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USGS2002

Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS)

The Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS) mission offers a novel approach to the provision of key scientific data with unprecedented radiometric accuracy for Earth Observation (EO) and solar studies, which will also establish well-calibrated reference targets/standards to support other EO missions. This paper will present the TRUTHS mission and its objectives. TRUTHS will be the first satellite mission to calibrate its instrumentation directly to SI in orbit, overcoming the usual uncertainties associated with drifts of sensor gain and spectral shape by using an electrical rather than an optical standard as the basis of its calibration. The range of instruments flown as part of the payload will also provide accurate input data to improve atmospheric radiative transfer codes by anchoring boundary conditions, through simultaneous measurements of aerosols, particulates and radiances at various heights. Therefore, TRUTHS will significantly improve the performance and accuracy of Earth observation missions with broad global or operational aims, as well as more dedicated missions. The provision of reference standards will also improve synergy between missions by reducing errors due to different calibration biases and offer cost reductions for future missions by reducing the demands for on-board calibration systems. Such improvements are important for the future success of strategies such as Global Monitoring for Environment and Security (GMES) and the implementation and monitoring of international treaties such as the Kyoto Protocol. TRUTHS will achieve these aims by measuring the geophysical variables of solar and lunar irradiance, together with both polarised and un-polarised spectral radiance of the Moon, and the Earth and its atmosphere.

Conference Paper↗

USGS2004

Littoral Assessment of Mine Burial Signatures (LAMBS) buried land mine/background spectral signature analyses

The objective of the Office of Naval Research (ONR) Rapid Overt Reconnaissance (ROR) program and the Airborne Littoral Reconnaissance Technologies (ALRT) project's LAMBS effort is to determine if electro-optical spectral discriminants exist that are useful for the detection of land mines in littoral regions. Statistically significant buried mine overburden and background signature data were collected over a wide spectral range (0.35 to 14 ??m) to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. LAMBS has expanded previously collected databases to littoral areas - primarily dry and wet sandy soils - where tidal, surf, and wind conditions can severely modify spectral signatures. At AeroSense 2003, we reported completion of three buried mine collections at an inland bay, Atlantic and Gulf of Mexico beach sites.1 We now report LAMBS spectral database analyses results using metrics which characterize the detection performance of general types of spectral detection algorithms. These metrics include mean contrast, spectral signal-to-clutter, covariance, information content, and spectral matched filter analyses. Detection performance of the buried land mines was analyzed with regard to burial age, background type, and environmental conditions. These analyses considered features observed due to particle size differences, surface roughness, surface moisture, and compositional differences.

Conference Paper↗

USGS2004

Visual enhancement of unmixed multispectral imagery using adaptive smoothing

Adaptive smoothing (AS) has been previously proposed as a method to smooth uniform regions of an image, retain contrast edges, and enhance edge boundaries. The method is an implementation of the anisotropic diffusion process which results in a gray scale image. This paper discusses modifications to the AS method for application to multi-band data which results in a color segmented image. The process was used to visually enhance the three most distinct abundance fraction images produced by the Lagrange constraint neural network learning-based unmixing of Landsat 7 Enhanced Thematic Mapper Plus multispectral sensor data. A mutual information-based method was applied to select the three most distinct fraction images for subsequent visualization as a red, green, and blue composite. A reported image restoration technique (partial restoration) was applied to the multispectral data to reduce unmixing error, although evaluation of the performance of this technique was beyond the scope of this paper. The modified smoothing process resulted in a color segmented image with homogeneous regions separated by sharpened, coregistered multiband edges. There was improved class separation with the segmented image, which has importance to subsequent operations involving data classification.

Conference Paper↗

USGS2005

Potential for calibration of geostationary meteorological satellite imagers using the Moon

Solar-band imagery from geostationary meteorological satellites has been utilized in a number of important applications in Earth Science that require radiometric calibration. Because these satellite systems typically lack on-board calibrators, various techniques have been employed to establish "ground truth", including observations of stable ground sites and oceans, and cross-calibrating with coincident observations made by instruments with on-board calibration systems. The Moon appears regularly in the margins and corners of full-disk operational images of the Earth acquired by meteorological instruments with a rectangular field of regard, typically several times each month, which provides an excellent opportunity for radiometric calibration. The USGS RObotic Lunar Observatory (ROLO) project has developed the capability for on-orbit calibration using the Moon via a model for lunar spectral irradiance that accommodates the geometries of illumination and viewing by a spacecraft. The ROLO model has been used to determine on-orbit response characteristics for several NASA EOS instruments in low Earth orbit. Relative response trending with precision approaching 0.1% per year has been achieved for SeaWiFS as a result of the long time-series of lunar observations collected by that instrument. The method has a demonstrated capability for cross-calibration of different instruments that have viewed the Moon. The Moon appears skewed in high-resolution meteorological images, primarily due to satellite orbital motion during acquisition; however, the geometric correction for this is straightforward. By integrating the lunar disk image to an equivalent irradiance, and using knowledge of the sensor's spectral response, a calibration can be developed through comparison against the ROLO lunar model. The inherent stability of the lunar surface means that lunar calibration can be applied to observations made at any time, including retroactively. Archived geostationary imager data that contains the Moon can be used to develop response histories for these instruments, regardless of their current operational status.

Conference Paper↗

USGS2008

L5 TM radiometric recalibration procedure using the internal calibration trends from the NLAPS trending database

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. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for this imagery used the instrument's response to the Internal Calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time causing radiometric calibration errors up to 20 percent. In May 2003 the National Landsat Archive Production System (NLAPS) was updated to use a gain model rather than the scene acquisition specific IC gains to calibrate TM data processed in the United States. Further modification of the gain model was performed in 2007. L5 TM data that were processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing Level-1 products. The best recalibration results are obtained if the work order report that was originally included in the standard data product delivery is available. However, many users may not have the original work order report. In such cases, the IC gain look-up table that was generated using the radiometric gain trends recorded in the NLAPS database can be used for recalibration. This paper discusses the procedure to recalibrate L5 TM data when the work order report originally used in processing is not available. A companion paper discusses the generation of the NLAPS IC gain and bias look-up tables required to perform the recalibration.

Conference Paper↗

USGS2011

Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

The Deepwater Horizon oil spill covered a very large geographical area in the Gulf of Mexico creating potentially serious environmental impacts on both marine life and the coastal shorelines. Knowing the oil's areal extent and thickness as well as denoting different categories of the oil's physical state is important for assessing these impacts. High spectral resolution data in hyperspectral imagery (HSI) sensors such as Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provide a valuable source of information that can be used for analysis by semi-automatic methods for tracking an oil spill's areal extent, oil thickness, and oil categories. However, the spectral behavior of oil in water is inherently a highly non-linear and variable phenomenon that changes depending on oil thickness and oil/water ratios. For certain oil thicknesses there are well-defined absorption features, whereas for very thin films sometimes there are almost no observable features. Feature-based imaging spectroscopy methods are particularly effective at classifying materials that exhibit specific well-defined spectral absorption features. Statistical methods are effective at classifying materials with spectra that exhibit a considerable amount of variability and that do not necessarily exhibit well-defined spectral absorption features. This study investigates feature-based and statistical methods for analyzing oil spills using hyperspectral imagery. The appropriate use of each approach is investigated and a combined feature-based and statistical method is proposed.

Proceedings of SPIE↗

USGS2011

Bias estimation for the Landsat 8 operational land imager

The Operational Land Imager (OLI) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM). This instrument is the latest in the line of Landsat imagers, and will continue to expand the archive of calibrated earth imagery. An important step in producing a calibrated image from instrument data is accurately accounting for the bias of the imaging detectors. Bias variability is one factor that contributes to error in bias estimation for OLI. Typically, the bias is simply estimated by averaging dark data on a per-detector basis. However, data acquired during OLI pre-launch testing exhibited bias variation that correlated well with the variation in concurrently collected data from a special set of detectors on the focal plane. These detectors are sensitive to certain electronic effects but not directly to incoming electromagnetic radiation. A method of using data from these special detectors to estimate the bias of the imaging detectors was developed, but found not to be beneficial at typical radiance levels as the detectors respond slightly when the focal plane is illuminated. In addition to bias variability, a systematic bias error is introduced by the truncation performed by the spacecraft of the 14-bit instrument data to 12-bit integers. This systematic error can be estimated and removed on average, but the per pixel quantization error remains. This paper describes the variability of the bias, the effectiveness of a new approach to estimate and compensate for it, as well as the errors due to truncation and how they are reduced.

Conference Paper↗

USGS2001

Geophysics: Silent slip on the Cascadia subduction interface

G eodetic satellites of the Global Positioning System (GPS) now permit continuous recording of surface motions around earthquake faults and volcanoes with millimeter precision. Data from distinct points on Earth's surface can be combined to infer the locations of the sources of deformation at depth in the crust. These data are radically altering our understanding of earthquake processes, justifying ambitious new sensor arrays to image active deformation sources in Earth's crust. In recent years, large continuous GPS arrays have been deployed in Japan and southern California, and smaller networks have been installed in other seismically active regions. On page 1525 of this issue ( 1 ), Dragert et al. provide a glimpse of the kinds of insights we can expect from these arrays.

Cascadia subduction zone↗

USGS1964

Infrared surveys of Hawaiian volcanoes

Aerial infrared-sensor surveys of Kilauea volcano have depicted the areal extent and the relative intensity of abnormal thermal features in the caldera area of the volcano and along its associated rift zones. Many of these anomalies show correlation with visible steaming and reflect convective transfer of heat to the surface from subterranean sources. Structural details of the volcano, some not evident from surface observation, are also delineated by their thermal abnormalities. Several changes were observed in the patterns of infrared emission during the period of study; two such changes show correlation in location with subsequent eruptions, but the cause-and-effect relationship is uncertain. Thermal anomalies were also observed on the southwest flank of Mauna Loa; images of other volcanoes on the island of Hawaii, and of Haleakala on the island of Maui, revealed no thermal abnormalities. Approximately 25 large springs issuing into the ocean around the periphery of Hawaii have been detected. Infrared emission varies widely with surface texture and composition, suggesting that similar observations may have value for estimating surface conditions on the moon or planets.

Hawai'i↗

USGS2020

Ecological insights from three decades of animal movement tracking across a changing Arctic

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

Science↗

USGS1997

Mapping hydrothermally altered rocks on Mount Rainier, Washington, with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data

Mount Rainier has produced numerous Holocene debris flows, the largest of which contain clays and other minerals derived from hydrothermally altered rocks on the volcano's edifice. Imagery from an advanced airborne sensor was used to map altered rocks at Mount Rainier and demonstrates their distinctly nonuniform distribution. The mapping of altered rocks helps to identify edifice failure surfaces and to recognize the source areas for the largest debris flow events. Remote sensing methods like those used at Mount Rainier can enhance ground-based mapping efforts and should prove useful for rapidly identifying hazardous sectors at other volcanoes.

Geology↗

USGS1998

Annual cycle of magmatic CO2 in a tree-kill soil at Mammoth Mountain, California: Implications for soil acidification

Time-series sensor data reveal significant short-term and seasonal variations of magmatic CO 2 in soil over a 12 month period in 1995–1996 at the largest tree-kill site on Mammoth Mountain, central-eastern California. Short-term variations leading to ground-level soil CO 2 concentrations hazardous and lethal to humans were triggered by shallow faulting in the absence of increased seismicity or intrusion, consistent with tapping a reservoir of accumulated CO 2 , rather than direct magma degassing. Hydrologic processes closely modulated seasonal variations in CO 2 concentrations, which rose to 65%–100% in soil gas under winter snowpack and plunged more than 25% in just days as the CO 2 dissolved in spring snowmelt. The high efflux of CO 2 through the tree-kill soils acts as an open-system CO 2 buffer causing infiltration of waters with pH values commonly of <4.2, acid loading of up to 7 keq H+ ṁha −1 ṁyr −1 , mobilization of toxic Al 3+ , and long-term decline of soil fertility.

Geology↗

USGS2021

Graph-based reinforcement learning for active learning in real time: An application in modeling river networks

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.

Conference Paper↗

USGS2024

Dams facilitate predation during Atlantic salmon (Salmo salar) smolt migration

Diadromous fish populations have incurred precipitous declines across the globe. Among many stressors, these species are threatened by anthropogenic barriers that impede movement, alter riverine habitat, and augment predator communities. In this study, we used acoustic transmitters ( n = 220) with predation and temperature sensors to characterize Atlantic salmon ( Salmo salar ) smolt predation risk in the Penobscot River, Maine, USA. Across two seasons, we documented 79 predation events through a 170 km migratory pathway, which included three hydropower projects and a large estuary. We detected tagged smolts that were predated by fish ( n = 42), marine mammals ( n = 28), and birds ( n = 9). Using a multistate mark-recapture framework, we estimated that 46% of smolts were predated during downstream migration, which accounted for at least 55% of all mortality. Relative predation risk was greatest through impoundments and the lower estuary, where on average, predation rates were 4.8-fold and 9.0-fold greater than free-flowing reaches, respectively. These results suggest that predation pressure on Atlantic salmon smolts is exacerbated by hydropower projects and that predation in the lower estuary may be greater than expected.

Maine↗

USGS2012

Storm surge modeling and applications in coastal areas

This chapter introduces the reader to a wide spectrum of storm surge modeling systems used to assess the impact of tropical cyclones, covering a range of numerical methods, model domains, forcing and boundary conditions, and purposes. New technologies to obtain data such as deployment of temporary sensors and remote sensing practices to support modeling are also presented. Extensive storm surge modeling applications have been made with existing modeling systems and some of them are described in this chapter. The authors recognize the importance of evaluating river-ocean interactions in coastal environments during tropical cyclones. Therefore, the coupling of hydraulic (riverine) and storm surge models is discussed. In addition, results from studies performed in the coast of India are shown which generated maps to help emergency managers and reduce risk due to coastal inundation.

Book chapter↗

USGS1995

Comparison of BASS and VACM current measurements during STRESS

Current measurements from a vector-averaging current meter (VACM) on a subsurface mooring and a benthic acoustic stress sensor (BASS) on a bottom tripod are compared to assess their relative accuracy. The instruments were deployed off northern California at a midshelf site (water depth approximately 90 m) as part of the STRESS (Sediment Transport Events on Shelves and Slopes) field program. The subsurface mooring and bottom tripod were within a few hundred meters of each other, with the BASS 5.0 m and the VACM 6.7 m above the bottom, during two tripod deployments of 49 and 32 days in the winter of 1988/89. Speed differences between the VACM and BASS current observations have a mean of 0.2 cm s −1 and a standard deviation of 1.2 cm s −1 . If the mean speed profile is logarithmic, the expected mean speed difference due to the vertical separation is about 0.4 cm s −1 . The average speed difference between the VACM and BASS increases as near-bottom wave orbital velocities get large relative to hourly averaged currents, consistent with laboratory studies of VACMs in oscillating flows. Direction differences have a mean of 1° and standard deviations of about 5° for speeds greater than 10 cm s −1 . The relative accuracy of the corresponding velocity measurements is ±2 cm s −1 (mean differences less than 0.6 cm s −1 and standard deviations of about 1 cm s −1 ). The equations used to convert VACM rotor rotation rates to current speed we based on a calibration study by Woodward and Appell rather than one based on a study by Cherriman that is routinely used at the Woods Hole Oceanographic Institution. The former yields closer agreement between the BASS and VACM speed measurements during STRESS (mean speed difference 0.2 cm s −1 versus 1.4 cm s −1 ).

Journal of Atmospheric and Oceanic Technology↗

USGS1996

Preflight and in-flight calibration plan for ASTER

Preflight and in-flight radiometric calibration plans are described for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) that is a multispectral optical imager of high spatial resolution. It is designed for the remote sensing from orbit of land surfaces and clouds, and is expected to be launched in 1998 on NASA's EOS AM-I spacecraft. ASTER acquires images in three separate spectral regions, the visible and near-infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR) with three imaging radiometer subsystems. The absolute radiometric accuracy is required to be better than 4% for VNIR and SWIR radiance measurements and 1 to 3 K, depending on the temperature regions from 200 to 370 K, for TIR temperature measurements. A reference beam is introduced at the entrance pupil of each imaging radiometer to provide the in-flight calibration. Thus, the ASTER instrument includes internal onboard calibration units that comprise incandescent lamps for the VNIR and SWIR and a blackbody radiator for the TIR as reference sources. The calibration reliability of the VNIR and SWIR is enhanced by a dual system of onboard calibration units as well as by high-stability halogen lamps. A ground calibration system of spectral radiances traceable to fixed-point blackbodies is used for the preflight VNIR and SWIR calibration. Because of the possibility of nonuniform contamination effects on the partial-aperture onboard calibration, it is desirable to check their results with respect to other methods. Reflectance- and radiance-based vicarious methods have been developed for this purpose. These, and methods involving in-flight cross-calibration with other sensors are also described.

Journal of Atmospheric and Oceanic Technology↗

USGS1997

Estimation of directional surface wave spectra from a towed research catamaran

During the High-Resolution Remote Sensing Main Experiment (1993), wave height was estimated from a moving catamaran using pitch-rate and roll-rate sensors, a three-axis accelerometer, and a capacitive wave wire. The wave spectrum in the frequency band ranging roughly from 0.08 to 0.3 Hz was verified by independent buoy measurements. To estimate the directional frequency spectrum from a wave-wire array, the Data-Adaptive Spectral Estimator is extended to include the Doppler shifting effects of a moving platform. The method is applied to data obtained from a fixed platform during the Risø Air–Sea Experiment (1994) and to data obtained from a moving platform during the Coastal Ocean Processes Experiment (1995). Both results show that the propagation direction of the peak wind waves compares well with the measured wind direction. When swells and local wind waves are not aligned, the method can resolve the difference of propagation directions. Using the fixed platform data a numerical test is conducted that shows that the method is able to distinguish two wave systems propagating at the same frequency but in two different directions.

Journal of Atmospheric and Oceanic Technology↗