Search Results (693)

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. Full article

Cadmium (Cd) is a non-essential heavy metal and a pervasive pollutant in agricultural soils. Despite numerous studies investigating Cd accumulation and tolerance in plants, there is a lack of systematic analysis of how various physio-biochemical indexes respond to Cd toxicity, particularly their indicative role in plant tolerance mechanisms. A pot experiment was conducted in greenhouse to assess the differences in Cd accumulation and tolerance among three Brassica napus L. cultivars (‘Hanyou 2’, ‘Hanyou 3’, and ‘Hanyou 16’) under the treatments of CK (0.18 mg kg⁻¹ in soil), T1 (2.18 mg kg⁻¹ in soil), T2 (4.18 mg kg⁻¹ in soil), and T3 (8.18 mg kg⁻¹ in soil). All three cultivars exhibited high tolerance indexes (TIs) and strong Cd tolerance when exposed to a Cd concentration of 2.18 mg kg⁻¹ in soil (T1). There were significant positive correlations between TI and chlorophyll a (Chla), chlorophyll b (Chlb), carotenoids (Car), net photosynthetic rate (Pn), transpiration rate (Tr), and activities of antioxidant enzymes and non-enzymatic antioxidants such as superoxide dismutase (SOD), ascorbate peroxidase (APX), glutathione (GSH), and ascorbic acid (ASA), while negatively correlating with intercellular CO₂ concentration (Ci) and malondialdehyde (MDA) content. These findings underscore the significant indicative role of these physio-biochemical indexes in elucidating Cd tolerance mechanisms in B. napus and may be used in breeding programs to develop cultivars with a high Cd-tolerance but low Cd uptake profile. However, this was a pot experiment only. Field experiments might be more useful in the future. Full article

In constructed wetlands (CWs) with multiple plant communities, population structure may change over time and these variations may ultimately influence water quality. However, in CWs with multiple plant communities, it is still unclear how population structure may change over time and how these variations ultimately influence water quality. Here, we established a CW featuring multiple plant species within a polder to investigate the variation in plant population structure and wastewater treatment effect for drainage water over the course of one year. Our results showed that the total species decreased from 52 to 36; however, 20 established species with different ecological types (emerged or submerged) remained with the same functional assembly for nutrient absorption, accounting for 94.69% of relative richness at the initial stage and 91.37% at the last state. The Shannon index showed no significant differences among the initial, middle, and last states. Meanwhile, regarding nutrient content, the total phosphorus (TP) concentration decreased by 57.66% at the middle stage and by 56.76% at the last state. Total nitrogen (TN) decreased by 50.86% and 49.30%, respectively. Chemical oxygen demand (COD) decreased by 36.83% and 38.47%, while chlorophyll a (Chla) decreased by 72.36% and 78.54%, respectively. Redundancy analysis (RDA) results indicated that none of the selected environmental variables significantly affected the species community except for conductivity. Our findings suggest that when utilizing multiple species for CWs, it is essential to focus on the well-established species within the plant community. By maintaining these well-established species, water purification in CWs can be sustained. Full article

In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term Memory (LSTM) neural network. Adopting the permutation feature importance method, time sequences of several geographical and physical variables, including longitude, latitude, time, sea surface temperature, salinity, sea level anomaly, wind field, etc., were selected and integrated to the reconstruction model as input parameters. Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. The average unbiased percentage difference (UPD) of reconstructed Chl-a concentration is 11.7%, which is 2.9%, 7.6%, 10.6%, and 10.5% smaller than that of the other four models, respectively. Over the majority of the study area, the root mean square error is less than 0.05 mg/m³ and the UPD is below 10%, indicating that the LSTM model has considerable potential in accurately reconstructing sea surface Chl-a concentrations in shallow waters. Full article

Rivers play a crucial role in nutrient cycling, yet are increasingly affected by eutrophication due to anthropogenic activities. This study focuses on the Barato River in Hokkaido, Japan, employing an integrated approach of field measurements and Sentinel-2 satellite remote sensing to monitor eutrophication as the river experiencing huge sewage effluents. Key parameters such as chlorophyll-a (Chla), dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and Secchi Disk Depth (SDD) were analyzed. The developed empirical models showed a strong predictive capability for water quality, particularly for Chla (R² = 0.87), DIP (R² = 0.61), and SDD (R² = 0.82). Seasonal analysis indicated peak Chla concentrations in October, reaching up to 92.4 μg/L, alongside significant decreases in DIN and DIP, suggesting high phytoplankton activity. Advanced machine learning models, specifically back propagation neural networks, improved the prediction accuracy with R² values up to 0.90 for Chla and 0.83 for DIN. Temporal analyses from 2018 to 2022 consistently revealed the Barato River’s eutrophic state, with severe eutrophication occurring for 33% of the year and moderate for over 50%, emphasizing the ongoing nutrient imbalance. The strong correlation between DIP and Chla highlights phosphorus as the main driver of eutrophication. These findings demonstrate the efficacy of integrating remote sensing and machine learning for dynamic monitoring of river eutrophication, providing critical insights for nutrient management and water quality improvement. Full article

The objective of this study is to better identify the state of eutrophication of coastal waters along the southern coast of the city of Jeddah in the Red Sea. Thirty-six samples from surface seawater were collected during the spring and autumn of 2021. Water temperature, pH, salinity, dissolved oxygen (DO), nutrients, and chlorophyll-a (Chl-a) content were examined as a guide of water quality indicators. The present data revealed low levels of Chl-a content (in the range of 0.11–0.24 µg L⁻¹). The average concentrations of DIN (dissolved inorganic nitrogen) forms follow the order NO₃-N > NH₄-N ~ NO₂-N (representing about 11.4–29.4% of the total nitrogen). To investigate the trophic status and water quality, numerical indicators were applied to the results of the analysis of chemical variables (NH₄-N, NO₃-N, and PO₄-P) and the biological analysis (Chl-a) in the aqueous environment within the study area. These indicators are simplified based on the specialist, the non-specialist, the decision-maker, and the one responsible for managing the coastal areas. We also obtain through this method a single numerical value that expresses the state of the coastal waters. According to the analysis of phosphorus and nitrogen data and a trophic index (TRIX), the study area’s trophic status was determined as oligotrophic, due to low nutrient concentrations in the seawater. The current study identified a total of 58 species of phytoplankton comprised four classes in the investigated areas; Bacillariophyceae was the dominant algal class (Diatoms 30 species), followed by Chlorophyceae (9 species), Dinophyceae (11 species), and Cyanophyceae (8 species). Seasonally, spring recorded the highest value of total phytoplankton, recording a value of 251 × 10³ cells/L with a percentage of 61%, while autumn recorded the lowest value of 186 × 10³ cells/L with a percentage of 39%. Phytoplankton classes can be arranged in order of prevalence as follows: Bacillariophyceae >> Dinophyceae > Chlorophyceae > Cyanophyceae. Full article

The subsurface chlorophyll maximum depth (SCMD) is an indicator of the spatial activity of marine organisms and changes in the ecological environment. Ubiquitous mesoscale eddies are among the important factors regulating the Kuroshio–Oyashio confluence region. In this study, we use satellite altimeter observations and high-resolution reanalysis data to explore seasonal variations in the SCMD and its responses to different types of eddies based on methods of composite averaging and normalization. The results show that variations in the SCMD induced by the evolution of the eddies were prominent in the summer and autumn. The monopoles of the SCMD exhibited internally shallow and externally deep features in the cyclonic eddies (CEs), while the contrary trend was observed in the anticyclonic eddies (ACEs). The SCMD was positively correlated with the intensity of the eddies and sea surface temperature, and was negatively correlated with the depth of the mixed layer. These correlations were more pronounced in the CEs (summer) and ACEs (autumn). Both the CEs and ACEs prompted the westward transport of chlorophyll-a (Chl-A), where ACEs transported it over a longer distance than the CEs. Full article

To examine the bio-optical response to coastal upwelling, we measured inherent optical properties (IOPs) and biogeochemical parameters simultaneously off Hainan Island in the northwest part of the South China Sea (SCS) during late summer 2013. Bio-optical relationships between IOPs and phytoplankton were used for calculating vertical profiles of the total chlorophyll a concentration (Chl-a) and the absorption by coloured detrital matter (CDM). These bio-optical properties, which showed distinct horizontal and vertical distributions across the continental shelf, were strongly influenced by upwelling processes, as well as the shelf topography. Phytoplankton biomass and CDM absorption in surface waters showed much higher values along the coast, with their spatial distributions related to topographic variability. Vertical distributions of phytoplankton were characterised by a subsurface chlorophyll maximum (SCM) layer. The strongest SCM (Chl-a = 4.22 mg m⁻³) was observed at 24 m depth in coastal waters near the northeast cape of Hainan Island. The depth of the SCM varied between 16 and 60 m at different stations, appearing to coincide with the isotherm of 22 °C. The SCM depth was inversely correlated with the magnitude of the SCM. Different shapes of Chl-a profiles were observed, which suggested that the vertical distributions of phytoplankton biomass were driven by different environmental factors. Elevated concentrations of CDM were mainly observed near the bottom, which suggest that the benthic nepheloid layer may be an important source of detrital material. The relationship between the absorption coefficient of CDM at 443 nm, a_CDM(443), and Chl-a exhibited distinct differences between waters in upper ocean and in bottom layers, with the threshold depth being modulated by shelf topography. Our results highlight the utility of bio-optical observations with high resolution for better understanding the coupling between physical forcing and biogeochemical variability. Full article

The East Sea is a jointly managed maritime area of Korea, Russia, and Japan, where the frequency of strong typhoons is anticipated to increase with climate change, affecting its marine ecosystem and regional climate regulation. This study investigated the environmental and ecological impacts of summer typhoons entering the East Sea by analyzing satellite-derived chlorophyll-a (Chl-a) data, Argo float measurements, and ERA5 wind data. Our findings revealed that summer typhoons generally increased surface Chl-a concentrations by 65.4%, with typhoon intensity substantially influencing this process. Weak typhoons caused marginal Chl-a increases attributed to redistribution rather than nutrient supply, whereas normal and strong typhoons increased Chl-a through enhanced vertical mixing and nutrient upwelling in the East Sea. Stronger typhoons notably impacted the mixed layer depth and isothermal layer depth, leading to greater Chl-a concentrations within the strong wind radius. However, the increased Chl-a magnitude was lower than that of other strong typhoons in other regions. The East Sea uniquely responds to typhoons with fewer upper environment changes, possibly due to a stable barrier layer limiting vertical mixing. These findings underscore the importance of continuous monitoring and integrated observational methods in order to better understand the ecological effects of typhoons, particularly as their intensity increases with climate change. Full article

In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R² values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. Full article

An online dissolved inorganic carbon (DIC) monitoring system was produced to achieve high spatial and temporal resolution in DIC data from the western Taiwan Strait (WTS) during the summer. Surface seawater DIC, salinity, dissolved organic carbon (DOC), Chl-a, and NO₃⁻ samples were collected, as well as the vertical profiles of DIC, to understand DIC variations in the WTS. The results showed that the range of DIC levels in the surface seawater from the WTS was from 1.68 to 2.21 mmol/L (Mmol), with an average of 1.93 ± 0.19 Mmol, which was consistent with the sampling results using titration determination, with an average of 1.98 ± 0.12 Mmol. A high correlation (R² = 0.96) was presented between the online monitoring and sampling detection of DIC, indicating that DIC could be measured with high accuracy using the online monitoring system. The spatial distribution of DIC was similar to that of salinity, but it was different from that of DOC and Chl-a. The DIC concentration positively correlated with salinity (R² = 0.51) and presented a negative correlation (R² = 0.92) with seawater temperature. However, the surface seawater DIC was almost independent from DOC and Chl-a in the observation sea areas. The DIC levels first increased and then decreased with the depth, with the highest DIC concentration occurring in the subsurface water at about 10 m, which was similar to the profiles of salinity and Chl-a in the northern and southern areas of the WTS. The profiles of DIC, salinity, NO₃⁻, and Chl-a were almost independent from the water depth in the central WST. This indicated that seawater DIC in the WTS was mainly affected by seawater temperature, salinity, and the vertical mixing of sea water, but it was less impacted by TOC and Chl-a. Full article

Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. Full article

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. Full article

This work aimed to characterize the impact of copper (Cu), at environmentally relevant concentrations, using the freshwater microalga Raphidocelis subcapitata. Algae were incubated with 33 or 53 µg/L Cu, in OECD medium, and toxic impacts were evaluated over 72 h, using different cellular and biochemical biomarkers. The exposure to 33 µg/L Cu had an algistatic effect: slowing growth and reducing algal population (53%, at 72 h) without compromising the cell membrane. This Cu concentration promoted a transient reduction in chlorophyll a (chla) content and typical markers of oxidative stress: increased levels of reactive oxygen species (ROS), augmented catalase (CAT) activity, and lipid peroxidation (malondialdehyde, MDA). Algae exposed to 53 µg/L Cu, suffered a severe effect with a 93% reduction in the number of cells, 50% decrease in chla content, and diminished (17%) maximum photochemical quantum yield of PSII (F_v/F_m). This population also presented increased levels of ROS and MDA, 33 and 20 times higher than the control, respectively, at 72 h, augmented CAT activity, and permeabilized cell membrane (5%, at 72 h). These findings provide valuable insights into Cu toxicity in aquatic ecosystems, highlighting the biochemical and physiological impacts at environmentally relevant concentrations. Full article