Total Suspended Matter Research Articles

Assessing water quality restoration measures in Lake Pampulha (Brazil) through remote sensing imagery.

Urban reservoirs are frequently exposed to impacts from high population density, polluting activities, and the absence of environmental control measures and monitoring. In this study, we investigated the use of satellite imagery to assess restoration measures and support decision-making in a hypereutrophic urban reservoir. Since 2016, Lake Pampulha (Brazil) has undergone restoration measures, including the application of Phoslock®, to mitigate its poor water quality conditions. Satellite images from Landsat-8 (L8) and Sentinel-2 (S2) and historical monitoring data were used to estimate total suspended matter (TSM), chlorophyll-a (Chla), and Secchi disk depth (SDD). We explored both established models from existing literature and novel alternatives, the latter employing machine learning approaches. Model performance was assessed through the coefficient of determination (R ) during calibration and using the root mean square error (RMSE) and its normalization by the observed mean (nRMSE) during validation. Random forest regressor presented superior performance for the three water quality parameters and both satellites. The performance metrics for Landsat-8 were TSM (n=44) R of 0.62; Chla (n=48) R of 0.74; and SDD (n=23) R of 0.67. For Sentinel-2, TSM (n=53) R of 0.72; Chla (n=54) R of 0.64; SDD (n=33) R of 0.89. The selected models were then applied to cloud-free images from 2013 to 2022 to provide modeled TSM, Chla, and SDD values. These were used for spatiotemporal analysis, encompassing cluster analysis, sample comparisons, and trend analysis. Three distinct regions within Lake Pampulha were identified, with the upstream region displaying poorer conditions for TSM, Chla, and SDD. Restoration measures have contributed positively to improving water quality in Lake Pampulha; however, rainy periods pose a challenge due to water quality deterioration associated with urban runoff. Remote sensing has proven to be a robust tool for assisting managers in assessing recovery measures in urban lakes.

Particle composition-based water classification method for estimating chlorophyll-a in coastal waters from OLCI images

The complex composition of seawater presents significant challenges for accurately estimating biogeochemical data through optical measurements, both in situ and via satellite data. To address the regional applicability of single bio-optical or remote sensing algorithms caused by these challenges, we explored a water optical classification method based on inherent optical properties and particle composition. The ratio of organic particulate matter to total suspended particulate matter concentration (POM/SPM) serves as an optical discriminator of water bodies based on the proportions of organic and mineral particles. The boundary value is determined by the relationship between the particulate backscattering coefficient bbp(λ) and POM/SPM. By analyzing in situ data collected from the coastal waters of Qinhuangdao in the Bohai Sea, China, we developed empirical algorithms to estimate both the POM/SPM ratio and chlorophyll-a (Chl-a) concentration, the latter being a key parameter derived from current ocean remote sensing that indicates phytoplankton abundance. The evaluation of our algorithms demonstrates that accounting for POM/SPM variations significantly improves Chl-a estimate accuracy across the optically-complex coastal waters near Qinhuangdao compared to algorithms that do not consider changes in particle composition, such as the well-known OC4Me algorithm. Furthermore, we determined the distribution of monthly averaged Chl-a concentration and POM/SPM ratio on the coast of Qinhuangdao, Bohai Sea, in 2023. Our results show, for the first time, that the monthly average variations of the POM/SPM ratio in the Bohai Sea and Chl-a concentrations exhibit pronounced seasonal fluctuations.

Long-term monitoring of total suspended matter concentration in fishponds in the Guangdong-Hong Kong-Macao Greater Bay Area.

Excessive total suspended matter (TSM) concentrations can exert a considerable impact on the growth of aquatic organisms in fishponds, representing a significant risk to aquaculture health. This study revised existing unified models using empirical data to develop an optimized TSM retrieval model tailored for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (R2 = 0.69, RMSE = 7.78mg/L, and MAPE = 0.23). Employing top-of-atmosphere (TOA) reflectance data from Landsat satellites, accessed via Google Earth Engine (GEE), the refined model facilitated the generation of TSM datasets for fishponds across the GBA from 1986 to 2019. Over these 34years, there was a marked decline in TSM levels in fishponds, with an approximate 50% reduction in annual average TSM. This decline was particularly notable in the northern, western, and eastern GBA regions, leading to a spatial distribution characterized by higher TSM concentrations in the central and southern regions and lower concentrations in the peripheral regions. Seasonally, TSM levels in GBA fishponds are significantly higher during spring and summer compared to autumn and winter. Regarding natural factors, wind speed shows a significant positive correlation with long-term TSM fluctuations in these environments (p < 0.01). Stocking density, regulated artificially, emerges as a pivotal factor affecting TSM fluctuations. Specifically, TSM concentrations are elevated during periods of high stocking density in the rapid growth phase, and decrease during the mature and harvesting phases when stocking densities are reduced. Furthermore, fishponds situated in impervious areas exhibit significantly higher TSM concentrations compared to those in cropland or forested areas. The economic costs associated with aquaculture drive variations in stocking densities across different land uses within the GBA, contributing to the observed spatial variations in TSM levels. Given the status of the GBA as one of China's most advanced aquaculture regions, the insights from this study hold substantial value from both economic and ecological viewpoints.

Long-Term Evaluation of GCOM-C/SGLI Reflectance and Water Quality Products: Variability Among JAXA G-Portal and JASMES

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance (Rrs) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides Rrs products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution Rrs products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal Rrs products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates Rrs values at shorter wavelengths, particularly at 412 nm. While the Rrs accuracy at 412 nm is relatively low, G-Portal’s Rrs products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower Rrs magnitudes and Chla concentrations below 5 mg/m3. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in Rrs data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments.

An Interpretable CatBoost Model Guided by Spectral Morphological Features for the Inversion of Coastal Water Quality Parameters

Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider the morphological characteristics of the spectrum. To address this limitation, we used Sentinel-3 OLCI data to construct an interpretable CatBoost model guided by spectral morphological characteristics for remote sensing monitoring of Chla and TSS along the coast of Fujian. The results show that the coastal waters of Fujian Province can be divided into five clusters, and the areas of different clusters will change with the alternation of seasons. Clusters 2 and 4 are the main types of coastal waters. The CatBoost model combined with spectral feature engineering has a high accuracy in predicting Chla and TSS, among which Chla is slightly better than TSS (R2 = 0.88, MSE = 8.21, MAPE = 1.10 for Chla predictions; R2 = 0.77, MSE = 380.49, MAPE = 2.48 for TSS predictions). We further conducted an interpretability analysis on the model output and found that the combination of BRI and TBI indexes composed of bands such as b8, b9, and b10 and the fluctuation of spectral curves will have a significant impact on the prediction of model output. The interpretable CatBoost model based on spectral morphological features proposed in this study can provide an effective technical means of estimating the chlorophyll-a and total suspended particulate matter concentrations in the coastal areas of Fujian.

Identifying TSM dynamics in arid inland lakes combining satellite imagery and wind speed

The Chah Nimeh Reservoirs (CNRs), located in Iran’s Sistan region, are critical arid inland lakes that support agriculture and supply drinking water to the region. A major concern regarding water quality in these reservoirs is the concentration of Total Suspended Matter (TSM), which has significant implications for both the local communities and the aquatic ecosystem. This study demonstrates the complicated connection between wind speed and TSM values, indicating that wind speed is an essential variable influencing TSM concentrations. By combining in-situ wind measurements with satellite imagery, we mapped TSM distributions using empirically derived models. Our investigation identified the Long Short-Term Memory (LSTM) and Attention-Mechanism-Based Dynamic Inner Partial Least Squares Long Short-Term Memory (ADiPLS-LSTM) models as effective predictors of TSM levels. Specifically, two distinct machine learning models were utilized: Rrs2TSM, which relies solely on Remote Sensing Reflectance (Rrs(λ)), and the more advanced Rrs&Wind2TSM, which incorporates both Rrs(λ) and wind data. The ADiPLS-LSTM model generated results with minimal variance, showing exceptional consistency. The Root Mean Square Error (RMSE) remains low at 0.617, while the R2 value maintained continually elevated at 0.997. It’s interesting to note that modest variations in TSM concentrations occurred when wind speed data was incorporated into the algorithm, especially during times of greater wind speeds. This study emphasizes the significant impact of wind speed on TSM dynamics in arid inland lakes, showcasing the value of satellite imagery in conducting such analyses. The findings provide essential insights for developing strategies that promote sustainable water resource management.

Assessment of GOCI-II satellite remote sensing products in Lake Taihu

IntroductionThe Geostationary Ocean Color Imager-II (GOCI-II), launched on February 19, 2020, offers increased observation times throughout the day and higher spatial resolution compared to its predecessor, the Geostationary Ocean Color Imager (GOCI), launched in 2010. To ensure the reliability of GOCI-II data for practical applications, the accuracy of remote sensing products needs to be validated. This study uses in situ data from Lake Taihu for validation.MethodsWe assessed the accuracy of GOCI-II remote sensing products, including remote sensing reflectance derived using two atmospheric correction algorithms: ultraviolet (UV) and near-infrared (NIR). The study also evaluated the accuracy of derived parameters, such as chlorophyll-a (Chl-a) concentration, total suspended matter (TSM) concentration, and phytoplankton absorption coefficient (aph), based on these atmospheric correction algorithms. In situ measurements from Lake Taihu were used as ground truth data for validation.ResultsOur results revealed that the UV atmospheric correction algorithm provided higher accuracy in Lake Taihu compared to the NIR algorithm. The average absolute percentage deviations (APDs) for remote sensing reflectance across different bands were: 25.17% (412 nm), 29.69% (443 nm), 22.27% (490 nm), 19.38% (555 nm), 36.83% (660 nm), and 33.0% (680 nm). Compared to NIR-derived products, the UV algorithm showed improved accuracy for Chl-a concentration, TSM concentration, and aph, with reductions in APD values by 16.92%, 3.32%, and 10.91%, respectively. When applying UV correction, the 412 nm band performed better than the 380 nm band, likely due to a lower signal-to-noise ratio at 380 nm and smaller extrapolation errors at 412 nm.DiscussionWhile the NIR algorithm is suitable for open ocean waters, the UV algorithm demonstrated higher accuracy in turbid environments such as Lake Taihu. Therefore, a combined UV-NIR atmospheric correction algorithm may be more effective for handling various types of water environments. Additionally, further research is needed to develop more suitable retrieval algorithms for Chl-a concentration and aph in eutrophic waters to improve accuracy.

Analysis of the Generation and Spatiotemporal Distributions of Dust During Tunnel Construction

The dust generated during tunnel construction poses serious health risks to workers, as it not only causes respiratory obstruction but also leads to pneumoconiosis and respiratory failure after prolonged exposure. However, most existing studies focus on specific construction stages or particular particle sizes and often assume an ideal airflow, neglecting the complex flow fields, vortex effects, and dust composition variations at different stages in tunnel and mine construction. This study systematically analyzes the spatiotemporal distribution characteristics of dust at various stages of tunnel construction and proposes targeted prevention and control strategies. On the basis of measured data from three construction stages—the working face, initial support, and secondary lining stages—and SPSS 27 statistical analysis, a dynamic analysis was conducted on the concentration and distribution patterns of total suspended particulates (TSPs) and particulate matter of different sizes (PM10, PM4, PM2.5, and PM1). The results show that coarse particles dominate during the working face stage, whereas fine particles gradually accumulate during the initial support and secondary lining stages. Finally, this work establishes a dust concentration–excavation time/tunnel depth equation and proposes targeted dust control measures. These findings offer important practical value for enhancing construction safety and air quality.

Enhancing indoor air quality and cardiopulmonary health in patients with asthma by photocatalytic oxidation and filters air cleaner

BackgroundAir purifiers can enhance indoor air quality and health outcomes, and studies have primarily focused on filters and particulate matter (PM) in households. Photocatalytic oxidation (PCO) is a promising technique for eliminating gaseous pollutants and bioaerosols. However, no field study was conducted in household. Therefore, this study evaluated the effects of the PCO and PCO + filters intervention on indoor air pollutants and cardiopulmonary endpoints in households. MethodsA randomized, double-blind crossover clinical trial was conducted. Indoor air pollutants, including PM, bioaerosols, and gaseous pollutants and cardiopulmonary endpoints including lung function, fractional exhaled nitric oxide (FeNO), respiratory symptoms, and blood pressure were assessed before and after intervention. FindingsThis was the first study to evaluate the effects of PCO and PCO + filters interventions on indoor air pollutants and cardiopulmonary health in households. Indoor total volatile organic compounds (TVOC) and sulfur dioxides (SO2) significantly reduced after PCO intervention, however, we also observed the significant reduction in percentage of predicted values of forced vital capacity (FVC%) and forced expiratory volume in 3 s (FEV3%) and increased in FeNO after 13 days of PCO intervention. The PCO + filters intervention significantly reduced the levels of indoor PM1, PM2.5, PM4, PM10, total suspended particulate matter, ultrafine particles, airborne bacteria, fungi, endotoxin, mites, TVOC, nitrogen dioxide, and SO2, and marginal reduction in carbon monoxide. However, indoor carbon dioxide significantly increased after PCO/PCO + filters intervention. As for cardiopulmonary health, FVC%, and FEV1 % marginally increased 7 days after the PCO + filters intervention.

Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers

ABSTRACT The Multispectral Imager (MII) onboard the newly launched SDGSAT-1 holds significant promise for monitoring and understanding variations in water quality within nearshore water systems, with an impressive spatial resolution of 10 m, seven spectral bands in the visible and near-infrared domain and a wide swath of 300 km. This paper evaluates the MII data in estimating the concentration of total suspended matter (TSM) in rivers and estuaries from clear to extremely turbid conditions. For atmospheric correction, the ACOLITE model showed superior performance compared to the FLAASH and 6SV, with average unbiased relative error (AURE) ranging from 11.8% to 26.4% in the four visible bands of MII. Through comparison, a TSM model based on the visible band ratio (i.e., Rrs (660)/Rrs (560)) was proposed to ensure the application of MII data in both clear and turbid waters, with an overall AURE of 40.9%. The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.

Agricultural Valorization of Treated Effluent from Korhogo Wastewater Treatment Plant

Aims: The main objective is to evaluate the effluents of Korhogo’s wastewater treatment plant for irrigation and fertilization in agriculture. Study Design: Characterization of outlet treated wastewaters has been firstly done. Then their suitability for irrigation has been determined and finally their fertilizing power has been assessed. Place and Duration of Study: Department of geosciences, Korhogo’s wastewater treatment plant and laboratory. Sampling and analyses were carried out in January 2023, before agricultural field implementation started in February 2023. Methodology: Firstly, Physico-chemical and microbiological analyses, allowed characterization of outlet treated wastewaters. A determination of their suitability for irrigation was then done through Sodium Adsorption Ratio calculation and water classification with Diagram of Richards. An assessment of their fertilizing capacity was finally carried out using these waters for irrigating and fertilizing crop plots (3x2 m), and consisting of determining firstly their biodegradability index, then their nutrient contents (Nitrogen, Phosphorus and potassium), and the influence of their use on crop development and yield. Results: The results show that several parameters meet standards for discharge of treated wastewater except total nitrogen, suspended matters and total phosphorus (67.67 mg/l, 271.33 mg/l and 18.30 mg/l). Sodium Adsorption Ratio (SAR) value=3.78 and C3S1 class indicate that these treated effluents can be used for irrigation without salinization risk. As fertilizer, treated effluents brought to crops respectively 603 kg/ha of nitrogen, 162 kg/ha of phosphorus and 99 kg/ha of potassium for maize; and 368.5 kg/ha of nitrogen, 99 kg/ha of phosphorus and 60.5 kg/ha of potassium respectively for Okra. A yield of 12.2 kg of fresh maize and 3.15 kg of fresh okra was obtained for the plot irrigated by treated effluents, compared with 4.80 kg of fresh maize and 0.36 kg of fresh okra for the plot irrigated by well water. Conclusion: This study has shown that outlet treated wastewater from Korhogo’s wastewater treatment plant is suitable for crops irrigation, contains sufficient nutrients enhancing crops development and yield.

Progress in Remote Sensing of Heavy Metals in Water

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms.

Attention-Enhanced Urban Fugitive Dust Source Segmentation in High-Resolution Remote Sensing Images

Fugitive dust is an important source of total suspended particulate matter in urban ambient air. The existing segmentation methods for dust sources face challenges in distinguishing key and secondary features, and they exhibit poor segmentation at the image edge. To address these issues, this paper proposes the Dust Source U-Net (DSU-Net), enhancing the U-Net model by incorporating VGG16 for feature extraction, and integrating the shuffle attention module into the jump connection branch to enhance feature acquisition. Furthermore, we combine Dice Loss, Focal Loss, and Activate Boundary Loss to improve the boundary extraction accuracy and reduce the loss oscillation. To evaluate the effectiveness of our model, we selected Jingmen City, Jingzhou City, and Yichang City in Hubei Province as the experimental area and established two dust source datasets from 0.5 m high-resolution remote sensing imagery acquired by the Jilin-1 satellite. Our created datasets include dataset HDSD-A for dust source segmentation and dataset HDSD-B for distinguishing the dust control measures. Comparative analyses of our proposed model with other typical segmentation models demonstrated that our proposed DSU-Net has the best detection performance, achieving a mIoU of 93% on dataset HDSD-A and 92% on dataset HDSD-B. In addition, we verified that it can be successfully applied to detect dust sources in urban areas.

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions.

Retrieval of total suspended matter concentration in the yellow river estuary offshore area based on QAA-RF model

ABSTRACT Total suspended matter is one of the crucial water quality parameters for both inland and marine environments, and a key role in evaluating the water quality of estuaries and offshore areas. Each year, the Yellow River carries a significant amount of sediment into the semi-enclosed Bohai Sea, results in a prolonged high concentration of total suspended matter in the offshore areas of the Yellow River Estuary. This study focuses on the offshore region of the Yellow River Estuary in China. Utilizing Sentinel-2 satellite imagery data from 2020 to 2023 and in-situ measured data from August 2020 to August 2022, to address the lack of physical mechanisms currently studied in machine learning retrieval methods, a model that integrates the physics-driven Quasi-Analytical Algorithm (QAA) and data-driven Random Forest (RF) is employed for the retrieval of total suspended matter concentration in the study area. The fused model (QAA-RF) is compared and analysed against regression models and standalone machine learning models. The results indicate that the accuracy of machine learning retrieval models is consistently higher than that regression models. The QAA-RF model demonstrates the highest accuracy (R 2 = 0.87, MAE = 5.01 mg L−1, RMSE = 6.39 mg L−1). Based on the QAA-RF model and the imagery data, monthly total suspended matter concentration is conducted in the study area. The retrieval results indicates that: (1) the total suspended matter concentrations is primarily concentrated in the near estuary region, with concentrations decreasing as distance from the estuary increases. (2) the total suspended matter concentration exhibits a distribution pattern with higher values in spring and winter, and lower values in summer and autumn. (3) the concentration of total suspended matter shows relatively small fluctuations at the annual scale from 2020 to 2023.

Chlorophyll-a and suspended matter variability in a data-scarce coastal-estuarine ecosystem

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change.

Spatial and seasonal variability of chlorophyll-a, total suspended matter, and colored dissolved organic matter in the Sundarban mangrove forest using earth observation and field data

The Sundarbans, the world's largest mangrove forest, confronts potential threats from various anthropogenic activities leading to degradation of its aquatic ecosystem. To examine the current status of the aquatic ecosystem, this study aimed to evaluate the spatial and seasonal fluctuation of three principal water quality attributes namely Chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and Colored Dissolved Organic Matter (CDOM) in the complex tidal river systems of the Sundarban mangroves forest using earth observation and in-situ data. A set of two bio-optical algorithms, Ocean color-2 (OC-2) and Ocean color-3 (OC-3), were applied to measure Chl-a concentration, Green/NIR and the Red/NIR band ratio algorithms were used for TSM and the Case-2 Regional Coast Color (C2RCC) processor in the SNAP software was applied to obtain CDOM concentration in study area. A total of 50 in-situ samples were collected during post-monsoon and pre-monsoon to validate the results. Our results clearly demonstrated seasonal variability with higher Chl-a concentrations in post-monsoon than pre-monsoon. This was due to the OC-2 algorithm which produced better results with R2 = 0.73, RMSE = 0.27 for post-monsoon and R2 = 0.55, RMSE = 0.32 for pre-monsoon. Whilst, TSM concentration performed the best with R2 = 0.77; RMSE = 15.82 and R2 = 0.65; RMSE = 33.96 in post-monsoon and pre-monsoon according to the Green/NIR band ratio method. The nearshore and narrow waterway regions had the highest concentrations of TSM and Chl-a, whereas the offshore regions had the lowest. Strong association were observed between the in-situ and satellite derive absorption coefficient, aCDOM (m−1). The R2 for a CDOM during pre-monsoon was 0.65 and throughout the post-monsoon, it was 0.74. Pre-monsoon concentrations were found to be higher due to marine sources and higher wind speeds, possibly due to sediment resuspension. This kind of baseline evaluation will help to detect threats, direct preventive measures for the protection of biodiversity, and deepen our knowledge of these distinct ecosystems. The results will help develop flexible management and preservation plans that can adjust to both natural and man-made changes.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Spatial and Temporal Variations of Total Suspended Matter Concentration during the Dry Season in Dongting Lake in the Past 35 Years

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake’s dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an R2 of 0.84, RMSE of 18.94 mg/L, and MRE of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes.

Tiber River-Driven Chlorophyll-a and Total Suspended Matter Dynamics and Their Impacts along the Central Tyrrhenian Sea Coast: A Sentinel-2 Approach

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating the coastal Chl-a concentration levels. The present research primarily focuses on understanding the significance of Tiber River −driven spatial dynamics of Chl-a and TSM along the central Tyrrhenian Sea coasts. The research also focuses on evaluating the applicability of Sentinel-2 and identifying a suitable method for estimating Chl-a and TSM from Sentinel-2. Neural networks and dark spectrum fitting techniques were applied using multiple algorithms to estimate the dynamic distribution of Chl-a and TSM driven by the Tiber River in the study area. Multiple statistical analyses were performed, and statistically significant relationships were observed. The Case-2 Regional Coast Colour Neural Network (C2RCC-Net) outperformed all other algorithms, with an R2 value of 0.903 for Chl-a and an R2 value of 0.966 for TSM. Furthermore, the present research also identified a positive pixel to pixel spatial correlation between Chl-a and TSM in all four seasons, highlighting the positive impact of Tiber River on maintaining Chl-a levels along the coasts of Tyrrhenian Sea. This stands in contrast with the negative trend seen in the Mediterranean scale.