Search Results (247)

Small lakes (<10 km²) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr⁻¹ for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m⁻³, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Over 40% of freshwater fish species in Europe are currently at risk of extinction, highlighting the need for improved conservation planning. This study examines whether the threat status is associated with life-history and ecological traits across 580 autochthonous (native and endemic) freshwater fish species in European inland waters. Using data from FishBase and the IUCN Red List, we assessed associations between threat level and both categorical (e.g., migratory behavior, commercial importance, reproductive guild, and body shape) and numerical traits (e.g., maximum length, weight, age, growth parameters, and maturity traits). Significant, though modest, associations were identified between species threat level and migratory behavior and reproductive guild. Non-migratory species exhibited higher median threat levels, while amphidromous species showed a non-significant trend toward higher threat, suggesting that limited dispersal ability and dependence on fragmented freshwater networks may increase extinction vulnerability. Species with unclassified reproductive strategies also showed elevated threat levels, possibly reflecting both actual risk and underlying data gaps. In contrast, body shape and trophic level were not significantly associated with threat status. Critically Endangered species tend to be larger, heavier, and mature later—traits characteristic of slow life history strategies that limit population recovery. Although length at maturity and maximum age did not differ significantly among IUCN categories, age at maturity was significantly higher in more threatened species, and growth rate (K) was negatively correlated with threat level. Together, these patterns suggest that slower-growing, later-maturing species face elevated extinction risk. Overall, the findings underscore that the threat level in European freshwater fish is shaped by complex interactions between intrinsic biological traits and external pressures. Trait-based approaches can enhance extinction risk assessments and conservation prioritization, especially in data-deficient freshwater ecosystems facing multifaceted environmental challenges. Full article

Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an R² of 0.9938 for TN, a mean absolute error (MAE) of 0.0728 for DO, a root mean square error (RMSE) of 0.3881 for total TSS, and a mean absolute percentage error (MAPE) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring. Full article

In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, important research value, and broad development prospects. However, despite existing research focusing on the application of spaceborne GNSS-R L1-level data, the potential value of raw intermediate-frequency (IF) signals has not been fully explored for special applications that require a high accuracy and spatiotemporal resolution. This article provides a comprehensive overview of the current status of the measurement of raw IF signals from spaceborne GNSS-R in multiple application fields. Firstly, the development of spaceborne GNSS-R microsatellites launch technology is introduced, including the ability of microsatellites to receive GNSS signals and receiver technique, as well as related frequency bands and technological advancements. Secondly, the key role of coherence detection in spaceborne GNSS-R is discussed. By analyzing the phase and amplitude information of the reflected signals, parameters such as scattering characteristics, roughness, and the shape of surface features are extracted. Then, the application of spaceborne GNSS-R in inland water monitoring is explored, including inland water detection and the measurement of the surface height of inland (or lake) water bodies. In addition, the widespread application of group delay sea surface height measurement and carrier-phase sea surface height measurement technology in the marine field are also discussed. Further research is conducted on the progress of spaceborne GNSS-R in the retrieval of ice height or ice sheet height, as well as tropospheric parameter monitoring and the study of atmospheric parameters. Finally, the existing research results are summarized, and suggestions for future prospects are put forward, including improving the accuracy of signal processing and reflection signal analysis, developing more advanced algorithms and technologies, and so on, to achieve more accurate and reliable Earth observation and remote sensing applications. These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval. Full article

This paper presents a study of the thermal structure, circulation, and mixing processes in an inland water body by taking into account horizontal heterogeneity using a three-dimensional thermohydrodynamics model. The object of this study was the Rybinsk Reservoir, a large inland water body with complex morphology. Field measurement data and ERA5 reanalysis data, as well as a three-dimensional numerical model (which is a development by the authors of this article), were used in this research. The results of the numerical experiments show that three-dimensional numerical modeling, which explicitly takes into account the contribution of seiche oscillations, allows us to describe the temperature distribution in a water body with sufficient accuracy, and this plays a fundamental role in studies of inland water objects. The obtained results could be useful for a model representation of the biochemical processes occurring in lakes and reservoirs. Thus, for models of less spatial detail that are, for example, used to hlrepresent lakes in Earth system models, it is necessary to take into account seiches in the form of parameterizations. Full article

Multispectral satellite imagery has been widely applied in water quality monitoring, but limitations in spatial–temporal resolution and acquisition delays often hinder accurate assessments in small water bodies. In this study, a DJI M600PRO UAV equipped with a Sequoia multispectral sensor was used to assess the water quality in Zhangshan Reservoir, a small inland reservoir in Chuzhou, Anhui, China. Two regression approaches—the Window Averaging Method (WAM) and the Matching Pixel-by-Pixel Method (MPP)—were used to link UAV-derived spectral indices with in situ measurements of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). Despite a limited sample size (n = 60) and single-day sampling, MPP outperformed WAM, achieving higher predictive accuracy (R² = 0.970 for TN, 0.902 for TP, and 0.695 for COD). The findings demonstrate that UAV-based MPP effectively captures fine-scale spatial heterogeneity and offers a promising solution for monitoring water quality in small and turbid reservoirs, overcoming key limitations of satellite-based remote sensing. However, the study is constrained by the temporal coverage and sample density, and future work should integrate multi-temporal UAV observations and expand the dataset to improve the model robustness and generalizability. Full article

The urban heat island (UHI) effect significantly impacts urban environments and quality of life, yet research comparing coastal and inland cities is relatively lacking. This study, using the MYD11A2 dataset, analyzes the spatiotemporal evolution of land surface temperature (LST) and the surface urban heat island intensity index (SUHIII) in Beijing (inland) and Dalian (coastal) from 2003 to 2023, exploring the driving factors from 2003 to 2018 and proposing mitigation strategies for similar cities. Key findings: (1) Beijing’s SUHIII is 0.45 °C higher than Dalian’s during summer days, while Dalian’s SUHIII is 0.24 °C stronger than Beijing’s during winter nights, likely due to Dalian’s maritime climate, which raises nighttime LSTs and intensifies the winter SUHIII. (2) Both cities show similar trends in LST and SUHIII, with fluctuations until 2010, an increase after 2011, and a peak in 2023, with the expansion of heat island areas occurring mainly in suburban regions. (3) From 2003 to 2018, TEMP is the primary factor promoting SUHIII, followed by ET and POP, with EVI as the main mitigating factor. Beijing’s PREP weakens SUHI, while Dalian’s PREP promotes it. Coastal cities should focus on water bodies and wetland planning to mitigate heat islands, especially in areas like Dalian which are affected by PREP. Full article

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology. Full article

To evaluate the change trends of plankton in inland saline–alkaline water bodies, this study investigated the ecological restoration and rational development of saline–alkaline lakes in northwest China. From June to October 2023, phytoplankton communities in a high-salinity lake in Alar City, Xinjiang, were analyzed using standard survey methods for inland natural waters. Biodiversity indices were calculated, and redundancy analysis (RDA), Spearman’s correlation analysis, and Mantel test were carried out to assess the functional community structure of phytoplankton and its environmental drivers. In total, 115 phytoplankton taxa belonging to seven phyla were identified. The densities ranged from 23.76 × 10⁵ to 53.54 × 10⁷ cells/L. Bacillariophyta and Cyanophyta were the dominant phyla, accounting for 41.7% and 27.8% of the total taxa, respectively. The dominant species included Microcystis spp., Merismopedia sp., Cyclotella meneghiniana, and other algae. Spearman correlation analysis revealed that salinity, water temperature (WT), Na⁺, TDS, HCO₃⁻, Cl⁻, and K⁺ were key environmental factors significantly influencing phytoplankton community structure. Mantel tests confirmed that salinity (SAL), TDS, DO, and major ions (K⁺, Na⁺, CO₃²⁻) served as key determinants of spatiotemporal phytoplankton community distribution (p < 0.05). RDA results indicated that WT, TDS, alkalinity (ALK), pH, salinity, and Na⁺ were the key factors driving seasonal variations in phytoplankton communities. Notably, decreasing salinity and ion concentrations stabilized the phytoplankton community structure, maintaining high-diversity indices. This highlights the positive impact of ecological restoration measures, such as fisheries-based alkalinity control and systematic environmental management, on the health of saline–alkaline lake ecosystems. These findings provide important insights for the sustainable development of saline–alkaline fisheries and the conservation of aquatic biodiversity in arid regions. Full article

Floating photovoltaic (FPV) systems can play an important role in energy transition. Yet, so far, not much is known about the effects of FPV systems on water quality and ecology. A sun-tracking FPV system (24% coverage) was installed on a shallow drinking water reservoir. We observed for the first time that benthic cyanobacteria (blue-green algae), which can deteriorate water quality, developed massively under the FPV system, while macrophytes and benthic algae, such as Chara (stonewort), mostly disappeared. Calculations of light availability explain this shift. The natural mixing of the water column was hardly affected, and the average temperature of the reservoir was not altered significantly. Biofouling of the water-submerged part of the FPV system consisted mostly of a massive attachment of Dreissena mussels, which affected water quality. Water bird numbers and concentrations of faecal bacteria were similar after the installation of the FPV system. Especially in shallow, transparent water bodies, there is a significant risk of FPV systems promoting the growth of undesirable benthic cyanobacteria. Overall, these new insights can aid water managers and governmental institutions in assessing the risks of FPV systems on water quality and the ecology of inland waters. Full article

The Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA and several international collaboration agencies, aims to achieve high-resolution two-dimensional observations of global surface water. Equipped with the advanced Ka-band radar interferometer (KaRIn), it significantly enhances the ability to monitor surface water and provides a new data source for obtaining large-scale water surface elevation (WSE) data at high temporal and spatial resolution. However, the accuracy and applicability of its scientific data products for inland water bodies still require validation. This study obtained three scientific data products from the SWOT satellite between August 2023 and December 2024: the Level 2 KaRIn high-rate river single-pass vector product (L2_HR_RiverSP), the Level 2 KaRIn high-rate lake single-pass vector product (L2_HR_LakeSP), and the Level 2 KaRIn high-rate water mask pixel cloud product (L2_HR_PIXC). These were compared with in situ water level data to validate their accuracy in retrieving inland water levels across eight different regions in the middle and lower reaches of the Yangtze River (MLRYR) and to evaluate the applicability of each product. The experimental results show the following: (1) The inversion accuracy of L2_HR_RiverSP and L2_HR_LakeSP varies significantly across different regions. In some areas, the extracted WSE aligns closely with the in situ water level trend, with a coefficient of determination ($R^2$) exceeding 0.9, while in other areas, the $R^2$ is lower (less than 0.8), and the error compared to in situ water levels is larger (with Root Mean Square Error (RMSE) greater than 1.0 m). (2) This study proposes a combined denoising method based on the Interquartile Range (IQR) and Adaptive Statistical Outlier Removal (ASOR). Compared to the L2_HR_RiverSP and L2_HR_LakeSP products, the L2_HR_PIXC product, after denoising, shows significant improvements in all accuracy metrics for water level inversion, with $R^2$ greater than 0.85, Mean Absolute Error (MAE) less than 0.4 m, and RMSE less than 0.5 m. Overall, the SWOT satellite demonstrates the capability to monitor inland water bodies with high precision, especially through the L2_HR_PIXC product, which shows broader application potential and will play an important role in global water dynamics monitoring and refined water resource management research. Full article

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ to 8.69 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$, and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

As sensitive human-environment systems, lake-intensive regions are critical governance areas for advancing global low-carbon development. Rapid economic growth has intensified the imbalance between economic carbon sources and ecological carbon sinks in these regions. However, methods for measuring territorial space carbon budgets tailored to “production–living–ecological” functions are underdeveloped, and the mechanisms driving carbon imbalance risks remain unclear. To address these issues, this study develops a spatial measurement model for “carbon sources-carbon sinks” in the Dongting Lake region. Using exploratory spatiotemporal data analysis, this study identifies grid-scale variation patterns in carbon budgets. Finally, using the logarithmic mean Divisia index (LMDI) decomposition model, this study examines the driving mechanisms of carbon budgets from a territorial space perspective. The results indicate the following: (1) The territorial space of the Dongting Lake region follows a pattern where “ecological spaces surround production spaces, with living spaces interspersed among water network spaces”. Between 2005 and 2020, functional transitions primarily occurred between agricultural production spaces and forest or water ecological spaces. (2) The study area’s territorial space carbon budgets increased annually, though the growth rate slowed. Construction land was the most significant carbon emission source in territorial space. Spatially, carbon budgets exhibit a radial pattern, with high values concentrated in plains near water bodies, gradually decreasing inland. Spatiotemporal differentiation followed a north–south development trend along the water system axis. High-High clusters were concentrated in municipal areas with dense water networks. In contrast, Low-Low clusters appeared in peripheral mountainous regions to the west, east, and south. (3) Land-use efficiency had the most potent inhibitory effect on carbon budgets, cumulatively reducing carbon emissions by 1.37 × 10⁸ tC. Economic development had the strongest positive effect, adding 1.31 × 10⁸ tC in carbon emissions. Therefore, the Dongting Lake region should promote intensive land use, adjust industrial structures, and develop a green ecological economy to achieve sustainable carbon source–sink management. Full article

Alpine wetland ecosystems, as important carbon sinks and water conservation areas, possess unique ecological functions. Driven by climate change and human activities, the spatial distribution changes in alpine wetlands directly affect the ecosystems and water resource management within a basin. To further refine the evolution processes of different types of alpine wetlands in different zones of a basin, this study combined multiple field surveys, unmanned aerial vehicle (UAV) flights, and high-resolution images. Based on the Google Earth Engine (GEE) cloud platform, we constructed a Random Forest model to identify and extract alpine wetlands in the Shule River Basin over a long-term period from 1987 to 2021. The results indicated that the accuracy of the extraction based on this method exceeded 90%; the main wetland types are marsh, swamp meadow, and river and lake water bodies; and the spatial–temporal distribution of each wetland type has obvious heterogeneity. In total, 90% of the swamp meadows areas were mainly scattered throughout the study area’s section 3700 to 4300 m above sea level (a.s.l.), and 80% of the marshes areas were concentrated in the Dang River source 3200 m above sea level. From 1987 to 2021, the alpine wetland in the study area showed an overall expansion trend. The total area of the wetland increased by 51,451.8 ha and the area increased by 53.5%. However, this expansion mainly occurred in the elevation zone below 4000 m after 2004, and low-altitude marsh wetland primarily dominated the expansion. The analysis of the spatial–temporal heterogeneity of alpine wetlands can provide a scientific basis for the attribution analysis of the change in alpine wetlands in inland water conservation areas, as well as for protection and rational development and utilization, and promote the healthy development of ecological environments in nature reserves. Full article