Search Results (954)

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

Chelidonichthys spinosus is a common fish distributed in the Northwest Pacific. To ensure sustainable utilization, it is crucial to understand the potential impacts of environmental changes on habitat suitability. A habitat suitability index (HSI) model was developed for C. spinosus based on seasonal bottom trawling survey data and remote-sensing oceanographic data collected in the East China Sea and southern Yellow Sea from 2015 to 2017. The model examined the relationships between the spatio-temporal distribution of fish and environmental variables. The suitable ranges of sea bottom temperature, sea bottom salinity, depth and chlorophyll-a for C. spinosus in four seasons were identified. Each variable was then combined into the HSI model with weights defined by the Gradient Boosting Regression Tree. The spatial distribution and the centroid of the HSI revealed that C. spinosus exhibits a seasonal southward and southwestward migratory pattern throughout the year. This migration pattern indicates the suitable habitats for reproductive, feeding, and overwintering activities. The conservation of C. spinosus resources is a matter of great urgency, and some of the feasible proposals have been put forth in this purpose. 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

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications. 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

Increasing atmospheric carbon dioxide (CO₂) from human activities underscores the need to understand air–sea CO₂ flux in marine environments, particularly in marginal seas like the South China Sea (SCS). This research analyzes the spatial and temporal patterns of air–sea CO₂ flux across four typical regions of the SCS: the northern SCS, western SCS, SCS basin, and northeastern SCS. Our results show that the SCS serves as a carbon source from spring to autumn and shifts to a carbon sink in winter. The northern SCS exhibits strong carbon sink behavior during winter, transitioning to a source in warmer months, while the western SCS and SCS basin consistently act as carbon sources year-round, with summer peaks. The northeastern SCS acts as a source in warmer months, becoming a weak sink in winter. Partial correlation analysis reveals that temperature and wind speed significantly influence air–sea CO₂ flux, though regional differences exist. Notably, chlorophyll-a in the northern SCS is negatively correlated with air–sea CO₂ flux, indicating that high primary productivity enhances CO₂ absorption, whereas other regions show contrasting relationships. These findings provide valuable insights into the complex carbon cycle mechanisms in the SCS. 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

Lidar technology is increasingly applied to the inversion of oceanic biological parameters and optical properties based on empirical and semi-empirical bio-optical models. However, these models cannot be directly applied to inland waters due to their complex composition, and research on the biological parameters and optical properties of inland waters remains limited. In this study, the Fernald method was employed to retrieve the vertical distribution of optical properties in Qiandao Lake for the first time using shipborne lidar data obtained in June 2019. By quantifying the depth-resolved optical contributions of biological components, the vertical distributions of chlorophyll-a concentration were mapped with greater precision. The lidar-estimated optical properties exhibited characteristic spatiotemporal distributions, which were closely related to water quality. At the inflow of Xin’an River, the attenuation and scattering coefficient showed a gradual increase with depth. At the north–south-oriented reservoir area and the outflow of Qiandao Lake, an apparently continuous subsurface layer with the maximum signal occurred at approximately 3.5 m. The vertical distributions of chlorophyll-a profiles were consistently classified as subsurface chlorophyll maxima, with the maximum value of chlorophyll-a concentration fluctuating between 4 and 12 μg/L. The subsurface phytoplankton layer was observed at water depths ranging from 1.5 to 3.5 m, with a thickness of 3 to 6 m. Furthermore, the influences of lidar ratio S_p(z) and reference value b_bp(z_m) were discussed as significant sources of inversion error in the Fernald method. These results indicate that lidar technology holds great potential for the long-term monitoring of lakes. Full article

Understanding the interactions among biochar, plants, soils, and microbial communities is essential for developing effective and eco-friendly soil remediation strategies. This study investigates the role of cystamine-modified biochar (Cys-BC) in alleviating cadmium (Cd) toxicity in lettuce, comparing its effects to those of raw biochar. Lettuce plants were exposed to Cd stress (1–5 mg kg⁻¹), and the effects of Cys-BC were assessed by measuring plant biomass, photosynthetic efficiency, antioxidant activity, Cd bioavailability, and soil microbial diversity. Cys-BC significantly enhanced plant biomass, with increases in above-ground growth (40.54–44.95%) and root biomass (37.54–47.44%) compared to Cd-stressed controls. Photosynthetic parameters improved by up to 91.02% for chlorophyll-a content and 37.93% for the net photosynthetic rate. Cys-BC mitigated oxidative stress, increasing antioxidant activities by 73.83% to 99.39%. Additionally, Cys-BC reduced available Cd levels in the soil, primarily through enhanced cation exchange rather than changes in pH. Plant responses to Cd stress included increased glutathione reductase activity and elevated cysteine levels, which further contributed to Cd passivation. Microbial diversity in the soil increased, particularly among sulfur- and nitrogen-cycling bacteria such as Deltaproteobacteria and Nitrospira, suggesting their role in mitigating Cd stress. These findings highlight the potential of Cys-BC as an effective agent for the remediation of Cd-contaminated soils. Full article