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1. Remote Sensing of Water Quantity and Quality in Geospatial Education: Lake Sidney Lanier, Georgia, USA

   Brookins-Jackson, C.D.; Boyd, Z.L.; Freeland, A.C.; Mann, B.L.; Perry, M.K.; Ignatius, A.R. (January 2022, Apalachicola-Chattahoochee-Flint Waters Conference: Shared Resources in Changing Times. Albany, GA)

   To increase geospatial awareness about local water resources, our team developed learning resources for the 150 km² Lake Sidney Lanier reservoir located in North Georgia, USA. The reservoir is vital for hydroelectric power generation, recreation, tourism, and consumptive uses. Using geospatial analysis in Google Earth Engine (GEE), we analyzed precipitation trends in the watershed using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. We also quantified expansion and contraction of reservoir surface area using Landsat-derived Global Surface Water data. As Lake Sidney Lanier is a managed reservoir, surface water extent fluctuations are related to climatic variables, consumptive use, and hydropower generation. Water temperature varies based on seasonality, water depth, water clarity, and lake stratification. Changing temperature dynamics affect ecosystem health and determine other important water quality parameters such as dissolved oxygen concentrations. Landsat 8 Thermal Infrared Sensor (TIRS) data were used to examine temperature trends over multiple years and investigate the timing of lake stratification and mixing. Highly turbid waters are associated with pollutants and lower water quality and can affect ecosystem productivity by minimizing sunlight penetration into the water column. Sentinel 2 MSI data were processed using a turbidity algorithm to analyze temporal trends and spatial correlations with reservoir inflows. Finally, high concentrations of chlorophyll a were used as a proxy to identify harmful algal blooms. The spatial differences in headwaters and near-dam locations were examined and near real-time satellite data were explored for potential development of early-warning systems to protect ecosystem and human health. 

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2. Satellite detection of dinoflagellate blooms off California by UV reflectance ratios

   https://doi.org/10.1525/elementa.2020.00157  

   Kahru, Mati; Anderson, Clarissa; Barton, Andrew D.; Carter, Melissa L.; Catlett, Dylan; Send, Uwe; Sosik, Heidi M.; Weiss, Elliot L.; Mitchell, B. Greg (January 2021, Elementa: Science of the Anthropocene)

   As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios <1 of remote sensing reflectance of the UV band at 380 nm to that of the blue band at 443 nm were used as an indicator of the dinoflagellate bloom. The satellite data indicated that an observed, long, and narrow nearshore band of elevated chlorophyll-a (Chl-a) concentrations, extending from northern Baja to Santa Monica Bay, was dominated by L. polyedra. In other high Chl-a regions, the ratios were >1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales. 

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3. Recipes for the Derivation of Water Quality Parameters Using the High-Spatial-Resolution Data from Sensors on Board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 Satellites

   https://doi.org/10.34133/remotesensing.0049  

   Tavora, Juliana; Jiang, Binbin; Kiffney, Thomas; Bourdin, Guillaume; Gray, Patrick Clifton; Carvalho, Lino Sander; Hesketh, Gabriel; Schild, Kristin M.; Souza, Luiz Faria; Brady, Damian C.; et al (January 2023, Journal of Remote Sensing)

   Satellites have provided high-resolution ( < 100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll- a , turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date. 

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4. UAV-Based Hyperspectral Imaging for River Algae Pigment Estimation

   https://doi.org/10.3390/rs15123148  

   Logan, Riley D.; Torrey, Madison A.; Feijó-Lima, Rafael; Colman, Benjamin P.; Valett, H. Maurice; Shaw, Joseph A. (June 2023, Remote Sensing)

   Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates. 

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5. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

   https://doi.org/10.3390/rs12050833  

   Song, Rui; Muller, Jan-Peter; Kharbouche, Said; Yin, Feng; Woodgate, William; Kitchen, Mark; Roland, Marilyn; Arriga, Nicola; Meyer, Wayne; Koerber, Georgia; et al (March 2020, Remote Sensing)

   null (Ed.)

   Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR). 

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