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Tyler King

Publications and source records attributed to Tyler King.

17 recordsLinked to original sources
USGS2026

National-scale remotely sensed lake surface temperature in the contiguous United States

Temperature is a master variable in aquatic ecosystems. Despite its importance for ecosystem function, our capacity to measure temperature is often limited to focal systems with intensive in situ measurements or larger systems with readily accessible data from disparate data streams. These limitations are consequential for holistically understanding aquatic ecosystem status and change, especially considering that most lakes globally do not have consistent in situ data. To empower researchers with macroscale surface temperature observations, we present the first remotely sensed lake temperature dataset that includes measurements from Landsat 8 made every ~ 16 d for 137,394 lakes of at least 4 ha in surface area throughout the contiguous United States from 2013 through 2025. By aggregating data in a cloud computing framework, the dataset is amenable for continual update, thereby allowing end users to track local-to-continental scale changes in lake water temperature over time.

contiguous United States↗
USGS2026

Cyanobacteria Assessment Network: Pilot study with Sentinel-2 derived chlorophyll data

Harmful algal blooms (HABs) affect global water quality, limiting uses like recreation and consumption because of excessive algal biomass and toxin production. These blooms cause surface scums, taste and odor issues, hypoxia, and negative health and socioeconomic impacts. The US Army Corps of Engineers (USACE) manages over 400 lakes and reservoirs and seeks to improve monitoring of HABs through Sentinel-2 (S2) satellite imagery. The purpose of this project is to develop workflows for a national chlorophyll-a product with 20 m spatial resolution, enabling HAB monitoring for over 270,000 lakes and reservoirs. The 1-year pilot study established a federal partnership to conduct preliminary S2 processing steps, using datasets from Florida, Ohio, and Oregon. Key steps included in situ data aggregation, data assimilation, S2 algorithm evaluation, spatial and temporal compositing, satellite sensor cross-validation, and web hosting of an example prototype product. The study covers 29 USACE reservoirs, with cross-validation using Sentinel-3 (S3) data from 212 lakes. The multiagency initiative aligns with federal agency missions to protect health and the environment, supports acts like the 2017 Harmful Algal Bloom and Hypoxia Research and Control Act, and has annual potential avoided costs of $42 million.

contiguous United States↗
USGS2026

Field sampling guidelines for developing and verifying satellite remote sensing chlorophyll a concentration and fluorescence models in inland waters

Harmful algal blooms are increasing in frequency in inland waters across the United States, resulting in a need to monitor phytoplankton bloom events to track ecosystem health and productivity. Remote sensing of chlorophyll a values offers a cost-effective and powerful method for early detection and characterization of bloom events and serves as an overall indicator of water quality and trophic state, with regular, repeated sampling of landscape-wide, high spatial resolution measurements. Field measurements are necessary for developing and verifying chlorophyll a retrieval models. For model verification, chlorophyll a concentration or fluorescence and light attenuation measurements are needed; for model development, turbidity and colored dissolved organic matter concentration measurements are additionally needed; and for model development and verification, radiometric measurements, taxonomic identification of phytoplankton, inherent optical properties, and cyanotoxin concentration are further measurements that can provide context. This report outlines detailed methods and priority considerations for collecting high-quality field data in inland waters (defined as rivers, lakes, reservoirs, estuaries, streams, and wetlands). The described methods include best practices for collecting and preparing discretely collected water samples and for calibration, maintenance, and quality assurance and quality control of field sensors. Whereas the priorities will vary between applications, some general guidelines are to collect field samples (1) as close in time to a satellite overpass as possible, (2) from representative areas of the waterbody to capture the range of spatial variability, and (3) near the surface to match remote sensing reflectance data.

Techniques and Methods↗
USGS2026

Sentinel-2 for chlorophyll-a water quality monitoring: A review of validation evidence and application potential

Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.

International Journal of Remote Sensing↗
USGS2026

Aquatic reflectance derived from Sentinel-2 Multispectral Imager data for inland waters in the conterminous United States

Satellite-based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real-time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic-focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis-ready, aquatic reflectance dataset for inland water derived from Sentinel-2 for the conterminous United States.

conterminous United States↗
USGS2026

A comparison of non-contact methods for measuring turbidity in the Colorado River

Monitoring suspended-sediment concentration (SSC) is essential to better understand how sediment transport could adversely affect water availability for human communities and ecosystems. Aquatic remote sensing methods are increasingly utilized to estimate SSC and turbidity in rivers; however, an evaluation of their quantitative performance is limited. This study evaluates the performance of three multispectral sensors, which vary in resolution and ease of deployment, to estimate turbidity in the Colorado River: the Multispectral Instrument (MSI) on board the European Space Agency’s Sentinel-2 satellite, an industrial-grade 10-band dual camera system mounted on a cable car, and a consumer-grade 6-band dual camera system positioned on the riverbank. We use multivariate linear regression to compare in situ turbidity measurements with concurrent spectral reflectance data from each sensor. Models for all three sensors selected similar spectral information and resulted in mean errors <35% in predicting turbidity. A cross-sensor comparison showed that little accuracy is lost when applying models developed for satellite-based systems to ground-based systems, and vice versa. Transferability of satellite-based models to ground-based systems could support continuous water-quality monitoring between satellite overpasses and avoid issues associated with cloud interference. Conversely, continuously operating ground-based systems could be used to rapidly establish datasets and models for application in satellite imagery, thus accelerating remote sensing applications. The encouraging performance of the consumer-grade system indicates that SSC could be monitored for low cost.

Colorado↗
USGS2025

Environmental characterization of Blue Mesa Reservoir and potential causes of and management strategies for harmful algal blooms, 1970 through 2023, Curecanti National Recreation Area, Colorado

Blue Mesa Reservoir, in the Curecanti National Recreation Area, is the largest storage reservoir in Colorado and consists of three distinct basins: Iola (the shallowest), Cebolla, and Sapinero. After algal toxins were first documented in Iola basin in 2018, the U.S. Geological Survey began a study in cooperation with the National Park Service, Colorado River Water Conservation District, Upper Gunnison River Water Conservancy District, Gunnison County, Project 7 Water Authority, and Uncompahgre Valley Water Users Association to better understand occurrence of toxic cyanobacteria harmful algal blooms (cyanoHABs) and identify possible causal mechanisms to potentially inform management strategies. Toxic cyanoHABS occurred when the algal toxin microcystin exceeded a concentration of 8 micrograms per liter primarily in Iola basin in 2018 and 2020–22, years having some of the lowest reservoir water-level elevations (reservoir levels) since 1984. The toxic cyanoHABs started in mid-September and continued through the fall months. Algal abundance was greatest in Iola basin compared to Cebolla and Sapinero basins, with Aphanizomenon , a toxin-producing cyanobacterium, being the most abundant. During blooms, enhanced algal photosynthesis caused elevated pH and dissolved oxygen concentrations especially in Iola basin. Continuous monitor data in Iola basin indicated peaks in phycocyanin fluorescence, pH, and dissolved oxygen concentration that preceded the onset of toxic cyanoHABs by about 2 weeks potentially indicating a useful early warning monitoring strategy for future response to toxic cyanoHABs. Long-term trends showed increases in mean air and surface-water temperatures and chlorophyll- a concentrations in the reservoir but no change in nutrient inputs from major tributaries. In Iola basin, reservoir level was positively correlated with Secchi disk depth and inversely correlated with total phosphorus concentration. Because of its shallow depth, the effect of low reservoir levels may disproportionately affect Iola basin compared to other basins, resulting in algal blooms and toxin production especially at reservoir levels below about 7,470 feet above North American Vertical Datum of 1988. Elevated phosphorus at low reservoir level likely was primarily phosphorus contained in algal tissue. This report indicates that the main driver for recent toxic cyanoHABs in Iola basin is low reservoir level that likely causes favorable conditions (shallow and warm) for algal growth and increased recruitment of algae from bottom sediments such as during wind-driven turbulence. Control of external nutrients to the reservoir is unlikely to help control algal blooms because Aphanizomenon fixes nitrogen from the atmosphere, and there is an abundant geogenic source of phosphorus. Maintenance of reservoir levels greater than about 7,470 feet might help minimize the occurrence of toxic cyanoHABs. Additional data could help better understand how the timing and duration of reservoir levels below 7,470 feet contribute to toxic cyanoHABs.

Colorado↗
USGS2025

From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York

Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll- a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation ( r 2 = 0.25) that improved substantially after splitting data by day (mean r 2 = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.

New Jersey, New York↗
USGS2025

Clarifying the trophic state concept to advance macroscale freshwater science and management

For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use-specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non-algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re-evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.

Ecosphere↗
USGS2025

Remote sensing of chlorophyll a and temperature to support algal bloom monitoring in Blue Mesa Reservoir, Colorado

We present methods to reconstruct historical chlorophyll a and surface water temperatures from satellite-based remote sensing products for Blue Mesa Reservoir, Colorado, to support algal bloom monitoring. A machine learning model was trained to construct chlorophyll a concentrations from Sentinel-2 satellite imagery and in situ measurements of chlorophyll a concentrations (out of bag RMSE = 1.9 μg/L, R 2 = 0.63) and reconstruct summertime chlorophyll a concentrations over the entire reservoir from 2016 through 2023. Concurrently, we developed an approach to retrieve remotely sensed water temperatures from the Landsat collection 2 provisional surface temperature product (MAE = 0.6°C) and reconstructed summertime surface water temperature records from 2000 through 2023. Finally, we demonstrate how the reconstructed chlorophyll a and temperature records can yield insight on reservoir dynamics. The chlorophyll a records indicate that algal blooms have a consistent spatial pattern across multiple years, initiating in the eastern end of the reservoir and spreading to the west over time. Water temperatures increased at a linearized rate of 0.3°C per decade from 2000 through 2023 and were inversely proportional to reservoir water surface elevation. Finally, mean summer remotely sensed chlorophyll a concentration had a moderately positive correlation with mean summer remotely sensed water temperature.

Colorado↗
USGS2025

Fate and effects to the benthic community of a copper treatment to eradicate invasive mussels in a large western river, USA

Copper-based chemical treatments are commonly used to eradicate invasive mussels in small ponds and lakes, but their use in large rivers has been limited. In 2023, in response to a detection of invasive quagga mussels, a 10-km reach of the Snake River (Idaho, USA), was treated with an unprecedented 19,300 kg of chelated copper molluscicide to a target concentration of 1,000 µg/L for 10 days. We assessed the transport and fate of the copper and its exposure and effects on the nontarget benthic community downstream. Water samples were collected at seven locations throughout the treatment period, and sediment, periphyton, and benthic macroinvertebrates were collected pre- and posttreatment. Nearly half of the original mass of copper was removed from the water column via sedimentation, sorption to algae, or biological uptake within the 10-km treatment reach and the first 15 km downstream. Even so, dissolved copper concentrations exceeded the acute toxicity threshold at least as far as 28 km downstream for more than 2 weeks. Sediment copper increased by up to 8.3-fold, exceeding the consensus-based sediment quality threshold effect concentration at several sites. Effects on benthic macroinvertebrates varied by taxa. From 0–28 km downstream, invertebrate abundances decreased 52%–94%, with gastropods among the most affected. Of the unique taxa present at these sites pretreatment, 52%–64% were not found posttreatment but were replaced by other taxa, indicating a reorganization of the base of the food web. Additionally, from 0–15 km downstream, the percentage of individuals from tolerant taxa increased two to15-fold. Findings from this study can help watershed managers plan future invasive mussel responses while protecting culturally, economically, and ecologically important nontarget species in large rivers.

Idaho↗
USGS2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Scientific Data↗
USGS2024

Software application for spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A tool for identifying cyanobacteria genera from remotely sensed data

Remote sensing is often used to detect algae, but standard techniques do not provide information on the types of algae present or their potential to form a harmful algal bloom (HAB). We developed a framework for identifying algal genera based on reflectance: SMASH, short for Spectral Mixture Analysis for Surveillance of HABs. The Software Application for SMASH (SAS) was developed in MATLAB and makes use of a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm implemented in Python but packaged as a standalone executable. SAS includes functions for importing hyperspectral images, resampling spectral libraries, evaluating endmember spectral separability, performing MESMA, and generating various output data products.

Journal of Open Research Software (JORS)↗
USGS2022

Evaluation of select velocity measurement techniques for estimating discharge in small streams across the United States

Multiple instruments and methods exist for collecting discrete streamflow measurements in small streams with low flows, defined here as less than 5.7 m 3 /s (200 ft3/s). Included in the available methods are low-cost approaches that are infrequently used, in part, because their uncertainty is not well known. In this work, we evaluated the accuracy and suitability of three low-cost velocity measurement methods (surface float [SF], velocity head rod [VR], and rising body [RB]) and three conventional current meters (acoustic Doppler velocimeter, and mechanical Price type AA and Price Pygmy meters) relative to discharge calculated from stable artificial hydraulic controls. A total of 231 measurements were made by 20 individuals during 88 site visits to 24 sites in eight states. Accuracies were assessed for all methods and precision was evaluated for the low-cost methods. The median percent error was below 5% for conventional methods, and below 20% for the low-cost methods. The SF was the most accurate (median absolute percent error 14%) and precise (mean percent precision of 11%) low-cost method. The RB and VR, respectively, had 15% and 20% median absolute percent error and 29% and 12% mean percent precision. Results suggest that low-cost methods, when used appropriately, can be used to estimate discharge data under low flow conditions when measurements with conventional methods are not feasible and the associated accuracies meet end-user measurement objectives.

continental United States↗
USGS2022

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll- a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon , and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella , a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

New York, Oregon, Texas↗
USGS2022

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Idaho, Oregon↗