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USGS2022

Early in mission Landsat 9 geometric performance

Landsat 9 (L9) was launched on September 27, 2021, from Vandenberg Space Force Base in California. The U. S. Geological Survey (USGS) released Level-1 data, geometrically orthorectified and radiometrically calibrated imagery in digital numbers that can be scaled to Top-of-Atmosphere reflectance, and Level-2 data, geometrically orthorectified and radiometrically calibrated surface reflectance imagery, to the public on February 10, 2022. From September 27, 2021 to early January of 2022, the satellite and its two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), were in their commissioning phase, updating key radiometric and geometric calibration parameters for both the spacecraft and the instruments. The data acquired during the commissioning phase of the spacecraft and instruments were reprocessed with the newly determined post-launch calibration parameters prior to the releasing of the data to the public. After the public release of the data, the calibration parameters of the sensors and the spacecraft continue to be monitored to ensure the data released to the public is of the same high quality as previous Landsat data products. This paper discusses three key geometric performance aspects of the L9 spacecraft and its instruments during its early mission time frame (September 27, 2021 to June 27, 2022) including geodetic accuracy, geometric accuracy, and within band registration accuracy of the L9 products generated.

SPIE Optics + Photonics 2022 - Conference Proceedi↗

USGS2000

Landsat 7 on-orbit geometric calibration and performance

The Landsat 7 Image Assessment System was developed to characterize and calibrate the radiometric and geometric performance of the Landsat 7 Enhanced Thematic Mapper Pius (ETM+) instrument. Algorithms and software assess the geometric performance of the Landsat 7 spacecraft and ETM+ sensor system and perform geometric calibration by estimating sensor and spacecraft geometric parameters. Following the initial on-orbit calibration, performed during the Landsat 7 on- orbit initialization and verification period, all geometric performance goals were met. Geometric characterization and calibration activities will continue for the life of the Landsat 7 mission.

Conference Paper↗

USGS2002

Archiving, processing, and disseminating ASTER products at the USGS EROS Data Center

The U.S. Geological Survey EROS Data Center archives, processes, and disseminates Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data products. The ASTER instrument is one of five sensors onboard the Earth Observing System's Terra satellite launched December 18, 1999. ASTER collects broad spectral coverage with high spatial resolution at near infrared, shortwave infrared, and thermal infrared wavelengths with ground resolutions of 15, 30, and 90 meters, respectively. The ASTER data are used in many ways to understand local and regional earth-surface processes. Applications include land-surface climatology, volcanology, hazards monitoring, geology, agronomy, land cover change, and hydrology. The ASTER data are available for purchase from the ASTER Ground Data System in Japan and from the Land Processes Distributed Active Archive Center in the United States, which receives level 1A and level 1B data from Japan on a routine basis. These products are archived and made available to the public within 48 hours of receipt. The level 1A and level 1B data are used to generate higher level products that include routine and on-demand decorrelation stretch, brightness temperature at the sensor, emissivity, surface reflectance, surface kinetic temperature, surface radiance, polar surface and cloud classification, and digital elevation models. This paper describes the processes and procedures used to archive, process, and disseminate standard and on-demand higher level ASTER products at the Land Processes Distributed Active Archive Center.

Conference Paper↗

USGS2008

Radiometric calibration stability and inter-calibration of solar-band instruments in orbit using the moon

With the increased emphasis on monitoring the Earth's climate from space, more stringent calibration requirements are being placed on the data products from remote sensing satellite instruments. Among these are stability over decade-length time scales and consistency across sensors and platforms. For radiometer instruments in the solar reflectance wavelength range (visible to shortwave infrared), maintaining calibration on orbit is difficult due to the lack of absolute radiometric standards suitable for flight use. The Moon presents a luminous source that can be viewed by all instruments in Earth orbit. Considered as a solar diffuser, the lunar surface is exceedingly stable. The chief difficulty with using the Moon is the strong variations in the Moon's brightness with illumination and viewing geometry. This mandates the use of a photometric model to compare lunar observations, either over time by the same instrument or between instruments. The U.S. Geological Survey in Flagstaff, Arizona, under NASA sponsorship, has developed a model for the lunar spectral irradiance that explicitly accounts for the effects of phase, the lunar librations, and the lunar surface reflectance properties. The model predicts variations in the Moon's brightness with precision ???1% over a continuous phase range from eclipse to the quarter lunar phases. Given a time series of Moon observations taken by an instrument, the geometric prediction capability of the lunar irradiance model enables sensor calibration stability with sub-percent per year precision. Cross-calibration of instruments with similar passbands can be achieved with precision comparable to the model precision. Although the Moon observations used for intercomparison can be widely separated in phase angle and/or time, SeaWiFS and MODIS have acquired lunar views closely spaced in time. These data provide an example to assess inter-calibration biases between these two instruments.

Conference Paper↗

USGS2010

An overview of the Landsat Data Continuity Mission

The Landsat Data Continuity Mission (LDCM) is the follow-on mission to Landsat 7 and will be the eighth mission in the Landsat series. The mission is in development via an interagency partnership between the National Aeronautics and Space Administration (NASA) and the Department of Interior (DOI) / United States Geological Survey (USGS). The LDCM satellite will carry two earth-observing sensors, the Operational Land Imager (OLI) to collect image data for nine spectral bands in the reflective portion of the spectrum and the Thermal Infrared Sensor (TIRS) to collect coincident image data for two thermal spectral bands. The LDCM ground segment will control the satellite and will receive, process, archive, and distribute the science data collected by the OLI and TIRS instruments. The USGS Earth Resources Observation & Science Center (EROS) will distribute LDCM data products at no cost to requestors. The mission objective is to continues the Landsat program's collection, archive, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earth's land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. The LDCM launch readiness date is currently December, 2012.

Conference Paper↗

USGS2010

The CEOS Cal/Val portal: A new instrument for the Cal/Val community

The exploitation of Earth Observation data depends with increasing importance on multi-source inter-calibrated data, as demonstrated, for example, in the ESA DUE GlobColour project. 1 The subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated a recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an Internet based system to provide sensor data, protocols and guidelines for the purposes of efficiently supporting sensor calibration, inter-calibration and product validation. ESA has taken the initiative and launched the version 1.0 of the Cal/Val Portal in October 2006 and the version 2.0 in mid 2009. This paper describes the Cal/Val portal webpage, components, and general content organization. It also gives an example of collaboration and Cal/Val facility.

Conference Paper↗

USGS2012

Spectroscopic remote sensing for material identification, vegetation characterization, and mapping

Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.

Conference Paper↗

USGS2015

Crowdsourced earthquake early warning

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Science Advances↗

USGS1969

Structural geologic interpretations from radar imagery

Certain structural geologic features may be more readily recognized on sidelooking airborne radar (SLAR) images than on conventional aerial photographs, other remote sensor imagery, or by ground observations. SLAR systems look obliquely to one or both sides and their images resemble aerial photographs taken at low sun angle with the sun directly behind the camera. They differ from air photos in geometry, resolution, and information content. Radar operates at much lower frequencies than the human eye, camera, or infrared sensors, and thus "sees" differently. The lower frequency enables it to penetrate most clouds and some precipitation, haze, dust, and some vegetation. Radar provides its own illumination, which can be closely controlled in intensity and frequency. It is narrow band, or essentially monochromatic. Low relief and subdued features are accentuated when viewed from the proper direction. Runs over the same area in significantly different directions (more than 45° from each other), show that images taken in one direction may emphasize features that are not emphasized on those taken in the other direction; optimum direction is determined by those features which need to be emphasized for study purposes. Lineaments interpreted as faults stand out on radar imagery of central and western Nevada; folded sedimentary rocks cut by faults can be clearly seen on radar imagery of northern Alabama. In these areas, certain structural and stratigraphic features are more pronounced on radar images than on conventional photographs; thus radar imagery materially aids structural interpretation.

Alabama;Nevada↗

USGS2023

Limited role of absolute humidity in intraurban heat variability

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Journal of Applied Meteorology and Climatology↗

USGS2013

Cross-scale modeling of surface temperature and tree seedling establishment inmountain landscapes

Abstract: Introduction: Estimating surface temperature from above-ground field measurements is important for understanding the complex landscape patterns of plant seedling survival and establishment, processes which occur at heights of only several centimeters. Currently, future climate models predict temperature at 2 m above ground, leaving ground-surface microclimate not well characterized. Methods: Using a network of field temperature sensors and climate models, a ground-surface temperature method was used to estimate microclimate variability of minimum and maximum temperature. Temperature lapse rates were derived from field temperature sensors and distributed across the landscape capturing differences in solar radiation and cold air drainages modeled at a 30-m spatial resolution. Results: The surface temperature estimation method used for this analysis successfully estimated minimum surface temperatures on north-facing, south-facing, valley, and ridgeline topographic settings, and when compared to measured temperatures yielded an R2 of 0.88, 0.80, 0.88, and 0.80, respectively. Maximum surface temperatures generally had slightly more spatial variability than minimum surface temperatures, resulting in R2 values of 0.86, 0.77, 0.72, and 0.79 for north-facing, south-facing, valley, and ridgeline topographic settings. Quasi-Poisson regressions predicting recruitment of Quercus kelloggii (black oak) seedlings from temperature variables were significantly improved using these estimates of surface temperature compared to air temperature modeled at 2 m. Conclusion: Predicting minimum and maximum ground-surface temperatures using a downscaled climate model coupled with temperature lapse rates estimated from field measurements provides a method for modeling temperature effects on plant recruitment. Such methods could be applied to improve projections of species’ range shifts under climate change. Areas of complex topography can provide intricate microclimates that may allow species to redistribute locally as climate changes.

Ecological Processes↗

USGS2025

Movements and survival of hatchery reared juvenile cisco (Coregonus artedi) in Saginaw Bay, Lake Huron

Background Cisco ( Coregonus artedi ) were historically abundant throughout Lake Huron, including Saginaw Bay, but only a few remnant populations remain in northern Lake Huron today. Reestablishment of cisco is an important component of management plans to restore sustainable fisheries in Lake Huron. Cisco restoration efforts have focused on the release of hatchery-reared fish, but the fate and behavior of stocked fish after release is unknown. Mortality due to predation and behavior of hatchery-reared fish after release may influence success of restoration stocking programs. Acoustic telemetry tags with predation sensors show promise for tracking movements and survival of juvenile fish; however, guidelines for designing receiver arrays to capture movements and determine the fate of juvenile fish are not well-established. Results We examined whether an acoustic receiver array with approximately 20 km 2 of coverage was sufficient to determine movements and fate of cisco during the first month after release. We implanted 26 juvenile cisco (mean total length = 161 mm) with acoustic tags equipped with a sensor to detect predation. Thirteen fish (50%) moved more than 4 km from the release location and out of the array, seven fish (27%) were consumed by predators while in the array within 17 days of release, and the fates of six fish (23%) were unknown. Of fish that left the array, 50% left between 4 and 7 days after release. No fish were detected after 17 days after release. Cisco moved with water currents during the first day after release, but this was not observed in subsequent days. Concurrent with fish release, detection range was estimated from stationary tags at three locations within the receiver array. Daily estimates of detection range were greater than 50% at 250 m during October 2021. Conclusions This study provides evidence that hatchery-reared juvenile cisco can move more than 4 km within 17 days of release but are vulnerable to predation. To fully quantify sources of mortality and spatial extent of movements by hatchery-reared cisco, future acoustic telemetry studies will require a receiver array designed to track movements of tagged fish and their predators over larger distances than monitored in this study.

Michigan↗

USGS2022

Analog field-scale acoustic study of volcanic eruption directivity using a tiltable liquid nitrogen-charged water cannon

Laterally directed explosive eruptions are responsible for multiple fatalities over the past decade and are an increasingly important volcanology problem. To understand the energy dynamics for these events, we collected field-scale explosion data from nine acoustic sensors surrounding a tiltable cannon as part of an exploratory experimental design. For each cannon discharge, the blast direction was varied systematically at 0°, 12°, and 24° from vertical, capturing acoustic wavefield directivity related to the tilt angle. While each event was similar in energy discharge potential, the resulting acoustic signal features were variable event-to-event, producing non-repetitious waveforms and spectra. Systematic features were observed in a subset of individual events for vertical and lateral discharges. For vertical discharges, the acoustic energy had a uniform radiation pattern. The lateral discharges showed an asymmetric radiation pattern with higher frequencies in the direction of the blast and depletion of those frequencies behind the cannon. Results suggest that, in natural volcanic systems, near-field blast directionality may be elucidated from acoustic sensors in absence of visual data, with implications for volcano monitoring and hazard assessment.

Earth, Planets and Space↗

USGS2008

ALLTEM UXO detection and discrimination

ALLTEM is a multi-axis electromagnetic induction system designed for unexploded ordnance (UXO) applications. It uses a continuous triangle-wave excitation and provides good late-time signal-to-noise ratio (SNR) especially for ferrous targets. Multi-axis transmitter (Tx) and receiver (Rx) systems such as ALLTEM provide a richer data set from which to invert for the target parameters required to distinguish between clutter and UXO. Inversions of field data over the Army's UXO Calibration Grid and Blind Test Grid at the Yuma Proving Ground (YPG), Arizona in 2006 produced polarizability moment values for many buried UXO items that were reasonable and generally repeatable for targets of the same type buried at different orientations and depths. In 2007 a test stand was constructed that allows for collection of data with varying spatial data density and accurate automated position control. The behavior of inverted ALLTEM test stand data as a function of spatial data density, sensor SNR, and position error has been investigated. The results indicate that the ALLTEM inversion algorithm is more tolerant of sensor noise and position error than has been reported for single-axis systems. A high confidence level in inversion-derived target parameters is required when a target is declared to be harmless scrap metal that may safely be left in the ground. Unless high confidence can be demonstrated, state regulators will likely require that targets be dug regardless of any "no-dig" classifications produced from inversions, in which case remediation costs would not be decreased.

Conference Paper↗

USGS2024

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.

California↗

USGS2007

Geometric correction and digital elevation extraction using multiple MTI datasets

Digital Elevation Models (DEMs) are traditionally acquired from a stereo pair of aerial photographs sequentially captured by an airborne metric camera. Standard DEM extraction techniques can be naturally extended to satellite imagery, but the particular characteristics of satellite imaging can cause difficulties. The spacecraft ephemeris with respect to the ground site during image collects is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy, while a large angle of separation can cause an excessive number of erroneous points in the DEM from occlusion of ground areas. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of some sensors, such as the Multispectral Thermal Imager (MTI), allows for multiple collects of the same area from different perspectives. This functionality of MTI makes it a good candidate for the implementation of a DEM extraction algorithm using multiple images for improved accuracy. Evaluation of this capability and development of algorithms to geometrically model the MTI sensor and extract DEMs from multi-look MTI imagery are described in this paper. An RMS elevation error of 6.3-meters is achieved using 11 ground test points, while the MTI band has a 5-meter ground sample distance.

Photogrammetric Engineering and Remote Sensing↗

USGS2009

Influence of resolution in irrigated area mapping and area estimation

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R 2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and non-irrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).

Krishna River basin↗

USGS1992

Tensiometers: Theory, construction, and use

Standard tensiometers are used to measure matric potential as low as −870 cm of water in the unsaturated zone by creating a saturated hydraulic link between the soil water and a pressure sensor. The direction and, in some cases, quantity of water flux can be determined using multiple installations. A variety of commercial and fabricated tensiometers are commonly used. Saturated porous ceramic materials, which form an interface between the soil water and the bulk water inside the instrument, are available in many shapes, sizes, and pore diameters. A gage, manometer, or electronic pressure transducer is connected to the porous material with small- or large-diameter tubing. Selection of these components allows the user to optimize one or more characteristics, such as accuracy, versatility, response time, durability, maintenance, extent of data collection, and cost. Special designs have extended the normal capabilities of tensiometers, allowing measurement in cold or remote areas, measurement of matric potential as low as −153 m of water (−15 bars), measurement at depths as deep as 6 m (recorded at land surface), and automatic measurement using as many as 22 tensiometers connected to a single pressure transducer. Continuous hydraulic connection between the porous material and soil, and minimal disturbance of the natural infiltration pattern are necessary for successful installation. Avoidance of errors caused by air invasion, nonequilibrium of the instrument, or pressure-sensor inaccuracy will produce reliable values of matric potential, a first step in characterizing unsaturated flow.

Geotechnical Testing Journal↗