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1. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

   https://doi.org/10.3390/hydrology10040078  

   Ekpetere, Kenneth; Abdelkader, Mohamed; Ishaya, Sunday; Makwe, Edith; Ekpetere, Peter (April 2023, Hydrology)

   The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics. 

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2. A database of in situ water temperatures for large inland lakes across the coterminous United States

   https://doi.org/10.1038/s41597-024-03103-8  

   Sorensen, Troy; Espey, Eamon; Kelley, John_G W; Kessler, James; Gronewold, Andrew D (December 2024, Scientific Data)

   Abstract Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing andin situdata for validation.In situmonitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizingin situlake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns. 

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3. Combining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigation

   https://doi.org/10.1109/OCEANS47191.2022.9977208  

   Rojas, Cesar A.; Padrao, Paulo V.; Fuentes, Jose E.; Albayrak, Arif R.; Osmanoglu, Batuhan; Bobadilla, Leonardo (December 2022, OCEANS 2022)

   Scientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance (Rrs) indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentine1-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem. 

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4. GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

   https://doi.org/10.1038/s41597-023-01973-y  

   Lehmann, Moritz K.; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; Conroy, Ted; Anstee, Janet; Balasubramanian, Sundarabalan V.; Barbosa, Cláudio C.; Binding, Caren; Bracher, Astrid; et al (December 2023, Scientific Data)

   Abstract The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount ofin situdata to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophylla, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA thede-factostate of knowledge ofin situcoastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring. 

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5. Trophic state resilience to hurricane disturbance of Lake Yojoa, Honduras

   https://doi.org/10.1038/s41598-023-32712-3  

   Fadum, J. M.; Waters, M. N.; Hall, E. K. (April 2023, Scientific Reports)

   Abstract Cyclones are a poorly described disturbance in tropical lakes, with the potential to alter ecosystems and compromise the services they provide. In November 2020, Hurricanes Eta and Iota made landfall near the Nicaragua-Honduras border, inundating the region with a large amount of late-season precipitation. To understand the impact of these storms on Lake Yojoa, Honduras, we compared 2020 and 2021 conditions using continuous (every 16 days) data collected from five pelagic locations. The storms resulted in increased Secchi depth and decreased algal abundance in December 2020, and January and February 2021, and lower-than-average accumulation of hypolimnetic nutrients from the onset of stratification (April 2021) until mixus in November 2021. Despite the reduced hypolimnetic nutrient concentrations, epilimnetic nutrient concentrations returned to (and in some cases exceeded) pre-hurricane levels following annual water column turnover in 2021. This response suggests that Lake Yojoa’s trophic state had only an ephemeral response to the disturbance imposed by the two hurricanes, likely due to internal input of sediment derived nutrients. These aseasonal storms acted as a large-scale experiment that resulted in nutrient dilution and demonstrated the resilience of Lake Yojoa’s trophic state to temporary nutrient reductions. 

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