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USGS1999

SAR studies in the Yuma Desert, Arizona: Sand penetration, geology, and the detection of military ordnance debris

Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also Compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).

Remote Sensing of Environment↗

USGS1999

The importance of SAR wavelength in penetrating blow sand in Northern Arizona

Multifrequency and polarimetric Airborne Synthetic Aperture Radar (AIRSAR) images of a sand streak in northern Arizona demonstrate the ability of C- (6-cm wavelength), L- (24 cm), and P-band (68 cm) radar signals to backscatter, respectively, from increasingly greater depths reaching 2 m or more in active blow sand. The studied sand streak, located on a salient of Ward Terrace at lat 35??48.4'N and long 111??15.0'W, is sourced in the normally dry Little Colorado River channel and migrates northeastward toward the Moenkopi Plateau Scarp. The sand streak is 'radar dark' compared to the 'radar-bright' surface of Ward Terrace on all C-band images. It is, however, penetrated completely at P-band and is invisible on these images. The sand streak is recognizable on the LHH image but is poorly defined on the LHV image. High resolution AIRSAR images constitute the primary data set analyzed; in addition, a C-band radar image acquired by the European Remote Sensing (ERS) satellite is also presented. The spaceborne image data confirms the results of the aircraft data at C-band.Multifrequency and polarimetric Airborne Synthetic Aperture Radar (AIRSAR) images of a sand streak in northern Arizona demonstrate the ability of C- (6-cm wavelength), L- (24 cm), and P-band (68 cm) radar signals to backscatter, respectively, from increasingly greater depths reaching 2 m or more in active blow sand. The studied sand streak, located on a salient of Ward Terrace at lat 35??48.4???N and long 111??15.0???W, is sourced in the normally dry Little Colorado River channel and migrates northeastward toward the Moenkopi Plateau Scarp. The sand streak is `radar dark' compared to the `radar-bright' surface of Ward Terrace on all C-band images. It is, however, penetrated completely at P-band and is invisible on these images. The sand streak is recognizable on the LHH image but is poorly defined on the LHV image. High resolution AIRSAR images constitute the primary data set analyzed; in addition, a C-band radar image acquired by the European Remote Sensing (ERS) satellite is also presented. The spaceborne image data confirms the results of the aircraft data at C-band.

Remote Sensing of Environment↗

USGS2000

Orbital radar studies of paleodrainages in the central Namib Desert

Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area. (C) Elsevier Science Inc., 2000.Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area.

Remote Sensing of Environment↗

USGS2000

A diurnal animation of thermal images from a day-night pair

Interpretation of thermal images is often complicated because the physical property information is contained in both the spatial and temporal variations of the data and thermal models are necessary to extract and display this information. A linearized radiative transfer solution to the surface flux has been used to derive a function that is invariant with respect to thermal inertia. This relationship makes it possible to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements (e.g., noon and midnight). An animation can then be constructed from a pair of day-night images to view both the spatial and temporal temperature changes throughout the diurnal cycle. A more complete solution for the invariant function, using the method of Laplace transforms and based on the linearized solution, was introduced. These results indicate that the linear model does not provide a sufficiently accurate estimate. Using standard conditions (latitude 30??, solar declination 0??, acquisition times at noon and midnight), this new relationship was used to predict temperature throughout the diurnal cycle to an rms error of 0.2??C, which is close to the system noise of most thermal scanners. The method was further extended to include the primary effects of topographic slope with similar accuracy. The temperature was computed at 48 equally spaced times in the diurnal cycle with this algorithm using a co-registered day and night TIMS (Thermal Infrared Multispectral Scanner) data pair (330 pixels, 450 lilies) acquired of the Carlin, Nevada, area and a co-registered DEM (Digital Elevation Model). (Any reader can view the results by downloading the animation file from an identified tip site). The results illustrate the power of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences. This 'visual change' approach could significantly increase the use of thermal data for environmental, hazard, and resource studies. Published by Elsevier Science Inc., 2000.A linearized radiative transfer solution of determining the surface flux is proposed to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements. An animation is constructed from a pair of day-night images to view the spatial and temporal temperature changes throughout the diurnal cycle. The results illustrate the effectiveness of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences.

Remote Sensing of Environment↗

USGS2014

Establishing a baseline for regional scale monitoring of eelgrass ( Zostera marina ) habitat on the lower Alaska Peninsula

Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass ( Zostera marina ) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.

Alaska↗

USGS2022

Animal tracing with sulfur isotopes: Spatial segregation and climate variability in Africa likely contribute to population trends of a migratory songbird

Climatic conditions affect animals but range-wide impacts at the population level remain largely unknown, especially in migratory species. However, studying climate–population relationships is still challenging in small migrants due to a lack of efficient and cost-effective geographic tracking method. Spatial distribution patterns of environmental stable isotopes (so called ‘isoscapes’) generally overcome these limitations but none of the currently available isoscapes provide a substantial longitudinal gradient in species-rich sub-Saharan Africa. In this region, sulphur ( δ 34 S) has not been sufficiently explored on a larger scale. We developed a δ 34 S isoscape to trace animal origins in sub-Saharan Africa by coupling known-origin samples from tracked migratory birds with continental remotely sensed environmental data building on environment– δ 34 S relationships using a flexible machine learning technique. Furthermore, we link population-specific nonbreeding grounds with interannual climatic variation that might translate to breeding population trends. The predicted δ 34 S isotopic map featured east–west and coast-to-inland isotopic gradients and was applied to predict nonbreeding grounds of three breeding populations of Eurasian Reed Warblers Acrocephalus scirpaceus with two distinct migratory phenotypes. Breeding populations as well as migratory phenotypes exhibited large-scale segregation within the African nonbreeding range. These regions also differed substantially in the interannual climatic variation, with higher interannual variability in the eastern part of the range during 2001–2012. Over the same period, the eastern European breeding population seemed to have experienced a more steep decline in population size. The link between migratory patterns and large-scale climatic variability appears important to better understand population trajectories in many declining migratory animals. We believe animal tracing using sulphur isotopes will facilitate these efforts and offers manifold ecological and forensic applications in the biodiversity hotspot of sub-Saharan Africa.

Journal of Animal Ecology↗

USGS2007

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

Fact Sheet↗

USGS2009

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

Fact Sheet↗

USGS2016

Removing sun glint from optical remote sensing images of shallow rivers

Sun glint is the specular reflection of light from the water surface, which often causes unusually bright pixel values that can dominate fluvial remote sensing imagery and obscure the water-leaving radiance signal of interest for mapping bathymetry, bottom type, or water column optical characteristics. Although sun glint is ubiquitous in fluvial remote sensing imagery, river-specific methods for removing sun glint are not yet available. We show that existing sun glint-removal methods developed for multispectral images of marine shallow water environments over-correct shallow portions of fluvial remote sensing imagery resulting in regions of unreliable data along channel margins. We build on existing marine glint-removal methods to develop a river-specific technique that removes sun glint from shallow areas of the channel without overcorrection by accounting for non-negligible water-leaving near-infrared radiance. This new sun glint-removal method can improve the accuracy of spectrally-based depth retrieval in cases where sun glint dominates the at-sensor radiance. For an example image of the gravel-bed Snake River, Wyoming, USA, observed-vs.-predicted R 2 values for depth retrieval improved from 0.66 to 0.76 following sun glint removal. The methodology presented here is straightforward to implement and could be incorporated into image processing workflows for multispectral images that include a near-infrared band.

Earth Surface Processes and Landforms↗

USGS2003

The U.S. Geological Survey Land Remote Sensing Program

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

Fact Sheet↗

USGS2015

Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR

The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.

Book chapter↗

USGS2010

Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery

High spatial and spectral resolution thermal infrared imagery (8.0–13.5 μm) from the SEBASS airborne sensor was used to analyze and map tree canopy spectral features at the State Arboretum of Virginia, near Boyce, Virginia. Fifty tree species were analyzed and about half were directly identified with varying degrees of success on the basis of spectral matched filtering that utilized laboratory-measured leaf spectra as the target signatures. Spectral averages of pixels extracted from SEBASS emissivity data compared favorably with laboratory spectra of leaves collected from individual tree species. Best results were obtained from species having relatively strong spectral contrast, wide and flat leaves, closed planophile canopies, and/or large canopy areas. Tree species having small leaves or unfavorable leaf orientations showed spectral attenuation likely resulting from cavity blackbody effects. Increased spatial resolution and better image calibration and atmospheric correction might lead to further improvements in thermal infrared plant species identification.

Remote Sensing of the Environment↗

USGS2013

Controls on variations in MODIS fire radiative power in Alaskan boreal forests: implications for fire severity conditions

Fire activity in the Alaskan boreal forest, though episodic at annual and intra-annual time scales, has experienced an increase over the last several decades. Increases in burned area and fire severity are not only releasing more carbon to the atmosphere, but likely shifting vegetation composition in the region towards greater deciduous dominance and a reduction in coniferous stands. While some recent studies have addressed qualitative differences between large and small fire years in the Alaskan boreal forest, the ecological effects of a greater proportion of burning occurring during large fire years and during late season fires have not yet been examined. Some characteristics of wildfires that can be detected remotely are related to fire severity and can provide new information on spatial and temporal patterns of burning. This analysis focused on boreal wildfire intensity (fire radiative power, or FRP) contained in the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire product from 2003 to 2010. We found that differences in FRP resulted from seasonality and intra-annual variability in fire activity levels, vegetation composition, latitudinal variation, and fire spread behavior. Our studies determined two general categories of active fire detections: new detections associated with the spread of the fire front and residual pixels in areas that had already experienced front burning. Residual pixels had a lower average FRP than front pixels, but represented a high percentage of all pixels during periods of high fire activity (large fire years, late season burning, and seasonal periods of high fire activity). As a result, the FRP from periods of high fire activity was less intense than those from periods of low fire activity. Differences related to latitude were greater than expected, with higher latitudes burning later in the season and at a higher intensity than lower latitudes. Differences in vegetation type indicate that coniferous vegetation is the most fire prone, but deciduous vegetation is not particularly fire resistant, as the proportion of active fire detections in deciduous stands is roughly the same as the fraction of deciduous vegetation in the region. Qualitative differences between periods of high and low fire activity are likely to reflect important differences in fire severity. Large fire years are likely to be more severe, characterized by more late season fires and a greater proportion of residual burning. Given the potential for severe fires to effect changes in vegetation cover, the shift toward a greater proportion of area burning during large fire years may influence vegetation patterns in the region over the medium to long term.

Alaska↗

USGS2021

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Alaska, Minnesota↗

USGS2022

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIR v ), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR v and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR v offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R 2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R 2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIR v and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIR v and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

Arizona, California, Colorado, Idaho, New Mexico, ↗

USGS2026

Aquatic reflectance derived from Sentinel-2 Multispectral Imager data for inland waters in the conterminous United States

Satellite-based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real-time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic-focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis-ready, aquatic reflectance dataset for inland water derived from Sentinel-2 for the conterminous United States.

conterminous United States↗

USGS2014

Spectral properties of Ca-sulfates: Gypsum, bassanite, and anhydrite

This study of the spectral properties of Ca-sulfates was initiated to support remote detection of these minerals on Mars. Gypsum, bassanite, and anhydrite are the currently known forms of Ca-sulfates. They are typically found in sedimentary evaporites on Earth, but can also form via reaction of acidic fluids associated with volcanic activity. Reflectance, emission, transmittance, and Raman spectra are discussed here for various sample forms. Gypsum and bassanite spectra exhibit characteristic and distinct triplet bands near 1.4–1.5 μm, a strong band near 1.93–1.94 μm, and multiple features near 2.1–2.3 μm attributed to H 2 O. Anhydrite, bassanite, and gypsum all have SO 4 combination and overtone features from 4.2–5 μm that are present in reflectance spectra. The mid-IR region spectra exhibit strong SO 4 ν 3 and ν 4 vibrational bands near 1150–1200 and 600–680 cm −1 (~8.5 and 16 μm), respectively. Additional weaker features are observed near 1005–1015 cm −1 (~10 μm) for ν 1 and near 470–510 cm −1 (~20 μm) for ν 2 . The mid-IR H 2 O bending vibration occurs near 1623–1630 cm −1 (~6.2 μm). The visible/near-infrared region spectra are brighter for the finer-grained samples. In reflectance and emission spectra of the mid-IR region the ν 4 bands begin to invert for the finer-grained samples, and the ν 1 vibration occurs as a band instead of a peak and has the strongest intensity for the finer-grained samples. The ν 2 vibration is a sharp band for anhydrite and a broad peak for gypsum. The band center of the ν 1 vibration follows a trend of decreasing frequency (increasing wavelength) with increasing hydration of the sample in the transmittance, Raman, and reflectance spectra. Anhydrite forms at elevated temperatures compared to gypsum, and at lower temperature, salt concentration, and pH than bassanite. The relative humidity controls whether bassanite or gypsum is stable. Thus, distinguishing among gypsum, bassanite, and anhydrite via remote sensing can provide constraints on the geochemical environment.

American Mineralogist↗

USGS1980

EROS: A space program for Earth resources

Within the technology of the space age lies a key to increased knowledge about the resources and environment of the Earth. This key is remote sensing detecting the nature of an object without actually touching it. Although the photographic camera is the most familiar remote-sensing device, other instrument systems, such as scanning radiometers and radar, also can produce photographs and images. On the basis of the potential of this technology, and in response to the critical need for greater knowledge of the Earth and its resources, the Department of the Interior established the Earth Resources Observation Systems (EROS) Program to gather and use remotely sensed data collected by satellite and aircraft of natural and manmade features on the Earth's surface.

South Dakota↗