Search USGS⌕ Search

SEARCH · Search USGS

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 91 records · Page 5Linked to original sources
USGS2009

Assessment of the short-term radiometric stability between Terra MODIS and Landsat 7 ETM+ sensors

Short-term radiometric stability was evaluated using continuous ETM+ scenes within a single orbit (contact period) and the corresponding MODIS scenes for the four matching solar reflective visible and near-infrared (VNIR) band pairs between the two sensors. The near-simultaneous earth observations were limited by the smaller swath size of ETM+ (183 km) compared to MODIS (2330 km). Two sets of continuous granules for Terra MODIS and Landsat 7 ETM+ were selected and mosaicked based on pixel geolocation information for noncloudy pixels over the African continent. The matching pixel pairs were resampled from a fine to a coarse pixel resolution, and the at-sensor spectral radiance values for a wide dynamic range of the sensors were compared and analyzed, covering various surface types. The following study focuses on radiometric stability analysis from the VNIR band-pairs of ETM+ and MODIS. The Libya-4 desert target was included in the path of this continuous orbit, which served as a verification point between the short-term and the long-term trending results from previous studies. MODTRAN at-sensor spectral radiance simulation is included for a representative desert surface type to evaluate the consistency of the results.

Conference Paper↗
USGS2004

Cross calibration of the Landsat-7 ETM+ and EO-1 ALI sensor

As part of the Earth Observer 1 (EO-1) Mission, the Advanced Land Imager (ALI) demonstrates a potential technological direction for Landsat Data Continuity Missions. To evaluate ALI's capabilities in this role, a cross-calibration methodology has been developed using image pairs from the Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) and EO-1 (ALI) to verify the radiometric calibration of ALI with respect to the well-calibrated L7 ETM+ sensor. Results have been obtained using two different approaches. The first approach involves calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors. The second approach uses vicarious calibration techniques to compare the predicted top-of-atmosphere radiance derived from ground reference data collected during the overpass to the measured radiance obtained from the sensor. The results indicate that the relative sensor chip assemblies gains agree with the ETM+ visible and near-infrared bands to within 2% and the shortwave infrared bands to within 4%.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2010

Radiometric, geometric, and image quality assessment of ALOS AVNIR-2 and PRISM sensors

The Advanced Land Observing Satellite (ALOS) was launched on January 24, 2006, by a Japan Aerospace Exploration Agency (JAXA) H-IIA launcher. It carries three remote-sensing sensors: 1) the Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2); 2) the Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM); and 3) the Phased-Array type L-band Synthetic Aperture Radar (PALSAR). Within the framework of ALOS Data European Node, as part of the European Space Agency (ESA), the European Space Research Institute worked alongside JAXA to provide contributions to the ALOS commissioning phase plan. This paper summarizes the strategy that was adopted by ESA to define and implement a data verification plan for missions operated by external agencies; these missions are classified by the ESA as third-party missions. The ESA was supported in the design and execution of this plan by GAEL Consultant. The verification of ALOS optical data from PRISM and AVNIR-2 sensors was initiated 4 months after satellite launch, and a team of principal investigators assembled to provide technical expertise. This paper includes a description of the verification plan and summarizes the methodologies that were used for radiometric, geometric, and image quality assessment. The successful completion of the commissioning phase has led to the sensors being declared fit for operations. The consolidated measurements indicate that the radiometric calibration of the AVNIR-2 sensor is stable and agrees with the Landsat-7 Enhanced Thematic Mapper Plus and the Envisat MEdium-Resolution Imaging Spectrometer calibration. The geometrical accuracy of PRISM and AVNIR-2 products improved significantly and remains under control. The PRISM modulation transfer function is monitored for improved characterization.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2018

Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization

Landsat-8 has been operating on-orbit for 5+ years. Its two sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), are continuing to produce high quality data. The OLI has been radiometrically stable at the better than 0.3% level on a band average basis for all but the shortest wavelength (443 nm) band, which has degraded about 1.3% since launch. All on-board calibration devices continue to perform well and consistently. No gaps in across track coverage exist as 100% operability of the detectors is maintained. The variability over time of detector responsivity within a band relative to the average is better than 0.05% (1 sigma), though there are occasional detectors that jump up to 1.5% in response in the Short-Wave InfraRed (SWIR) bands. Signal-to-Noise performance continues at 2-3x better than requirements, with a small degradation in the 443 nm band commensurate with the loss in sensitivity. Pre-launch error analysis, combined with the stability of the OLI indicates that the absolute reflectance calibration uncertainty is better than 3%; comparisons to ground measurements and comparisons to other sensors are consistent with this. The Landsat-8 TIRS is similarly radiometrically stable, showing changes of at most 0.3% over the mission. The uncertainty in the absolute calibration as well as the detector to detector variability are largely driven by the stray light response of TIRS. The current processing corrects most of the stray light effects, resulting in absolute uncertainties of ~1% and reduced striping. Efforts continue to further reduce the striping. Noise equivalent delta temperature is about 50 mK at typical temperatures and 100% detector operability is maintained. Landsat-9 is currently under development with a launch no earlier than December 2020. The nearly identical OLI-2 and upgraded TIRS-2 sensors have completed integration and are in the process of instrument level performance characterization including spectral, spatial, radiometric and geometric testing. Component and assembly level measurements of the OLI-2, which include spectral response, radiometric response and stray light indicate comparable performance to OLI. The first functional tests occurred in July 2018 and spatial performance testing in vacuum is scheduled for August 2018. Similarly, for TIRS-2, partially integrated instrument level testing indicated spectral and spatial responses comparable to TIRS, with stray light reduced by approximately an order of magnitude from TIRS.

Conference Paper↗
USGS2006

Absolute calibration accuracy of L4 TM and L5 TM sensor image pairs

The Landsat suite of satellites has collected the longest continuous archive of multispectral data of any land-observing space program. From the Landsat program's inception in 1972 to the present, the Earth science user community has benefited from a historical record of remotely sensed data. However, little attention has been paid to ensuring that the data are calibrated and comparable from mission to mission, Launched in 1982 and 1984 respectively, the Landsat 4 (L4) and Landsat 5 (L5) Thematic Mappers (TM) are the backbone of an extensive archive of moderate resolution Earth imagery. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The approach involves comparing image statistics derived from large common areas observed eight days apart by the two sensors. The average percent differences in reflectance estimates obtained from the L4 TM agree with those from the L5 TM to within 15 percent. Additional work to characterize the absolute differences between the two sensors over the entire mission is in progress.

Conference Paper↗
USGS2018

Observations of rotational motions from local earthquakes using two temporary portable sensors in Waynoka, Oklahoma

Characterizing rotational motions from earthquakes at local distances has the potential to improve earthquake engineering and seismic gradiometry by better characterizing the complete seismic wavefield. Applied Technology Associates (ATA) has developed a proto‐seismic magnetohydrodynamic (SMHD) three‐component rotational rate sensor. We deploy two ATA rotational rate sensors at a temporary aftershock station in Waynoka, Oklahoma. From 27 April to 6 June 2017, we recorded the translational and rotational motions of 155 earthquakes of ML≥2.0 within 220 km of the station. Using the recorded events, we compare peak ground rotation rate (⁠ PGω˙ ⁠) with peak ground velocity (PGV) and with peak ground acceleration (PGA). Our results support previously identified potential relationships between the two quantities. We also compare peak ground rotations (⁠ PGω ⁠) as a function of seismic moment and distance. We found that PGω˙ decays with an exponent of approximately −4.0km−1 for both horizontal and vertical components. On the other hand, PGA decays with an exponent of approximately −1.8km−1 for all components. We compute apparent phase velocity directly from the rotational data for both horizontally polarized shear waves (SH; 379m/s with a standard deviation of 114m/s ⁠) and vertically polarized compression and shear waves (P‐SV; 387m/s with a standard deviation of 121m/s ⁠). Finally, by comparing various rotational and translational components, we look at potential implications for estimating local event source parameters. We found that the absolute correlation of nearby earthquakes decays at a rate of approximately 0.39/km for rotational sensors. This decay rate of absolute correlation is faster on translational sensors with a decay rate of 0.44/km. The latter may help in identifying phenomena such as repeating earthquakes by using differences in correlations as a function of distance and how these differences compare with translational correlations.

Oklahoma↗
USGS2018

Sensor suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite

To standardize parameters used in seismometer testing and calibration and to make these algorithms accessible to the seismological community, we have developed a new seismometer testing software package called Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. This software is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. Our goal is not to be all‐inclusive but instead to focus on a few of the instrumentation tests we view as critical when verifying a sensor’s performance. The tests include self‐noise, relative azimuth, relative gain, and estimation of the poles and zeros. For the self‐noise and the relative azimuth, we also include three‐component versions of these tests to allow for the case of sensors with potentially different orientations (e.g., boreholes). The software has been made available on GitHub with the hope that it will be useful for other seismologists who need to quickly verify various sensor parameters without having to write their own versions of the algorithms. Furthermore, by using a common platform and processing algorithms, it becomes possible to compare results among different tests with similar processing methods being used for both.

Seismological Research Letters↗
USGS2018

Laboratory evaluation of the Sea-Bird Scientific HydroCycle-PO4 phosphate sensor

Sea-Bird Scientific’s HydroCycle-PO 4 phosphate sensor is a single-analyte wet-chemistry sensor designed for in situ environmental monitoring. The unit was evaluated at the U.S. Geological Survey Hydrologic Instrumentation Facility to assess the accuracy of the sensor in solutions with known phosphorous concentration and to test the effects of chromophoric (colored) dissolved organic matter (CDOM) and natural water matrixes on sensor accuracy. Accuracy was tested with three standards: 0.110, 0.174, and 0.260 milligram per liter, as phosphorous (mg/L as P). The 0.110- and 0.260-mg/L standards were made from a dilution of a National Institute of Standards and Technology-traceable phosphate-phosphorous standard with Type I deionized water (DIW). Average measured phosphate concentrations of the tested standards (0.110, 0.174, and 0.260 mg/L as P) in DIW were 0.132, 0.181, and 0.310 mg/L as P, for differences of 20, 4, and 19 percent, respectively. Measured phosphate concentration of a tested standard was biased by the addition of tea water filtered through a 0.45-micrometer pore size filter (filtered tea water [FTW]) simulating the effect of CDOM. An aliquot of the filtered tea solution was sent to a certified environmental laboratory, which reported a less than reporting level (<0.004 mg/L as P) phosphate concentration. True color of the FTW was measured at 380 platinum-cobalt units by using Standard Methods 8025. For this FTW test, the measured phosphate concentration for the clear 0.260 mg/L as P standard averaged 0.366 mg/L as P. This concentration increased to an average of 0.653 mg/L as P with the addition of 10 percent FTW, and to an average of 0.859 mg/L as P with the addition of 20 percent FTW. These test results indicate a positive bias of up to 40 percent of the concentrations of the measured phosphate concentrations when CDOM is present and indicate a proportional increase in an apparent concentration of phosphorus instrument response as CDOM concentration increases.

Open-File Report↗
USGS2025

System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing. We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager. The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.

Open-File Report↗
USGS2023

Comparison of turbidity sensors at U.S. Geological Survey supergages in Indiana from November 2018 to December 2021

Beginning in September 2010, the U.S. Geological Survey installed continuous water-quality monitors at several streamgages across Indiana as part of a network of supergages to meet cooperator information needs. Two types (or models) of water-quality monitors deployed at each site measured and recorded water temperature, dissolved oxygen, specific conductance, pH, and turbidity every 15 minutes during the study period. Associated discrete water samples were collected at regular intervals and analyzed for concentrations of suspended sediment and total phosphorus. Surrogate regression models were developed between the continuously measured turbidity values and turbidity values in the associated samples to compute continuous concentrations and loads of suspended sediment and total phosphorus. Starting in November 2018, the original extended deployment system monitors were replaced with the newest model of multiparameter water-quality monitors and were equipped with turbidity smart sensors because the older monitors were phased out of production. The updated monitor and smart sensor yield different but relatable turbidity values. Turbidity data collected concurrently by the two sensors from November 2018 to December 2021 were compared and analyzed to quantify the relation between them at six supergage sites in northwestern Indiana and one site in the town of Zionsville in central Indiana. Ordinary least squares regression was used to calculate site-specific conversion factors so that turbidity data from the newer monitors can be used in published surrogate models based on the older monitor data. Regression analyses explained approximately 98 percent of the variation in turbidity readings between the two sensors. From these analyses, conversion factors were developed that may be applied to older turbidity readings to calculate near real-time concentrations of phosphorus and suspended sediment.

Indiana↗
USGS2022

Soft pressure sensor for underwater sea lamprey detection

In this paper, an economical and effective soft pressure sensor for underwater sea lamprey detection is proposed, which consists of an array of piezoresistive elements between two layers of perpendicular copper tape electrodes, forming a passive resistor network. With multiplexers, the apparent resistance corresponding to each pixel of the sensing matrix can be measured directly, where the pixel is identified with the row and the column of the respective electrodes. However, this measured two-point resistance is not equal to the actual cell resistance for that pixel due to the crosstalk effect in the resistor network. Since the cell resistance reflects directly the pressure applied on each pixel, the relationship between the cell resistance and the measured two-point resistance is analyzed for a passive matrix of any size. More importantly, several regularized least-squares algorithms are proposed to reconstruct the cell resistance profile from the two-point resistance measurements, with enhanced robustness of the reconstruction in the presence of measurement noises and modeling errors. The proposed pressure sensor is applied to detect the suction attachment of sea lampreys, a devastating invasive species in the Great Lakes region. Experimental results demonstrate that the pressure sensor can successfully capture the rim profile of the lamprey’s sucking mouth. Moreover, the performance and computational complexity of the reconstruction algorithms with different regularization functions are compared.

IEEE Sensors Journal↗
USGS2017

Comparing catchment hydrologic response to a regional storm using specific conductivity sensors

A better understanding of stormwater generation and solute sources is needed to improve the protection of aquatic ecosystems, infrastructure, and human health from large runoff events. Much of our understanding of water and solutes produced during stormflow comes from studies of individual, small headwater catchments. This study compared many different types of catchments during a single large event to help isolate landscape controls on streamwater and solute generation, including human-impacted land cover. We used a distributed network of specific electrical conductivity sensors to trace storm response during the post-tropical cyclone Sandy event of October 2012 at 29 catchments across the state of New Hampshire. A citizen science sensor network, Lotic Volunteer for Temperature, Electrical Conductivity, and Stage, provided a unique opportunity to investigate high-temporal resolution stream behavior at a broad spatial scale. Three storm response metrics were analyzed in this study: (a) fraction of new water contributing to the hydrograph; (b) presence of first flush (mobilization of solutes during the beginning of the rain event); and (c) magnitude of first flush. We compared new water and first flush to 64 predictor attributes related to land cover, soil, topography, and precipitation. The new water fraction was positively correlated with low and medium intensity development in the catchment and riparian buffers and with the precipitation from a rain event 9 days prior to Sandy. The presence of first flush was most closely related (positively) to soil organic matter. Magnitude of first flush was not strongly related to any of the catchment variables. Our results highlight the potentially important role of human landscape modification in runoff generation at multiple spatial scales and the lack of a clear role in solute flushing. Further development of regional-scale in situ sensor networks will provide better understanding of stormflow and solute generation across a wide range of landscape conditions.

New Hampshire↗
USGS2004

Moisture content measurements of moss (Sphagnum spp.) using commercial sensors

Sphagnum (spp.) is widely distributed in permafrost regions around the arctic and subarctic. The moisture content of the moss layer affects the thermal insulative capacity and preservation of permafrost. It also controls the growth and collapse history of palsas and other peat mounds, and is relevant, in general terms, to permafrost thaw (thermokarst). In this study, we test and calibrate seven different soil moisture sensors for measuring the moisture content of Sphagnum moss under laboratory conditions. The soil volume to which each probe is sensitive is one of the important parameters influencing moisture measurement, particularly in a heterogeneous medium such as moss. Each sensor has a unique response to changing moisture content levels, solution salinity, moss bulk density and to the orientation (structure) of the Sphagnum relative to the sensor. All of the probes examined here require unique polynomial calibration equations to obtain moisture content from probe output. We provide polynomial equations for dead and live Sphagnum moss (R2 > 0.99. Copyright ?? 2004 John Wiley & Sons, Ltd.

Permafrost and Periglacial Processes↗
USGS2025

Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions

Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses ( n = 797), legumes ( n = 264), and brassicas ( n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses ( R 2 = 0.51 – 0.64) and legumes ( R 2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses ( R 2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models ( R 2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha −1 for legumes and ∼4000 kg ha −1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled ( R 2 = 0.58 – 0.61) and ACS-214-estimated biomass ( R 2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.

Alabama, Florida, Indiana, Iowa, Kansas, Maryland,↗
USGS2009

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and each frame into slots. A parent node determines the children's frame assignment based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. First, given any node, at any time slot, there is at most one active sender in its neighborhood (including itself). Second, the packet scheduling with TreeMAC is bufferless, which therefore minimizes the probability of network congestion. Third, the data throughput to the gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24-node testbed show that TreeMAC protocol significantly improves network throughput, fairness, and energy efficiency compared to TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC. Partial results of this paper were published in Song, Huang, Shirazi and Lahusen [W.-Z. Song, R. Huang, B. Shirazi, and R. Lahusen, TreeMAC: Localized TDMA MAC protocol for high-throughput and fairness in sensor networks, in: The 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom, March 2009]. Our new contributions include analyses of the performance of TreeMAC from various aspects. We also present more implementation detail and evaluate TreeMAC from other aspects. ?? 2009 Elsevier B.V.

Pervasive and Mobile Computing↗
USGS2012

Complementarity of ResourceSat-1 AWiFS and Landsat TM/ETM+ sensors

Considerable interest has been given to forming an international collaboration to develop a virtual moderate spatial resolution land observation constellation through aggregation of data sets from comparable national observatories such as the US Landsat, the Indian ResourceSat and related systems. This study explores the complementarity of India's ResourceSat-1 Advanced Wide Field Sensor (AWiFS) with the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The analysis focuses on the comparative radiometry, geometry, and spectral properties of the two sensors. Two applied assessments of these data are also explored to examine the strengths and limitations of these alternate sources of moderate resolution land imagery with specific application domains. There are significant technical differences in these imaging systems including spectral band response, pixel dimensions, swath width, and radiometric resolution which produce differences in observation data sets. None of these differences was found to strongly limit comparable analyses in agricultural and forestry applications. Overall, we found that the AWiFS and Landsat TM/ETM+ imagery are comparable and in some ways complementary, particularly with respect to temporal repeat frequency. We have found that there are limits to our understanding of the AWiFS performance, for example, multi-camera design and stability of radiometric calibration over time, that leave some uncertainty that has been better addressed for Landsat through the Image Assessment System and related cross-sensor calibration studies. Such work still needs to be undertaken for AWiFS and similar observatories that may play roles in the Global Earth Observation System of Systems Land Surface Imaging Constellation.

Remote Sensing of Environment↗
USGS2025

Regenerable membrane sensors for ultrasensitive nanoplastic quantification enabled by a data-driven Raman spectral processing algorithm

The detection of nanoplastics (NPs) in complex natural water systems is hindered by matrix interferences and limitations in current analytical techniques. This study presents Pre_seg, a Raman spectral processing algorithm integrated with regenerable anodic aluminum oxide (AAO) membrane sensors, for ultrasensitive, rapid, and quantitative NP detection at the single-particle level. The AAO membranes function as both filtration substrates and Raman sensors, reducing sample loss and contamination. Pre_seg incorporates statistically determined thresholds for signal-to-noise ratios (SNRs) and full width at half maximums (fwhms) across segmented spectral ranges, effectively minimizing noise and enhancing accuracy and sensitivity of NP detection. Pre_seg achieved 93.5% prediction accuracy of NPs and ≥90.4% rejection accuracy for non-NP entries. Mixed NPs were quantified at the lowest concentration of 0.5 μg L –1 . The robustness of Pre_seg was validated in eutrophic and oligotrophic lake matrices following oxidation digestion pretreatment to mitigate organic interferences. Furthermore, the AAO membrane sensors demonstrated stability through multiple regeneration and reuse cycles. This innovative approach advances NP detection by enabling scalable, customizable, and environmentally relevant monitoring.

Environmental Science and Technology↗
USGS2012

Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms

The new satellite ocean color sensors offer a means of detecting and monitoring algal blooms in the ocean and coastal zone. Beginning with SeaWiFS (Sea Wide Field-of-view Sensor) in September 1997, these sensors provide coverage every 1 to 2 days with 1-km pixel view at nadir. Atmospheric correction algorithms designed for the coastal zone combined with regional chlorophyll algorithms can provide good and reproducible estimates of chlorophyll, providing the means of monitoring various algal blooms. Harmful algal blooms (HABs) caused by Karenia brevis in the Gulf of Mexico are particularly amenable to remote observation. The Gulf of Mexico has relatively clear water and K. brevis , in bloom conditions, tends to produce a major portion of the phytoplankton biomass. A monitoring program has begun in the Gulf of Mexico that integrates field data from state monitoring programs with satellite imagery, providing an improved capability for the monitoring of K. brevis blooms.

Gulf of Mexico↗