Search Results (1,399)

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R² in the range of 0.71–0.83 across all models for depths < 3.5 m, while in depths > 3.5 m, an RMSE = 1.43–1.78 m and R² = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics. Full article

The rapid growth of phytoplankton and microalgae has presented considerable environmental and societal challenges to the sustainable development of human society. Given the inherent limitations of satellite-based algal bloom detection techniques that rely on chlorophyll and fluorescence methods, this study proposes a method that employs hyperspectral data to calculate water chromatic indices (WCIs), including hue, saturation (S), dominant wavelength (λd), and integrated apparent visual wavelength (IAVW), to identify algal blooms. A global in situ hyperspectral dataset was constructed, comprising 13,110 entries, of which 9595 were for normal waters and 3515 for algal bloom waters. The findings of our investigation indicate statistically significant discrepancies in chromaticity parameters between normal and algal bloom waters, with a p-value of 0.05. It has been demonstrated that different algal blooms exhibit distinct chromatic characteristics. For algae of the same type, the chromaticity parameters increase exponentially with chlorophyll concentration for hue and λd, while S shows low correlation and IAVW displays a good linear relationship with chlorophyll concentration. The application of this method to the Bohai Sea (coastal) and Taihu Lake (inland water) for the extraction of algal blooms revealed a clear separation in chromaticity parameters between normal and algal bloom waters. Moreover, the method can be applied to satellite data, offering an alternative approach for the detection of algal blooms based on satellite data. The indices can serve as ground truth values for colorimetric indices and provide a benchmark for the validation of satellite chromatic products. Full article

Sustainable potable groundwater supply is crucial for human development and the preservation of natural habitats. The largest endorheic inland lake in Kazakhstan, Balkhash Lake, is the main water resource for the arid southeastern part of the country. Several ore enrichment plants that are located along its shore have heavy metal pollution potential. The study area is located around a plant that has an evident anthropogenic impact on the Balkhash Lake aquatic ecological system, with ten known heavy metal toxic hotspots endangering fragile habitats, including some indigenous human communities. This study assessed the risk of heavy metal contamination from tailing dump operations, storage ponds, and related facilities and suggested management practices for preventing this risk. The coastal zone risk assessment analysis used an innovative integrated groundwater numerical flow and transport model that predicted the spread of groundwater contamination from tailing dump operations under several mitigation strategies. Heavy metal pollution prevention models included a no-action scenario, a filtration barrier construction scenario, and two scenarios involving the drilling of drainage wells between the pollution sources and the lake. The scenario assessment indicates that drilling ten drainage wells down to the bedrock between the existing drainage channel and the lake is the optimal engineering solution for confining pollution. Under these conditions, pollution from tailings will not reach Lake Balkhash during the forecast period. The methods and tools used in this study to enable mining activity without environmental implications for the region can be applied to sites with similar anthropogenic influences worldwide. Full article

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R² = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R² = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R² = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. Full article

The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions. Full article

Desertification and salt stress are major causes of terrestrial ecosystem loss worldwide, and the Gobi, representing a salt-stressed area in inland China, has a major impact on the ecosystems and biodiversity of its surrounding environment. The restoration of the Gobi Desert is an important way to control its expansion, but there are few studies on the evaluation of restoration. In this study, soils under different restoration scenarios, namely, soils in restored areas (R1, R2), semi-restored areas (SR1, SR2), and unrestored control areas (C1, C2), were used to investigate differences in microbial diversity and physicochemical properties. The results showed that the soil was mainly dominated by particles of 4–63 μm (26.45–37.94%) and >63 μm (57.95–72.87%). Across the different restoration levels, the soil pH (7.96–8.43) remained basically unchanged, salinity decreased from 9.23–2.26 to 0.24–0.25, and water content remained constant (10.98–12.27%) except for one restored sample in which it was higher (22.32%). The effective Al, Cu, and Zn in the soil increased, but only slightly. Total organic matter (TOM) decreased from 3.86–5.20% to 1.31–1.47%, and total organic nitrogen (TON) decreased from 0.03–0.06% to 0.01–0.02%, but the difference in total organic carbon (TOC) was not significant. High-throughput testing revealed that the bacterial population of the restored area was dominated by A4b (6.33–9.18%), MND1 (4.94–7.39%), and Vicinamibacteraceae (7.04–7.39%). Regarding archaea, samples from the restored areas were dominated by Marine Group II (76.17–81.49%) and Candidatus Nitrososphaera (6.07–9.75%). PCoA showed that the different restoration levels were the main cause of the differences between the samples. Additionally, salinity was the dominant factor that induced this difference, but it was inhibited by the restoration and targeted enrichment of some of these functional genera. Desert restoration should therefore focus on conserving water rather than adding nutrients. Planting salt- and drought-tolerant vegetation will contribute to the initial restoration of the desert and the restoration of the microbiological content of the soil as it migrates over time, creating a cycle of elements. Restoration stimulates and enhances the microbial diversity of the soil via beneficial microorganisms. Full article

The Atlantic pearl oyster Pinctada radiata (Leach, 1814), the first documented Lessepsian bivalve species to enter the Mediterranean basin, is present in various coastal areas in Greece, and constitutes, almost exclusively, a domestic commercial bivalve resource. The present study aimed to contribute to the limited information available on P. radiata population structure and dynamics in Hellenic waters, especially following the recent enforcement of legislation for regulation of its fishery. A total of 703 individuals were collected using scuba diving from the South Evoikos Gulf. The male-to-female ratio (1:1.70) significantly departed from 1:1. A higher probability for female prevalence was exhibited for shell heights over 50.77 mm. Significant differences were exhibited in the shell height–total weight relationship between the sexes. The fourth-year class was the dominant cohort, comprising 50.09% of the population, out of the seven age classes identified. Asymptotic length was estimated at 109.1 mm and growth index at 3.35, respectively. Longevity was estimated at 15.7 years, with natural mortality (M) at 0.39 and total mortality (Z) at 0.76. The probability of capture (LC₅₀) was estimated at 50.72 mm at 2.8 years. Biological reference points F_MSY and E_MSY were higher than the fishing mortality and current exploitation rate, respectively, indicating the potential for further population exploitation. Full article

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. Full article

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region. Full article

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson’s correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of ‘moderate drought’ or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month’s drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea’s drought monitoring system to support sustainable water management. Full article

Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based predictive model for regional mapping over time. When matching ground and satellite data, positional and temporal discrepancies are unavoidable due particularly to dynamic lake surfaces, thereby biasing the model calibration. This limitation has long been recognized but so far has not been addressed explicitly. To mitigate such effects of data mismatching, we proposed an Akaike Information Criterion (AIC)-like weighted regression algorithm that relies on an error-based heuristic to automatically favor “good” data points and downplay “bad” points. We evaluated the algorithm for estimating Chl-a over inland lakes in Ohio using Harmonized Landsat Sentinel-2. The AIC-like weighted regression estimates showed superior performance with an R² of 0.91 and an error variance (${\sigma }_E^2$) of 0.29 μg/L, outperforming linear regression (R² = 0.34, ${\sigma }_E^2$ = 2.34 μg/L) and random forest (R² = 0.82, ${\sigma }_E^2$ = 0.92 μg/L). We also noticed the poorest performance occurred in the spring due to low reflectance variation in clear water and low Chl-a concentration. Our weighted regression scheme is adaptive and generically applicable. Future studies may adopt our scheme to tackle other remote sensing estimation problems (e.g., terrestrial applications) for alleviating the adverse effects of geolocation errors and temporal discrepancies. Full article

Inland arid basins face the challenge of ecological deterioration due to insufficient water availability. The irrigation water consumption depletes the water flowing into the downstream tailrace ecological wetland, leading to increasing ecological deterioration. It is urgent to optimize the management of irrigation water resources in the middle reaches and improve the ecological sustainability of the lower reaches. To ensure sustainable development, improving water use efficiency and preserving the health of basin ecosystems should be simultaneously considered in the agricultural water management of these regions. Therefore, a 0–1 integer multi-objective programming approach was proposed to optimize midstream crop planting. This method has advantages in (1) effectively balancing ecological sustainability, agricultural production, and water-saving goals; (2) linking irrigation district management with grid geographic information to develop land use strategies; and (3) obtaining optimal solutions for multi-objective synergies. The proposed approach is applied to a typical inland river basin in China, the Heihe River Basin in Gansu Province. Results indicate that the optimization schemes can increase agricultural benefits, crop suitability, water use efficiency, and ecological quality by 12.37%, 6.82%, 13.00%, and 8.04% (compared to 2022), respectively, while irrigation water can be saved about 7.53%. The optimization results and proposed approach can help decision-makers manage water resources in the Heihe River Basin and similar regions. 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

Continuous software development and widespread access to satellite imagery allow for obtaining increasingly accurate data on the natural environment. They play an important role in hydrosphere research, and one of the most frequently addressed issues in the era of climate change is the thermal dynamics of its components. Interesting research opportunities in this area are provided by the utilization of data obtained from the moderate resolution imaging spectroradiometer (MODIS). These data have been collected for over two decades and have already been used to study water temperature in lakes. In the case of Poland, there is a long history of studying the thermal regime of lakes based on in situ observations, but so far, MODIS data have not been used in these studies. In this study, the available products, such as 1-day and 8-day MODIS land surface temperature (LST), were validated. The obtained data were compared with in situ measurements, and the reliability of using these data to estimate long-term thermal changes in lake waters was also assessed. The analysis was conducted based on the example of two coastal lakes located in Poland. The results of 1-day LST MODIS generally showed a good fit compared to in situ measurements (average RMSE 1.9 °C). However, the analysis of long-term trends of water temperature changes revealed diverse results compared to such an approach based on field measurements. This situation is a result of the limited number of satellite data, which is dictated by environmental factors associated with high cloud cover reaching 60% during the analysis period. Full article

Diamond and oil and gas production carries risks to the aquatic ecosystem. In Eastern Siberia, on the territory of the Central Yakut Plain, mining development of the Yakut diamond-bearing province and Tas-Yuryakh oil and gas condensate field has been underway for several decades. But the problem of studying negative consequences in the region is covered only from the point of view of the impact on terrestrial ecosystems. The purpose of this study was to assess the impact of diamond and oil and gas production on the aquatic ecosystems of the region using the bioindicative properties of diatoms. The work used previously widely tested methods of ecological mapping, JASP, and species–environments relationship analysis. The results of chemical analysis of water showed that in oil and gas production areas, there is no pollution with petroleum products, but the concentration of silicon and zinc is increased. The study identified key pollutants in the Central Yakut Plain and demonstrated the effectiveness of diatoms as bioindicators. Elevated chloride levels were found in diamond mining areas, and increased copper levels were noted in oil production regions. In the diatom flora of the region, 144 species were identified, of which 137 are indicator species. Natural and anthropogenic clusters of environmental factors are identified, characterized by a specific effect on the species composition and taxonomic structure of diatom communities. The results obtained are suitable for assessing the level of anthropogenic impact on aquatic communities of photosynthetic microorganisms in permafrost conditions. Full article