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

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At least 145 records · Page 8Linked to original sources
USGS1996

Establishing the moon as a spectral radiance standard

A new automated observatory dedicated to the radiometry of the moon has been constructed to provide new radiance information for calibration of earth-orbiting imaging instruments, particularly Earth Observing System instruments. Instrumentation includes an imaging photometer with 4.5-in. resolution on a fully digital mount and a full-aperture radiance calibration source. Interference filters within 0.35–0.95 µm correspond to standard stellar magnitude systems, accommodate wavelengths of lunar spectral contrast, and approximate some band-passes of planned earth-orbiting instruments (ASTER, Landsat-7 ETM, MISR, MODIS, and SeaWIFS). The same equipment is used for lunar and stellar observations, with the use of an aperture stop in lunar imaging to comply with Nyquist's theorem and lengthen exposure times to avoid scintillation effects. A typical robotic night run involves observation of about 60 photometric standard stars and the moon; about 10 of the standard stars are observed repeatedly to determine atmospheric extinction, and the moon is observed several times. Observations are to be made on every photometric night during the bright half of the month for at least 4.5 years to adequately cover phase and libration variation. Each lunar image is reduced to absolute exoatmospheric radiance and reprojected to a fixed selenographic grid system. The collection of these images at various liberators and phase angles will be reduced to photometric models for each of the approximately 120 000 points in the lunar grid for each filter. Radiance models of the moon can then be produced for the precise geometry of an orbiting instrument observation. Expected errors are under 1% relative and 2.5% absolute. A second telescope operating from 1.0 to 2.5 µm is planned.

Journal of Atmospheric and Oceanic Technology↗
USGS2025

Advancements in satellite observations of inland and coastal waters: Building towards a global validation network

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide suggestions for future field campaigns to improve coordination with remote sensing data collection and to ensure that field data is well suited for use in model or algorithm development.

Remote Sensing↗
USGS1996

Mapping playa evaporite minerals and associated sediments in Death Valley, California, with multispectral thermal infrared images

Efflorescent salt crusts and associated sediments in Death Valley, California, were studied with remote-sensing data acquired by the NASA thermal infrared multispectral scanner (TIMS). Nine spectral classes that represent a variety of surface materials were distinguished, including several classes that reflect important aspects of the playa groundwater chemistry and hydrology. Evaporite crusts containing abundant thenardite (sodium sulfate) were mapped along the northern and eastern margins of the Cottonball Basin, areas where the inflow waters are rich in sodium. Gypsum (calcium sulfate) crusts were more common in the Badwater Basin, particularly near springs associated with calcic groundwaters along the western basin margin. Evaporite-rich crusts generally marked areas where groundwater is periodically near the surface and thus able to replenish the crusts though capillary evaporation. Detrital silicate minerals were prevalent in other parts of the salt pan where shallow groundwater does not affect the surface composition. The surface features in Death Valley change in response to climatic variations on several different timescales. For example, salt crusts on low-lying mudflats form and redissolve during seasonal-to-interannual cycles of wetting and desiccation. In contrast, recent flooding and erosion of rough-salt surfaces in Death Valley probably reflect increased regional precipitation spanning several decades. Remote-sensing observations of playas can provide a means for monitoring changes in evaporite facies and for better understanding the associated climatic processes. At present, such studies are limited by the availability of suitable airborne scanner data. However, with the launch of the Earth Observing System (EOS) AM-1 Platform in 1998, multispectral visible/near-infrared and thermal infrared remote-sensing data will become globally available.

Journal of Geophysical Research B: Solid Earth↗
USGS2008

MODIS and SeaWIFS on-orbit lunar calibration

The Moon plays an important role in the radiometric stability monitoring of the NASA Earth Observing System's (EOS) remote sensors. The MODIS and SeaWIFS are two of the key instruments for NASA's EOS missions. The MODIS Protoflight Model (PFM) on-board the Terra spacecraft and the MODIS Flight Model 1 (FM1) on-board the Aqua spacecraft were launched on December 18, 1999 and May 4, 2002, respectively. They view the Moon through the Space View (SV) port approximately once a month to monitor the long-term radiometric stability of their Reflective Solar Bands (RSB). SeaWIFS was launched on-board the OrbView-2 spacecraft on August 1, 1997. The SeaWiFS lunar calibrations are obtained once a month at a nominal phase angle of 7??. The lunar irradiance observed by these instruments depends on the viewing geometry. The USGS photometric model of the Moon (the ROLO model) has been developed to provide the geometric corrections for the lunar observations. For MODIS, the lunar view responses with corrections for the viewing geometry are used to track the gain change for its reflective solar bands (RSB). They trend the system response degradation at the Angle Of Incidence (AOI) of sensor's SV port. With both the lunar observation and the on-board Solar Diffuser (SD) calibration, it is shown that the MODIS system response degradation is wavelength, mirror side, and AOI dependent. Time-dependent Response Versus Scan angle (RVS) Look-Up Tables (LUT) are applied in MODIS RSB calibration and lunar observations play a key role in RVS derivation. The corrections provided by the RVS in the Terra and Aqua MODIS data from the 412 nm band are as large as 16% and 13%, respectively. For SeaWIFS lunar calibrations, the spacecraft is pitched across the Moon so that the instrument views the Moon near nadir through the same optical path as it views the Earth. The SeaWiFS system gain changes for its eight bands are calibrated using the geometrically-corrected lunar observations. The radiometric corrections to the SeaWiFS data, after more than ten years on orbit, are 19% at 865 nm, 8% at 765 nm, and 1-3% in the other bands. In this report, the lunar calibration algorithms are reviewed and the RSB gain changes observed by the lunar observations are shown for all three sensors. The lunar observations for the three instruments are compared using the USGS photometric model. The USGS lunar model facilitates the cross calibration of instruments with different spectra bandpasses whose measurements of the Moon differ in time and observing geometry.

Conference Paper↗
USGS2013

Essential biodiversity variables

Reducing the rate of biodiversity loss and averting dangerous biodiversity change are international goals, reasserted by the Aichi Targets for 2020 by Parties to the United Nations (UN) Convention on Biological Diversity (CBD) after failure to meet the 2010 target (1, 2). However, there is no global, harmonized observation system for delivering regular, timely data on biodiversity change (3). With the first plenary meeting of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) soon under way, partners from the Group on Earth Observations Biodiversity Observation Network (GEO BON) (4) are developing—and seeking consensus around—Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programs worldwide.

Science↗
USGS2001

The Landsat 7 mission: terrestrial research and applications for the 21st century

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation, and seasonally repetitive monitoring. Substantial improvements in calibration procedures, both prior to launch and during normal operations, have also been made to ensure long-term stability in the acquired spectral radiometry. Landsat 7 data acquisitions are being driven by a long-term data acquisition plan that was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the US for all land areas of the globe. NASA competitively selected a Landsat Science Team, consisting of representatives from US universities and government agencies, to exploit the Landsat 7 record for global change research. This team is addressing the technical and analytical means to process and analyze the core of this observation record, and for the first time in the history of the Landsat mission, the technical and operational aspects of the mission are being driven by the goals of the US science community. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational, and research purposes. Pathways to achieve effective Landsat continuity in the early decades of the 21st century are also being given careful attention, and there is no question that the lessons learned from the Landsat 7 mission will strongly influence these next-generation sensor systems.

Remote Sensing of Environment↗
USGS1995

Historical Landsat data comparisons: illustrations of the Earth's changing surface

The U.S. Geological Survey's (USGS) EROS Data Center (EDC) has managed the Landsat data archive for more than two decades. This archive provides a rich collection of information about the Earth's land surface. Major changes to the surface of the planet can be detected, measured, and analyzed using Landsat data. The effects of desertification, deforestation, pollution, cataclysmic volcanic activity, and other natural and anthropogenic events can be examined using data acquired from the Landsat series of Earth-observing satellites. The information obtainable from the historical and current Landsat data play a key role in studying surface changes through time. This document provides an overview of the Landsat program and illustrates the application of the data to monitor changes occurring on the surface of the Earth. To reveal changes that have taken place within the past 20 years, pairs and triplicates of images were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical record of the Earth's land surface from the early 1970's to the early 1990's. Landsat TM data provide land surface information from the early 1980's to the present.

Report↗
USGS2015

Barrier Island Shorelines Extracted from Landsat Imagery

Introduction Changes to barrier islands occur at time scales that vary from the few hours it takes an individual storm to pass (Morton, 2008) to the millennia it takes for coastal systems to undergo geologic evolution. Developing an understanding of how barrier islands will respond to climate change, sea level rise, and major storms over a range of time scales is relevant to studies of physical, geological, ecological, and societal processes and will help to guide and improve management of our coastal resources (Sallenger and others, 1987). Observations of coastal processes made over a range of spatial and temporal scales and from a variety of instrument platforms (for example, in situ and remote remote sensing) are required to understand and eventually predict the evolution of coastal systems. The deployment of Landsat and other earth-observing satellites within the last few decades has provided an opportunity to observe barrier islands at frequent intervals, often many times a year. This sample frequency is much higher and the spatial coverage much greater than most routine high-resolution topographic surveys (Guy and others, 2014). In addition, the historical record of these datasets have become long enough to document shorter- (that is, annual) and longer-term (that is, decadal) changes from a single data source. Certain aspects of barrier island morphology, such as island size, shape, and position, can be determined from these images and can indicate erosion, land loss, and island breakup (McBride and others, 1989; Plant and Guy, 2013). The shoreline is a common variable used as a metric for coastal erosion or change (Himmelstoss and others, 2010). Although shorelines are often extracted from topographic data (for example, ground-based surveys and light detection and ranging [lidar]), image-based shorelines, corrected for their inherent uncertainties (Moore and others, 2006), have provided much of our understanding of long-term shoreline change because they pre-date routine lidar elevation survey methods. Image-based shorelines continue to be valuable because of their higher temporal resolution compared to costly airborne lidar surveys. A method for extracting sandy shorelines from 30-meter (m) resolution Landsat imagery is presented here.

Open-File Report↗
USGS2021

Airborne geophysical surveys of the lower Mississippi Valley demonstrate system-scale mapping of subsurface architecture

The Mississippi Alluvial Plain hosts one of the most prolific shallow aquifer systems in the United States but is experiencing chronic groundwater decline. The Reelfoot rift and New Madrid seismic zone underlie the region and represent an important and poorly understood seismic hazard. Despite its societal and economic importance, the shallow subsurface architecture has not been mapped with the spatial resolution needed for effective management. Here, we present airborne electromagnetic, magnetic, and radiometric observations, measured over more than 43,000 flight-line-kilometers, which collectively provide a system-scale snapshot of the entire region. We develop detailed maps of aquifer connectivity and shallow geologic structure, infer relationships between structure and groundwater age, and identify previously unseen paleochannels and shallow fault structures. This dataset demonstrates how regional-scale airborne geophysics can close a scale gap in Earth observation by providing observational data at suitable scales and resolutions to improve our understanding of subsurface structures.

Mississippi River Valley↗
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↗
USGS2021

Can Landsat 7 preserve its science capability with a drifting orbit?

Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–75 0 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.

Science of Remote Sensing↗
USGS1990

Historical Landsat data comparisons: illustrations of land surface change

The U.S. Geological Survey's (USGS) EROS Data Center (EDC) has archived two decades of Landsat data, providing a rich collection of information about the dynamics of the Earth's land surface. Major changes to the surface features of the planet can be detected, measured, and studied using Landsat data. The effects of desertification, deforestation, pollution, cataclysmic volcanic activity, and other natural and anthropogenic events can be examined by resource scientists using data acquired from the Landsat series of Earth-observing satellites. The availability of a nearly uninterrupted flow of information from the Landsats, in a consistent data format, gives researchers an important tool for studying surface changes over time. This booklet provides an overview of the Landsat program and shows the application of the data to monitor changes occurring on the surface of the Earth. To show changes that have taken place within the last 20 years or less, image pairs were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical global record of the land surface from the early 1970's to present. Landsat TM data provide land surface information from the early 1980's to present.

Report↗
USGS2022

Pennsylvania and Landsat

The Commonwealth of Pennsylvania straddles an array of landscapes. From east to west, its 46,055 square miles connect the sea-level lowlands of the Atlantic seaboard with the rolling hills of the Midwest. It also acts as a bridge between regions from north to south, with the Appalachian Mountains swooping through its center from its northern border with New York to its southern borders with Maryland and West Virginia. The Declaration of Independence and U.S. Constitution were signed in the Commonwealth’s largest city, Philadelphia, and it was the second of the original 13 colonies to ratify the Constitution. The pivotal Civil War battle of Gettysburg took place in the southern part of Pennsylvania. Western Pennsylvania was an early hub for the coal, oil, and steel industries that fueled the Nation’s growth in the 20th century and still factor into Pennsylvania’s economy. The varied natural features of Pennsylvania have been an important proving ground for the value of satellite imagery from the USGS Landsat Program. Scientists began to lean on Landsat observations early in the program’s history to map the effect of invasive moths on Commonwealth forests. That activity continues to this day. Space-based imagery has also contributed to the study and management of land change from energy development, urban growth, and shifting land use patterns. The Landsat Program’s unparalleled 50-year archive of repeat Earth observations remains a critical public resource for Pennsylvania as environmental changes and land use patterns present new challenges to land managers and urban planners in the Keystone State.

Pennsylvania↗
USGS2019

2019 Joint Agency Commercial Imagery Evaluation—Land remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the first edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗
USGS2020

2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the second edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗
USGS2022

2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between six Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the third edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗
USGS2009

Review of FEWS NET biophysical monitoring requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users; it focused upon operational requirements to determine additional useful remote sensing data and, subsequently, to assess whether such data would be beneficial as FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard; that high resolution remote sensing products continue to be in demand; and that rainfall and vegetation products are valued as data that provide actionable food security information.

Environmental Research Letters↗
USGS2022

Ecological Coastal Units – Standardized global shoreline characteristics

A new set of resources is now available that describe global shoreline characteristics. High resolution (30 m), globally comprehensive Coastal Segment Units (CSUs) and Ecological Coastal Units (ECUs) were developed in a collaboration between the U.S. Geological Survey (USGS), Esri, and the Marine Biodiversity Observation Network (MBON). The data were produced from a segmentation and characterization of a global shoreline vector extracted from year 2014 Landsat imagery. A total of 4 million 1 km shoreline segments were attributed with values from ten variables which describe the ecological settings in which the coastline occurs, including water-side properties, landside properties, and properties of the coastline itself. These data were developed as part of a Group on Earth Observations (GEO) global ecosystem mapping initiative called GEO Ecosystems (GEO ECO). The development of the resource and its intended utility are reviewed herein.

Conference Paper↗