Search Results (179)

Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of a_CDOM(355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R² = 0.85, RMSE = 0.71 m⁻¹). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R² = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: a_CDOM(355) in spring (6.23 m⁻¹) was higher than that in summer (5.3 m⁻¹) and autumn (4.74 m⁻¹). The a_CDOM(355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle. Full article

Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies. Full article

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management. Full article

Atmospheric correction, the removal of the atmospheric signal from a satellite image, still poses a challenge over optically complex coastal water. Here, we present the first atmospheric correction validation study performed in optically complex Norwegian fjords. We compare in situ reflectance measurements and chlorophyll-a concentrations from Western Norwegian fjords with atmospherically corrected Sentinel-3 Ocean and Land Colour Instrument observations and chlorophyll-a retrievals. Measurements were taken in Hardangerfjord, Bjørnafjord and Møkstrafjord during a bright green coccolithophore bloom in May 2022, and during a period of no apparent discoloration in April 2023. Coccolithophore blooms generally peak in the blue region (490 nm), but spectra measured in this bloom peaked in the green region (559 nm), possibly due to absorption by colored dissolved organic matter (a_CDOM(440) = 0.18 ± 0.01 m⁻¹) or due to high cell counts (up to 15 million cells/L). We tested a wide range of atmospheric correction algorithms, including ACOLITE, BAC, C2RCC, iCOR, L2gen, POLYMER and the SNAP Rayleigh correction. Surprisingly, atmospheric correction algorithms generally performed better during the bloom (average MAE = 1.25) rather than in the less scattering water in the following year (average MAE = 4.67), possibly because the high water-leaving radiances due to the high backscattering by coccolithophores outweighed the adjacency effect. However, atmospheric correction algorithms consistently underestimated water-leaving reflectance in the bloom. In non-bloom matchups, most atmospheric correction algorithms overestimated the water-leaving reflectance. POLYMER appears unsuitable for use over coccolithophore blooms but performed well in non-bloom matchups. Neither BAC, used in the official Level-2 OLCI products, nor C2RCC performed well in the bloom. Nine chlorophyll-a retrieval algorithms, including two algorithms based on neural nets, four based on red and near-infrared bands and three maximum band-ratio algorithms, were also tested. Most chlorophyll-a retrieval algorithms did not perform well in either year, although several did perform within the 70% accuracy threshold for case-2 waters. A red-edge algorithm performed best in the coccolithophore blooms, while a maximum band-ratio algorithm performed best in the following year. Full article

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated for the estimation of CDOM concentrations in surface lake water as the absorption coefficient at 440 nm ($a_{cdom}^{440}$, m⁻¹). Of the five atmospheric corrections evaluated, the QUAC (Quick Atmospheric Correction) method demonstrated the highest accuracy for the remote estimation of CDOM. The application of separate models for deep and shallow lakes yielded superior results compared to a combined model, with R² values of 0.76 and 0.82 and mean absolute percentage errors (MAPEs) of 14% and 22% for deep and shallow lakes, respectively. The spatio-temporal variability of CDOM was characterized over a five-year period using satellite-derived $a_{cdom}^{440}$ values. CDOM concentrations varied widely, with very low values in deep lakes and moderate values in shallow lakes. Additionally, significant seasonal fluctuations were evident. Lower CDOM concentrations were observed during the summer to early autumn period, while higher concentrations were observed in the winter to spring period. A gradient boosting regression tree analysis revealed that inter-lake differences were primarily influenced by the lake perimeter to lake area ratio, mean lake depth, and watershed area to lake volume ratio. However, seasonal CDOM variation was largely influenced by Lake Nahuel Huapi water storage (a proxy for water level variability at a regional scale), followed by precipitation, air temperature, and wind. This research presents a robust method for estimating low to moderate CDOM concentrations, improving environmental monitoring of North Andean Patagonian Lake ecosystems. The results deepen the understanding of CDOM dynamics in low-impact lakes and its main environmental drivers, enhance the ability to estimate lacustrine carbon stocks on a regional scale, and help to predict the effects of climate change on this important variable. Full article

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, a_cdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating a_cdom at the wavelength 412 nm (a_cdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, R_rs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite R_rs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate a_cdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m⁻¹ from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived a_cdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of a_cdom (412) is provided for a coastal transect in the south of Vietnam. Full article

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region. Full article

The Vibrio bacteria known to cause infections to humans and wildlife have been largely overlooked in coastal environments affected by beach wrack accumulations from seaweed or seagrasses. This study presents findings on the presence and distribution of potentially pathogenic Vibrio species on coastal beaches that are used for recreation and are affected by red-algae-dominated wrack. Using species-specific primers and 16S rRNA gene amplicon sequencing, we identified V. vulnificus, V. cholerae (non-toxigenic), and V. alginolyticus, along with 14 operational taxonomic units (OTUs) belonging to the Vibrio genus in such an environment. V. vulnificus and V. cholerae were most frequently found in water at wrack accumulation sites and within the wrack itself compared to sites without wrack. Several OTUs were exclusive to wrack accumulation sites. For the abundance and presence of V. vulnificus and the presence of V. cholerae, the most important factors in the water were the proportion of V. fucoides in the wrack, chl-a, and CDOM. Specific Vibrio OTUs correlated with salinity, water temperature, cryptophyte, and blue-green algae concentrations. To better understand the role of wrack accumulations in Vibrio abundance and community composition, future research should include different degradation stages of wrack, evaluate the link with nutrient release, and investigate microbial food-web interactions within such ecosystems, focusing on potentially pathogenic Vibrio species that could be harmful both for humans and wildlife. Full article

Researchers and environmental managers need big datasets spanning long time periods to accurately assess current and historical water quality conditions in fresh and estuarine waters. Using remote sensing data, we can survey many water bodies simultaneously and evaluate water quality conditions with greater frequency. The combination of existing and historical water quality data with remote sensing imagery into a unified database allows researchers to improve remote sensing algorithms and improves understanding of mechanisms causing blooms. We report on the development of a water quality database “EstuarySAT” which combines data from the Sentinel-2 multi-spectral instrument (MSI) remote sensing platform and water quality data throughout the coastal USA. EstuarySAT builds upon an existing database and set of methods developed by the creators of AquaSat, whose region of interest is primarily larger freshwater lakes in the USA. Following the same basic methods, EstuarySAT utilizes open-source tools: R v. 3.24+ (statistical software), Python (dynamic programming environment), and Google Earth Engine (GEE) to develop a combined water quality data and remote sensing imagery database (EstuarySAT) for smaller coastal estuarine and freshwater tidal riverine systems. EstuarySAT fills a data gap that exists between freshwater and estuarine water bodies. We are able to evaluate smaller systems due to the higher spatial resolution of Sentinel-2 (10 m pixel image resolution) vs. the Landsat platform used by AquaSat (30 m pixel resolution). Sentinel-2 also has a more frequent revisit (overpass) schedule of every 5 to 10 days vs. Landsat 7 which is every 17 days. EstuarySAT incorporates publicly available water quality data from 23 individual water quality data sources spanning 1984–2021 and spatially matches them with Sentinel-2 imagery from 2015–2021. EstuarySAT currently contains 299,851 matched observations distributed across the coastal USA. EstuarySAT’s primary focus is on collecting chlorophyll data; however, it also contains other ancillary water quality data, including temperature, salinity, pH, dissolved oxygen, dissolved organic carbon, and turbidity (where available). As compared to other ocean color databases used for developing predictive chlorophyll algorithms, this coastal database contains spectral profiles more typical of CDOM-dominated systems. This database can assist researchers and managers in evaluating algal bloom causes and predicting the occurrence of future blooms. Full article

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data. Full article

Autochthonic recalcitrant organic carbon is the most stable component in karst aquatic systems. Still, the processes of its generation and transformation remain unclear, which hinders the study of the mechanisms and quantitative calculations of carbon sinks in karst aquatic systems. This study collected water samples from the Li River, a typical surface karst river in Southwest China. Through in situ microbial cultivation and the chromophoric dissolved organic matter (CDOM) spectrum, changes in organic carbon components and their contents during the transformation of autochthonic dissolved organic carbon (Auto-DOC) to autochthonic dissolved recalcitrant organic carbon (Auto-RDOC) were analyzed to investigate the inert transformation processes of endogenous organic carbon. This study found that microbial carbon pumps (MCPs) promote the tyrosine-like component condensed into microbial-derived fulvic and humic components via heterotrophic bacteria metabolism, forming Auto-RDOC. During the dry season, the high level of Auto-DOC provides abundant organic substrates for heterotrophic bacteria, resulting in significantly higher Auto-RDOC production compared to the rainy season. This study provides fundamental information on the formation mechanisms of Auto-DOC in karst aquatic systems, which contributes to the assessment of carbon sinks in karst aquatic systems. Full article

For insight into the productive and storage mechanisms of biogas in the Qaidam Basin, efforts were made to investigate the groundwater recharge and the processes of hydrocarbon generation by CDOM-EEM (fluorescence excitation-emission matrix of Chromophoric dissolved organic matter) spectrum, hydrogen and oxygen isotopes, and geochemical characters in the central depression of the Qaidam Basin, China. The samples contain formation water from three gas fields (TN, SB, and YH) and surrounding surface water (fresh river and brine lake). The results indicate that modern precipitation significantly controls the salinity distribution and organic matter leaching in the groundwater system of the central depression of the Qaidam Basin. Higher salinity levels inhibit microbial activity, which leads to organic matter degradation and to gas generation efficiency being limited in the groundwater. The inhabitation effect is demonstrated by the notable negative correlation between the extent of organic matter degradation and its concentration with hydrogen and oxygen isotopes. The conclusion of this study indicated that modern precipitation emerges as a crucial factor affecting the biogas production and storage in the Qaidam Basin by influencing the ultimate salinity and organic matter concentration in the formation, which provides theoretical insight for the maintenance of modern gas production wells and the assessment of gas production potential. Full article

Water conservation efforts and studies receive special attention, versatile and constantly developing remote sensing methods especially so. The quality and quantity of algae fundamentally influence the ecosystems of water bodies. Inland lakes are less-frequently studied despite their essential ecological role compared to ocean and sea waters. One of the reasons for this is the small-scale surface extension, which poses challenges during satellite remote sensing. In this study, we investigated the correlations between remote-sensing- (via Seninel-2 satellite) and laboratory-based results in different chlorophyll-a concentration ranges. In the case of low chlorophyll-a concentrations, the measured values were between 15 µg L⁻¹ and 35 µg L⁻¹. In the case of medium chlorophyll-a concentrations, the measured values ranged between 35 and 80 µg L⁻¹. During high chlorophyll-a concentrations, the results were higher than 80 µg L⁻¹. Finally, under extreme environmental conditions (algal bloom), the values were higher than 180 µg L⁻¹. We also studied the accuracy and correlation and the different algorithms applied through the Acolite (20231023.0) processing software. The chl_re_mishra algorithm of the Acolite software gave the highest correlation. The strong positive correlations prove the applicability of the Sentinel-2 images and the Acolite software in the indication of chlorophyll-a. Because of the high CDOM concentration of Lake Naplás, the blue–green band ratio underestimated the concentration of chlorophyll-a. In summer, higher chlorophyll-a was detected in both laboratory and satellite investigations. In the case of extremely high chlorophyll-a concentrations, it is significantly underestimated by satellite remote sensing. This study proved the applicability of remote sensing to detect chlorophyll-a content but also pointed out the current limitations, thus assigning future development and research directions. Full article

Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables R_rs (452/497), R_rs (497/580), R_rs (497/618), and R_rs (684/618), which had the highest correlation with the CDOM absorption coefficient a_CDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m⁻¹. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R², absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m⁻¹, respectively, compared to the original algorithm’s test values of 0.722, 0.493, and 0.802 m⁻¹. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance. Full article

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration ($C_{\mathit{tsm}}$). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate $C_{\mathit{tsm}}$. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations $B_{6/3}$ and $B_{6/5}$ for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving $C_{\mathit{tsm}}$ was formed, which was successfully verified by using the modeling data (R² = 0.97, RMSE = 4.89 g/m³, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R² = 0.87, RMSE = 3.92 g/m³, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring $C_{\mathit{tsm}}$ was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of $C_{\mathit{tsm}}$, as well as monitoring and improving the water environment. Full article