Satellite-based observation of particulate organic carbon in the northern Bay of Bengal

Rouf, Muhammad Abdur; Golder, Md Rony; Sumana, Zareen Afroje

doi:10.1016/j.envadv.2021.100124

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…However, the correlation analysis during these nineteen years also reveals that SST does not always significantly influence POC (e.g., February and March). Another factor may induce seasonal POC variations in Indonesian waters, such as fresh water fluxes [16,23], currents [24] or winds [25]. Besides that, biological processes are also largely responsible for the higher levels of POC [18].…”

Section: Resultsmentioning

confidence: 99%

The influence of increasing sea surface temperature on particulate organic carbon in Indonesian waters based on MODIS Aqua satellite

Yati

Kushardono

Gaol

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Both Sea Surface Temperature (SST) and Particulate Organic Carbon (POC) have important roles in the marine ecosystem and carbon cycle. Therefore, we analyze the influence of SST on POC in Indonesian water. We used SST and POC products from the moderate-resolution imaging spectroradiometer (MODIS) Aqua satellite from 2003 to 2021 (nineteen years). Regression and a temporal anomaly correlation approach were applied to assess the effect of SST on POC in Indonesian waters. Our results show that the southeast Indonesian waters, south of Java Island and around the Riau Islands, mostly have a high SST variation. On the other hand, the high variation of POC mostly appeared in Indonesian coastal. During the last nineteen years, the increasing SST anomalies (SSTA) in Indonesian waters have been followed by decreasing POC anomalies (POCA). These results indicate that rising SST reduces the ocean’s ability to absorb carbon in Indonesian waters. A strong negative relation between SSTA on POCA was found from April to August and October to January in a seasonal timescale. In the interannual timescale, our result reveals that the strong negative relationship between SSTA and POCA is likely linked to the El Niño Southern Oscillation, the Indian Ocean Dipole, and global warming.

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Section: Resultsmentioning

confidence: 99%

The influence of increasing sea surface temperature on particulate organic carbon in Indonesian waters based on MODIS Aqua satellite

Yati

Kushardono

Gaol

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…POC (Particulate Organic Carbon) is a fundamental component in the carbon cycle and biogeochemical processes in the oceans (Kandasamy et al, 2019;Rouf et al, 2021a;Yu et al, 2019). For the understanding of biophysical interactions in the ocean, in situ sampling of recent marine conditions is essential.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The use of satellite products has allowed for the estimation of spatial and temporal variability of POC in regions such as the Bay of Bengal (Kandasamy et al, 2019;Rouf et al, 2021a), the North Paci c (Yu et al, 2019), the coasts of Indonesia (Triana et al, 2021), and the North Atlantic (Cetinić et al, 2012;Stramska, 2014). Although the inverse relationship between POC and Sea Surface Temperature (SST) indicates a strong in uence of physical processes on the spatial and temporal variability of POC in some regions (Kandasamy et al, 2019;Rouf et al, 2021a;Yu et al, 2019), Yu et al (2019) reported in the Paci c Ocean, above 45°N, positive associations re ecting a reduction in the permanent POC reservoir in surface waters due to rapid sinking of larger-sized particles. On the other hand, the in uence of biological processes on the spatial and temporal distribution of POC has been evaluated through the POC:Chlorophyll ratio (POC:Chla) in some marine regions of the world (Kandasamy et al, 2019;Rouf et al, 2021a;Yu et al, 2019), assuming that phytoplankton is a dominant factor in POC variability, making it a su ciently robust relationship to explain the in uence of biological processes on POC variability.…”

Section: Introductionmentioning

confidence: 99%

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

De Jesús Morales-Acuña,

Aguíñiga-García,

Cervantes-Duarte

et al. 2024

Environ Sci Pollut Res

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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. Data from POC and Chlorophyll-a (Chla-a) from MODIS-Aqua and sea surface temperature (SST) from MUR were used. Initially, a match-up analysis was applied to compare the satellite products with in situmeasurements obtained from a sediment trap installed in Alfonso Basin, Mexico. Subsequently, spatial and seasonal variability patterns of POC were analyzed using Empirical Orthogonal Functions (EOFs), power spectral density (PSD), geospatial correlations of each pair of variables, the POC:Chla-a ratio, and multivariate techniques. The results of the matchmaking analysis between in situ and satellite series show that the satellite estimates consistently represent the variability and mean values measured in situ. The distribution of POC characterizes two seasons with their respective transition periods and ve subregions where POC exhibits its maximum variability. Two of these subregions coincide with the reported locations of winter and summer eddies in BP, while the remaining three correspond to the coastal zone and the two regions where LPB interacts with the GC. Associations, variability nodes, and multiple linear regression analysis suggest that POC uctuations in LPB are mainly driven by biological processes and, to some extent, by seasonal SST and wind intensity. Finally, based on the concentrations obtained in this study, this area can be considered as a reservoir of POC for the marine and atmospheric regions of northwest Mexico.

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“…Satellite remote sensing monitoring of POC concentration distribution in surface water can provide an effective method for quantifying the long-term trend of POC in lakes, which can support lake biogeochemical cycle and ecosystem research. Due to the selective absorption and scattering of POC in visible and near-infrared bands, the increase in the concentration of POC will lead to a decrease in the propagation distance of light and an increase in the decay rate, and the intensity of light is closely related to the concentration and physical properties of particulate matter [5]. At the same time, the different proportions of organic particles and inorganic particles in the particles have different absorption and reflection characteristics for different wavelengths of light [6].…”

Section: Introductionmentioning

confidence: 99%

A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

Pan,

Yu,

Cheng

et al. 2023

Sustainability

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As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R2) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes.

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Satellite-based observation of particulate organic carbon in the northern Bay of Bengal

Cited by 5 publications

References 49 publications

The influence of increasing sea surface temperature on particulate organic carbon in Indonesian waters based on MODIS Aqua satellite

The influence of increasing sea surface temperature on particulate organic carbon in Indonesian waters based on MODIS Aqua satellite

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

A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

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