Validation of Chlorophyll-a Algorithms in the Coastal Waters of Bay of Bengal Initial Validation Results from OCM-2

Nagamani, P. V.; Hussain, M. Iftikhar; Choudhury, S. B.; Panda, C. R.; Sanghamitra, Priyadarsini; Kar, RN; Das, Anindita; Ramana, I. V.; Rao, K. H.

doi:10.1007/s12524-012-0203-x

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Several studies have supported the usage of blue-green bands on the assumption that any changes in these wavelengths are driven by changes in phytoplankton concentrations [32,33]. The performance of blue-green ratioed algorithms for retrieving Chl-a was tested independently in different environments [34][35][36][37][38]. We, therefore, employed the OC3 algorithm [11], which uses the water leaving reflectance (R rs ) in wavelength 443, 482, and 561 for Landsat 8 (Equations (1)-(4)), and 490 and 560 for Sentinel 2 sensor (Equations (1)-(3), and (5)).…”

Section: Chlorophyll-a Retrievalmentioning

confidence: 99%

Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing

Avtar

Kumar

Supe

et al. 2020

Water

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The novel coronavirus pandemic (COVID-19) has brought countries around the world to a standstill in the early part of 2020. Several nations and territories around the world insisted their population stay indoors for practicing social distance in order to avoid infecting the disease. Consequently, industrial activities, businesses, and all modes of traveling have halted. On the other hand, the pollution level decreased ‘temporarily’ in our living environment. As fewer pollutants are supplied in to the hydrosphere, and human recreational activities are stopped completely during the lockdown period, we hypothesize that the hydrological residence time (HRT) has increased in the semi-enclosed or closed lake bodies, which can in turn increase the primary productivity. To validate our hypothesis, and to understand the effect of lockdown on primary productivity in aquatic systems, we quantitatively estimated the chlorophyll-a (Chl-a) concentrations in different lake bodies using established Chl-a retrieval algorithm. The Chl-a monitored using Landsat-8 and Sentinel-2 sensor in the lake bodies of Wuhan, China, showed an elevated concentration of Chl-a. In contrast, no significant changes in Chl-a are observed for Vembanad Lake in India. Further analysis of different geo-environments is necessary to validate the hypothesis.

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Section: Chlorophyll-a Retrievalmentioning

confidence: 99%

Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing

Avtar

Kumar

Supe

et al. 2020

Water

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“…However, as long as the data are consistent through time, the anomalies should actually reflect the changes and thus could be used for many purposes [18][19][20][21]. Moreover, intensive validation work [22][23][24] showed that the standard Chl-a for coastal waters was linearly related to field-measured Chl-a, and thus may be used to study the long-term relative changes in Chl-a and primary production in the Yellow Sea and East China Sea [25,26]. Figures 3 and 4 show an example of how the MAB occurrence on 15 July 2009 influenced the standard MODIS Chl-a product.…”

Section: Modis and Seawifs Level-3 Chl-a Productsmentioning

confidence: 99%

World’s Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations

Xing

Tang

et al. 2015

Remote Sensing

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Since 2008, the world's largest blooms of the green macroalgae, Ulva prolifera, have occurred every summer in the Yellow Sea, posing the question of whether these macroalgal blooms (MABs) have changed the phytoplankton biomass due to their perturbations of nutrient dynamics. We have attempted to address this question using long-term Moderate Resolution Imaging Spectroradiometer (MODIS) observations. A new MODIS monthly time-series of chlorophyll-a concentrations (Chl-a, an index of phytoplankton biomass) was generated after removing the macroalgae-contaminated pixels that were characterized by unexpectedly high values in the daily Chl-a products. 2008-2012 (the MAB period) exhibited significant increases in the offshore Yellow Sea waters (rich in macroalgae), with mean Chl-a increased by 98% from 0.64 µg/L to 1.26 µg/L in the study region. In contrast, no significant Chl-a changes were observed during June between the two periods. After analyzing sea surface temperature, photosynthetically available radiation, and nutrient availability, we speculate that the observed Chl-a changes are due to nutrient competition between macroalgae and phytoplankton: during the MAB period, the fast-growing macroalgae would uptake the increased nutrients from the origin of Jiangsu Shoal; thus, the nutrients available to phytoplankton were reduced, leading to no apparent increases in biomass in the offshore Yellow Sea in June.

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“…9). Nagamani et al (2013) observed a high correlation coefficient of 0.842 between the in-situ and satellite data in the coastal water of the BoB. Haëntjens et al (2017) also found an R 2 value of approximately 0.67 for the southern ocean, while compared the in-situ Chl-a concentration data with MODIS Aqua satellite Chl-a concentration data using the OCI algorithm.…”

Section: Validation Of Satellite Measurements By In-situ Measurementsmentioning

confidence: 97%

“…The lowest Chl-a concentration was found in the shelf region of the northern BoB during the pre-monsoon season (March-May). It occurred due to a gradual increase in SST (detailed relationship between Chl-a concentration and SST are described in the subsequent section) and a decrease in wind speed (Nagamani et al 2013). The compensation of the northeast and southwest monsoons during the transition period from winter to summer neutralizes the propagated strong currents (Suwannathatsa & Wongwises 2013), which explains the decrease in Chl-a concentration to the lowest level in the year.…”

Section: Annual Trend In Chl-a Concentrationmentioning

confidence: 98%

“…Several efforts have been made to study Chl-a variability in the Arabian Sea, which is adjacent to the Bay of Bengal (BoB; Sathyendranath et al 1991;Bhattathiri et al 1996). Although some studies discuss the Chl-a distribution in the BoB, most of them are based on the southwestern and western parts of BoB (Prasad & Singh 2010;Nagamani et al 2011;Nagamani et al 2013;Poornima et al 2013;Suwannathatsa & Wongwises 2013). Thus, no studies were found that would focus particularly on the shelf region of the northern part of the BoB.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal and annual variability in chlorophyll-a in the shelf region of the Northern Bay of Bengal using MODIS-Aqua data

Rouf

Antu

Noor

2020

Oceanological and Hydrobiological Studies

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Chlorophyll-a (Chl-a) concentration is an important issue in ocean ecosystem management and research. This study investigates seasonal and annual variability in Chl-a and its relationship with sea surface temperature (SST) and river discharge in the shelf region of the Northern Bay of Bengal (BoB), as well as validates satellite data against in-situ data. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite data on Chl-a concentration and SST from 2002–2018 were used in this study. River discharge data were obtained from the Bangladesh Water Development Board (BWDB). The annual Chl-a concentration ranged from 2.08 to 2.94 mg m−3, with an average of 2.43 ± 0.24 mg m−3. The Chl-a concentration was found higher (2.21 ± 0.56 mg m−3) during the northeast monsoon (October–February) and lower (1.81 ± 1.14 mg m−3) during the pre-monsoon season (March–May). The study revealed a declining trend in Chl-a concentration from 2002 to 2018, and the rate of change was −0.0183 mg m−3 year−1. Chl-a concentration showed a weak inverse relationship with SST, both annually and seasonally, especially in the pre-monsoon season. River discharge masked the effect of SST on Chl-a variability during the southwest and northeast monsoon. A reasonable correlation (r = 0.78) was found between the MODIS-Aqua data and in-situ Chl-a observations.

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Validation of Chlorophyll-a Algorithms in the Coastal Waters of Bay of Bengal Initial Validation Results from OCM-2

Cited by 12 publications

References 2 publications

Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing

Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing

World’s Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations

Seasonal and annual variability in chlorophyll-a in the shelf region of the Northern Bay of Bengal using MODIS-Aqua data

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