MODIS-Aqua Satellite Research Articles

İskenderun Körfezi kıyı alanlarında sıcaklık ve klorofil-a için uydu ve model temelli veri setlerinin temsil yeteneği üzerine bir değerlendirme

This study investigates the monitoring of sea surface temperature (SST) and chlorophyll-a (chl-a) levels in Iskenderun Bay using satellite and modeling data and evaluates the possible use of these datasets for monitoring marine ecosystems. Datasets derived from MODIS-Aqua satellite imagery and modeling data obtained from the Copernicus MyOcean and in-situ measurements were used in the study. According to the analysis on paried data sets of the distribution of SST and chl-a, sattelite and model datasets showed statistically significant correlations with in-situ measurements for SST. However, only satellite dataset showed significant correlations for Chl-a. Evaluations on uncertainty of the data sets revealed that the satellite dataset had a narrower range and less outlier distribution for SST. For chlorophyll-a, both datasets had wide uncertainty ranges and required further improvement. This study highlights the potential of satellite and model datasets for monitoring SST and chl-a variations in Iskenderun Bay.

Analysing Sea Surface Temperature and Chlorophyll-a Distribution Along Visakhapatnam Coast Using MODIS Data

Satellite images along with Geographic Information Systems (GIS) are indispensable instruments for the identification, monitoring, and analysis of environmental transformations and management. The primary focus of this study is to investigate the relationship between Sea Surface Temperature (SST) and Chlorophyll-a concentration in the Bay of Bengal along the Visakhapatnam Coast by utilizing MODIS data. The research utilized remote sensing methodologies to accumulate data on SST and Chlorophyll-a concentration from the MODIS Aqua satellite, which provided daily observations for the period of January to December 2022. The investigation has unveiled important findings indicating a strong correlation between SST and Chlorophyll-a concentration, in general, higher SST levels are found to be linked with lower Chlorophyll-a concentrations, and conversely. The majority of months demonstrated notable determination coefficient (r2), except July, August, September, and October, wherein weaker correlations were observed due to increased precipitation levels in these specific months. Specifically, the determination coefficients were as follows: r2 = -0.58, -0.38, -0.35, -0.61, 0.16, -0.25, -0.06, and -0.59, with the exceptions being July (r2 = 0.01), August (r2 = 0.19), September (r2 = 0.33), and October (r2 = 0.24). Additionally, seasonal fluctuations were evident, with increased SST levels and reduced Chlorophyll-a concentrations during the summer season, and decreased SST levels and heightened Chlorophyll-a concentrations during the monsoon period. To effectively conclude, extensive research utilizing MODIS data has revealed significant trends and relationships between SST and Chlorophyll-a concentrations over the Bay of Bengal along the Visakhapatnam Coast. SST has been observed to increase significantly over recent decades, while Chlorophyll-a concentrations peak during the northeast monsoon season and decline during the southwest monsoon. A robust inverse correlation exists between SST and Chlorophyll-a concentrations, indicating that elevated SSTs are associated with decreased Chlorophyll-a levels, with exceptions during periods of heightened precipitation. Seasonal fluctuations further emphasize the dynamic nature of these parameters, with implications for marine ecosystem management and conservation efforts in the face of climate change, overfishing, and pollution. Remote sensing remains a crucial tool for monitoring these variables and understanding the marine ecosystem's dynamics in the Bay of Bengal.

Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea

Sea Surface Salinity (SSS) is a key variable in physical oceanography and biogeochemical research. Microwave remote sensing technology has been used to monitor SSS in open ocean areas. However, the limited spatial resolution and suboptimal precision exhibited by microwave remote sensing derived SSS data have led to the omission of crucial oceanic information, consequently constraining its utilization. In this study, an artificial neural network (ANN) model for the regional SSS inversion was constructed utilizing in situ SSS measurements in the northern South China Sea and remote sensing sea surface temperature and reflectance at wavelengths of 412, 443, 488, 555 and 667 nm derived from MODIS-Aqua satellite Level 2 data. The model showed a good performance of root mean square error (RMSE) of 0.32 and 0.34 for the model training and testing datasets, with mean bias (MB) of 0 and 0.01, and correlation coefficients (R2) reaching 0.93. The model validation indicated that the ANN model reproduced the spatial distribution of SSS highly consistent with the measured SSS (RMSE = 2.75, MB = −1.67, R2 = 0.95), demonstrating its strong applicability in the NSCS. In contrast to other local SSS inversion models, our ANN model clearly showed the fine-scale SSS feature under the intrusion of Pearl River plume near to Dongsha Island with the help of anticyclonic-cyclonic eddies. This prediction closely aligned with the observations from the Soil Moisture Active Passive SSS dataset. The ANN model was applied to MODIS-Aqua data to generate the monthly SSS from 2003 to 2022. A decreasing SSS trend in SSS of 0.02 psu y−1 during 2005–2013 and 2017–2020 and a slightly increasing SSS trend of 0.01 psu y−1 from 2013 to 2017 were observed. Further analysis shows the low-frequency variability of SSS is significantly correlated with the Pacific Decadal Oscillation.

Assessing Chlorophyll-a Variations in Caspian Sea during the COVID-19 Pandemic.

The COVID-19 pandemic's disruptions to human activities prompted serious environmental changes. Here, we assessed the variations in coastal water quality along the Caspian Sea, with a focus on the Iranian coastline, during the lockdown. Utilizing Chlorophyll-a data from MODIS-AQUA satellite from 2015 to 2023 and Singular Spectrum Analysis for temporal trends, we found a 22% Chlorophyll-a concentration decrease along the coast, from 3.2 to 2.5mg/m³. Additionally, using a deep learning algorithm known as Long Short-Term Memory Networks, we found that, in the absence of lockdown, the Chlorophyll-a concentration would have been 20% higher during the 2020-2023 period. Furthermore, our spatial analysis revealed that 98% of areas experienced about 18% Chlorophyll-a decline. The identified improvement in coastal water quality presents significant opportunities for policymakers to enact regulations and make local administrative decisions aimed at curbing coastal water pollution, particularly in areas experiencing considerable anthropogenic stress.

Spatiotemporal Expansion of Algal Blooms in Coastal China Seas.

The coastal seas of China are increasingly threatened by algal blooms, yet their comprehensive spatiotemporal mapping and understanding of underlying drivers remain challenging due to high turbidity and heterogeneous water conditions. We developed a singular value decomposition-based algorithm to map these blooms using two decades of MODIS-Aqua satellite data, spanning from 2003 to 2022. Our findings indicate significant algal activity along the Chinese coastline, impacting an average annual area of approximately 1.8 × 105 km2. The blooms exhibit peak intensity in August, while the maximum affected area occurs in September, featuring multifrequency outbreaks in spring, and pronounced large-scale events in summer and autumn. Notably, our analysis demonstrates a robust 67% increase in bloom occurrences over the study period. This expansion is primarily attributed to increased nutrient inflow from terrestrial sources linked to human activity and precipitation, compounded by rising global sea surface temperatures. These spatiotemporal insights are critical for devising effective management strategies and policies to mitigate the impacts of algal blooms.

Evaluation of particulate organic carbon from MODIS-Aqua in a marine-coastal water body.

La Paz Bay (LPB) in Mexico is one of the largest marine-coastal bodies of water in the Gulf of California (GC) and is ecologically important for the feeding, reproduction, and refuge of marine species. Although particulate organic carbon (POC) is an important reservoir of oceanic carbon and an indicator of productivity in the euphotic zone, studies in this region are scarce. This study evaluates the performance of satellite-derived POC in LPB from January 2003 to December 2020. The metrics obtained for COP ( ; y ), Chla-a ( ; y ), and SST ( ; y ) establish that although in some cases there was a slight over/underestimation, the satellite estimates consistently represent the variability and average values measured in situ. On the other hand, the spatio-temporal analysis of the POC allowed us to identify two seasons with their respective transition periods and five subregions in which the POC is characterized by having its maximum variability; two of these coincide with the locations of the eddies reported for the winter and summer seasons in the LPB, while the following three are located: one in the coastal zone and in the two areas in which the LPB interacts with the GC. The associations, variability nodes, and multiple linear regression analysis suggest that POC fluctuations in the LPB respond mainly to biological processes and, to some extent, to the seasonality of SST and wind. Finally, our results justify the use of the MODIS-Aqua satellite POC for studies in marine-coastal water bodies with similar characteristics to the LPB and suggest that this water body can be considered a reservoir for the marine region of northwestern Mexico.

Toward the future integration of land-to-ocean observing systems to characterize organic carbon fluxes from storms

Storms can drive massive fluxes of carbon from land to ocean via rivers, contributing a large but relatively uncertain term to coastal carbon budgets. To estimate storm-driven aquatic organic carbon fluxes and develop a framework for future observations, we conducted a case study of extreme precipitation followed by river discharge to the Chesapeake Bay watershed, on the east coast of the United States over two decades from June 2001 to July 2021. Anomalous high precipitation events were defined as the top 5% of five-day accumulated precipitation over the watershed and we identified 80 distinct large storms over the Chesapeake Bay region. The top 1% of five-day accumulated river discharge from the United States Geological Survey (USGS) gage stations provided secondary storm criteria to distinguish extreme precipitation that manifest in very high flow: over the same period, we identified 16 storms that led to very high streamflow and combined streamflow with observations of POC and DOC to estimate daily mass flux from the seven major rivers. An extremely large convective storm and subsequent river discharge in July 2018 was studied to determine the association between precipitation, river discharge, and satellite ocean color in combination with in situ observations. MODIS-Aqua satellite derived dissolved organic carbon (DOC) concentration was calculated for all images in 2004–2021 and the estimated DOC values in 2018 were used to assess the feasibility of the optical satellites to monitor coastal storm fluxes of carbon. Using the USGS Load Estimator stream flow model and historical observations, the 2018 event exported approximately 12.9 Gg C particulate organic carbon (POC) and 12.5 Gg C DOC into the bay for 7.2% and 4.4% of the annual total, respectively. The largest storm over the last 20 years exported up to 16% of the annual total POC and 11% of the annual total of DOC from the major rivers over 10 days. However, monitoring the evolution of riverine carbon in estuarine environments from optical satellites alone remains challenging due to cloud cover, thus additional observing platforms assimilated through models are essential.

Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1-km grid, using seven ocean color features from the MODIS-Aqua satellite platform (2002-2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth-dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K-nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;20:1432-1446. © 2024 SETAC.

Detection of upwelling events in the Caspian Sea using thermal satellite image processing

In this article, we studied the upwelling phenomena observed in the surface waters of the Caspian Sea. Studying upwelling is crucial for understanding and managing the complex interactions between the ocean, climate, and marine life, with implications for both environmental conservation and human well-being. To accomplish this, we utilized data from MODIS-Aqua satellite observations in the infrared range of 11 microns. These observations had a spatial resolution of 4 km and covered the period from 2003 to 2021. The data was accessed through the NASA Giovanni online data system databases. Our findings indicate that upwelling phenomena are primarily observed in the eastern part of the Middle and South Caspian from May to September. The most intense upwelling occurs along the eastern coast of the Middle Caspian in July and August. Based on long-term averaged data, the upwelling phenomenon is typically observed between 40-44º latitude during this period. The width of the upwelling zone increases gradually from north to south, extending approximately 60-70 km towards the Kazakh Gulf before decreasing towards the south. In the upwelling zone, the temperature gradient can at times reach 4.0ºC per 100 km. In certain years, the upwelling zone that initially forms along the eastern coasts can extend over long distances and even reach the western coasts. Generally, the upwelling phenomenon occurs alongside the advection of warm waters from the South Caspian towards the Middle Caspian.

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.

Influence of Oceanographic Parameters on The Results of The Catch of Squid (Loligo sp.) in The Java Sea (WPP-RI 712)

Information on oceanographic conditions is an important aspect to pay attention to because it is closely related to the existence of abundant squid resources. Most types of squid make seasonal migrations in response to changes in temperature. Squids will look for a place to attach their eggs under certain oceanographic conditions, such as the right salinity and water depth. Data collection was carried out primarily in the field using a 5 in 1 salinometer and secondarily using the AQUA MODIS satellite. Sea surface temperatures observed from the end of March to the beginning of May experienced a slow increase in temperature from day to day. Regarding water salinity, the data obtained is that the further out to sea, the greater the salinity tends to be. In chlorophyll a, there was a significant increase in March with the highest distribution. The influence of the measured oceanographic parameters on the number of squid catches, after chlorophyll-a cross-correlation was carried out, was 36.5% or a fairly strong correlation and the other 63.5% was estimated to influence other factors in the form of light, bulla, water depth, and current strength, as well as the crew's skills in operating fishing gear.

Cross-Satellite Atmospheric Correction for Consistent Remote Sensing Reflectance from Multiple Ocean Color Satellites: Concept and Demonstrations

Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites, thereby enhancing spatial coverage and extending temporal spans. However, due to factors such as sensor specifics and separate data processing algorithms, bio-optical properties (e.g., remote sensing reflectance, R rs ) from different ocean color missions exhibit varying discrepancies in oceanic, coastal, and inland waters. Here, we introduce a cross-satellite atmospheric correction (CSAC) scheme, which could greatly improve the consistency of R rs products between MODIS-Aqua and other satellite ocean color missions. Specifically, using an inclusive high-quality R rs dataset of oceanic waters obtained from MODIS-Aqua as the reference, and as an example, top-of-atmosphere reflectance from SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) is directly processed to MODIS-Aqua-equivalent R rs ( R rs MA − eqv ) via CSAC. As a demonstration, for independent space-time matched measurements between MODIS-Aqua and SeaWiFS, the mean absolute percent difference ( MAPD ) between R rs MA − eqv and MODIS-Aqua R rs ranges from 5.9% to 22.2% across wavelengths from 412 to 667 nm. In contrast, the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua R rs products range from 10.1% to 55.1% for the same spectral bands. These results highlight the potential of CSAC in obtaining consistent R rs products and, subsequently, R rs -derived bio-optical properties, from various ocean color satellites, facilitating extensive and long-term ocean color observations of the global ocean.

EARLY WARNING SYSTEM (EWS) FOR ALGAL BLOOMS USING SATELLITE IMAGERY IN JAKARTA BAY

Jakarta Bay is experiencing eutrophication, primarily due to nutrient inflows from agriculture, industry, and urban sources. This abundance of nutrients has led to significant algae blooms. A study using Terra and Aqua MODIS satellite data from 2004 to 2007 monitored these blooms by measuring chlorophyll-a levels. During this period, large-scale fish kills were observed directly related to the algal blooms, as evidenced by high chlorophyll-a concentrations and blooms covering more than a quarter of the bay. Interestingly, not all intense blooms resulted in massive fish kills. The study suggests that this mortality is primarily due to oxygen depletion after peak bloom periods, compounded by poor water circulation in the bay. Using satellite imagery to monitor algal blooms is a practical tool for implementing an early warning system (EWS) in Jakarta Bay. Satellite imagery has proven effective in monitoring these blooms and could help develop an early warning system in Jakarta Bay despite limitations such as cloud cover.

Mapping of Sea Surface Temperatures on The Coast Probolinggo With Satellite Image Data

Indonesia has the advantage of abundant marine resources. One of them is on the coast of Probolinggo , where the average community there utilizes marine natural resources for livelihoods. Monitoring ocean conditions such as sea surface temperature (SST) is used to facilitate and streamline the utilization of marine resources, namely by using remote sensing technology from Aqua MODIS satellite images which can later be used as a database in the field of Civil Engineering in the planning and construction of water buildings such as ports and docks. In this study, it can be used for SPL mapping using field data ( in situ ) and data from Aqua MODIS satellite imagery with the help of the SeaDAS application. The data used from Aqua MODIS satellite imagery is data from 2019-2023, with wavelengths of 412 nm, 531 nm and 667 nm. The results of the analysis and discussion of satellite image data concluded that the best data is the 2023 image data. For SPL with a wavelength of 531 nm, the logarithmic equation with the SPL = 3.7797ln(x) + 51.436 algorithm model, R 2 value = 0.1201 and correlation value R = 0.4567300 which has a moderate positive correlation.

The Utilization of Seasonal Variation of Ocean Wave Characteristics Identification in Monitoring the Chlorophyll-a Distribution in South Sulawesi

South Sulawesi is one of Indonesia’s provinces with various forms of waters around its territory, such as the sea, strait, the Spermonde archipelago, and bay. This geographical condition makes it interesting to examine what phenomena can affect the characteristics and variability of ocean waves in each type to increase efficiency and effectiveness in carrying out activities in the waters, especially fishing. By utilizing the BMKG Ina-Waves wave model and NASA’s Aqua MODIS satellite image, the characteristics and variations of ocean waves and the level of fertility of the waters around South Sulawesi Province can be seen. Based on the climatological analysis of wind, ocean waves, and the distribution of chlorophyll-a in this study, the results showed that the Asian and Australian monsoon phenomena greatly influenced these three parameters. The monsoon winds increase the average wave height and affect the distribution of chlorophyll-a concentrations in the waters of South Sulawesi and its surroundings. Efficiency and effectiveness of fishing at sea are carried out during the transition season in the coastal waters of South Sulawesi so that the risk of sea accidents can be minimized.

Regional Algorithm of Quantitative Assessment of Cyanobacteria Blooms in the Eastern Part of the Gulf of Finland Using Satellite Ocean Color Data

Summer blooms of potentially harmful cyanobacteria are common in the Baltic Sea. Under clear sky conditions, the cyanobacterial blooms are easily detectable from space. We propose a new regional algorithm for cyanobacteria biomass estimation from satellite data in the eastern part of the Gulf of Finland, developed on the basis of field measurements in July–August 2012–2014. The multi-regression equation defines the cyanobacteria biomass as a function of the particle backscattering coefficient and chlorophyll concentration. The use of this equation provides the best performance in comparison to the linear one, which is reflected in both R2 and RMSE values (0.61 and 272 mg m−3 respectively). Unlike other algorithms, which determine only the cyanobacteria bloom area in the Baltic Sea, our algorithm allows the determination of both a bloom area and its intensity. Considering the algorithm errors, the bloom detection threshold has been shifted from the 200 mg m−3 determined by biologists to 300 mg m−3. Based on data from the MODIS-Aqua satellite ocean color scanner, the spatial and temporal variability of cyanobacterial blooms in this region from 2003 to 2022 was analyzed. Significant interannual variability of cyanobacteria biomass was revealed in the central part of the studied region, with minimum values in 2014 and maximum in 2004. The record bloom during the studied period occurred in July 2004 (the average cyanobacteria biomass was 780 mg m−3). The weakest blooms were observed in 2009, 2010, and 2014, when both in July and August, the bloom areas did not exceed 30% of the study region.

Assessment of satellite-based Net Primary Productivity models in different biogeochemical provinces over the northern Indian Ocean

ABSTRACT The biological productivity in the oceanic ecosystem plays a vital role in the global carbon cycle and energy flow in the oceanic food chain. Therefore, proper monitoring of phytoplankton primary production in the ocean is essential to understand the role and responses of aquatic ecosystems to global climate change. Its quantification is also important for higher trophic-level studies and sustainable ecosystem management. The advent of satellite technology has provided an opportunity to retrieve Net Primary Productivity (NPP) at high spatial and temporal resolution. However, for efficient utilization of satellite NPP it is imperative to assess the models for their accuracy and variability. The present study described NPP estimated from MODIS-Aqua satellite data (2003–2021) using five models (VGPM, Eppley-VGPM, CbPM, CAFE, and SABPM) and their variability in the four biogeochemical provinces (ARAB, MONS, INDW and INDE) in the northern Indian Ocean. The temporal variability indicated all models exhibit seasonal variability in ARAB and INDW with two prominent peaks during peak southwest monsoon (June-August) and winter monsoon (December-January). However, there was significant difference in the magnitude. The seasonal peak was primarily due to increase in NPP due to summer upwelling and winter mixing. The spatially averaged diversity index indicated maximum variability in INDW (74.6–77.1%), followed by INDE (77.2–78.0%), ARAB (77.2–78.4%), and MONS (78.6–78.9%). The satellite retrieved NPP by various models showed maximum diversity in the region above 15°N and west of 60°E in AS and 20°N and east 90°E in the BoB indicating a significant influence of discharge from perennial rivers resulting in poor performance of the models in these regions. The variability of other models was then assessed with respect CbPM as the model was known to perform better. The Eppley-VGPM showed a maximum (55%) and SABPM showed a minimum (11%) Mean Absolute Percentage Difference with respect to CbPM. The long-term spatio-temporal and quantifiable variability between the NPP models, and biogeochemical provinces, indicated that the study provided useful information on the selection and utilization of an appropriate model for future sustainability and climate change studies.

Blue Color Indices as a Reference for Remote Sensing of Black Sea Water

In this paper, we propose to analyze the values of the “blue” color index for further use in additional atmospheric correction of Level 2 remote sensing reflectance data for the waters of the Black Sea. Regardless of seasonal phenomena, atmospheric conditions, and the type of water, the average color index in the short-wave region, according to in situ measurements CI(412/443), varies from 0.77 to 0.83. The most frequently observed value is 0.8. In turn, the values of the “blue” color index CI(412/443) according to the satellite data of MODIS Aqua/Terra, VIIRS SNPP, and OLCI Sentinel 3A scanners showed a large scatter in values based on the standard deviation of the sample. The paper proposes to introduce the value of the minimum allowable threshold CI(412/443) > 0.59 based on the small variance found from in situ measurements, as well as on the basis of a theoretical estimate of the possible values of the index CI(412/443) when varying the backscattering exponent and the exponent for the absorption approximation. The quality check of the remote sensing data showed that, according to this selection criterion, 15% of data are physically incorrect for MODIS Aqua, 30% for MODIS Terra, 20% for Sentinel 3A, and 26% for VIIRS SNPP. In the course of the work, it was shown that the MODIS Aqua satellite provides the most high-quality and reliable information about the optical characteristics of the Black Sea.

Benthic Characterization of Mesophotic Communities Based on Optical Depths in the Southern Mexican Pacific Coast (Oaxaca)

The distinction between shallow coral reefs and mesophotic coral ecosystems (MCEs) has not been fully clarified yet. The original definition of MCEs, by depths of 30–150 m, fixes their bathymetrical limits and fails to accommodate environmental and biological variation. Recent studies have indicated that water transparency and light availability may explain why MCEs do not occur at fixed depths but vary among localities. This study aimed to evaluate the presence and distribution of MCEs, along the central coast of Oaxaca, through optical depths and the associated benthic community. Using MODIS-Aqua satellite data (Kd490), we estimated the mesophotic optical depths monthly (z10%, z1%, z0.1%), over the last four years. In addition, to characterize benthic community structure, we conducted underwater photo quadrat surveys at two locations on the southern Mexican Pacific coast from 10 to 55 m depth. Significant differences between depths and locations were found in benthic communities. Furthermore, the lower distribution of photosynthetic taxa was different between the two locations but indicative to the z10% and z1% in both cases. Those differences were associated with the upwelling season, which reduces, drastically and differentially, the light availability for benthic communities between the two locations and limits the development of MCEs on the central coast of Oaxaca.

Cross frontal variability in bio-optical characteristics in the Indian sector of the Southern Ocean during an austral summer

Bio-optical properties, which play a crucial role in the understanding of the radiation budget of optical active substances (OAS), phytoplankton community, biological pump, and detection of algal blooms have been sparsely studied in the Southern Ocean (SO). We assessed how bio-optical characteristics change across the frontal regions and during phytoplankton bloom and non-bloom domains in the Indian sector of the SO (ISSO) during the austral summer of 2011. In both the meridional transect (47 oE and 57.5 oE), the surface Chlorophyll-a, and phytoplankton absorption coefficient (aph(443)) increased with latitude. Whereas Chlorophyll-a specific phytoplankton absorption (a*ph(443)), an index for the “pigment package effect”, decreased with latitude, implying that the size of phytoplankton increases with latitude. The 57.5 oE transect showed higher quantities of OAS than 47 oE, except for Polar Front 2 (PF-2). Moreover, the vertical profiles of Chlorophyll-a in PF-2 at 47 oE showed a massive phytoplankton bloom with a thickness of 60 m, which could be confirmed in the surface Chlorophyll-a images from the MODIS Aqua satellite. The massive bloom occurred due to distinct physical processes (nutrients supplied by pack ice), causing high productivity. The vertical profiles of Chlorophyll-a, aph(443), and a*ph(443) identified subsurface chlorophyll maxima (SCM) and different phytoplankton communities across the fronts. The interrelationships between bio-optical properties exhibited distinct features in bloom and non-bloom domains. The aCDOM(443) and its spectral slope (S300−500) increased and decreased with latitude, respectively. Using the vertical distribution of CDOM and its relationships with slope and Chlorophyll-a, multiple sources of CDOM could be identified. The percentage-contribution of OAS in the Polar Front-1 (PF-1) exhibited lower aph, lower detrital absorption (ad), and higher aCDOM indicating its low productivity compared to other fronts. This study facilitates the understanding of the phytoplankton size-class (pico, nano and micro) and productivity using the bio-optical properties in this lesser-studied polar region.