Detecting Climate Driven Changes in Chlorophyll-a Using High Frequency Monitoring: The Impact of the 2019 European Heatwave in Three Contrasting Aquatic Systems

Remote sensing for mapping algal blooms in freshwater lakes: a review

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.

Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies

A consistent dataset of lake surface water temperature, ice cover, water-leaving reflectance, water level and extent is presented. The collection constitutes the Lakes Essential Climate Variable (ECV) for inland waters. The data span combined satellite observations from 1992 to 2020 inclusive and quantifies over 2000 relatively large lakes, which represent a small fraction of the number of lakes worldwide but a significant fraction of global freshwater surface. Visible and near-infrared optical imagery, thermal imagery and microwave radar data from satellites have been exploited. All observations are provided in a common grid at 1/120° latitude-longitude resolution, jointly in daily files. The data/algorithms have been validated against in situ measurements where possible. Consistency analysis between the variables has guided the development of the joint dataset. It is the most complete collection of consistent satellite observations of the Lakes ECV currently available. Lakes are of significant interest to scientific disciplines such as hydrology, limnology, climatology, biogeochemistry and geodesy. They are a vital resource for freshwater supply, and key sentinels for global environmental change.

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Water clarity (Secchi disk depth, SDD), as a proxy of water transparency, provides important information on the light availability to the lake ecosystem, making it one of the key indicators for evaluating the water ecological environment, particularly in nutrient-rich inland lakes. Hulun Lake, the fifth largest lake in China, has faced severe water quality challenges in the past few decades, e.g., high levels of phosphorus and nitrogen, leading to lake eutrophication. However, under such a serious context, the temporal and spatial dynamics of SDD in Hulun Lake are still unclear. In this paper, we obtained the best model input parameters by using stepwise linear regression models to test field measurements against remote sensing band information, and then developed the SDD satellite algorithm suitable for Hulun Lake by comparing six models (i.e., linear, quadratic, cubic, exponential, power, and logarithmic). The results showed that (1) B3/(B1 + B4) [red/(blue-near-infrared)] was the most sensitive parameter for transparency (R = 0.84) and the exponential model was the most suitable transparency inversion model for Hulun Lake (RMSE = 0.055 m, MAE = 0.003 m), (2) The annual mean SDD of Hulun Lake was higher in summer than in autumn, the summer SDD decreased from 2010 (0.23 m) to 2020 (0.17 m), and the autumn SDD increased from 2010 (0.06 m) to 2020 (0.16 m). The SDD in the littoral zones of Hulun Lake was less than that in the central part; (3) meteorological conditions (i.e., precipitation and wind speed) were highly correlated with the variation of SDD. Cropland expansion was the possible reason for the low SDD at the entrance of Hulun Lake flow. The findings of this study have important implications for the development and implementation of ecological protection and restoration strategies in the Hulun Lake basin.