Water Sensors Presentations and Publications

On this page:

Learn about EPA's Water Sensors Toolbox.

Satellite-Based Sensors

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

Publication Highlights

  • Assessing Potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for Water Quality Monitoring Across the Coastal United States (2023) The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) is a geostationary sensor funded by the National Aeronautics and Space Administration (NASA) Earth Venture Instrument program anticipated to launch in 2027 and will provide high temporal frequency observations of the United States coastal waters. The spatial, temporal, and radiometric resolutions of GLIMR are evaluated and compared to other satellites typically used for water quality measures. GLIMR has the potential to provide unprecedented observations of coastal dynamics, in addition to harmful algal bloom and oil spill event response, valuable for management applications.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems (2018). Use of satellites sensors to monitor loss of biodiversity in coastal ecosystems.
  • Remote Sensing of Selected Water-Quality Indicators with the Hyperspectral Imager for the Coastal Ocean (HICO) Sensor (2014). Satellite imagery and spectral data from the Hyperspectral Imager for Coastal Ocean on the International Space Station was used to map the magnitude and spatial extent of water quality indicators such chlorophyll, turbidity, and colored dissolved organic matter at multiple spatial scales for Pensacola Bay, Choctawhatchee Bay, St. Andrew Bay and St. Joseph Bay along the Florida Panhandle from 2009-2012. 

Presentation Highlights

Field-Based Sensors

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.

Publication Highlights

  • 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 on line 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.

  • 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.

  • Development of a Disposable AChE Sensor for As(III) and Field Analysis Method; Tests with Groundwater Samples from Shepley’s Hill Landfill (2021) This interim report summarizes the findings in the development of a disposable sensor for in-situ arsenic determination, highlight the achievements and issues identified, and propose the path forward.
  • Effects of Experimental Conditions on the Signaling Fidelity of Impedance-Based Nucleic Acid Sensors (2021) This collaborative effort, led by scientists at the University of Cincinnati, investigated the use of electrochemical impedance spectroscopy (EIS), an extremely sensitive analytical technique used for the electrochemical detection of target analytes in a broad range of sensor applications. The work improved understanding of the effect of multiple factors on EIS signal response and optimized the experimental conditions for development of sensitive and reproducible sensors.
  • Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results. This 2009 EPA report gives EPA’s results from investigating water quality monitoring sensor technologies that could have been part of a real-time contamination warning system (CWS).
  • Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results - An Updated Guide for Sensor for Manufacturers and Water Utilities (2021) EPA testing of water quality sensors provides information to sensor manufacturers and utilities. New sensors have come on the market since this handbook was originally published in 2009. This update provides the results from 10 additional sensors tested at EPA Test and Evaluation facility.
  • 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
  • Microbial Biosensors for Recreational and Source Waters (2020) Biosensors are finding new places in science, and the growth of this technology will lead to dramatic improvements in the ability to detect microorganisms in recreational and source waters for the protection of public health. This review provides a summary of the state of the science for microbial biosensors.
  • A Comprehensive Review: Development of Electrochemical Biosensors for Detection of Cyanotoxins in Freshwater (2019) Cyanobacteria harmful algal blooms are increasing in frequency and cyanotoxins have become an environmental and public concern in the U.S. and worldwide. In this review, conducted by University of Cincinnati and EPA scientists, the majority of reported studies and developments of electrochemical affinity biosensors for cyanotoxins are critically reviewed and discussed. This review, developed by scientists from University of Cincinnati and US EPA, aims to serve as a valuable source to scientists and engineers entering the interdisciplinary field of electrochemical biosensors for detection of cyanotoxins in freshwaters.
  • 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 Disposable Acetylcholine Esterase Sensor for As(iii) Determination in Groundwater Matrix Based on 4-Acetoxyphenol Hydrolysis (2019). To address the issue of the lack of field-compatible analytical methods for the speciation of As(III) to characterize groundwater pollution at anthropogenic sites, an inhibition-based acetylcholine esterase (AchE) sensor was developed. The sensor was used to determine As(III) in groundwater. 4-Acetoxyphenol was employed to develop an amperometric assay for AchE activity. This assay was used to guide the fabrication of an AchE sensor with screen-printed carbon electrode. An As(III) determination protocol was developed based on the pseudo-irreversible inhibition mechanism. 
  • 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. 
  • 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.
  • Smart Data Infrastructure for Wet Weather Control and Decision Support (2018). Summarizes key aspects of utility operations where smart data systems can provide significant benefits. 
  • Adaptive Water Sensor Signal Processing: Experimental Results and Implications for Online Contaminant Warning System (2007). The capability of a contaminant detection method is used to analyze output from water quality sensors measuring free and total chlorine, chloride, pH, dissolved oxygen, conductivity, oxidation reduction potential and turbidity in experiments with 16 herbicide, pesticide, inorganic and biological contaminants. The results helped identify water quality parameter changes and chlorine reactivity differences that can be used to establish a contaminant detection system.

Presentation Highlights

Water Research

Contact Us about Water Research
Contact Us to ask a question, provide feedback, or report a problem.
Last updated on January 5, 2024