Silicate is a significant prerequisite for the growth and development of primary producers, mainly in diatoms, it remains a prevalent contributor. Satellite ocean colour sensors data are broadly utilized for the identification, mapping and monitoring the phytoplankton characteristics, spatial and temporal. In this study, an empirical algorithm was developed for mapping the silicate concentration an important nutrient for planktonic diatoms depending on the relationship between chlorophyll- a , Sea Surface Temperature (SST) and silicate at a high spatio-temporal resolution. Three dimensional polynomial functions, such as plane, paraboloid, Gaussian and Lorentzian functions were used to correlate SST, chlorophyll- a and silicate. Among these the paraboloid function provided significant relationship between the variables with an R 2 value of 0.828. Validation of Visible Infrared Imaging Radiometer Suite (VIIRS) derived SST ( R 2 = 0.634, Mean Normalized Bias (MNB) = 0.006, Root Mean Square Error (RMSE) = 0.280 and Standard Error of Estimation (SEE) = +/- 0.227) and chlorophyll- a ( R 2 = 0.523, MNB = 0.369, RMSE = 0.846, and SEE = +/- 0.632) observed better synchronization with in situ measurements of SST and chlorophyll- a , respectively. The VIIRS-derived silicate algorithm provided better agreements with in situ silicate concentration ( R 2 = 0.784, MNB = -0.001, RMSE = 1.394 and SEE = +/- 0.839) along the Southwest Bay of Bengal.

This study presents a synthesis of surface water partial pressure of CO2 (pCO2) and nutrient measurement in the southwest Bay of Bengal (swBoB) from 2014 to 2020 and characterizes the spatial and temporal variability. pCO2 rates found to be high (1191μatm) during the 2015 monsoon and low (176 μatm) in the summer season during the month of May 2015. The inter-annual CO2 fluxes varied from −4.79 to 9.97 mmol Cm−2d−1. The significant a negative CO2 flux (−4.79 mmol Cm−2d−1) was recorded during the summer season in the year 2019, whereas a positive significant CO2 flux (9.97 mmol Cm−2d−1) was observed during the monsoon in 2014. Major physical parameters are at their highest during summer owing to increased high solar radiation during cloud-free circumstances, reduced or inadequate riverine flux, and a lack of vertical mixing of the water column, which results in the lowest nutrients concentration, Dissolved Oxygen (DO), Dissolved Inorganic Nitrogen (DIN), Dissolved Organic Carbon (DOC), chlorophyll-a, Particulate Organic Carbon (POC), pCO2, that leads to negative CO2 flux to the atmosphere. In contrast during monsoon season colossal discharge of freshwater high DO, DIN, DOC, chlorophyll-a, POC, pCO2 as results source CO2 flux to the atmosphere. Statistical analysis the correlation coefficient depicts Total Alkalinity (TA), DIC, POC, DIN, and DO found a positive correlation with pCO2 and fCO2 during the monsoon season. In the swBoB, pCO2 had a negative relationship with sea surface temperature (SST), sea surface salinity (SSS), and pH because CO2 solubility changes with SST and increases in cold water rather than warm water. In this study, we examine the association between all carbonate variables and the SSS and SST to better understand seasonal fluctuations.

Understanding the relationship between atmospheric and oceanic carbon cycles necessitates measuring geographical and temporal variations of surface water partial pressure of carbon dioxide (pCO2). The seasonal pCO2 maps have been developed using MODIS-derived SST and chlorophyll for four different seasons to calculate air–sea flux of CO2 at basin scale. Summer in 2017 had the lowest pCO2 value (263 µatm), whereas monsoon season in 2016 had the highest (553 µatm). From 2010 to 2019, atmospheric pCO2 level fluctuated from 371 to 396 µatm with progressive growth of atmospheric pCO2 at 2.5 μatm year−1. The inter-annual CO2 flux ranged between − 3.58 and 3.64 mmol C m−2 day−1. Significant negative CO2 flux (− 3.58 mmol C m−2 day−1) was observed in 2015 premonsoon, indicating that the Bay of Bengal was a net sink for atmospheric CO2, while served as a net source of CO2 to the atmosphere in 2013 monsoon season with a significant positive flux of CO2 (3.64 mmol C m−2 day−1) to the atmosphere. The annual CO2 sink was active in 2015 (− 1.17 mmol C m−2 day−1) which showed increased trend from 2014 to 2018 with a range of − 1.17 to − 0.26 mmol C m−2 day−1. The Bay of Bengal was found to be a substantial CO2 contributor to the atmosphere in 2013 (3.64 mmol C m−2 day−1) and 2012 (3.27 mmol C m−2 day−1). In this context, the southwest Bay of Bengal serves as a net sink of atmospheric pCO2 during summer season on an annual scale, and a weak sink during postmonsoon and premonsoon seasons, while served as a strong source of CO2 to the atmosphere during monsoon season from 2010 to 2019 with super saturation of CO2.

The seasonal and inter-annual variability of Total Alkalinity (TA) concentration was studied in the Bay of Bengal from 2003 to 2019 by using MODIS-Aqua derived sea surface temperature (SST) and sea surface salinity (SSS) products. The satellite derived TA showed a positive relationship with in-situ TA with (R2 = 0.67, RMSE = ±27.53 μMol/kg, SEE = ±32.16 and uncertainty error = 2287μMol/kg). The seasonal SST, SSS and TA portray the clear seasonal pattern between the seasons without any rapid change increase or decrease in trend observed over the years. In contrast to other seasons, the spring inter-monsoon was observed to have a warm surface water temperature with high salinity and TA. Strong wind and excessive cloud cover during the summer monsoon result in the reduction of ocean surface heat, which favours sea surface cooling and shallow mixed layer depth, resulting in low SST, SSS, and TA compared to the spring inter-monsoon. During fall inter-monsoon, the reversal of East India coastal current directs warm water from north to south and the weak wind that prevails in this region enhances stratification. During winter, low-saline water compensates the static stability loss by thermal inversion from the sea surface resulting in surface cooling with coldest SST, low SSS and TA during this period.

Time-series observations of the Vellar estuary between May 2013 and December 2019 showed clear variability with respect to space and time in the distribution of nutrients, partial pressure of carbon dioxide (pCO(2)) and air-water CO2 exchange. Lower and higher salinities revealed significant seasonality in estuarine pCO(2), as well as variations in the seasonal pattern due to the freshwater discharges during monsoon rainfall. The pCO(2) attained the highest levels (8457 mu atm) during monsoon which coincided with the lowest pH (7.498) and the undersaturation of pCO(2) (322 mu atm) was observed with maximum pH (8.182) during pre-monsoon. The Principal Component Analysis (PCA) identified four components that accounted for 77.28 % of the total variance and explained the significant influence of nutrients, chlorophyll and temperature on pCO(2) distribution. Similarly, the multiple linear regression analysis showed significant influence of environmental variables on pCO(2) variability with a R-2 of 0.957, SEE +/- 230.816, p < 0.001. The surveyed area of the Vellar estuary had an overall pCO(2 )of 1068 mu atm and was supersaturated with regard to the atmospheric pCO(2 )throughout the year, with an average CO2 flux of 4.13 +/- 5.59 mmol C m(-2 )d(-1) to the atmosphere. During the study period, the Vellar estuary actively supplied 650.2 mol C m(-2) Y-1 to the atmosphere. Hence, the metabolic balance of the estuarine ecosystem is aided by land derived organic carbon accompanied with freshwater flows from the Vellar river, constituting the estuary as a substantial source of atmospheric CO2.

A spatial and temporal variation of sea surface salinity (SSS) is vital to understand the dynamics of the seasonal and inter-annual changes in the marine environment. In the present study, Soil Moisture Active–Passive (SMAP) have assessed the accuracy of satellite derived product of SSS and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) remote sensing reflectance (Rrs)-based SSS images (Algorithm by Qing et al. Remote Sens Environ 136: 117–125 2013), applied in the coastal and offshore region of the Bay of Bengal (BoB). SMAP data validation with in situ data (offshore and coastal water, 10 and 15 points) showed good correlation in offshore water and less correlation in coastal waters (R2 = 0.707/0.499, SEE =  ± 0.291/ ± 0.546, MNB =  − 0.0029/ − 0.0089 and RMSE =  ± 0.092/ ± 0.139), respectively. Similarly, MODIS-Aqua Rrs-derived salinity data validated with in situ SSS observed the correlation as follows: R2 = 0.908/0.891, SEE =  ± 2.395/ ± 1.512, MNB = 0.0718/0.0361, RMSE =  ± 0.760/ ± 0.316 in offshore and coastal water, respectively, during April and August 2019. The salinity data were observed in the range from 32 to 34.5 psu. High SSS mean (35.6–35.8psu) was observed during the spring inter-monsoon, and a low salinity range (34.6–34.9 psu) were observed during winter monsoon phase as depicted in the decadal-scale interpretation. The present study infers that MODIS-Aqua-derived SSS is better than SMAP-based SSS of coastal and offshore waters of the western BoB, irrespective of their resolution and spectral differences.

The partial pressure of carbon dioxide (pCO2) is one of the most effective measurements of carbon dioxide in seawater, and the increases in pCO2 profoundly affect the marine carbonate system. The role of SST on pCO2 is analyzed to develop a regional pCO2 algorithm using in-situ SST and calculated pCO2 by employing the polynomial regression functions such as linear, quadratic, and cubic to develop a pCO2 map and the best-fit algorithm of the cubic function developed for the postmonsoon season with an R2 of 0.537 and SEE of ± 36.543 has been validated for remote sensing applications. Evaluation of satellite-derived pCO2 with calculated pCO2 showed R2 of 0.498 and the root means square error (RMSE) of ±30.922 µatm with 75% of overestimation of calculated pCO2 by the satellite-derived pCO2. The satellite-derived pCO2 map error is mainly because of the inbound errors in MODIS-derived SST products. Hence, improvement in sensor technology and retrieval algorithm would improve the retrieval of input parameters (SST), which is useful in estimating pCO2 precisely. This would enable us to understand the biogeochemical processes behind the variability of CO2 in the surface waters of the southwest Bay of Bengal.

Multivariate statistical analysis such as multiple linear regression (MLR) and principal component analysis (PCA) are used to study the effect of physico-chemical parameters on chlorophyll distribution along the southwest Bay of Bengal from January 2012 to June 2014. Physical properties recorded showed clear seasonal patterns in sea surface temperature (26.2 - 32.8 degrees C), salinity (24 - 36 PSU), pH (7.808 to 8.428), photosynthetic photon flux (522 - 1220.4 mu M m-2s-1) with the minimum and maximum values during monsoon and summer seasons, respectively. In contrast, the chemical variables such as nitrite (0.15 to 2.35 mu M), nitrate (1.02 to 6.58 mu M), ammonia (0.11 - 5.22 mu M), total nitrogen (1.04 to 11.58 mu M), inorganic phosphate (0.16 - 2.97 mu M), total phosphorus (0.55 - 8.60 mu M) and reactive silicate (2.00 to 23.95 mu M) showed the minimum and maximum concentration during summer and monsoon seasons, respectively. The high and low chlorophyll (0.10 to 6.92 mu g l-1) and dissolved oxygen (4.07 and 7.884 mg l-1) concentrations are observed during summer and pre-monsoon seasons, respectively. PCA found that nitrogenous nutrients and chlorophyll are positively loaded and sea surface temperature (SST) was negatively loaded in all the seasons except during summer season. Inter-comparison of modeled and in-situ chlorophyll-a (chl-a) concentration showed a significant correlation during monsoon season by 93 % of matchup with a R2 = 0.930, N = 60 and SEE = +/- 0.369 compared to other seasons. Regression analysis also predicted the positive influence of nitrate and ammonia and negative influence of SST with chl-a.

MODIS Aqua derived primary production and sea surface temperature (SST) data are used for calculating the f ratio and new production using previously developed models.Developed models were validated with in-situ new production and f ratio values from cruise dataset collected during September 2014.The regression between derived and bottle measured Pnew value showed R 2 =0.553,SEE=±51.632,MNB=-0.414 and RMSE=±266.398.Similarly f ratio is validated with in-situ derived value showed the clear underestimation with a R 2 =0.537,SEE=±0.016,MNB=-0.560 and RMSE=±0.182.The seasonal and inter-annual variation of primary production, f ratio and new production showed the clear seasonal pattern between the seasons.The high (133.64mgCm -2 d -1 ) and low (31.69 mgCm -2 d -1 ) new production ranges are observed during premonsoon and summer seasons.The integrated primary production and f ratio of all the seasons provides a significant R 2 =0.585.This is used to estimate the total annual primary production of Bay of Bengal as ~11%.The minimum (0.028 GTCyr -1 ) and maximum (0.037GTCyr -1 ) annual new production observed for the years of 2014 and 2006.In the entire processes of primary production, new production rate was less and it indicates Bay of Bengal largely as the regeneration based basin than the new production.

The seasonal and interannual variability of sea surface nitrate was studied in the southwest Bay of Bengal. MODIS-Aqua-derived SST and chlorophyll data are used to develop seasonal nitrate maps for the period of 2002–2017. Seasonally developed nitrate images were validated for the year 2013 with corresponding in situ datasets, and the validation of this nitrate model provides the statistically significant relationship for postmonsoon (R2 = 0.612), summer (R2 = 0.535), premonsoon (R2 = 0.554) and monsoon (R2 = 0.533) seasons. The seasonal SST, chlorophyll a, nitrate and wind speed images depict the clear seasonal pattern between the seasons without any abrupt increase or decrease in trend observed during all these years. This was also confirmed by the Kruskal–Wallis one-way analysis of variance on ranks with a statistical significance (P = < 0.001) between the seasons. From the results, this is a clear indication that the nitrate concentration is under the natural control without any anthropogenic contaminations.

Physico-chemical observations made from January 2013 to March 2015 in coastal waters of the southwest Bay of Bengal show pronounced seasonal variation in physicochemical parameters including total alkalinity (TA: 1927.390-4088.642 mu mol kg(-1)), chlorophyll (0.13-19.41 mu gl(-1)) and also calculated dissolved inorganic carbon (DIC: 1574.219-3790.954 mu mol kg(-1)), partial pressure of carbon dioxide (pCO(2): 155.520-1488.607 mu atm) and air-sea CO2 flux (FCO2: -4.808 to 11.255 mmol Cm-2 d(-1)). Most of the physical parameters are at their maximum during summer due to the increased solar radiation at cloud free conditions, less or no riverine inputs, and lack of vertical mixing of water column which leads to the lowest nutrients concentration, dissolved oxygen (DO), biological production, pCO(2) and negative flux of CO2 to the atmosphere. Chlorophyll and DO concentrations enhanced due to increased nutrients during premonsoon and monsoon season due to the vertical mixing of water column driven by the strong winds and external inputs at respective seasons. The constant positive loading of nutrients, TA, DIC, chlorophyll, pCO(2) and FCO2 against atmospheric temperature (AT), lux, sea surface temperature (SST), pH and salinity observed in principal component analysis (PCA) suggested that physical and biological parameters play vital role in the seasonal distribution of pCO(2) along the southwest Bay of Bengal. The annual variability of CO2 flux clearly depicted that the southwest Bay of Bengal switch from sink (2013) to source status in the recent years (2014 and 2015) and it act as significant source of CO2 to the atmosphere with a mean flux of 0.204 +/- 1.449 mmol Cm-2 d(-1). (C) 2016 Elsevier B.V. All rights reserved.

The in-situ sensor based nitrate algorithm is developed by using SUNA and thermal logger derived nitrate and temperature data along the coastal waters of southwest Bay of Bengal. Comparison was carried out between SUNA and in-situ nitrate depicted 88% overestimation in open ocean waters and the regression analysis provided R2=0.568 with SEE=±0.360. The nitrate algorithm is derived from the empirical relationship between SUNA and thermal logger derived nitrate and SST during postmonsoon season. Overall regression analysis, sigmoid function provides the better relationships of R2=0.872 and SEE=±0.776. MODIS Aqua derived SST is used to develop the instrument based algorithm to retrieve nitrate image whereas MODIS Aqua derived SST and chlorophyll are applied with conventional algorithm proposed earlier and compared. The two different nitrate algorithms are regressed with in-situ nitrate value for testing the accuracy of the algorithms. The regression between sensor and laboratory analyzed nitrate showed the R2=0.619, SEE=±0.501, MNB=1.303 and RMSE=1.727µM, whereas the conventional model nitrate showed the R2=0.695, SEE=±0.175, MNB=0.603 and RMSE=1.054µM. Thus, the present results confirm that chlorophyll as an important input for developing nitrate algorithms whatever the way the data have been collected.

Seasonal and inter-annual variability of hydrological parameters and its impact on chlorophyll distribution was studied from January 2009 to December 2011 at four coastal stations along the southwest Bay of Bengal. Statistical analysis (principal component analysis (PCA), two-way analysis of variance (ANOVA) and correlation analysis) showed the significant impact of hydrological parameters on chlorophyll distribution in the study area. The ranges of different parameters recorded were 23.8–33.8°C (SST), 4.00–36.00 (salinity), 7.0–9.2 (pH), 4.41–8.32 mg/L (dissolved oxygen), 0.04–2.45 μmol/L (nitrite), 0.33–16.10 μmol/L (nitrate), 0.02–2.51 μmol/L (ammonia), 0.04–3.32 μmol/L (inorganic phosphate), 10.09–85.28 μmol/L (reactive silicate) and 0.04–13.8 μg/L (chlorophyll). PCA analysis carried out for different seasons found variations in the relationship between physico-chemical parameters and chlorophyll in which nitrate and chlorophyll were positively loaded at PC1 (principal component 1) during spring inter-monsoon and at PC2 (principal component 2) during other seasons. Likewise correlation analysis also showed significant positive relationship between chlorophyll and nutrients especially with nitrate (r=0.734). Distribution of hydrobiological parameters between stations and distances was significantly varying as evidenced from the ANOVA results. The study found that the spatial and temporal distribution of chlorophyll was highly dependent on the availability of nutrients especially, nitrate in the southwest Bay of Bengal coastal waters.

In situ datasets of nitrate, sea surface temperature (SST), and chlorophyll a (chl a) collected during the monthly coastal samplings and organized cruises along the Tamilnadu and Andhra Pradesh coast between 2009 and 2013 were used to develop seasonal nitrate algorithms. The nitrate algorithms have been built up based on the three-dimensional regressions between SST, chl a, and nitrate in situ data using linear, Gaussian, Lorentzian, and paraboloid function fittings. Among these four functions, paraboloid was found to be better with the highest co-efficient of determination (postmonsoon: R2=0.711, n=357; summer: R2=0.635, n=302; premonsoon: R2=0.829, n=249; and monsoon: R2=0.692, n=272) for all seasons. Based on these fittings, seasonal nitrate images were generated using the concurrent satellite data of SST from Moderate Resolution Imaging Spectroradiometer (MODIS) and chlorophyll (chl) from Ocean Color Monitor (OCM-2) and MODIS. The best retrieval of modeled nitrate (R2=0.527, root mean square error (RMSE)=3.72, and mean normalized bias (MNB)=0.821) was observed for the postmonsoon season due to the better retrieval of both SST MODIS (28 February 2012, R2=0.651, RMSE=2.037, and MNB=0.068) and chl OCM-2 (R2=0.534, RMSE=0.317, and MNB=0.27). Present results confirm that the chl OCM-2 and SST MODIS retrieve nitrate well than the MODIS-derived chl and SST largely due to the better retrieval of chl by OCM-2 than MODIS.

Monthly coastal sampling and ship-cruise-measured in situ datasets of nitrate, sea surface temperature (SST), and chlorophyll in the SW Bay of Bengal covering Tamilnadu and Andhra Pradesh coasts of India were used to develop nitrate algorithm. A total of 15 datasets prepared during 2009-11 covered cruise data and all monthly datasets, nine datasets followed with better results with paraboloid function, and others followed linear, Gaussian, and Lorentzian function regression fits. Data collection through cruises (397 points), monthly (482 points) coastal sampling, total monthly and cruise (879 points) covering four seasons were used, and the three-dimensional (3D)-paraboloid function showed better results during the seasonal scale study with the R-2 values 0.670, 0.635, 0.465, and 0.693 for the postmonsoon, summer, premonsoon, and monsoon seasons respectively. In the current study, there has been considerable improvement in R-2 (0.670 with 236 points) than the earlier study using postmonsoon data (0.560 with 105 data points). Through this algorithm, a nitrate map was generated for 11 March 2011 using Oceansat-2 Ocean Color Monitor (OCM) and MODIS-aqua-derived chlorophyll and SST data, respectively. The retrieved nitrate map has been validated with in situ dataset of the same date with an R-2 value of 0.718, which suggests that the developed nitrate algorithm was statistically significant with mean normalized bias (MNB) = 0.078, root mean square error (RMSE) = 0.412, and standard error of estimate (SEE) = +/- 0.4032, and the algorithm was observed to be working satisfactorily over the SW Bay of Bengal region.

Elemental ratio of nutrients and its influence on chlorophyll a distribution was studied along the central coast of Bay of Bengal using multivariate statistical methods. High chlorophyll concentration was observed during summer (1.81 μg l⁻¹) and premonsoon (1.95 μgl⁻¹), however, it was high in top 20 m during premonsoon season in tandem with high nitrate (N) and silicate (Si) concentration. N:P (phosphate) ratio was less than Redfield ratio (16:1) during all seasons, indicating the Bay of Bengal as nitrate limited and confirmed the results of Principal ComponentAnalysis (PCA) with positive loading and multiple regression analysis showing negative correlation between this ratio and chlorophyll concentration during all seasons. Whereas, N:Si ratio was < 1 and Si:P ratio > 7 in top 20 m during all seasons explained the deficiency of phosphorus and enrichment of silicate in the central Bay of Bengal. Regression analysis between Si:P and N:Si ratios with chlorophyll showed negative correlation during premonsoon and summer respectively. Thus, the present results confirmed that nutrient molar ratios such as N:P<16; Si:P>7 and N:Si<1 was indicative of a potential N and Si limitation and are the primary limiting nutrients in the central Bay of Bengal in determining chlorophyll concentration.

In-situ nutrient enrichment experiment was conducted to know the relationship between chlorophyll and phosphate so as to assess the significant role of phosphate in the phytoplankton growth. During the experiment period temperature (27.9-33.7°C), salinity (31-35 ‰) and pH (7.74-8.07) values were not shown dramatic changes as the experimental studies are conducted for the short period only. DO was found to show an oscillating trend (2.77-6.68 mgl -1 ) with phytoplankton population variations. Nutrient concentrations (NO 3 :1.05-12.63 µM, NO 2 :0.29-1.67 µM, PO 4 : 0.07-28.32 µM and SiO 3 :4.19-23.89 µM) showed maximum concentration at the first and second day of the experiment in enrichment tanks and it gradually decreased (PO 4 and SiO 3 ) on last day of the experiment period. The pronounced maximum chlorophyll concentration in tank 5 on 5 th day corresponding with addition of highest concentration of phosphorus, clearly pointed out that phosphorus addition had influenced the plankton growth. Increased utilization of PO 4 and support of SiO 3 indicates diatoms prefer silicate and phosphate for growth than the nitrates. Nitrate enrichment in the tank in later part of the experiment indicates nitrogen cycling processes. Increased phytoplankton uptake and growth rate at tank 5 when compared to control, substantially proved the uptake of phosphate by phytoplankton under culture system.

In the present study, nutrient enrichment experiments were carried out to understand the role and importance of silicate in the phytoplankton blooming. Water temperature (26.6 – 29.8oC), salinity (33 - 37‰), pH (8.01 – 8.64), and DO (4.26 – 6.38mgl -1 ) not showed significant variation and not play important role in the phytoplankton growth. At the first day of the experiment, nutrient concentrations (NO 3 - 9.44µM, NO 2 – 1.51µM, PO 4 – 24.40µM and SiO 3 – 45.60µM) were at its maximum concentration in enrichment tanks and it gradually decreased (PO 4 – 14.19µM and SiO 3 – 6.11µM) at the end of experiment period, suggested uptake of nutrients by the experimental microalgal species. The pronounced maximum chlorophyll concentration (8.90µgl -1 ) in tank5 corresponding with addition of highest concentration of silicate (25µM), clearly pointed out that silicate addition has influenced the diatom dominated microalgal growth evidenced by the increased chlorophyll concentration. Increased utilization of SiO 3 supported by PO 4 indicates the nutrient preference by the diatom. The regression trend recorded in the present study could be used as the positive signal in mapping silicate using remote sensing techniques. The regression analysis between silicate and chlorophyll showed significant correlation coefficient (R 2 = 0.609) and increased phytoplankton growth rate (0.277 d -1 ) at tank 5 when compared to control substantially proved the uptake of silicate by phytoplankton community dominated by diatoms.

Spatial and temporal distribution of chlorophyll a (chl a) and Total Suspended Matter (TSM) and inter comparison of Ocean Color Monitor-2 (OCM-2) and Moderate Resolution Imaging Spectro-radiometer (MODIS-Aqua) derived chlorophyll a and TSM was made along the southwest Bay of Bengal (BoB). The in-situ chl a and TSM concentration measured during different seasons were ranged from 0.09 to 10.63 μgl−1 and 11.04–43.75 mgl−1 respectively. OCM-2 and MODIS derived chl a showed the maximum (6–8 μgl−1) at nearshore waters and the minimum (0–1 μgl−1) along the offshore waters. OCM-2 derived TSM imageries showed the maximum (50–60 mgl−1) along the nearshore waters of Palk Strait and the moderate concentration (2–5 mgl−1) was observed in the offshore waters. MODIS derived minimum TSM concentration (13.244 mgl−1) was recorded along the offshore waters, while the maximum concentration of 15.78 mgl−1 was found along the Kodiakarai region. The inter-comparison of OCM-2 and MODIS chl a data (R 2  = 0.549, n = 49, p < 0.001, SEE = ±0.117) indicate that MODIS data overestimates chl a concentration in the nearshore waters of the southern BoB compared to the OCM-2. The correlation between OCM-2 and MODIS-Aqua TSM data (R 2  = 0.508, N = 53, P < 0.001 and SEE = ±0.024) confirms that variation in the range of values measured by OCM-2 (2–60 mgl−1) and the MODIS (13–16 mgl−1) derived TSM values. Despite problems in range of measurements, persistent cloud cover etc., the launch of satellites like OCM-2 with relatively high spatial resolutions makes job easier and possible to monitor chl a distribution and sediment discharges on day to day basis in the southwest BoB.

The Chlorophyll and Total Suspended Matter (TSM) data retrieved from Ocean Colour Monitor (OCM-2) onboard Oceansat-2 were tested for the accuracy using in-situ measurements made along the southwest Bay of Bengal coast during cruises and monthly samplings synchronized with satellite overpass from January 2010 to May 2011. The observed range of in-situ chlorophyll a and TSM concentrations were 0.10–4.60 μgl − 1 and 12.70–34.56 mgl − 1 respectively, while OCM-2 derived chlorophyll a and TSM concentration ranged from 0.324 to 1.552 μgl − 1 and 3.537 to 32.11 mgl − 1 , respectively. The in-situ dataset was grouped into low (0.1–0.5 μgl − 1 ), moderate (0.51–1.0 μgl − 1 ) and high (>1 μgl − 1 ) chlorophyll concentration and low (12.7–17.81 mgl − 1 ), moderate (18.1–29.0 mgl − 1 ) and high (>30 mgl − 1 ) TSM concentration for evaluating the performance of algorithms against different ranges of field measurements. The OCM-2 chlorophyll retrieval algorithm (OC4V4) showed a systematic and large overestimation of low chlorophyll values with r 2 = 0.607, root mean square error (RMSE) = 0.33 μgl − 1 and mean normalized bias (MNB) = 1.57 and consistent underestimation of high chlorophyll values with r 2 = 0.497, RMSE = 1.486 μgl − 1 and MNB = 0.52 especially at nearshore waters due to the interference of suspended matter and coloured dissolved organic matter. However, moderate range of chlorophyll values showed better performance of OC4V4 algorithm in chlorophyll retrieval with r 2 = 0.676, RMSE = 0.254 μgl − 1 and MNB = 0.09 when compared to low and high chlorophyll values. The TSM algorithm (modified algorithm of Tassan 1994 ) showed large underestimation in TSM retrievals and this was proved by the statistical results which shown maximum r 2 = 0.551 for low TSM values with less RMSE = 0.909 mgl − 1 and MNB = 0.616 error compared to moderate and high TSM values. OCM-2 retrieved TSM values were not well correlated with in-situ TSM concentration and constantly underestimates four times lesser than the in-situ measurements especially near the coast when TSM concentration was measured high. Though there was significant correlation exists between OCM-2 retrieved chlorophyll and TSM with in-situ measurements, the empirical algorithms employed did not give logical retrieval of both chlorophyll and TSM for the southwest Bay of Bengal (BoB). Thus, the present study revealed that the OCM-2 chlorophyll and TSM retrieval algorithms need to be tested further with extensive in-situ dataset around BoB to improve the regional algorithms for accurate measurements of chlorophyll and TSM in this region.