Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Sensors”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
USGS2009

Cross-comparison of the IRS-P6 AWiFS sensor with the L5 TM, L7 ETM+, & Terra MODIS sensors

As scientists and decision makers increasingly rely on multiple Earth-observing satellites to address urgent global issues, it is imperative that they can rely on the accuracy of Earth-observing data products. This paper focuses on the crosscomparison of the Indian Remote Sensing (IRS-P6) Advanced Wide Field Sensor (AWiFS) with the Landsat 5 (L5) Thematic Mapper (TM), Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The cross-comparison was performed using image statistics based on large common areas observed by the sensors within 30 minutes. Because of the limited availability of simultaneous observations between the AWiFS and the Landsat and MODIS sensors, only a few images were analyzed. These initial results are presented. Regression curves and coefficients of determination for the top-of-atmosphere (TOA) trends from these sensors were generated to quantify the uncertainty in these relationships and to provide an assessment of the calibration differences between these sensors. ?? 2009 SPIE.

Conference Paper↗
USGS2020

Planetary sensor models interoperability using the community sensor model specification

This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation ( https://github.com/USGS-Astrogeology/usgscsm ). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.

Earth and Space Science↗
USGS2024

Landed planetary sensor model support with the community sensor model

This note describes recent work to develop landed planetary sensor model support conforming to the widely adopted Community Sensor Model (CSM) standard. We describe the CSM specification and its use in the planetary sciences, introduce the test sensors carried aboard the Mars Science Laboratory (MSL) Curiosity Rover, and describe the engineering research requirements fulfilled. Ultimately, we demonstrate use of a CSM framing sensor model for the NavCam and MastCam instruments including stereo terrain extraction and map projection using the NASA Ames Stereopipeline over a multi-day traverse and multi-image correspondence identification needed as an input to stereophotogrammetric block bundle adjustment using the USGS maintained ISIS library.

Conference Paper↗
USGS2021

Technical note—Relative variability of selected turbidity standards and sensors in use by the U.S. Geological Survey

The challenges associated with field measurements of turbidity are well known and result primarily from differences in reported values that depend on instrument design and the resulting need for reporting units that are specific to those designs. A critical challenge for making comparable turbidity measurements is the selection and use of appropriate turbidity standards for sensor calibration. The accepted primary standards for turbidity measurements use formazin made from scratch; all others should relate back to readings obtained using standard formazin. However, because turbidity is a qualitative property of water, comparing standards is not as simple as it is for many chemical measurements. The U.S. Geological Survey “National Field Manual for the Collection of Water-Quality Data” currently allows for the use of two standards, formazin and polymer beads, for the calibration of field turbidimeters. Another challenge for making comparable turbidity measurements is selection of turbidity sensors. A turbidity sensor commonly used in the U.S. Geological Survey, the Yellow Springs Instruments (YSI) 6136, has been replaced by the manufacturer with the YSI EXO turbidity sensor. Both sensors operate on the same principles but have slight design differences that result in readings that are not directly comparable on a 1:1 basis. Differences in calibration standards and sensors are a cause of concern in ongoing studies that require switching calibration standards or sensor types, and for comparisons of data collected with sensors calibrated by using different calibration standards, different sensor types, or both. The objectives of this study were to evaluate the response of two YSI turbidity sensors in both formazin-based standards (StablCal) and polymer turbidity standards (in this case YSI brand; however, other brands are available) and to compare the performance of the YSI EXO and YSI 6136 turbidity sensors under similar laboratory and environmental (field) conditions. To quantify these differences, a series of laboratory and field side-by-side comparisons were conducted. Nine field comparisons of YSI EXO and YSI 6136 sensors were performed at site locations in Kansas and Virginia. Two field comparisons of StablCal and polymer calibration standards were performed in Kansas, both using YSI EXO turbidity sensors. Five laboratory comparisons between the YSI EXO and YSI 6136 turbidity sensors were performed, and seven laboratory comparisons between StablCal and polymer turbidity standards were performed using YSI EXO turbidity sensors. The results can help the USGS and others better understand how turbidity data can differ depending on the sensors and calibration standards used. Key findings and conclusions include the following— Regardless of the comparison, strong linear associations were typically found across all measures of turbidity under field and laboratory conditions, but linear associations were not necessarily 1:1 and varied by type of standard, type of sensor, and field and laboratory conditions. The mean relative percentage differences for all but a few comparisons were greater than 10 percent but less than 30 percent. However, differences were inconsistent across the laboratory and field conditions measured in this study, precluding the ability to formulate definitive statements of consistent directional bias depending on the type of standard or sensor used. Across all tests and a range of 0 to 1,000 formazin nephelometric units (FNU), no consistent bias between the YSI EXO and YSI 6136 turbidity sensors was observed, but either regression relations were near 1:1 or the YSI EXO turbidity readings were lower than those measured with the YSI 6136. Relative percentage differences typically exceeded the 10- and 30-percent benchmarks. However, most (about 95 percent) of the absolute differences between turbidity values measured with the YSI EXO and those measured with the YSI 6136 sensors were less than 20 FNU. In laboratory experiments, when turbidity was “constant,” the absolute difference between YSI EXO and YSI 6136-measured turbidity values ranged from near 0 FNU to nearly 400 FNU (over a range of 0 to 1,000 FNU). Substantial variability in turbidity measurements makes comparison between standards and sensor types challenging. Given the inherent variability in turbidity measurements and the lack of consistent bias between calibration standards or sensors, changing methods during an ongoing study would compromise the comparability of the data. The effect of changing methods ultimately depends on study objectives. If method changes are required, laboratory- and field-based comparisons across a range of conditions ideally would be conducted to determine whether site-specific biases can be identified.

Open-File Report↗
USGS2026

Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors

Executive Summary The use of field-deployable fluorescence sensors to better understand dissolved organic matter concentrations and composition has grown immensely in recent years. Applications of these sensors to critical monitoring efforts have also grown, encompassing post-fire monitoring, wastewater tracking, and use as a proxy for various contaminants. Despite the growth, it is well known that these sensors require corrections for temperature (Watras and others, 2011) and are subject to many light-field interferences caused by both scattering and absorbance due to dissolved and particulate substances (Downing and others, 2012; Lee and others, 2015; Booth and others, 2023). The most common fluorescence sensors used by the U.S. Geological Survey (USGS) include those targeting fluorescent dissolved organic matter (fDOM) and chlorophylls. Because fDOM sensors primarily measure fluorescence in the dissolved to colloidal phases, corrections to the interferences caused by particulates can be made relatively easily. By the end of 2024, the USGS had 69 fDOM sensors deployed within official water quality monitoring networks included on the USGS National Water Dashboard ( https://dashboard.waterdata.usgs.gov/app/nwd/en/ ) and numerous others used in surveys and research applications across the Nation. Although temperature corrections are widely applicable across sensor models, interference corrections can be model specific due to differences in design specifications across manufacturers and models (Booth and others, 2023). The corrections are also potentially subject to changes in manufacturing within a specific sensor model. Recently, USGS staff obtained information regarding possible changes in the manufacturing of its most widely-used fDOM sensor model, raising concerns about data consistency and quality in the USGS fDOM sensor networks. Furthermore, changes in turbidity sensors since the corrections guidance was performed may also affect the performance of the corrections. The turbidity sensor used in the original experiments (Downing and others, 2012) was determined to have a signal output approximately 1.3 times higher than the output of the turbidity sensor currently used in an extensive field comparison study (Messner and others, 2023). With these changes, it is imperative that the corrections be reevaluated to maintain data consistency and continuity across the USGS. In this study, we evaluated turbidity corrections for fDOM sensors over a range of serial numbers covering manufacturing dates 2015 through 2022 and turbidity serial numbers covering the range 2013 through 2022. The goal was to determine whether reported changes in the manufacturing process of the fDOM and turbidity sensors affected the correction approach developed by Downing and others (2012) such that additional guidance would be required to address this manufacturing change. To evaluate, we repeated a laboratory-based test similar to that performed by Downing and others (2012) in which a series of tank experiments with multiple sensors were deployed in a suspension of Elliot Silt Loam (ESL). High turbidities of the ESL suspension were maintained throughout the tank by turbulent recirculation using submersible pumps. Particulates were removed using a recirculated line equipped with a capsule filter (0.45 micron). Measurements were collected throughout the filtration until turbidities reached approximately 5 formazin nephelometric units (FNU; data available in Baxter and others, 2023). Each experimental run included a mixture of unique sensor combinations to account for variability imposed by the turbidity and temperature sensors. The fDOM correction factor was calculated for each combination of fDOM and turbidity sensors included in the test. We observed no systematic change in fDOM correction coefficients across serial numbers representing manufacturing years 2015 through 2022. However, the results highlighted questions raised about the corrections for high-turbidity samples, as noted in USGS Techniques and Methods (Booth and others, 2023). Applying the inverse of the commonly-used fDOM ratio with a quadratic fit performed better than the exponential fits when correcting fDOM data for turbidity in the ESL laboratory filtration test and generated a simple scale factor correction equation. This approach also served as a better indicator of data quality than the exponential fit approach. Similar to fDOM, more rigorous quality assurance measures may be necessary to evaluate turbidity sensor calibrations and performance. Sensors exceeding a certain age may need to be replaced despite passing quality assurance checks during calibration. Further testing of the turbidity corrections for different sediment and water types is warranted to better understand the variations in the fits and correctable ranges of turbidity in different systems.

Open-File Report↗
USGS2015

Evaluation of Xylem EXO water-quality sondes and sensors

Two models of multiparameter sondes manufactured by Xylem, parent company of Yellow Springs Incorporated (YSI)—EXO1 and EXO2—equipped with EXO conductivity/temperature (C/T), pH, dissolved oxygen (DO), and turbidity sensors, were evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility. The sondes and sensors were evaluated in two phases for compliance with the manufacturer’s specifications and the USGS acceptance criteria for continuous water-quality monitors. Phase one tested the accuracy of the water-quality sondes equipped: (a) with a C/T, pH, DO, and turbidity sensor by comparing the EXO sensors’ measured values to those of an equivalently configured YSI 6920 V2-2 sensor, and (b) with multiple sensors of the same parameter type (such as three pH sensors and a C/T sensor) on a single sonde at room temperature and at an extended temperature range. In addition to accuracy, the communication protocols and the manufacturing specifications for range of detection and operating temperature were also tested during this phase. Phase two evaluated the sondes’ performance in a surface-water environment by deploying an EXO1 and an EXO2 equipped with pH, C/T, DO, and turbidity sensors at USGS site 02492620 located at East Pearl River near Bay Saint Louis, Mississippi. The EXO sondes’ temperature deviations from a certified YSI 4600 digital thermometer were within the ±0.2 degree Celsius (°C) USGS criteria, but were greater than the ±0.01 °C manufacturing specification. The conductivity sensors met the ±3 percent USGS criteria for specific conductance greater than 100 microsiemens per centimeter. The sensors met the more stringent ±0.5 percent manufacturing specification only at room temperature in the 250 microsiemens per centimeter (µS/cm) standard. The manufacturing and USGS criteria (±0.2 pH unit) were met in pH standards 4, 9.2, 10, and 12.45, but were not met in pH 1.68 standard. The DO sensors met both the ±0.3 milligram per liter (mg/L) USGS criteria and the ±1 percent manufacturing specification. The ±5 percent USGS criteria for turbidity in waters not exceeding 2,000 formazin nephelometric units (FNU) were met by the five turbidity sensors tested; however, all five sensors failed to meet these requirements at turbidities exceeding 2,000 FNU. The more stringent ±2 percent manufacturing turbidity specification for water with less than 1,000 FNU was met by only one of the five sensors tested. The results from the field deployment indicated acceptable agreement in temperature, specific conductance, pH, and DO between the EXO sondes, the site sonde, and the reference sonde. The EXO1 and EXO2 turbidity measurements differed from the site sonde by approximately 23 and 25 percent, respectively.

Open-File Report↗
USGS2017

Laboratory evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor

Sequoia Scientific’s LISST-ABS is an acoustic backscatter sensor designed to measure suspended-sediment concentration at a point source. Three LISST-ABS were evaluated at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF). Serial numbers 6010, 6039, and 6058 were assessed for accuracy in solutions with varying particle-size distributions and for the effect of temperature on sensor accuracy. Certified sediment samples composed of different ranges of particle size were purchased from Powder Technology Inc. These sediment samples were 30–80-micron (µm) Arizona Test Dust; less than 22-µm ISO 12103-1, A1 Ultrafine Test Dust; and 149-µm MIL-STD 810E Silica Dust. The sensor was able to accurately measure suspended-sediment concentration when calibrated with sediment of the same particle-size distribution as the measured. Overall testing demonstrated that sensors calibrated with finer sized sediments overdetect sediment concentrations with coarser sized sediments, and sensors calibrated with coarser sized sediments do not detect increases in sediment concentrations from small and fine sediments. These test results are not unexpected for an acoustic-backscatter device and stress the need for using accurate site-specific particle-size distributions during sensor calibration. When calibrated for ultrafine dust with a less than 22-µm particle size (silt) and with the Arizona Test Dust with a 30–80-µm range, the data from sensor 6039 were biased high when fractions of the coarser (149-µm) Silica Dust were added. Data from sensor 6058 showed similar results with an elevated response to coarser material when calibrated with a finer particle-size distribution and a lack of detection when subjected to finer particle-size sediment. Sensor 6010 was also tested for the effect of dissimilar particle size during the calibration and showed little effect. Subsequent testing revealed problems with this sensor, including an inadequate temperature compensation, making this data questionable. The sensor was replaced by Sequoia Scientific with serial number 6039. Results from the extended temperature testing showed proper temperature compensation for sensor 6039, and results from the dissimilar calibration/testing particle-size distribution closely corroborated the results from sensor 6058.

Open-File Report↗
USGS2010

Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and watersurface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The generation of EDEN waterlevel surfaces is derived from real-time data. Real-time data are automatically checked for outliers using minimum, maximum, and rate-of-change thresholds for each station. Smaller errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the gages. Correcting smaller errors in the data often is time consuming and water-level data may not be finalized for several months. To provide water-level surfaces on a daily basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous waterlevel data. A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Florida↗
USGS2015

Applications of optical sensors for high-frequency water-quality monitoring and research

The recent commercial availability of in-situ optical sensors, together with new techniques for data collection and analysis, provides the opportunity to monitor a wide range of water-quality constituents over time scales during which environmental conditions actually change. Traditional approaches for data collection (daily to monthly discrete samples) are often limited by high sample collection, processing, and analytical costs, difficult site access, and logistical challenges, particularly for long-term sampling at a large number of sites. Optical sensors that continuously measure constituents in the environment by absorbance or fluorescence properties (Figure 1) have had a long history of use in oceanography for measuring highly resolved concentrations and fluxes of organic matter, nutrients, and algal material. However, much of the work using commercially-available optical sensors in rivers and streams has taken place in only the last few years. Figure 1. [NOT SHOWN] Optical sensor technology is now sufficiently developed to warrant broader application for research and monitoring in coastal and freshwater systems, and the United States Geological Survey (a U.S. science agency) is now using these sensors in a variety of research and monitoring programs to better understand water quality in-situ and in real-time. Examples are numerous and range from the applications of nitrate sensors for calculating loads to estuaries susceptible to hypoxia (Pellerin et al., 2014) to the use of fluorometers to estimate methymercury fluxes (Bergamaschi et al., 2011) and disinfection byproduct formation (Carpenter et al., 2013). Transmitting these data in real-time provides information that can be used for early trend detection, help identify monitoring gaps critical for water management, and provide science-based decision support across a range of issues related to water quality, freshwater ecosystems, and human health. Despite the value of these sensors, collecting data that meet high-quality standards requires investment in and adherence to tested and established methods and protocols for sensor operation and data management (Pellerin et al., 2013). For example, optical sensor measurements can be strongly influenced by a variety of matrix effects, including water temperature, inner filtering from highly colored water, and scattering of light by suspended particles (Downing et al., 2012). Characterizing and correcting sensors for these effects – as well as the continued development of common methodologies and protocols for sensor use – will be critical to ensuring comparable measurements across sites and over time. In addition, collaborative efforts such as the Nutrient Sensor Challenge (www.nutrients-challenge.org) will continue to accelerate the development, production and use of affordable, reliable and accurate sensors for a range of environments. REFERENCES Bergamaschi .B.A., Fleck J.A., Downing B.D., Boss E., Pellerin B.A., Ganju N.K., Schoellhamer D.H., Byington A.A., Heim W.A., Stephenson M., Fujii R. (2011), Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4): 1355-1371. Carpenter K.D., Kraus T.E.C., Goldman J.H., Saraceno J., Downing B.D., Bergamaschi B.A., McGhee G., Triplett T. (2013), Sources and Characteristics of Organic Matter in the Clackamas River, Oregon, Related to the Formation of Disinfection By-products in Treated Drinking Water: U.S. Geological Survey Scientific Investigations Report 2013–5001, 78 p. Downing .B.D., Pellerin B.A., Bergamaschi B.A., Saraceno J., Kraus T.E.K. (2012), Seeing the light: The effects of particles, temperature and inner filtering on in situ CDOM fluorescence in rivers and streams. Limnology and Oceanography: Methods, 10: 767-775. Pellerin B.A., Bergamaschi B.A., Downing B.D., Saraceno J., Garrett J.D., Olsen L.D. (2013), Optical Techniques for the Determination of Nitrate in En

Conference Paper↗
USGS2016

The effect of suspended sediment and color on ultraviolet spectrophotometric nitrate sensors

Four commercially available ultraviolet nitrate spectrophotometric sensors were evaluated by the U.S. Geological Survey Hydrologic Instrumentation Facility (HIF) to determine the effects of suspended sediment concentration (SSC) and colored dissolved organic matter (CDOM) on sensor accuracy. The evaluated sensors were: the Hach NITRATAX plus sc (5-millimeters (mm) path length), Hach NITRATAX plus sc (2 mm), S::CAN Spectro::lyser (5 mm), and the Satlantic SUNA V2 (5 mm). A National Institute of Standards and Technology-traceable nitrate-free sediment standard was purchased and used to create the turbid environment, and an easily made filtered tea solution was used for the CDOM test. All four sensors performed well in the test that evaluated the effect of suspended sediment on accuracy. The Hach 5 mm, Hach 2 mm, and the SUNA V2 met their respective manufacturer accuracy specifications up to concentrations of 4,500 milligrams per liter (mg/L) SSC. The S::CAN failed to meet its accuracy specifications when the SSC concentrations exceeded 4,000 mg/L. Test results from the effect of CDOM on accuracy indicated a significant skewing of data from all four sensors and showed an artificial elevation of measured nitrate to varying amounts. Of the four sensors tested, the Satlantic SUNA V2’s accuracy was affected the least in the CDOM test. The nitrate concentration measured by the SUNA V2 was approximately 24 percent higher than the actual concentration when estimated total organic carbon values exceeded 44 mg/L. Measured nitrate concentration falsely increased 49 percent when measured by the Hach 5 mm, and 75 percent when measured by the Hach 2 mm. The S::CAN’s reported nitrate concentration increased 96 percent. Path length plays an important role in the sensor’s ability to compensate measurements for matrix interferences, but does not solely determine how well a sensor can handle all interferences. The sensor’s proprietary algorithms also play a key role in matrix interference compensation. The sensors’ ability to compensate for CDOM varied significantly during the tests, even among the three with 5-mm path lengths. Results of this evaluation suggest that the proprietary algorithms of the nitrate analyzers are more effective compensating for suspended sediment, and less effective compensating for CDOM (color) when sensor path length remains constant.

Open-File Report↗
USGS2018

Evaluation of the Radar Stage Sensor manufactured by Forest Technology Systems—Results of laboratory and field testing

Two identical Radar Stage Sensors from Forest Technology Systems were evaluated to determine if they are suitable for U.S. Geological Survey (USGS) hydrologic data collection. The sensors were evaluated in laboratory conditions to evaluate the distance accuracy of the sensor over the manufacturer’s specified operating temperatures and distance to water ranges. Laboratory results were compared to the manufacturer’s accuracy specification of ±0.007 foot (ft) and the USGS Office of Surface Water (OSW) policy requirement that water-level sensors have a measurement uncertainty of no more than 0.01 ft or 0.20 percent of the indicated reading. Both of the sensors tested were within the OSW policy requirement in both laboratory tests and within the manufacturer’s specification in the distance to water test over tested distances from 3 to 15 ft. In the temperature chamber test, both sensors were within the manufacturer’s specification for more than 90 percent of the data points collected over a temperature range of –40 to +60 degrees Celsius at a fixed distance of 8 ft. One sensor was subjected to an SDI-12 communication test, which it passed. A field test was conducted on one sensor at a USGS field site near Landon, Mississippi, from February 5 to March 29, 2016. Water-level measurements made by the radar during the field test were in agreement with those made by the Sutron Accubar Constant Flow Bubble Gauge. Upon the manufacturer’s release of updated firmware version 1.09, additional SDI-12 and temperature testing was performed to evaluate added SDI-12 functions and verify that performance was unaffected by the update. At this time, an Axiom data logger is required to perform a firmware update on this sensor. The data confirmed the results of the original test. Based on the test results, the Radar Stage Sensor is a suitable choice for USGS hydrologic data collection.

Open-File Report↗
USGS2024

Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020

In response to the increasing frequency of cyanobacterial harmful algal blooms (CyanoHABs) in the Finger Lakes region of New York State, a pilot study by the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, was conducted to enhance CyanoHAB monitoring and understanding. High-frequency sensors were deployed on open water monitoring-station platforms at Seneca Lake in 2019–20, at Owasco Lake in 2019–20, and at Skaneateles Lake in 2019. One of the goals of this study was to evaluate the ability of in-place sensors to make representative measurements of dissolved organic matter, nutrients, and algal pigments (as indicators of phytoplankton biomass) while collecting routine field parameters (water temperature, specific conductance, pH, dissolved oxygen, turbidity, weather, and light) to provide additional information about environmental conditions. Despite challenges like power issues and sensor fouling, the sensors performed well overall. However, correlation analyses between sensor readings and laboratory measurements revealed variable performance. Results indicate the relation between the fluorescent dissolved organic matter sensor and laboratory-measured dissolved organic carbon was weak at all study lakes. The nitrate sensors can be sensitive to ambient temperature and have a substantial power requirement, and the relation between sensor- and laboratory-measured nitrate values differed among lakes. The orthophosphate sensors, which were complex and prone to data loss, yielded results that were difficult to interpret because orthophosphate detections are rare in the study lakes. The multichannel fluorometer was also complex to use and required several unique procedures for its operation. Chlorophyll measurements from the fluorometers correlated moderately well with laboratory-measured chlorophyll- a , although relations with total phytoplankton biovolume were weaker. Relations between phycocyanin concentration measurements from the dual-channel fluorometers and cyanobacterial biovolume were not significant; however, the cyanobacterial biovolume correlation was moderately strong with chlorophyll contribution from cyanobacteria measurements from the multichannel fluorometer. Of all collected parameters, water temperature was among the strongest correlated with chlorophyll- a , total phytoplankton biovolume, and cyanobacterial biovolume. Stepwise regression analysis was used to identify the best parameters for modeling variance in laboratory measures of phytoplankton biomass. This analysis included factors such as chlorophyll fluorescence, pH, water temperature, and others, which varied by lake. Overall, the models had limited explanatory power for chlorophyll- a and other biovolumes, possibly due to the absence of CyanoHABs at the open-water monitoring locations. Multivariate models did not outperform simple fluorescence-based models. Notably, turbidity was a more significant indicator of cyanobacterial biovolume variability than phycocyanin from dual-channel fluorometers. The study concludes that while single and multivariate models based on sensor data are useful, they did not explain any more variance than fluorescence-based models. Broader data collection, including more CyanoHAB events, is necessary to refine these models. Integrating machine learning could leverage large, complex datasets to improve CyanoHAB predictions, thereby enhancing the management and understanding of these blooms.

New York↗
USGS2024

Adaptable plasmonic membrane sensors for fast and reliable detection of trace low micrometer microplastics in lake water

In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1–10 μm) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% ± 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 μg/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.

Environmental Science and Technology↗
USGS2012

Field comparison of optical and clark cell dissolved-oxygen sensors

Three multi-parameter water-quality monitors equipped with either Clark cell type or optical type dissolved-oxygen sensors were deployed for 30 days in a brackish (salinity <10 parts per thousand) environment to determine the sensitivity of the sensors to biofouling. The dissolved-oxygen sensors compared periodically to a hand-held dissolved oxygen sensor, but were not serviced or cleaned during the deployment. One of the Clark cell sensors and the optical sensor performed similarly during the deployment. The remaining Clark cell sensor was not aged correctly prior to deployment and did not perform as well as the other sensors. All sensors experienced substantial biofouling that gradually degraded the accuracy of the dissolved-oxygen measurement during the last half of the deployment period. Copyright ASCE 2005.

Conference Paper↗
USGS2012

Radiometric calibration of the Landsat MSS sensor series

Multispectral remote sensing of the Earth using Landsat sensors was ushered on July 23, 1972, with the launch of Landsat-1. Following that success, four more Landsat satellites were launched, and each of these carried the Multispectral Scanner System (MSS). These five sensors provided the only consistent multispectral space-based imagery of the Earth's surface from 1972 to 1982. This work focuses on developing both a consistent and absolute radiometric calibration of this sensor system. Cross-calibration of the MSS was performed through the use of pseudoinvariant calibration sites (PICSs). Since these sites have been shown to be stable for long periods of time, changes in MSS observations of these sites were attributed to changes in the sensors themselves. In addition, simultaneous data collections were available for some MSS sensor pairs, and these were also used for cross-calibration. Results indicated substantial differences existed between instruments, up to 16%, and these were reduced to 5% or less across all MSS sensors and bands. Lastly, this paper takes the calibration through the final step and places the MSS sensors on an absolute radiometric scale. The methodology used to achieve this was based on simultaneous data collections by the Landsat-5 MSS and Thematic Mapper (TM) instruments. Through analysis of image data from a PICS location and through compensating for the spectral differences between the two instruments, the Landsat-5 MSS sensor was placed on an absolute radiometric scale based on the Landsat-5 TM sensor. Uncertainties associated with this calibration are considered to be less than 5%.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2016

Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data

The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data. The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Florida↗