Search Results (169)

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

The concentration of colored dissolved organic matter (CDOM) in lakes is strongly influenced by climate, land cover, and topographic settings, but it is not known how drought may affect the relative importance of these controls. Here, we evaluate the controls of CDOM during two summers with strongly contrasting values of the Palmer drought index (PDI), indicating wet vs. dry conditions. We hypothesized that lake CDOM during a wet summer season is regulated mainly by the surrounding land cover to which the lakes are hydrologically connected, while, during drought, the lakes are disconnected from the catchment and CDOM is regulated by climatic and morphometric factors that govern the internal turnover of CDOM in the lakes. A suite of climate, land cover, and morphometric variables was assembled and used to explain remotely sensed CDOM values for 255 boreal lakes distributed across broad environmental and geographic gradients in Sweden and Norway. We found that PDI explained the variability in CDOM among lakes in a dry year, but not in a wet year, and that severe drought strongly decreased CDOM during the dry year. Large lakes, especially, with a presumed high degree of catchment uncoupling, showed low CDOM during the dry year. However, in disagreement with our hypothesis, climate, land cover, and morphometry all showed a stronger impact on lake CDOM in wet vs. dry years. Thus, drought systematically weakened the predictability of CDOM variations at the same time as CDOM was offset toward lower values. Our results show that drought not only has a direct effect on CDOM, but also acts indirectly by changing the spatial regulation of CDOM in boreal lakes. Full article

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions ${\mathrm{Ca}}^{2+}$, ${\mathrm{Cl}}^{-}$, and ${\mathrm{Na}}^{+}$. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. Full article

Dissolved organic matter (DOM) is a crucial component of continental aquatic ecosystems. It plays a vital role in the carbon cycle by serving as a significant source and reservoir of carbon in water. DOM provides energy and nutrients to organisms, affecting primary productivity, organic composition, and the food chain. This study presents empirical bio-optical models for estimating the absorption of colored dissolved organic matter (aCDOM) in the Negro River using in situ remote sensing reflectance (Rrs) data. Physical–chemical data (TSS, DOC, and POC) and optical data (aCDOM and Rrs) were collected from the Negro River, its tributaries, and lakes and empirical relationships between aCDOM at 440 nm, single band, and the ratio bands of Rrs were assessed. The analysis of spectral slope shows no statistically significant correlations with DOC concentration or aCDOM absorption coefficient. However, strong relationships were observed between DOC and aCDOM (R² = 0.72), aCDOM and Rrs at 650 nm (R² > 0.80 and RMSE < 1.75 m⁻¹), as well as aCDOM and the green/red band ratio (R² > 0.80 and RMSE < 2.30 m⁻¹). aCDOM displayed large spatial and temporal variations, varying from 1.9 up to 20.1 m⁻¹, with higher values in rivers of the upper course of the Negro basin and lower values in rivers with total solids suspended > 10 mg·L⁻¹. Environmental factors that influence the production of dissolved organic matter include soil type, dense forest cover, high precipitation, and low erosion rates. This study demonstrated that aCDOM can serve as an indicator of DOC, and Rrs can serve as an indicator of aCDOM in the Negro basin. Our findings offer a starting point for future research on the optical properties of Amazonian black-water rivers. Full article

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO₄), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O₂), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. Full article

Dissolved organic carbon (DOC) in lakes, as a regulatory agent and light-absorbing compound, is a key component of the global carbon cycling in lacustrine ecosystems. Hence, continuous monitoring of the DOC concentration in arid regions is extremely important. This study utilizes the QAA-CDOM semi-analytical model, which has good accuracy in retrieving the CDOM (colored dissolved organic matter) concentration of Lake Ebinur. We chose to invert the CDOM time-series data from May to October during the 2018–2022 period. A DOC estimation model was then established using the linear regression approach based on the CDOM inversion data and the field DOC measurements. In general, the DOC concentration in Lake Ebinur exhibited an increasing trend from 2018 to 2022, typically lower in May and higher in June. When comparing the average values of DOC in Lake Ebinur for the same months across different years, it can be observed that the month of September exhibits the greatest variability, whereas June shows the least variability. In sum, this study successfully retrieved CDOM concentrations for a saline lake within an arid region and developed a DOC estimation model, thereby providing a reference for investigating carbon cycling in typical lakes of arid areas. Full article

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) in the respective catchment of each bay. The optical properties were measured in situ over the last decade by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8/OLI data. The absorption coefficient of colored dissolve organic matter (CDOM) at 440 nm, a_CDOM (440), was significantly correlated to Wetlands which may act as sources of CDOM, while Developed areas (Agricultural and Urban classes) were negatively correlated. The Agriculture class was also negatively related to suspended particulate organic matter (SPOM), whilst Coniferous Forests and Mixed Forests as well as Meadows were positively correlated. SPOM seems thus to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land use and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale. Full article

Colored dissolved organic matter (CDOM) is a significant constituent of aquatic systems and biogeochemical cycles. Satellite CDOM retrievals are challenging in inland waters, due to overlapped absorption properties of bio-optical parameters, like Total Suspended Matter (TSM). In this framework, we defined an accurate CDOM model using Sentinel2-MSI (S2-MSI) data in Pertusillo Lake (Southern Italy) adopting a classification scheme based on satellite TSM data. Empirical relationships were established between the CDOM absorption coefficient, a_CDOM (440), and reflectance band ratios using ground-based measurements. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed good relationships (R² ≥ 0.75), which were further improved according to sub-region division (R² up to 0.93). The best accuracy of B3/B4 in the match-ups between S2-MSI-derived and in situ band ratios proved the exportability on S2-MSI data of two B3/B4-based a_CDOM (440) models, namely the fixed (for the whole PL) and the switching one (according to sub-region division). Although they both exhibited good agreements in a_CDOM (440) retrievals (R² ≥ 0.69), the switching model showed the highest accuracy (RMSE of 0.0155 m⁻¹). Finally, the identification of areas exposed to different TSM patterns can assist with refining the calibration/validation procedures to achieve more accurate a_CDOM (440) retrievals. 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

Water quality monitoring in coastal areas and estuaries poses significant challenges due to the intricate interplay of hydrodynamic, chemical, and biological processes, regardless of the chosen monitoring methods. In this study, we analyzed the applicability of different monitoring sources using in situ data, uncrewed-aerial-vehicle (UAV)-mounted hyperspectral sensing, and Sentinel-2-based multispectral imagery. In the first part of the study, we evaluated the applicability of existing empirical algorithms for water quality (WQ) parameter retrieval using hyperspectral, simulated multispectral, and satellite multispectral datasets and in situ measurements. In particular, we focused on three optically active WQ parameters: chlorophyll a ($Chl,a$), turbidity (TUR), and colored dissolved organic matter (CDOM) in oligotrophic coastal waters. We observed that most existing algorithms performed poorly when applied to different reflectance datasets, similar to previous findings in small and optically complex water bodies. Hence, we proposed a novel set of locally based empirical algorithms tailored for determining water quality parameters, which constituted the second part of our study. The newly developed regression-based algorithms utilized all possible combinations of spectral bands derived from UAV-generated hyperspectral data and exhibited coefficients of determination exceeding 0.9 for the three considered WQ parameters. The presented two-part approach was demonstrated in the semi-enclosed area of Kaštela Bay and the Jadro River estuary in the Central Eastern Adriatic Sea. This study introduces a promising and efficient screening method for UAV-based water quality monitoring in coastal areas worldwide. Such an approach may support decision-making processes related to coastal management and ultimately contribute to the conservation of coastal water ecosystems. Full article

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range. Full article