinsp;E, 8°&thinsp;N&ndash;13°&thinsp;N) and the southern tip of India (TIP, 74°&thinsp;E&ndash;78°&thinsp;E, 5°&thinsp;N&ndash;8°&thinsp;N) shows a steady decreasing trend, though not statistically significant. The EAS also has a higher warming trend when compared to the western basin during the study period. Our analysis has shown that in the EAS, local surface winds, remote forcing by equatorial winds and the surface and sub-surface oceanic circulation features are less (respectively more) favorable for inducing coastal upwelling during positive (respectively negative) IOD years. The Dipole Mode Index (DMI) and surface chlorophyll concentration in the EAS is significantly and negatively correlated, pointing to the fact that in the event of occurrence of frequent positive IOD years under a global warming regime, the surface chlorophyll concentration is likely to decrease during fall.</p>

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“…Third, with the development of hyperspectral remote sensing technology, some of the existing OWT schemes that initially were developed within limited multispectral bands may not encompass all characteristic bands reflecting the bio-optical features of water. Fourth, the existing OWT schemes were mostly designed for one specific ocean color instrument, such as SeaWiFS [11,13,14,17,22], MODIS [10,15], MERIS [15,16,19,20,30], or OLCI [18]; thus it is difficult to migrate these OWT schemes to other satellite sensors. Therefore, it is necessary to develop an OWT scheme that is measurement error-free, suitable for the global ocean waters, and multi-satellite sensors.…”

Section: Introductionmentioning

confidence: 99%

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

2021

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The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.

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Optical Classification of the Coastal Waters of the Northern Indian Ocean

Cited by 19 publications

References 41 publications

Decline in Phytoplankton Biomass along Indian Coastal Waters due to COVID-19 Lockdown

Decline in Phytoplankton Biomass along Indian Coastal Waters due to COVID-19 Lockdown

The influence of tropical Indian Ocean warming and Indian Ocean Dipole on the surface chlorophyll concentration in the eastern Arabian Sea

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

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