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1. Modeling Coastal Water Clarity Using Landsat‐8 and Sentinel‐2

   https://doi.org/10.1029/2022EA002579  

   Lang, Sarah E.; Luis, Kelly M. A.; Doney, Scott C.; Cronin‐Golomb, Olivia; Castorani, Max C. N. (July 2023, Earth and Space Science)

   Abstract Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. 

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2. Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

   https://doi.org/10.3390/rs16060996  

   Waczak, John; Aker, Adam; Wijeratne, Lakitha_O H; Talebi, Shawhin; Fernando, Ashen; Dewage, Prabuddha_M H; Iqbal, Mazhar; Lary, Matthew; Schaefer, David; Lary, David J (March 2024, Remote Sensing)

   Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. 

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3. Assessment of Atmospheric Correction Algorithms for Sentinel-3 OLCI in the Amazon River Continuum

   https://doi.org/10.3390/rs16142663  

   Valerio, Aline M; Kampel, Milton; Vantrepotte, Vincent; Ballester, Victoria; Richey, Jeffrey (July 2024, Remote Sensing)

   Xia, Junshi; Kishcha, Pavel; Roberts, Dar; VanDeventer, Heidi; Niculescu, Simona (Ed.)

   Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were evaluated against in situ remote sensing reflectance (Rrs) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital Rrs had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ Rrs. OC-SMART Rrs products showed better spectral quality compared to Rrs derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum. 

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4. 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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5. Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

   https://doi.org/10.3390/rs13152899  

   El Serafy, Ghada Y.H.; Schaeffer, Blake A.; Neely, Merrie-Beth; Spinosa, Anna; Odermatt, Daniel; Weathers, Kathleen C.; Baracchini, Theo; Bouffard, Damien; Carvalho, Laurence; Conmy, Robyn N.; et al (August 2021, Remote Sensing)

   Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. 

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