Monitoring Of Water Bodies Research Articles

Chemical composition of sediment and macrozoobenthos of small urban lakes, the Republic of Karelia, Russia

Assessing the benthic communities in water bodies under increasing anthropogenic pressure seems to be an urgent task of scientific research aimed at short-term and long-term monitoring of water bodies. This study focuses on the chemical composition of modern sediment and the current state of macrozoobenthos of small lakes in the Republic of Karelia (Kitaiskoe and Plotichie) influenced by anthropogenic factors. It was discovered that the lakes of Medvezhyegorsk have a higher concentration of heavy metals (such as lead (Pb), cadmium (Cd), molybdenum (Mo), and antimony (Sb),) compared to the Earth’s crust and natural background levels. The current study established the significant impact of industry and transportation on the movement and build up of pollutants in these lakes. The qualitative and quantitative parameters analysed for the benthic communities in the littoral and profundal zones included the species diversity, the structure of dominant species, and their percentage in the samples. According to the level of macrozoobenthos development, both reservoirs (lakes) were classified as mesotrophic, and according to the values of the chironomid index “K” as moderately polluted. The Pantle–Buck method (saprobic index) showed that the lakes can be classified as polluted (water quality class 4). Macrozoobenthos groups most tolerant to environmental conditions, for example, Chironomidae larvae, prevailed in the studied urban lakes. The economic development of the lakes leads to ecosystem transformation and long-term eutrophication over time affecting the biotic indicators of surface water quality. The results obtained can serve as a basis for complex environmental monitoring of the urbanized territories in the taiga zone.

Dynamic monitoring of open-surface water bodies in the middle reaches of the Tarim River based on the GEE cloud platform

ABSTRACT This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.

Changes in river morphology and influencing factors in the upper Yellow River over the past 25 years

Changes to the morphology of the upper Yellow River (UYR) had various impacts on the surrounding ecology and society, as well as the entire basin. However, low-spatial-resolution imagery (e.g., MODIS, AVHRR) cannot capture sufficient spatial details for monitoring complex water bodies, while high-spatial-resolution imagery (e.g., SPOT, Quickbird, Ikonos) lacks spatial coverage and the revisit frequency necessary for large-scale water body monitoring. To address these limitations, this study utilized the Google Earth Engine (GEE) and ArcGIS spatial analysis tools, applied pan-sharpening to downscale Landsat imagery of the study area from 1999 to 2023, performed river extraction, and calculated the spatiotemporal changes in river morphology in the UYR using river morphological parameters (i.e. area, channel width, centerline length, sinuosity index, lateral migration rate, channel stability). The Automated Water Extraction Index (AWEIsh) effectively characterized the study area, and pan-sharpening technology improved the extraction accuracy of small water bodies. Finally, the overall accuracy and Kappa coefficient were 0.993 and 0.985, respectively. Over the past 25 years, the area and average width of the entire reach of the UYR changed significantly, with the maximum value being 1.3 times the minimum value, whereas the centerline length and sinuosity index showed no apparent changes, and the lateral migration rate varied minimally, with the average annual movement ranging from 4.67 m to 10.18 m. In typical river segments without human activity, although single-channel reaches exhibited stronger stability than multi-channel reaches, natural factors (i.e. annual precipitation, annual runoff, annual sediment discharge) had a noticeable impact on the morphology of both single-channel and multi-channel reaches. Large-scale cascade hydropower development in the UYR has significantly impacted river morphology over a short period. Meanwhile, in river sections unaffected by human activities, the changes occurred gradually. This study provides support for better understanding complex river morphologies at large regional and long-term scales and a scientific basis for water resource management and sustainable development in the UYR.

Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Implications of Water Quality Index and Multivariate Statistics for Improved Environmental Regulation in the Irtysh River Basin (Kazakhstan)

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities.

Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

Analysis of nitrogen migration and transformation in the typical deep and large reservoir of the Lancang River — Evidence from nitrogen and oxygen isotopes

The environmental effect resulting from dam construction and the formation of large reservoirs has long been a topic of significant concern. While current research at a macro scale provides a well understanding regarding the evolution of nitrogen nutrient status in river-reservoir ecosystems, the vertical migration and transformation characteristics of nitrogen in reservoirs under changing runoff remain less defined. This study centers on a typical deep reservoir along the Lancang River-Nuozhadu (NZD) Reservoir in April and December. The monitoring reveals a distinct vertical stratification phenomenon in the NZD Reservoir in the vertical profile in April, which can be categorized into a mixed layer (ML) (0–5 m), a thermocline layer (TL) (5–40 m), and an isothermal layer (IL) (>40 m) based on the water temperature. However, little stratification in the profile is occurred in December. The results of nitrogen and oxygen isotopes in April indicate that the biochemical process of nitrogen in the NZD Reservoir is predominantly driven by nitrification. However, different nitrogen biochemical processes are coupled in various stratified regions in the vertical direction. In the ML, simultaneous assimilation of ammonia nitrogen by phytoplankton occurs. In the TL, there is a process of oxidation and decomposition of particulate organic nitrogen due to a significant stratification effect and an extended hydraulic retention time. In the region of minimal dissolved oxygen (30–40 m), simultaneous denitrification may take place. In December, the main process is nitrogen assimilation in reservoir surface (ML). Statistical analysis of environmental factors highlights water temperature (T) and dissolved oxygen (DO) as crucial environmental factors influencing nitrogen migration and transformation within the reservoir. This study suggests that in deep reservoirs experiencing vertical thermal stratification, the characteristics of DO and nitrogen migration and transformation exhibit significant vertical spatial heterogeneity. This phenomenon may lead to diverse ecological environmental effects. Future efforts should focus on enhancing the fine monitoring of stratified water bodies in deep and large reservoirs and analyzing the ecological environmental effects of material cycling in greater detail.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Investigating a surface water quality monitoring approach for QwaQwa, South Africa, by combining biological in vitro tests and chemical analyses

IntroductionWater quality assessment is a fundamental requirement for comprehensive water management. Access to water is not exclusively a quantitative issue, as human activities often lead to negative impacts on water quality due to pollution. This results in a need for the systematic monitoring of water bodies to surveil ongoing pollution dynamics and, if needed, facilitate the implementation of suitable solutions for water quality management.MethodsTo that effect, in March 2022 and July 2023, we conducted two sampling campaigns in four headwater streams in QwaQwa, South Africa, to assess the water quality under high (summer) and low (winter) flow conditions. The overarching aim was to support local decision-makers with relevant information on water quality in a semirural and less studied area. Beside the hydrological characterization of the catchment, which drains the mountainous areas around Phuthaditjhaba (the main town of the region), physicochemical and biological monitoring were conducted, including in vitro tests detecting endocrine disruption, dioxin-like impacts, and genotoxic potentials in the water.ResultsThe elemental analysis showed that at several sampling sites, Al, Cu, Mn, Fe, and Zn were above the Target-Water-Quality-Range (TWQR) for aquatic ecosystems and Fe, Al, and Mn were additionally above the TWQR for domestic use. Interestingly, the organic micropollutants (OMPs) revealed a region with a high incidence of HIV infections, given the occurrence of the drugs efavirenz, its transformation product 8-OH-efavirenz and nevirapine, among other micropollutants. Possibly, the signals from the in vitro tests for endocrine disruption are also induced by the detected micropollutants. Our results ultimately show that even though anthropogenic impacts increase from upstream to downstream, none of the sampling sites are without concern. This indicates the urgent need for measures to increase the water quality of these headwater streams in this region.

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Understanding the spatial and temporal distribution of small water bodies is essential for managing water resources, crafting conservation policies, and preserving watershed ecosystems and biodiversity. However, existing studies often rely on a single remote sensing data source (optical or microwave), focusing on large-scale, flat areas and lacking comprehensive monitoring of small water bodies in complex terrain. Therefore, considering the complementary advantages of multisource remote sensing (multispectral and SAR), this paper proposes a multispectral and SAR fusion algorithm, named Multispectral and SAR Fusion algorithm (MASF), to better capture the remote sensing characteristics of small water bodies in complex areas. Based on this, a dataset containing spectral, texture, and geometric features is constructed, and multi-scale segmentation and random forest algorithms are applied for identification of small water bodies in complex terrain. The results demonstrate that the proposed fusion algorithm MASF exhibits minimal spectral distortion (SAM < 3.5, ERGAS <21, RMSE <0.01) and robust spatial feature enhancement (PSNR >40, SSIM >0.999, CC > 0.99). The Overall Accuracy (OA) and Kappa coefficients for both experimental areas surpassed 0.9. For rivers and reservoirs, both Producer's Accuracy (PA) and User's Accuracy (UA) exceeded 0.9. The UA for agricultural ponds exceeded 0.8. Comparative analysis with three other types of water-related data products shows that the freshwater identification results in this study have certain advantages in local small water bodies. Our research holds significant implications for the utilization of water resources in mountainous areas, prevention and control of floods and floods, as well as the development of aquaculture industry.

Ecological patterns of phytoplankton across lake cross-section: insights into co-evolution of physicochemical conditions in Chashma Lake on Indus River.

Physicochemical properties of water influence planktonic diversity and distribution, which is essential in obtaining basic knowledge of aquatic biodiversity. Thus current study aims to investigate the spatiotemporal diversity, abundance ratio, and distribution of phytoplankton species and their association with water quality parameters of Chashma Lake, Pakistan. During the study period from 2018 to 2019, we measured 13 physicochemical parameters across three selected sampling sites (S1, S2, and S3) in Chashma Lake, revealing both spatial and temporal variability. Dissolved oxygen (DO) was higher in S3, while S1 exhibited higher alkalinity levels, carbon dioxide, phosphorus, and chloride levels. The study identified 77 phytoplankton species grouped into five taxonomic categories, with Cyanobacteria dominating (39.90%), followed by Chlorophyta (33.4%) and Bacillariophyta (24.88%). Euglenozoa and Ochrophyta were less abundant (1.3% and 0.41%, respectively). Spatial variations in phytoplankton distribution were noted, with Chlorophyta being more abundant at S2, Bacillariophyta and Cyanobacteria at S1, and Euglenozoa dominating at S3. Canonical Correspondence Analysis (CCA) revealed the influence of various physicochemical parameters on phytoplankton distribution. This comprehensive study provides valuable insights for the ecological assessment and monitoring of water bodies. It is recommended that continuous monitoring is required to capture long-term trends, further explore the specific environmental drivers impacting phytoplankton dynamics, and consider management strategies for maintaining water quality and biodiversity in Chashma Lake.

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies.

Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs' capacities.

IoT-Enabled Embedded Virtual Sensor for Energy-Efficient Scalable Online Water Quality Monitoring System

Pollutants introduced into water sources by industries and individuals have made it difficult to obtain enough clean drinking water in the modern world. This calls for a mechanism that periodically assesses the water's quality. There are portable instruments available to measure the factors impacting water quality. But the user needs solutions that are inexpensive, automated, reliable, and responsive. For monitoring of large water bodies, a low-cost, scalable, edge processing system in real-time is created to satisfy the current needs. It is designed using a Raspberry Pi, a display unit, and standard sensors for variables including temperature, ORP, pH, conductivity, and DO. Edge intelligence along with tiny ML is used to deploy the virtual sensor for estimation of the degree of pollution in water which otherwise requires a costly and tedious process. With an edge reaction time of 2.70ns and a notable Nash-Sutcliffe efficiency coefficient of 0.99 for BOD and COD estimation in water, this model is created utilizing the randomized tree classification. With the use of a power-efficient algorithm, the 8.4w system energy consumption of the created prototype allows it to operate on AC and solar power and can be effective in difficult climatic conditions.

Sorption of the Cu, Gd, V, Mn, and Fe elements by hydrogels with identification of their complexes in the solid phase of the polymer ligand using EPR

The search for new sorbents for the concentration of substances is among the important tasks of analytical chemistry regarding the monitoring of water bodies in modern times. It is necessary to regularly control water supply sources, and the quality of natural water is also an indicator of the health of ecosystems. The use of sorbents when collecting water for analysis simplifies the preparation of complex samples, allowing to use a solid concentrate to determine the absorbate and quickly record the results using multi-element in situ instrumental methods. We suggested using cross-linked polyacrylates (CLPs) based on polyacrylamide as a new type of sorbent for these purposes. Unlike traditional sorbents, hydrogels noticeably swell in water (рН 4-7), which allows conducting the sorption of elements not only on the surface but also inside CLP granules. Sorption is conducted in a static mode by placing a weighed portion of CLP in a certain volume of water sample and by further drying in air at 70-100°C. The purpose of this work was to obtain and study solid polymer concentrates of CLP using EPR spectroscopy in order to confirm the sorption of elements in CLP for the further development of methods for the preparation of samples of various water bodies. To do this, we obtained individual and binary polymer complexes of paramagnetic elements Cu(II), Gd(III), V(IV), Mn(II), and Fe(III) from model solutions of their salts (from 10-7 (INAA) to 10-3 (EPR) mol/dm3) and dried them To confirm the sorption and reliable binding of elements into polymer complexes, we used ESR spectroscopy at 293 K. The experimentally obtained new EPR spectra were compared with the theoretical ones. Complex EPR spectra were simulated using original software developed at the Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences. It was shown that upon drying CLPs completely disengaged from water molecules, while the sorption of metals depended on the рН and grew with an increase in the solution’s рН. In case of joint presence, metal ions formed more complex compounds as compared to individual sorption. The hyperfine structure constant (HSC) was calculated for the spectra of complexes with CLPs and g-factor was calculated for all samples. It was determined that in the course of the formation of metal complexes with CLPs, the nearest coordination sphere was greatly distorted, while the nature of the distortion of the ligand environment depended on the element. In case of the Cu(II) and V(IV) system, we observed additivity of the EPR spectra, but in case of Cu and Gd(III) it was not recorded. In any case, the formation of strong and stable polymer complexes was confirmed. Sorption concentration using CLPs was proposed as a method of sample preparation of natural and technogenic water systems by transferring the determined ions into a solid hydrogel concentrate in order to be further analysed using multi-element instrumental methods (for example, X-ray fluorescence analysis (XRF), instrumental neutron activation analysis (INAA), etc.).

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

Analysis of methods for assessing the degree of water pollution

The most important modern methods for the monitoring of water bodies, their state of pollution and the assessment of water quality are considered. Commonly accepted, standard-ized methods for assessing water quality in water bodies include ground-based sampling, which is untimely and expensive under certain conditions. Best suited for practical use in large areas is the remote approach, which combines ground and satellite measurements to classify changes and detect pollution in closed water bodies. Satellite monitoring is a reliable tool for obtaining operational information on the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availabil-ity of archived data becomes a significant advantage, opening up the possibility of comparing the results obtained and analyzing the factors influencing the state of water bodies and the environment. The paper describes the main methods and datasets that can be used for the ap-plication of remote monitoring methods based on machine learning. The use of Landsat mul-tispectral sensors, including TM (Thematic Mapper), MSS (Multispectral Scanner), ETM (Enhanced Thematic Mapper), and OLI (Operational Land Imager), is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of wa-ter pollution over large areas will be analyzed. Possible machine learning methods for solv-ing an applied problem are presented.

A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

Evolving a Methodology for Assessing Pesticide Pressure on Water Bodies under Data Scarce Conditions: A Case Study on the Marmara Basin in Türkiye

Evolving a Methodology for Assessing Pesticide Pressure on Water Bodies under Data Scarce Conditions: A Case Study on the Marmara Basin in Türkiye

Spatio-temporal dynamics monitoring of surface water bodies in Nhat Le River Basin, Vietnam, by Google Earth Engine

Abstract Consistent monitoring of surface water dynamics is essential for water resources, flood risk management, and addressing the challenges posed by climate change, urbanization. Nhat Le River Basin witnesses significant and noticeable dynamics in surface water on a yearly basis due to water-related disasters like floods and droughts. This article presents the first comprehensive study to systematically map and analyse the long-term (2016–2022) spatiotemporal dynamics of surface water in the Nhat Le River Basin of Vietnam, utilizing Sentinel-1 data. The results reveal that the optimal threshold for separating water from non-water pixels is −19 dB, with an overall accuracy of 0.93–0.94 and a Kappa coefficient of 0.77–0.82. Through quantitative analysis, the study characterizes seasonal and interannual variations in the surface water extent, contributing to an enhanced understanding of flood patterns and associated risks in a data-scarce region. Our analysis reveals the Kien Giang river delta as the most flooding-vulnerable sub-region, underscoring the importance of targeted risk management and adaptation planning in this area. A Google Earth Engine Tool is developed for automatic detecting, monitoring, and accessing the spatiotemporal dynamics of surface water in Nhat Le River Basin over the period 2016–2022 and is available on GitHub (https://github.com/MinhVu25/Surface_Water_Dynamics_2023).