Search Results (64)

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article

Elucidating the compositions, sources and mixing processes of dissolved organic matter (DOM) is crucial for a gaining deeper understanding of the coastal carbon cycle and global carbon budget. Hangzhou Bay (HZB), a vital estuary in China, receives freshwater inputs in the upper bay, borders the Changjiang River Estuary (CRE) to the north and is adjacent to Zhoushan Islands Region (ZIR) to the east. In HZB, the DOM sources and their compositions in estuaries remain unclear due to the complexity of this dynamic environment. In this study, we aimed to explore the chemical composition and sources of the DOM in the HZB and its adjacent coastal waters based on chromophoric DOM, fluorescent DOM indices and other hydrochemical parameters in the winter. The results showed that the DOM compositions in HZB have significant differences in the upper bay, middle bay and lower bay. The highest concentration of DOC was found in the CRE, close to the northern lower HZB, with high humification index (HIX), low biological index (BIX) and high proportion of humic-like fluorescent component (C1), indicating terrestrial inputs. In contrast, the DOM in the upper bay had high BIX and low HIX, being dominated by protein-like fluorescent components (C2 and C3), indicating an autochthonous source. The DOM in the middle bay showed mixed composition characteristics indicated by the chromophoric DOM (CDOM) and fluorescent DOM (FDOM) indices. Moreover, the terrestrial DOM transported via CDW intrusion accounted for a large proportion of the DOM in Northern HZB. Our study shows that, even in coastal estuaries with very strong hydrodynamics, the DOM composition can still retain its unique source signal, which, in turn, affects its migration and transformation processes. The results of this study provide supplement insights into the global carbon cycle and carbon budget estimation. Full article

The delimitation of an ecotone is an important reference for ecosystem conservation; however, the assessment of a marine ecotone from an ecological point of view represents a knowledge gap. The Yellow River Estuary (YRE) serves as both spawning and feeding grounds for numerous economically important organisms. Delineating the boundary of YRE and assessing the boundary change have great importance in maintaining its ecosystem health. This study attempts to apply a Moving Split Window (MSW) to determine marine boundary in YRE. Level 2 remote sensing satellite data spanning from 2012 to 2020 sourced from the Geostationary Ocean Color Imager (GOCI) were utilized. Chlorophyll-a, Chromophoric Dissolved Organic Matter (CDOM), and Total Suspended Solids (TSS) were employed as variables, with Squared Euclidean Distance (SED) serving as the determinant for identifying the marine ecological ecotone within the Yellow Estuary and its adjacent waters. Results indicate the following: (1) SED values exhibit distinct peaks and valleys, facilitating the accurate identification of marine ecotones via MSW. (2) Evident ecotones are observable in both the gate and coastal regions. (3) The influence range of TSS on the gate spans between 10 km and 14 km. In synthesis, the ensuing conclusions are drawn: MSW proves to be a reliable method for quantitatively determining ecotones in marine environments. Furthermore, MSW introduces a novel approach to the delineation of marine ecotones. Full article

The alpine peatlands in western Sichuan Province are currently experiencing aridification. To understand the effects of aridification on the characteristics of organic carbon release from alpine soils, the soil in the northwest Sichuan Plateau was investigated. Soil columns were incubated under different moisture conditions in situ and in the laboratory, and ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy were used to assess the soil dissolved organic carbon (DOC) levels. The results revealed that (1) the cumulative release of DOC from alpine soil in the northwest Sichuan Plateau decreased with decreasing moisture content. The cumulative release of soil DOC in the laboratory (0–5 cm soil reached 1.93 ± 0.43 g/kg) was greater than that from soil incubated in situ (0–5 cm soil reached 1.40 ± 0.13 g/kg); (2) the cumulative release of DOC in 0–5 cm soil exhibited the greatest response to changes in water content, and the cumulative release of DOC from the 0–5 cm soil layer (1.40 ± 0.13 g/kg) was greater than that from the 5–15 cm soil layer (1.25 ± 0.03 g/kg); and (3) UV-visible absorption spectra and 3D fluorescence spectral characteristics indicated that aridification increases the content of chromophoric dissolved organic matter (CDOM) components with strong hydrophobicity, especially tyrosine components (surface soil increased 39.59~63.31%), in alpine soil DOC. This increase in hydrophobic CDOM components enhances the aromaticity and degree of humification of DOC. Our results revealed that drought inhibits the release of soil DOC, which is unfavorable for the sequestration of organic carbon in alpine soils, potentially resulting in the loss of soil carbon pools and further degradation of alpine ecosystem functions. Full article

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

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

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

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

Chromophoric dissolved organic matter (CDOM) is a significant contributor to the biogeochemical cycle and energy dynamics within aquatic ecosystems. Hence, the implementation of a systematic and comprehensive monitoring and governance framework for the CDOM in inland waters holds significant importance. This study conducted the retrieval of CDOM in Lake Khanka. Specifically, we use the GBDT (R² = 0.84) algorithm which performed best in retrieving CDOM levels and an empirical relationship based on the situ data between CDOM and dissolved organic carbon (DOC) to indicate the distribution of DOC indirectly. The performance of the CDOM-DOC retrieval scheme was reasonably good, achieving an R² value of 0.69. The empirical algorithms were utilized for the analysis of Sentinel-3 datasets from the period 2016 to 2020 in Lake Khanka. The potential factors that contributed to the sources of DOM were also analyzed with the humification index (HIX). The significant relationship between CDOM and DOC (HIX and chemical oxygen demand (COD)) indicated the potential remote sensing application of water quality monitoring for water management. An analysis of our findings suggests that the water quality of the Great Khanka is superior to that of the Small Khanka. Moreover, the distribution of diverse organic matter exhibits a pattern where concentrations are generally higher along the shoreline compared to the center of the lake. Efficient measures should be promptly implemented to safeguard the water resources in international boundary lakes such as Lake Khanka and comprehensive monitoring systems including DOM distribution, DOM sources, and water quality management would be essential for water resource protection and government management. Full article

Along the shoreline of the White Sea, due to the post-glacial uplift of the coast, some water bodies with stable stratification have been formed. They have been classified as meromictic as they are at different stages of isolation from the Sea. As separation progresses, significant changes occur in the water column, including the composition of chromophoric dissolved organic matter (CDOM) and the structure of the aquatic microbial community. In this work, we searched for optical proxies of euxinia (anoxic conditions with accumulated hydrogen sulfide) in the water column of the meromictic lagoon on Zeleny Cape. The lagoon is separated from the White Sea basin by a shallow threshold that completely isolates the lagoon during low tide, but marine water enters the lagoon during high tide. The ecosystem in the lagoon is characterized by the marine salinity of water and a high organic matter content in the bottom water and sediments. In this study, spectral methods were used to obtain the depth distribution of CDOM, chlorophyll, and bacteriochlorophyll in the lagoon with strong water stratification and euxinic conditions in the bottom water. The measured optical CDOM characteristics were compared with hydrochemical data (water salinity, Eh, pH, dissolved oxygen), phytoplankton (oxygenic phototrophs), and green sulfur bacteria (anoxygenic phototrophs) distribution along the water column. The spectroscopic methods showed to have the advantages of not requiring water sample pre-treatment and allowing rapid sensing of CDOM and photosynthetic pigments at each horizon. Full article

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R² value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m⁻¹, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R² value, the machine learning model we established unveils fresh and novel results. Full article

Chromophoric dissolved organic matter (CDOM) is the main sunlight absorber in surface waters and a very important photosensitiser towards the generation of photochemically produced reactive intermediates (PPRIs), which take part in pollutant degradation. The absorption spectrum of CDOM (A_CDOM(λ), unitless) can be described by an exponential function that decays with increasing wavelength: A_CDOM(λ) = 100 d DOC A_o e^− Sλ, where d [m] is water depth, DOC [mg_C L⁻¹] is dissolved organic carbon, A_o [L mg_C⁻¹ cm⁻¹] is a pre-exponential factor, and S [nm⁻¹] is the spectral slope. Sunlight absorption by CDOM is higher when A_o and DOC are higher and S is lower, and vice versa. By the use of models, here we investigate the impact of changes in CDOM spectral parameters (A_o and S) on the steady-state concentrations of three PPRIs: the hydroxyl radical (^•OH), the carbonate radical (CO₃^•−), and CDOM excited triplet states (³CDOM*). A first finding is that variations in both A_o and S have impacts comparable to DOC variations on the photochemistry of CDOM, when reasonable parameter values are considered. Therefore, natural variability of the spectral parameters or their modifications cannot be neglected. In the natural environment, spectral parameters could, for instance, change because of photobleaching (prolonged exposure of CDOM to sunlight, which decreases A_o and increases S) or of the complex and still poorly predictable effects of climate change. A second finding is that, while the steady-state [³CDOM*] would increase with increasing A_CDOM (increasing A_o, decreasing S), the effect of spectral parameters on [^•OH] and [CO₃^•−] depends on the relative roles of CDOM vs. NO₃⁻ and NO₂⁻ as photochemical ^•OH sources. Full article

Based on the monthly monitoring of Bao’an Lake in Hubei Province from 2018 to 2020, the eutrophication level of Bao’an Lake in the middle reaches of the Yangtze River is investigated using the comprehensive trophic level index (TLI), chromophoric dissolved organic matter (CDOM) absorption coefficient, and the phytoplankton water quality biological method. The influencing factors are then identified. The results demonstrate that the overall water quality of Bao’an Lake remained at levels III–V during 2018–2020. Due to different eutrophication assessment methods, the results are different, but all show that Bao’an Lake is in a eutrophication state as a whole. The eutrophication level of Bao’an Lake is observed to vary with time, exhibiting an increasing then decreasing trend between 2018–2020, while levels are high in summer and autumn, and low in winter and spring. Moreover, the eutrophication level of Bao’an Lake presents an obviously varying spatial distribution. Potamogeton crispus is the dominant species of the Bao’an Lake, the water quality is good in spring when Potamogeton crispus vigorously grows, but poor in summer and autumn. The permanganate index (COD_Mn) and total phosphorous (TP), total nitrogen (TN), and chlorophyll a (Chl-a) contents are identified as the main influencing factors of the eutrophication level of Bao’an Lake, with a significant relationship observed between Chl-a and TP (p < 0.01). The above results provide a solid theoretical basis for the ecological restoration of Bao’an Lake. Full article