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At least 37 records · Page 2Linked to original sources
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↗
USGS2006

Cross-calibration of MODIS with ETM+ and ALI sensors for long-term monitoring of land surface processes

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. Although higher-level products (e.g., vegetation cover, albedo, surface temperature) derived from different sensors can be validated independently, the degree to which these sensors and their products can be compared to one another is vastly improved if their relative spectroradiometric responses are known. Most often, sensors are directly calibrated to diffuse solar irradiation or vicariously to ground targets. However, space-based targets are not traceable to metrological standards, and vicarious calibrations are expensive and provide a poor sampling of a sensor's full dynamic range. Crosscalibration of two sensors can augment these methods if certain conditions can be met: (1) the spectral responses are similar, (2) the observations are reasonably concurrent (similar atmospheric & solar illumination conditions), (3) errors due to misregistrations of inhomogeneous surfaces can be minimized (including scale differences), and (4) the viewing geometry is similar (or, some reasonable knowledge of surface bi-directional reflectance distribution functions is available). This study explores the impacts of cross-calibrating sensors when such conditions are met to some degree but not perfectly. In order to constrain the range of conditions at some level, the analysis is limited to sensors where cross-calibration studies have been conducted (Enhanced Thematic Mapper Plus (ETM+) on Landsat-7 (L7), Advance Land Imager (ALI) and Hyperion on Earth Observer-1 (EO-1)) and including systems having somewhat dissimilar geometry, spatial resolution & spectral response characteristics but are still part of the so-called "A.M. constellation" (Moderate Resolution Imaging Spectrometer (MODIS) aboard the Terra platform). Measures for spectral response differences and methods for cross calibrating such sensors are provided in this study. These instruments are cross calibrated using the Railroad Valley playa in Nevada. Best fit linear coefficients (slope and offset) are provided for ALI-to-MODIS and ETM+-to-MODIS cross calibrations, and root-mean-squared errors (RMSEs) and correlation coefficients are provided to quantify the uncertainty in these relationships. In theory, the linear fits and uncertainties can be used to compare radiance and reflectance products derived from each instrument.

Conference Paper↗
USGS2006

Cross-calibration of A.M. constellation sensors for long term monitoring of land surface processes

Data from multiple sensors must be used together to gain a more complete understanding of land surface processes at a variety of scales. Although higher-level products derived from different sensors (e.g., vegetation cover, albedo, surface temperature) can be validated independently, the degree to which these sensors and their products can be compared to one another is vastly improved if their relative spectro-radiometric responses are known. Most often, sensors are directly calibrated to diffuse solar irradiation or vicariously to ground targets. However, space-based targets are not traceable to metrological standards, and vicarious calibrations are expensive and provide a poor sampling of a sensor's full dynamic range. Cross-calibration of two sensors can augment these methods if certain conditions can be met: (1) the spectral responses are similar, (2) the observations are reasonably concurrent (similar atmospheric & solar illumination conditions), (3) errors due to misregistrations of inhomogeneous surfaces can be minimized (including scale differences), and (4) the viewing geometry is similar (or, some reasonable knowledge of surface bi-directional reflectance distribution functions is available). This study extends on a previous study of Terra/MODIS and Landsat/ETM+ cross calibration by including the Terra/ASTER and EO-1/ALI sensors, exploring the impacts of cross-calibrating sensors when conditions described above are met to some degree but not perfectly. Measures for spectral response differences and methods for cross calibrating such sensors are provided in this study. These instruments are cross calibrated using the Railroad Valley playa in Nevada. Best fit linear coefficients (slope and offset) are provided for ALI-to-MODIS and ETM+-to-MODIS cross calibrations, and root-mean-squared errors (RMSEs) and correlation coefficients are provided to quantify the uncertainty in these relationships. Due to problems with direct calibration of ASTER data, linear fits were developed between ASTER and ETM+ to assess the impacts of spectral bandpass differences between the two systems. In theory, the linear fits and uncertainties can be used to compare radiance and reflectance products derived from each instrument.

Conference Paper↗
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↗
USGS2026

Evaluating chlorophyll fluorescence sensors as a proxy for chlorophyll a concentrations in the Illinois River, Illinois, water years 2018–24

The U.S. Geological Survey deployed instream chlorophyll fluorescence sensors on the Illinois River in Illinois to better understand the formation, dynamics, and persistence of riverine algal blooms. At five sensor locations across four U.S. Geological Survey streamgages, chlorophyll fluorescence (fChl) sensors were set to measure instantaneous fChl in micrograms per liter as chlorophyll at 15-minute intervals. These sensors estimate fChl based on the fluorescence response of algal pigments but do not directly measure chlorophyll a (Chl) concentrations or algal biomass. To evaluate the reliability of fChl as a proxy for Chl, fChl measurements were compared to laboratory-measured Chl concentrations. These Chl concentrations were obtained from water-quality samples collected at the sensor locations and analyzed using the standard chromatographic-fluorometric method with acetone extraction. A total of 209 paired fChl and Chl observations were analyzed using Pearson’s correlation and model II regression. Statistically significant, positive correlations (Pearson’s correlation coefficient of 0.68–0.95) were detected at all sensor locations, indicating that fChl generally tracked with Chl. However, model II regression detected proportional and fixed bias at three of the five locations and proportional bias at another location. At locations with proportional biases, model II regression slopes were less than 1, indicating that fChl increasingly underestimated Chl as concentrations increased; however, paired observations at high Chl concentrations were sparse. Substantial variability in bias was also observed across sensor locations with slopes ranging from 0.43 to 0.93. Overall, the results indicated that fChl correlates well with Chl in the Illinois River, but fChl may not reliably represent Chl concentrations without adjustment. For applications requiring accurate Chl estimates, a sensor location-specific surrogate model could be beneficial. These findings underscore the importance of field validation, consistent sensor placement, and careful interpretation of fChl data, particularly when reported in micrograms per liter.

Illinois, Indiana, Wisconsin↗
USGS2025

Absolute radiometric calibration evaluation of Uncrewed Aerial System (UAS) Headwall and MicaSense sensors and improving data quality using the Empirical Line Method

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy.

Remote Sensing↗
USGS2017

A wideband magnetoresistive sensor for monitoring dynamic fault slip in laboratory fault friction experiments

A non-contact, wideband method of sensing dynamic fault slip in laboratory geophysical experiments employs an inexpensive magnetoresistive sensor, a small neodymium rare earth magnet, and user built application-specific wideband signal conditioning. The magnetoresistive sensor generates a voltage proportional to the changing angles of magnetic flux lines, generated by differential motion or rotation of the near-by magnet, through the sensor. The performance of an array of these sensors compares favorably to other conventional position sensing methods employed at multiple locations along a 2 m long × 0.4 m deep laboratory strike-slip fault. For these magnetoresistive sensors, the lack of resonance signals commonly encountered with cantilever-type position sensor mounting, the wide band response (DC to ≈ 100 kHz) that exceeds the capabilities of many traditional position sensors, and the small space required on the sample, make them attractive options for capturing high speed fault slip measurements in these laboratory experiments. An unanticipated observation of this study is the apparent sensitivity of this sensor to high frequency electomagnetic signals associated with fault rupture and (or) rupture propagation, which may offer new insights into the physics of earthquake faulting.

Sensors↗
USGS2008

Evaluation and comparison of the IRS-P6 and the landsat sensors

The Indian Remote Sensing Satellite (IRS-P6), also called ResourceSat-1, was launched in a polar sun-synchronous orbit on October 17, 2003. It carries three sensors: the highresolution Linear Imaging Self-Scanner (LISS-IV), the mediumresolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide-Field Sensor (AWiFS). These three sensors provide images of different resolutions and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to images from the Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced TM Plus (ETM+) sensors. The approach involves calibration of surface observations based on image statistics from areas observed nearly simultaneously by the two sensors. This paper also evaluated the viability of data from these nextgeneration imagers for use in creating three National Land Cover Dataset (NLCD) products: land cover, percent tree canopy, and percent impervious surface. Individual products were consistent with previous studies but had slightly lower overall accuracies as compared to data from the Landsat sensors.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2006

Cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM with the ResourceSat-1 (IRS-P6) AWiFS and LISS-III sensors

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. The Landsat suite of satellites has collected the longest continuous archive of multispectral data. The ResourceSat-1 Satellite (also called as IRS-P6) was launched into the polar sunsynchronous orbit on Oct 17, 2003. It carries three remote sensing sensors: the High Resolution Linear Imaging Self-Scanner (LISS-IV), Medium Resolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide Field Sensor (AWiFS). These three sensors are used together to provide images with different resolution and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to the Landsat-5 TM and Landsat-7 ETM+ sensors. The approach involved the calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors.

Conference Paper↗
USGS2007

Comparison of outgassing models for the Landsat thematic mapper sensors

The Thematic Mapper (TM) is a multi-spectral electro-optical sensor featured onboard both the Landsat 4 (L4) and Landsat 5 (L5) satellites. TM sensors have seven spectral bands with center wavelengths of approximately 0.49, 0.56, 0.66, 0.83, 1.65, 11.5 and 2.21 μm, respectively. The visible near-infrared (VNIR) bands are located on the primary focal plane (PFP), and two short-wave infrared (SWIR) bands and the thermal infrared (TIR) band are located on the cold focal plane (CFP). The CFP bands are maintained at cryogenic temperatures of about 91 K, to reduce thermal noise effects. Due to the cold temperature, an ice film accumulates on the CFP dewar window, which introduces oscillations in SWIR and an exponential decay in TIR band responses. This process is usually monitored and characterized by the detector responses to the internal calibrator (IC) lamps and the blackbody. The ice contamination on the dewar window is an effect of the sensor outgassing in a vacuum of the space environment. Outgassing models have been developed, which are based on the thin-film optical interference phenomenon. They provide the coefficients for correction for outgassing effects for the entire mission's lifetime. While the L4 TM ceased imaging in August 1993, the L5 TM continues to operate even after more than 23 years in orbit. The process of outgassing in L5 TM is still occurring, though at a much lower rate than during early years of mission. Although the L4 and L5 TM sensors are essentially identical, they exhibit slightly different responses to the outgassing effects. The work presented in the paper summarizes the results of modeling outgassing effects in each of the sensors and provides a detailed analysis of differences among the estimated modeling parameters. For both sensors, water ice was confirmed as a reasonable candidate for contaminant material, the contaminant growth rate was found to be gradually decreasing with the time since launch, and the indications exist that some film may remain after the CFP warm-up procedures, which are periodically initiated to remove accumulated contamination. The observed difference between the models could be contributed to differences in the operational history for the sensors, the content and amount of contaminant impurities, the sensor spectral filter responses, and the internal calibrator systems.

Conference Paper↗
USGS2015

Validation of mercury tip-switch and accelerometer activity sensors for identifying resting and active behavior in bears

Activity sensors are often included in wildlife transmitters and can provide information on the behavior and activity patterns of animals remotely. However, interpreting activity-sensor data relative to animal behavior can be difficult if animals cannot be continuously observed. In this study, we examined the performance of a mercury tip-switch and a tri-axial accelerometer housed in collars to determine whether sensor data can be accurately classified as resting and active behaviors and whether data are comparable for the 2 sensor types. Five captive bears (3 polar [ Ursus maritimus ] and 2 brown [ U. arctos horribilis ]) were fitted with a collar specially designed to internally house the sensors. The bears’ behaviors were recorded, classified, and then compared with sensor readings. A separate tri-axial accelerometer that sampled continuously at a higher frequency and provided raw acceleration values from 3 axes was also mounted on the collar to compare with the lower resolution sensors. Both accelerometers more accurately identified resting and active behaviors at time intervals ranging from 1 minute to 1 hour (≥91.1% accuracy) compared with the mercury tip-switch (range = 75.5–86.3%). However, mercury tip-switch accuracy improved when sampled at longer intervals (e.g., 30–60 min). Data from the lower resolution accelerometer, but not the mercury tip-switch, accurately predicted the percentage of time spent resting during an hour. Although the number of bears available for this study was small, our results suggest that these activity sensors can remotely identify resting versus active behaviors across most time intervals. We recommend that investigators consider both study objectives and the variation in accuracy of classifying resting and active behaviors reported here when determining sampling interval.

Ursus↗
USGS2009

Global Positioning System surveys of storm-surge sensors deployed during Hurricane Ike, Seadrift, Texas, to Lake Charles, Louisiana, 2008

The U.S. Geological Survey installed a network of pressure sensors at 65 sites along the Gulf Coast from Seadrift, Texas, northeast to Lake Charles, Louisiana, to record the timing, areal extent, and magnitude of inland storm surge and coastal flooding caused by Hurricane Ike in September 2008. A Global Positioning System was used to obtain elevations of reference marks near each sensor. A combination of real-time kinematic (RTK) and static Global Positioning System surveys were done to obtain elevations of reference marks. Leveling relative to reference marks was done to obtain elevations of sensor orifices above the reference marks. This report summarizes the Global Positioning System data collected and processed to obtain reference mark and storm-sensor-orifice elevations for 59 storm-surge sensors recovered from the original 65 installed as a necessary prelude to computation of storm-surge elevations. National Geodetic Survey benchmarks were used for RTK surveying. Where National Geodetic Survey benchmarks were not within 12 kilometers of a sensor site, static surveying was done. Additional control points for static surveying were in the form of newly established benchmarks or reestablished existing benchmarks. RTK surveying was used to obtain positions and elevations of reference marks for 29 sensor sites. Static surveying was used to obtain positions and elevations of reference marks for 34 sensor sites; four sites were surveyed using both methods. Multiple quality checks on the RTK-survey and static-survey data were applied. The results of all quality checks indicate that the desired elevation accuracy for the surveys of this report, less than 0.1-meter error, was achieved.

Louisiana, Texas↗
USGS2012

In situ optical water-quality sensor networks - Workshop summary report

Advanced in situ optical water-quality sensors and new techniques for data analysis hold enormous promise for furthering scientific understanding of aquatic systems. These sensors measure important biogeochemical parameters for long deployments, enabling the capture of data at time scales over which they vary most meaningfully. The high-frequency, real-time water-quality data they generate provide opportunities for early warning of water-quality deterioration, trend detection, and science-based decision support. However, developing networks of optical sensors in freshwater systems that report reliable and comparable data across and between sites remains a challenge to the research and monitoring community. To address this, the U. S. Geological Survey (USGS) and the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) convened a joint 3-day workshop (June 8-10, 2011) at the National Conservation Training Center in Shepardstown, West Virginia, to explore ways to coordinate development of standards and applications for optical sensors, and improve handling, storing, and analyzing the continuous data they produce. The workshop brought together more than 60 scientists, program managers, and vendors from universities, government agencies, and the private sector. Several important outcomes emerged from the presentations and breakout sessions. There was general consensus that making intercalibrated measurements requires that both manufacturers and users better characterize and calibrate the sensors under field conditions. For example, the influence of suspended particles, highly colored water, and temperature on optical sensors remains poorly understood, but consistently accounting for these factors is critical to successful deployment and for interpreting results in different settings. This, in turn, highlights the lack of appropriate standards for sensor calibrations, field checks, and characterizing interferences, as well as methods for data validation, treatment, and analysis of resulting measurements. Participants discussed a wide range of logistical considerations for successful sensor deployments, including key physical infrastructure, data loggers, and remote-communication techniques. Tools to manage, assure, and control quality, and explore large streams of continuous water-quality data are being developed by the USGS, CUAHSI, and other organizations, and will be critical to making full use of these highfrequency data for research and monitoring.

Open-File Report↗
USGS2015

ShakeNet: a portable wireless sensor network for instrumenting large civil structures

We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software.

Open-File Report↗
USGS2020

Field comparison of five in situ turbidity sensors

Five commercially available turbidity sensors were field tested by the U.S. Geological Survey Hydrologic Instrumentation Facility for accuracy and data comparability. The tested sensors were the Xylem EXO (EXO), the Hach Solitax sc (Solitax), the In Situ Aqua TROLL sensor installed onto a TROLL 600 sonde (TROLL 600), the Campbell Scientific OBS501 (OBS501), and the Observator ANALITE NEP–5000 (NEP–5000). The sensors were deployed at Pearl River at National Space Technology Laboratories Station, Mississippi (U.S. Geological Survey site 02492620), and were serviced weekly. In addition to the five in situ turbidity sensors, corresponding discrete samples were collected and analyzed during the evaluation on a calibrated Hach 2100N benchtop turbidimeter. The OBS501 malfunctioned early in the evaluation and eventually failed, resulting in few data from the sensor. During this study, the four remaining sensors (minus the OBS501) changed similarly throughout the field test; however, sensor data from the EXO consistently demonstrated lower results than the Solitax, TROLL 600, and NEP–5000, possibly because of the variation in raw signal processing among manufacturers. Results from a single factor analysis of variance test and a Tukey Honestly Significant Difference test verified the low bias observed in the EXO data and indicated there was a significant difference between the EXO data and data from the Solitax, TROLL 600, and NEP–5000 but an insignificant difference among the data when the Solitax, TROLL 600, and NEP–5000 were compared to each other.

Mississippi↗
USGS2023

Interim guidance for calibration checks on a submersible acoustic backscatter sediment sensor

Over the past two decades, the U.S. Geological Survey (USGS) and other agencies have pioneered the use of active acoustic sensors to monitor suspended-sediment concentrations and particle sizes in rivers and streams at the subdaily time scale. The LISST-ABS submersible acoustic backscatter sediment sensor (or “ABS sensor”) was developed by Sequoia Scientific, Inc., as an alternative to turbidity sensors for monitoring suspended-sediment concentrations in surface waters. The ABS sensor is different than traditional active acoustic instruments because it is small, lower in cost, lightweight, and requires less power; and the sampling volume is within the first 15 centimeters of the transducer face. Initial testing by the USGS indicated the ABS sensor had utility as a novel, cost-effective, off-the-shelf tool for monitoring suspended-sediment concentration in surface waters, and its use within the agency has increased in since its introduction around 2016. However, initial testing did not account for the potential of transducer calibration drift over longer deployments. As part of its mission to unify and standardize research and development activities of Federal agencies involved in fluvial sediment studies, the Federal Interagency Sedimentation Project partnered with the USGS Wyoming-Montana and New Mexico Water Science Centers to examine the potential for use of standard, low-tech laboratory equipment to perform calibration checks on ABS sensors on long-term deployments. The experiments were intended to provide USGS scientists and the public with interim guidance to assist in operating and maintaining the ABS sensor.

Open-File Report↗
USGS1980

Considerations for blending data from various sensors

A project is being proposed at the EROS Data Center to blend the information from sensors aboard various satellites. The problems of, and considerations for, blending data from several satellite-borne sensors are discussed. System descriptions of the sensors aboard the HCMM, TIROS-N, GOES-D, Landsat 3, Landsat D, Seasat, SPOT, Stereosat, and NOSS satellites, and the quantity, quality, image dimensions, and availability of these data are summaries to define attributes of a multi-sensor satellite data base. Unique configurations of equipment, storage, media, and specialized hardware to meet the data system requirement are described as well as archival media and improved sensors that will be on-line within the next 5 years. Definitions and rigor required for blending various sensor data are given. Problems of merging data from the same sensor (intrasensor comparison) and from different sensors (intersensor comparison), the characteristics and advantages of cross-calibration of data, and integration of data into a product matrix field are addressed. Data processing considerations as affected by formation, resolution, and problems of merging large data sets, and organization of data bases for blending data are presented. Examples utilizing GOES and Landsat data are presented to demonstrate techniques of data blending, and recommendations for future implementation of a set of standard scenes and their characteristics necessary for optimal data blending are discussed.

Sixth Annual Pecora Symposium and Exposition↗
USGS2010

Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites

The ability to detect and quantify changes in the Earth's environment depends on sensors that can provide calibrated, consistent measurements of the Earth's surface features through time. A critical step in this process is to put image data from different sensors onto a common radiometric scale. This work focuses on monitoring the long-term on-orbit calibration stability of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensors using the Committee on Earth Observation Satellites (CEOS) reference standard pseudo-invariant test sites (Libya 4, Mauritania 1/2, Algeria 3, Libya 1, and Algeria 5). These sites have been frequently used as radiometric targets because of their relatively stable surface conditions temporally. This study was performed using all cloud-free calibrated images from the Terra MODIS and the L7 ETM+ sensors, acquired from launch to December 2008. Homogeneous regions of interest (ROI) were selected in the calibrated images and the mean target statistics were derived from sensor measurements in terms of top-of-atmosphere (TOA) reflectance. For each band pair, a set of fitted coefficients (slope and offset) is provided to monitor the long-term stability over very stable pseudo-invariant test sites. The average percent differences in intercept from the long-term trends obtained from the ETM + TOA reflectance estimates relative to the MODIS for all the CEOS reference standard test sites range from 2.5% to 15%. This gives an estimate of the collective differences due to the Relative Spectral Response (RSR) characteristics of each sensor, bi-directional reflectance distribution function (BRDF), spectral signature of the ground target, and atmospheric composition. The lifetime TOA reflectance trends from both sensors over 10 years are extremely stable, changing by no more than 0.4% per year in its TOA reflectance over the CEOS reference standard test sites.

Remote Sensing of Environment↗