Chong Fang

Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning.

Turbidity is a crucial indicator for evaluating water quality. This study obtained the long-term spatial distribution of water turbidity across Northeast China from 1985 to 2023. A combination of the geographically and temporally weighted regression (GTWR) model, the Lindeman, Merenda, and Gold (LMG) method, and statistical data analysis methods were employed to quantify the spatiotemporal impacts of driving factors on turbidity changes. The stepwise regression model was able to credibly estimate turbidity, achieving a low RMSE of 18.432 Nephelometric Turbidity Units (NTU). Temporal variations in turbidity showed that 69.90% of lakes exhibited a decreasing trend. Spatial variations revealed that lakes with significantly increased turbidity were predominantly concentrated in the Songnen and Sanjiang Plains, whereas lakes with lower turbidity were situated in the Eastern Mountains regions and Liaohe Plain. Temporal changes were closely associated with socioeconomic development and anthropogenic interventions implemented by governments on the aquatic environment. Vegetation coverage, precipitation, and elevation demonstrated significant contributions (exceeding 16.39%) to turbidity variations in the Lesser Khingan and Eastern Mountains regions, where natural factors played a more dominant role. In contrast, cropland area, wind speed, and impervious surface area showed higher contribution rates of above 14.00% in the Songnen, Sanjiang, and Liaohe Plains, where anthropogenic factors were dominant. These findings provide valuable insights for informed decision-making in water environmental management in Northeast China and facilitate the aquatic ecosystem sustainability under human activities and climate change.

Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still in the initial and exploratory stage, and the research results are relatively few. Especially for mid-to-high latitude lakes, the research is even more blank. Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. Remote sensing models were developed to estimate the biomass abundance of three major algal groups: Cyanophyta, Chlorophyta, and Bacillariophyta. This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). The results indicated that XGBoost exhibited the highest accuracy (R² = 0.92, RMSE = 1.78%, MAPE = 9.96%) in estimating Cyanophyta’s biomass abundance. The RF model demonstrated the highest accuracy for estimating Chlorophyta’s biomass abundance (R² = 0.72, RMSE = 6.57%, MAPE = 50.8%), while the GBDT model exhibited the highest accuracy for estimating Bacillariophyta’s biomass abundance (R² = 0.9, RMSE = 4.66%, MAPE = 47.87%). The models were subsequently applied to all cloud-free OLCI images from Hulun Lake during the ice-free periods from 2016 to 2023, producing spatiotemporal distribution maps of the different phytoplankton biomass abundance. Cyanophyta dominated the biomass abundance (44.62 ± 3.47%), followed by Bacillariophyta (36.35 ± 2.68%), and Chlorophyta had the lowest proportion (10.42 ± 1.08%). Together, these three algae groups constituted 91.4 ± 1.55% of all phytoplankton in Hulun Lake. Significant annual variations in the biomass abundance of Cyanophyta and Bacillariophyta were observed, whereas those of Chlorophyta remained stable. Additionally, this study examined the effects of climatic factors and water quality parameters on the biomass abundance of algae. The findings suggest that temperature, wind speed, and atmospheric pressure are critical factors influencing the biomass abundance of the different algae groups. This study not only fills the gaps in the related field, but also provides a new method for monitoring algae, as well as a strong support for realizing the goals of sustainable management of water resources and ecological protection.

Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R² = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R² = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R² = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R² = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km²) around the globe over a 37‐year time span (1982–2018). Out of the 176032 lakes with area >1 km² detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.