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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 541 records · Page 30Linked to original sources
USGS2008

The effect of variations in relative spectral response on the retrieval of land surface parameters from multiple sources of remotely sensed imagery

Airborne visible infrared imaging spectrometer (AVIRIS) images, collected over Sioux Falls, South Dakota, were used to quantify the effect of spectral response on different surface materials and to develop spectral "figures-of-merit" for spectral responses covering similar, but not identical spectral bands. In this simulation, AVIRIS images were converted to radiance, then spectrally resampled to six wavelength bands commonly used for terrestrial observation. Preliminary results indicate that differences between the simulations can be attributed to variations in surface reflectance within spectral bands, and suggest influences due to water vapor absorption. Radiance simulated from the spectrally narrow Moderate Resolution Imaging Spectroradiometer (MODIS) relative spectral responses (RSR) was generally higher than that using the broader Enhanced Thematic Mapper Plus (ETM+) RSRs over most targets encountered over the test area. This is consistent with many MODIS bands being biased toward shorter wavelengths compared to corresponding ETM+ bands when viewing targets whose radiance decreases with wavelength. In some cases the higher radiance values appeared to occur where the MODIS RSR is better situated over peak reflected wavelengths. Simulation differences between MODIS & ETM+ bands in the near-infrared indicated higher MODIS radiance values that suggest the influence of water vapor absorption at 820 nanometers. This result agreed with water vapor values retrieved from the AVIRIS images themselves at around 2.7 cm precipitable water, and measurements made at a nearby AERONET node at around 2.8 cm during the AVIRIS overflight.

Conference Paper↗
USGS2017

New method to integrate remotely sensed hydrothermal alteration mapping into quantitative mineral resource assessments

Hydrothermal alteration data mapped using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were compiled into hydrothermal alteration polygons for use in an assessment of porphyry copper mineral resource potential in the southwestern United States. Hydrothermal alteration polygons along with geochemistry, gravity and magnetic, lithologic, and deposit and prospects data were compiled in a GIS to produce a quantitative set of physical properties for each polygon that were effectively used in making estimates of undiscovered deposits for each permissive tract. Results show a higher estimate of potential undiscovered deposits (17 vs 14) for permissive tracts when ASTER alteration data were used in the assessment.

Conference Paper↗
USGS2023

Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States

The increase of developed land changes the Earth’s ecosystems and, in doing so, impacts the natural environment and further affects the services it provides to humans. Urban growth and associated land cover transitions alter the thermal and physical properties of the land surface, resulting in surface temperature change in urban areas. In this study, we integrated both land cover and surface temperature information to characterize surface temperature spatiotemporal variations using the recently available time series of Landsat land surface temperature and annual land change products. We analyzed over thirty-year trends of land surface temperature (LST) in urban and surrounding non-urban lands. We found that the transitions of different land cover types to urban affected urban LST trends differently, and further impacted the temporal trend of urban heat island intensity.

continental United States↗
USGS2020

Integrating airborne remote sensing and field campaigns for ecology and Earth system science

In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.

Colorado↗
USGS2025

Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing

California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

California↗
USGS2005

An integrated approach to flood hazard assessment on alluvial fans using numerical modeling, field mapping, and remote sensing

Millions of people in the western United States live near the dynamic, distributary channel networks of alluvial fans where flood behavior is complex and poorly constrained. Here we test a new comprehensive approach to alluvial-fan flood hazard assessment that uses four complementary methods: two-dimensional raster-based hydraulic modeling, satellite-image change detection, field-based mapping of recent flood inundation, and surficial geologic mapping. Each of these methods provides spatial detail lacking in the standard method and each provides critical information for a comprehensive assessment. Our numerical model simultaneously solves the continuity equation and Manning's equation (Chow, 1959) using an implicit numerical method. It provides a robust numerical tool for predicting flood flows using the large, high-resolution Digital Elevation Models (DEMs) necessary to resolve the numerous small channels on the typical alluvial fan. Inundation extents and flow depths of historic floods can be reconstructed with the numerical model and validated against field- and satellite-based flood maps. A probabilistic flood hazard map can also be constructed by modeling multiple flood events with a range of specified discharges. This map can be used in conjunction with a surficial geologic map to further refine floodplain delineation on fans. To test the accuracy of the numerical model, we compared model predictions of flood inundation and flow depths against field- and satellite-based flood maps for two recent extreme events on the southern Tortolita and Harquahala piedmonts in Arizona. Model predictions match the field- and satellite-based maps closely. Probabilistic flood hazard maps based on the 10 yr, 100 yr, and maximum floods were also constructed for the study areas using stream gage records and paleoflood deposits. The resulting maps predict spatially complex flood hazards that strongly reflect small-scale topography and are consistent with surficial geology. In contrast, FEMA Flood Insurance Rate Maps (FIRMs) based on the FAN model predict uniformly high flood risk across the study areas without regard for small-scale topography and surficial geology.

Arizona↗
USGS2007

Quantitative remote sensing study indicates doubling of coastal erosion rate in past 50 yr along a segment of the Arctic coast of Alaska

A new quantitative coastal land gained-and-lost method uses image analysis of topographic maps and Landsat thematic mapper short-wave infrared data to document accelerated coastal land loss and thermokarst lake expansion and drainage. The data span 1955-2005 along the Beaufort Sea coast north of Teshekpuk Lake in the National Petroleum Reserve in Alaska. Some areas have undergone as much as 0.9 km of coastal erosion in the past 50 yr. Land loss attributed to coastal erosion more than doubled, from 0.48 km2 yr-1 during 1955-1985 to 1.08 km2 yr-1 during 1985-2005. Coastal erosion has breached thermokarst lakes, causing initial draining of the lakes followed by marine floodng. Although inland thermokarst lakes show some uniform expansion, lakes breached by coastal erosion display lake expansion several orders of magnitude greater than inland lakes. ?? 2007 The Geological Society of America.

Geology↗
USGS2004

Hydrologic scales, cloud variability, remote sensing, and models: Implications for forecasting snowmelt and streamflow

Accurate prediction of available water supply from snowmelt is needed if the myriad of human, environmental, agricultural, and industrial demands for water are to be satisfied, especially given legislatively imposed conditions on its allocation. Robust retrievals of hydrologic basin model variables (e.g., insolation or areal extent of snow cover) provide several advantages over the current operational use of either point measurements or parameterizations to help to meet this requirement. Insolation can be provided at hourly time scales (or better if needed during rapid melt events associated with flooding) and at 1-km spatial resolution. These satellite-based retrievals incorporate the effects of highly variable (both in space and time) and unpredictable cloud cover on estimates of insolation. The insolation estimates are further adjusted for the effects of basin topography using a high- resolution digital elevation model prior to model input. Simulations of two Sierra Nevada rivers in the snowmelt seasons of 1998 and 1999 indicate that even the simplest improvements in modeled insolation can improve snowmelt simulations, with 10%–20% reductions in root-mean-square errors. Direct retrieval of the areal extent of snow cover may mitigate the need to rely entirely on internal calculations of this variable, a reliance that can yield large errors that are difficult to correct until long after the season is complete and that often leads to persistent underestimates or overestimates of the volumes of the water to operational reservoirs. Agencies responsible for accurately predicting available water resources from the melt of snowpack [e.g., both federal (the National Weather Service River Forecast Centers) and state (the California Department of Water Resources)] can benefit by incorporating concepts developed herein into their operational forecasting procedures.

Weather and Forecasting↗
USGS2015

Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture

The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

Journal of Hydrometeorology↗
USGS2018

Fifty-years of advances in hyperspectral remote sensing of agriculture and vegetation: Summary, insights, and highlights of volume III

The goal of this summary chapter is twofold. The first is to provide the reader an overview of the content of the preceding chapters. This they can read at the very beginning, before moving on to individual chapters in detail. Alternatively, they may read it at the very end to refresh their memory and to summarize the contents of the Volume. Second, this summary provides the editors’ perspective, bringing in their rich collective experience and expertise to guide the reader. We have kept the summaries brief and illustrative, so that the reader can quickly gather essential initial knowledge and guidance. The in-depth details can be found in the individual chapters.

Book chapter↗
USGS2024

Remote sensing of actual evapotranspiration from croplands

Agriculture accounted for the majority of human water use and for more than 90% of global freshwater consumption during the twentieth century (Hoekstra and Mekonnen, 2012; Shiklomanov, 2000). Streamflow depletion due to enhanced evapotranspiration (ET) from irrigated crops impacts freshwater ecosystems globally (Foley et al., 2005). Water scarcity limits crop production in many arid and semiarid regions, and water is likely to be a key resource limiting food production and food security in the twenty-first century (Foley et al., 2011; Vorosmarty et al., 2000). Despite this, estimates of the location and temporal dynamics of ET from croplands are often uncertain at a variety of spatial and temporal scales. Better information on ET can be useful in several applications at a range of spatial scales, including water resources, agronomy, and meteorology (e.g., Rivas and Caselles, 2004). At the scale of irrigation projects, maps of ET can assist with irrigation scheduling and demand assessment. Measurements of ET are required for monitoring plant water requirements, plant growth, and productivity, as well as for irrigation management and deciding when to carry out cultivation procedures (e.g., Consolli et al., 2006; Glenn et al., 2007; Yang et al., 2010).

Book chapter↗
USGS2011

Hyperspectral remote sensing of vegetation

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

Book↗