Water Sensors Presentations and Publications

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Satellite-Based Sensor Publication Highlights

Sunrise over the Earth from space
A view of Earth from space.

Water Quality Monitoring

Mapping

  • Providing a Framework for Seagrass Mapping in United States Coastal Ecosystems Using High Spatial Resolution Satellite Imagery (2023) Traditional seagrass monitoring approaches can be costly and time-consuming. Commercial satellite imagery provides sensor technology with high spatial resolution for monitoring seagrass across the continental United States.  This study provides instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

  • High-Frequency Time Series Comparison of Sentinel-1 and Sentinel-2 Satellites for Mapping Open and Vegetated Water Across the United States (2017–2021) (2023) Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for both of these satellites at 12 sites across the conterminous United States (CONUS). The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

  • Improved Mapping of Coastal Salt Marsh Habitat Change at Barnegat Bay (NJ, USA) Using Object-Based Image Analysis of High-Resolution Aerial Imagery (2023) Tidal wetlands are valued for the ecosystem services they provide yet are vulnerable to loss due to anthropogenic disturbances such as land conversion, hydrologic modifications, and the impacts of climate change, especially accelerating rates of sea level rise. To effectively manage tidal wetlands in face of multiple stressors, accurate studies of wetland extent and trends based on high-resolution imagery are needed. This study demonstrates the suitability of high-resolution imagery for the detection of open water features. For the purposes of salt marsh change detection and the identification of change drivers, management and conservation agencies should make use of high-resolution imagery whenever feasible.

  • Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-Automated Remote Sensing Analysis (2022) However, Accurate quantification of seagrass and their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat satellite imagery to quantify seagrass extent, leaf area index, and belowground organic carbon in an estuary.
  • Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a (2021) Chlorophyll concentration provides a common metric of water quality, and is frequently used to indicate lake trophic state. This study demonstrates the satellite sensor ability to assess lake trophic state across more than 2,000 lakes across the contiguous United States, and a tool to assess the strengths and weaknesses of applying a single algorithm across multiple water systems.
  • Recent Advancement in Mangrove Forests Mapping and Monitoring of the World Using Earth Observation Satellite Data (2021) Mangrove forests are distributed in the inter-tidal region between the sea and the land in the tropical and subtropical regions of the world. They are one of the most productive and biologically complex ecosystems in the world. Recent findings suggest that mangroves annually sequester two to four times more carbon compared to mature tropical forests, and store three to four times more carbon per equivalent area than tropic forests. Advancement in remote sensing with the availability of higher spatial, spectral, and temporal resolution and availability of historical remote sensing data provides an opportunity to better characterize, map, and monitor mangrove forests.

Harmful Algal Blooms/Cyanobacteria/Chlorophyll-a

  • Forecasting freshwater cyanobacterial harmful algal blooms (2024). Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems but no quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Fortunately, publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the U.S. In this study, weekly cyanobacteria abundance was quantified from the Sentinel-3 satellite and used to develop an effective approach for forecasting the occurrence of cyanoHABs. These forecasts will be publicly available during the active bloom season at Cyanobacterial Harmful Algal Blooms Forecasting Research.

  • Expanding the application of Sentinel-2 chlorophyll monitoring across United States lakes (2024). Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) is able to quantify chlorophyll a, an indicator of water quality and trophic state, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms were applied to S2 MSI data and evaluated. The study reports algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

  • Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning (2023). Harmful algal blooms of cyanobacteria (CyanoHABs) have emerged as a serious environmental concern in large and small water bodies including many inland lakes. The growth dynamics of CyanoHABs can be chaotic at very short timescales but predictable at coarser timescales. In Lake Erie, cyanobacteria blooms occur in the spring-summer months, which, at annual timescale, are controlled by the total spring phosphorus (TP) load into the lake. This study, led by EPA and Purdue University, aimed to forecast CyanoHAB cell count at sub-monthly (e.g., 10-day) timescales. Satellite-derived cyanobacterial index (CI) was used as a surrogate measure of CyanoHAB cell count.

  • Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States (2023). Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

  • Satellite and in situ cyanobacteria monitoring: Understanding the impact of monitoring frequency on management decisions (2023). Cyanobacterial harmful algal blooms (CyanoHABs) in reservoirs can be transported to downstream waters via scheduled discharges. Transport dynamics are difficult to capture in traditional CyanoHAB monitoring, which can be spatially disparate and temporally discontinuous. The introduction of satellite remote sensing for CyanoHAB monitoring provides opportunities to detect where CyanoHABs occur in relation to reservoir release locations, like canal inlets. The study objectives were to assess (1) differences in reservoir CyanoHAB frequencies as determined by in situ and remotely sensed data and (2) the feasibility of using satellite imagery to identify conditions associated with release-driven CyanoHAB export. This study, conducted by EPA and North Carolina State University scientists, demonstrates how remote sensing can complement traditional CyanoHAB monitoring to inform reservoir release decision making.

  • Recent Changes in Cyanobacteria Algal Bloom Magnitude in Large Lakes Across the Contiguous United States (2023) Cyanobacterial blooms in inland lakes produce large quantities of biomass that can impact drinking water systems, recreation, and tourism. This study analyzed nine years of satellite-derived bloom records and compared how the bloom magnitude has changed across the largest lakes in the contiguous United States.
  • Identifying Lakes at Risk of Toxic Cyanobacterial Blooms Using Satellite Imagery and Field Surveys Across the United States (2023) Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The approach taken in this study represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.
  • Sub-Monthly Time Scale Forecasting of Harmful Algal Blooms Intensity in Lake Erie Using Remote Sensing and Machine Learning (2023) Harmful algal blooms of cyanobacteria (CyanoHAB) have emerged as a serious environmental concern in large and small water bodies including many inland lakes. The growth dynamics of CyanoHAB can be chaotic at very short timescales but predictable at coarser timescales. This study aimed to forecast CyanoHAB cell count at sub-monthly (e.g., 10-day) timescales with satellite-derived cyanobacterial index (CI) used as a surrogate measure of CyanoHAB cell count.
  • A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes (2022) This study examined the presence-absence agreement between state reported cyanoHAB advisories and events and cyanobacteria biomass estimated by a satellite. Satellite measured magnitude, spatial extent, and temporal frequency of cyanobacteria confirmed each of these three metrics were greater during state recreation advisories compared to non-advisory times. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.
  • Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales (2022) There is limited capability to quantify cyanobacterial biomass across broad geographic scales and at regular intervals. This study quantified the spatial extent of cyanobacteria using satellites from the European Space Agency including MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. Spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States.
  • Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales (2021) Satellite imagery was used to assess the annual frequency of cyanobacterial biomass, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Pixel-scale results can assist in identifying portions of a lake that are more prone to cyanobacterial, while lake- and state-scale results can assist in the prioritization of sampling resources and mitigation efforts.
  • Algal Bloom Monitoring: Remote Sensing (2020) This chapter provides an overview of the use of satellite remote sensing techniques and technology to observe, forecast, and monitor the temporal and spatial extent of phytoplankton and cyanobacteria concentrations. It concludes with a discussion of the challenges that the scientific and environmental communities face when incorporating remotely sensed data into monitoring strategies within the framework of monitoring to protect public recreation and drinking water supplies.
  • Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing (2020) This study provides a metric to quantify the percentage of lakes across the contiguous US experiencing cyanobacterial blooms for each week. Using satellite data, the percentage of lakes with a bloom, without a bloom, and the with no valid data for each weekly composite were reported. Results from this research can be used to monitor annual trends in the presence of cyanobacteria in inland lakes across the contiguous US.

  • Exploring the Potential Value of Satellite Remote Sensing to Monitor Chlorophyll-A for U.S. Lakes and Reservoirs (2020) Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment in situ measures. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events. The development of algorithms and sensors for monitoring HABs help address the need for higher spatial and temporal frequency of the data, which would be prohibitively costly to collect using traditional methods.

  • Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing (2019) A method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs is developed and tested. The magnitude was defined as the spatiotemporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. Lakes can be ranked even with issues such as variable data collection frequency and across different satellites.

Emergency Response/Event Detection

  • Advances in Underwater Oil Plume Detection Capabilities (2021). For subsurface spills over large space and time scales, Autonomous Underwater Vehicles (AUVs) can be used to provide subsurface plume footprints and estimate oil concentrations. For smaller, more frequent spills, tethered compact Remotely Operated Vehicles (ROVs) may be more appropriate as they are easy to deploy for rapid detection.
  • A Global Compilation of In Situ Aquatic High Spectral Resolution Inherent and Apparent Optical Property Data for Remote Sensing Applications (2020) An increase in spectral resolution of future satellites, such as the Plankton-Aerosol-Cloud ocean-Ecosystem (PACE) and the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR), is expected to lead to new or improved capabilities to characterize aquatic ecosystems.  In anticipation of these missions, this scientists have developed a dataset of geographically diverse, quality-controlled, high spectral resolution aquatic optical data for developing new measures.
  • Potential for commercial PlanetScope satellites in oil response monitoring (2022) Petroleum extraction may lead to oil spills in aquatic environments. Commercial satellites provide high resolution images and increased spatial coverage across the globe. Combining commercial satellites with other existing government satellites may increase monitoring coverage when responding to oil spill events.

Land Use/Land Cover

  • Frontiers in Global Mangrove Forest Monitoring (2023). This is a review article that describes the use of remote sensing technology in monitoring the status of mangrove forests.

  • Mangrove Forest Cover Change in the Conterminous United States from 1980–2020 (2023). Mangrove forests in developed and developing countries are experiencing substantial transformations driven by natural and anthropogenic factors. This study focuses on the conterminous United States, including Florida, Texas, and Louisiana, where coastal development, urbanization, hydrological pattern alterations, global warming, sea level rise, and natural disasters such as hurricanes contribute to mangrove forest changes. Using time-series Landsat data and image processing techniques in a cloud computing platform, we analyzed the dynamics of mangrove forests every five years from 1980 to 2020.  Our results can aid policymakers and conservationists in developing targeted strategies for preserving the ecological and socio-economic value of mangrove forests in the conterminous United States. Additionally, all the datasets generated from this study have been released to the public.

  • Thematic accuracy assessment of the 2019 National Land Cover Database for the conterminous United States (2023). The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. The study implements a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 and to evaluate the accuracy of the data.  

  • Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database (2020). The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes satellite-based land cover data in the continental U.S., including new information on land change patterns from 2001 to 2016. In this timeframe, significant changes were noted, with almost 8% of the landscape having experienced a land cover change at least once during this period. More specifically, nearly 50% of that change involves forest, with the overall being a decline in forested areas. Agricultural change represented 15.9% of the change, with a slight increase in agricultural extent, however there was a substantial decline in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and was heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Lastly, the amount of developed land increased, adding almost 29,000 km2 over 15 years (5.6%), with southern states exhibiting expansion much faster than most of the northern states. The authors noted that rates of development increased in 2001–2006 at twice the rate of 2011–2016, perhaps reflecting a slowdown in economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between production and delivery of land cover products, and improving accuracy and expanding the variety of products available in an integrated database.

  • The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data (2014). Land cover refers to the physical nature of a given area of the Earth’s surface. Examples of land cover are forest, wetlands, agricultural fields and open water. The MRLC is a consortium of 10 U.S. Federal Agencies that coordinate the production of five different products that provide information about land cover using satellite data. This includes the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

Presentation Highlights

Field-Based Sensor Publication Highlights

A man near a small river using an electronic device to take water measurements.
An EPA Researcher using a hand-held water sensor in a small river.

Water Quality Monitoring

  • Evaluation of real-time fluorescence sensors and benchtop fluorescence for tracking and predicting sewage contamination in the Tijuana River Esturary at the US-Mexico border (2024). Cross-border flow of untreated sewage from Mexico into the U.S. via the Tijuana River is public health issue with negative consequences for coastal communities. This study evaluates the potential application of fluorescence-based, submersible tryptophan-like (TRP) and humic-like (CDOM) fluorescence sensors for real-time tracking of wastewater pollution in an estuarine environment. This study, initiated by EPA and done in collaboration with scientists from San Diego State University and the Tijuana River National Estuarine Research Reserve, showed that the greatest amount of untreated wastewater in the estuary's surface layer during cross-border flow events was estimated at >80 % and occurred during neap tides, when concentrated, sewage-laden freshwater flowed over dense saline seawater due to stratification and lack of mixing in the estuary.

  • Freshwater Salinization Syndrome Alters Nitrogen Transport in Urban Watersheds (2023). Anthropogenic salt inputs have impacted many streams in the U.S. for over a century. Urban stream salinity is often chronically elevated and punctuated by episodic salinization events, which can last hours to days after snowstorms and the application of road salt. Here, University of Maryland, along with scientists from EPA and University of Delaware,  investigated the impacts of freshwater salinization on total dissolved nitrogen and specific nitrogen ion concentrations and fluxes across time in urban watersheds in the Baltimore-Washington D.C. metropolitan area of the Chesapeake Bay region. 

  • Sensors Track Mobilization of 'Chemical Cocktails' in Streams Impacted by Road Salts in the Chesapeake Bay Watershed (2021). University of Maryland and EPA scientists collaborated on an investigation into stream water chemistry across five urban watersheds in the Baltimore-Washington, USA metropolitan region through combined grab-sampling and high-frequency monitoring by USGS sensors. This work demonstrated that specific conductance could be used as a proxy to predict concentrations of major ions and trace metals. High-frequency sensor monitoring and proxies associated with freshwater salination may help better predict contaminant pulses and contaminant exceedances in response to salinization and impacts on aquatic life, infrastructure, and drinking water.

  • Measuring Coastal Acidification Using In Situ Sensors in the National Estuary Program (2021). This report details the experiences of ten National Estuary Programs and their partners in conducting coastal acidification monitoring using autonomous pH and pCO2 sensors from 2015 to 2020. It illustrates the monitoring goals, deployment methods, data analysis, costs, preliminary results, lessons learned and the role of partnerships in their successes.

  • Biosensors for Monitoring Water Pollutants: A Case Study with Arsenic in Groundwater (2019). Biosensors provide the opportunity for simple-to-use, disposable, or continuous tests, for monitoring many of the common contaminants and emerging contaminants that water-quality personnel are facing today. In this book chapter, an introduction to biosensors is provided along with a discussion on arsenic biosensors that are developed for field applications. In addition, the future of biosensors for emerging contaminants is discussed.
  • A Comprehensive Review: Development of Electrochemical Biosensors for Detection of Cyanotoxins in Freshwater (2019). This review is a valuable source for scientists and engineers in the field of electrochemical biosensors for detecting cyanotoxins in freshwater. Conventional analytical methods for cyanotoxins are usually conducted in certified laboratories using advanced instrumentation. However, most of these techniques are cumbersome, expensive, time-consuming and not suitable for point-of-use water monitoring. This review addresses the need for the development of an advanced, small and portable device that can overcome the drawbacks of current methods and be used in situ and on-line or real-time.
  • Signal Decomposition of Conductivity Sensor Measurements on the Allegheny River, Pennsylvania (2018). Surface water conductivity measurements were used to evaluate the combined contribution of anions in western Pennsylvania from brines discharged by sources such as oil and gas wastewater treatment, coal-fired power plants, and coal mining activities. Intermittent discharges, such as oil and gas wastewater, and continuous sources contributing to the conductivity were quantified using constrained and adaptive decomposition of time-series frequency analysis. 

Sensor Development/Technology

Distribution Systems

Emergency Response/Event Monitoring

  • Monitoring Spore Washoff During a Biological Contamination Incident Response Using Automated Stormwater Samplers and Sensors to Predict Contamination Movement (2023). This study examined the washoff of Bacillus globigii (Bg) spores from concrete, asphalt, and grass surfaces by stormwater. With regard to sensors, the study compared rainfall data from 4 tipping bucket rain gauges and a laser disdrometer and found they performed similarly for values of total rainfall accumulation while the laser disdrometer provided additional information (total storm kinetic energy) useful in comparing the seven different rain events. In addition, the soil moisture probes are recommended for assistance in predicting when to sample sites with intermittent runoff. Collectively the data are useful for emergency responders faced with remediation decisions after a biological agent incident.
  • Summary of Detection and Response Data from Source Water Contamination Incidents (2022). This report summarizes a research study on detection of contamination in source waters using online water quality sensors. Specifically, it describes sensor response data from two common source waters which contain contaminants that could affect source water quality. 
  • Advances in Underwater Oil Plume Detection Capabilities (2021). Historically, visual observation is an emergency responder’s first ‘tool’ in identifying spilled oil. Optical detection has since expanded to include a myriad of signals from space, aircraft, drone, vessel and submersible platforms that can provide critical information for decision-making during spill response efforts. Spill monitoring efforts below the air-water interface have been vastly improved by advances with in situ optical sensors and vehicle platform technology. This paper provides an overview of recent advances in the use of sensors in underwater oil plumes.
  • Investigation Clogging Dynamic of Permeable Pavement Systems Using Embedded Sensors (2018). A study of clogging in an 80-acre permeable pavement parking lot at a school in Fort Riley, Kansas. The results generally support the hypothesis that the clogging progresses from the upgradient to the downgradient edge. The magnitude of the contributing drainage area and rainfall characteristics are effective factors on rate and progression of clogging.

Presentation Highlights

Water Research

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Last updated on March 27, 2025