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At least 181 records · Page 10Linked to original sources

USGS1992

Evaluation of the use of remote-sensing data to identify crop types and estimate irrigated acreage, Uvalde and Medina counties, Texas, 1989

Remote-sensing data were used to estimate that 190,000 acre-feet of water was pumped from the Edwards aquifer in 1989 to irrigate crops in Uvalde and Medina Counties. Landsat digital satellite images for March and July 1989 were combined and classified to identify the areas of crops irrigated with water from the Edwards aquifer in the two counties. Normalized difference, the difference between the infrared and red reflectance values divided by the total of those values, was used to discriminate vegetative from nonvegetative ground cover. The images subsequently were classified using maximum likelihood, an unsupervised classification procedure. Detailed vegetation distribution maps of two calibration sites in the study area, and boundaries of the areas probably irrigated in 1989, helped to interpret the results and to separate probable irrigated areas from the rest of the image. Results were verified using crop acreages reported by the U.S. Department of Agriculture, Agricultural Stabilization and Conservation Service (ASCS). The total areas for all irrigated crops estimated using remote-sensing data were about 8 percent higher for Uvalde County and about 4 percent higher for Medina County than the areas reported by the ASCS. Irrigated-crop areas subsequently were multiplied by the respective duties of water to calculate the total quantity of water pumped from the aquifer for irrigation. Pumpage did not differ for the two estimates of crop areas for Uvalde County and differed by about 3 percent for Medina County.

Texas↗

USGS1978

Remote sensing data products: types and characteristics

Objectives: To identify and define remote sending data products available for analysis of wildlife management problems. To ascribe characteristics and formats as they relate to a choice of the data product to select for a particular analysis. To identify the various remote sensing data products discussed, displayed, and presented at the symposium.

Pecora IV: Application of Remote Sensing Data to W↗

USGS2021

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR), SPOT (Système Pour l’Observation de la Terre), and the Indian Remote Sensing (IRS) satellites. These systems typically provide 4 to 9 broad spectral wavebands spread from 400 to 2500 nm, often with one or two additional bands in the thermal range. Significant advances in the study of the Earth have been made based on these data [Thenkabail et al., 2018a,b,c,d; Thenkabail et al., 2015a,b,c]. Possibilities of great advances that can be made using HNB data over MBB data are well established based on studies conducted using hyperspectral sensors such as the hand-held spectroradiometers, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and spaceborne Earth Observing -1 (EO-1) Hyperion [Thenkabail 2018a,b,c,d]. The twenty-first century is already seeing the dawn of hyperspectral imaging data from sensors such as the German Aerospace Center’s (DLR’s) DESIS (DLR Earth Sensing Imaging Spectrometer) onboard the MUSES (Multi-User System for Earth Sensing) platform on the International Space Station (ISS), the polar-orbiting Italian Space Agency’s (ASI) PRISMA (PRecursore IperSpettrale della Missione Applicativa), and many other upcoming sensors such as the NASA Surface Biology and Geology (SBG) [Thenkabail et al., 2018a,b,c,d]. These satellites acquire data in hundreds of narrow spectral bands of 1 to 10 nm width, typically between 400 to 2500 nm; also future planned missions will be extending HNBs to the thermal (9,000 to 14,000 nm) electromagnetic spectrum. This expansion creates a quantum leap in new data, new information, and myriad possible new applications in the study of the Earth in addition to great advances in existing applications. Given the above, the objective of this article is to provide insights on the gigantic leap in our understanding, modeling, mapping, and monitoring of the Earth that can be made using HNB relative to MBB by focusing on agricultural and vegetation applications. We will address this in four aspects: 1. Comparison between HNB and MBB data; 2. Spectral libraries of agricultural crops; 3. HNB data analysis in general; and 4. HNB analysis using machine learning (ML) and cloud computing.

Photogrammetric Engineering and Remote Sensing.↗

USGS2022

Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry

Multi-dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two-dimensional (2D) and three-dimensional (3D) hydrodynamic models for a 1.6-km reach of a large gravel-bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth-averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near-bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high-resolution bathymetry used as model input. Model predictions of winter-run Chinook salmon ( Oncorhynchus tshawytscha ) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear-flowing, relatively shallow large rivers with sufficient accuracy to support multi-dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.

California↗

USGS2009

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service-Southeast Watershed Research Lab (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2-4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity.

Journal of Hydrology↗

USGS2025

Offsetting the noise: A framework for applying phenological offset corrections in remotely sensed burn severity assessments

Background Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored. Aims We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous US. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds and potential bias. Key results Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence with observed burn severity categories. Conclusions Offset corrections offer benefits but also present trade-offs to accurately representing remotely sensed burn severity. Implications The utility of offset corrections depends on the environment, methods and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

conterminous United States↗

USGS2003

Comparison of Irrigation Water Use Estimates Calculated from Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season

Increased demand for water in the Lake Altus drainage basin requires more accurate estimates of water use for irrigation. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Reclamation, is investigating new techniques to improve water-use estimates for irrigation purposes in the Lake Altus drainage basin. Empirical estimates of reference evapotranspiration, crop evapotranspiration, and crop irrigation water requirements for nine major crops were calculated from September 1999 to October 2000 using a solar radiation-based evapotranspiration model. Estimates of irrigation water use were calculated using remotely sensed irrigated crop acres derived from Landsat 7 Enhanced Thematic Mapper Plus imagery and were compared with irrigation water-use estimates calculated from irrigated crop acres reported by the Oklahoma Water Resources Board and the Texas Water Development Board for the 2000 growing season. The techniques presented will help manage water resources in the Lake Altus drainage basin and may be transferable to other areas with similar water management needs. Irrigation water use calculated from the remotely sensed irrigated acres was estimated at 154,920 acre-feet; whereas, irrigation water use calculated from state reported irrigated crop acres was 196,026 acre-feet, a 23 percent difference. The greatest difference in irrigation water use was in Carson County, Texas. Irrigation water use for Carson County, Texas, calculated from the remotely sensed irrigated acres was 58,555 acrefeet; whereas, irrigation water use calculated from state reported irrigated acres was 138,180 acre-feet, an 81 percent difference. The second greatest difference in irrigation water use occurred in Beckham County, Oklahoma. Differences between the two irrigation water use estimates are due to the differences of irrigated crop acres derived from the mapping process and those reported by the Oklahoma Water Resources Board and Texas Water Development Board.

Water-Resources Investigations Report↗

USGS2023

Remotely sensed short-crested breaking waves in a laboratory directional wave basin

Short-crested breaking waves that result from directionally spread wave conditions dissipate energy and generate turbulence within the surf zone, altering sediment transport processes, wave runup, and forces on structures. Additionally, vertical vorticity generated near crest ends during breaking, which depends on the gradient in wave height along a crest, may enhance nearshore dispersion of pollutants, nutrients, and larvae. Although directionally spread irregular wave fields are ubiquitous on ocean and large lake coastlines, the dependence of short-crested breaking wave characteristics (including the along-crest length and number of crest ends) on offshore wave conditions is not well established. To assess this relationship, laboratory experiments with alongshore-uniform barred bathymetry were performed in a large-scale directional wave basin. A three-dimensional scanning lidar, trinocular camera stereo processing methods, and in situ measurements were used to study short-crested wave field breaking characteristics in the laboratory, yielding a dataset with dense spatio-temporal coverage relative to prior laboratory or field measurements. Wave height estimates are similar for remotely sensed and in situ observations, except in the outer surf zone where plunging breaking occurred. Directional wave properties estimated with an array of in situ or remotely sensed sea-surface elevation estimates are similar and yield smaller directional spreads than single-point colocated pressure and velocity based in situ estimates when waves are less directionally spread. Using a breaking crest identification procedure combining visible imagery and stereo sea-surface elevation, we find that the average along-crest length of breaking waves decreases and the average number of crest ends increases with increasing directional spread. Relative to observations, a parameterized relationship between directional spread and crest characteristics based on theory for non-breaking, refracting waves generally over-estimates breaking crest lengths and is similar to or underestimates the total number of crest ends observed in the surf zone. The wave-field-dependent breaking-wave characteristics examined in the laboratory with remote sensing techniques can inform future investigations of depth-limited short-crested wave breaking and resulting surfzone eddy processes.

Coastal Engineering↗

USGS2003

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration ( R ). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO 2 fluxes ( F day ) and nighttime CO 2 fluxes ( R n ) using remote sensing and other data. F day and R n were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush ( Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict F day and R n . We found that iNDVI was a strong predictor of F day ( R 2 =0.79, n =66, P <0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F day ( R 2 =0.82, n =66, P <0.0001). Crossvalidation indicated that regression tree predictions of F day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R n quite well ( R 2 =0.75–0.77, n =66) with the regression tree model being slightly more robust in crossvalidation. Temporal mapping of F day and R n is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic F day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F day . These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.

Idaho↗

USGS2019

Remote sensing of river bathymetry: Evaluating a range of sensors, platforms, and algorithms on the upper Sacramento River, California, USA

Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements from a 1.6 km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths from a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis (OBRA) and the K nearest neighbors regression (KNN) machine learning algorithm. Linear, quadratic, exponential, power, and lowess OBRA models enabled more flexible curve-fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. KNN increased observed vs. predicted R2 values, particularly for the satellite image; we also found that pre-processing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

California↗

USGS2026

Thermal infrared and ultraviolet remote sensing of sulfur dioxide gas emitted during the 2018 lower East Rift Zone eruption of Kilauea, Hawaiʻi

Ultraviolet (UV) remote sensing is widely used to detect volcanic sulfur dioxide (SO₂) due to its high sensitivity and favorable spatial and temporal resolution. However, significant discrepancies have been reported between ground-based and satellite-based UV observations of dense volcanic plumes. A notable example is the 2018 lower East Rift Zone eruption of Kīlauea, where SO₂ emission rates derived from ground-based Differential Optical Absorption Spectroscopy (DOAS) measurements differed substantially from those obtained by the spaceborne Tropospheric Monitoring Instrument (TROPOMI). In this study, we investigate these differences by applying thermal infrared (TIR) satellite retrievals using a modified version of the SO₂-ALTA algorithm to Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The resulting TIR-derived SO₂ fluxes are compared with ground-based DOAS data, satellite UV observations, and petrological estimates of gas emissions. Our results show strong agreement between TIR-derived fluxes, ground-based DOAS measurements, and petrological estimates, particularly during the peak and plateau phases of the eruption. In contrast, satellite UV-derived SO₂ emissions are systematically lower. We find that TIR observations are more effective in quantifying high-concentration SO₂ plumes in the near-vent region, while UV measurements are more sensitive under lower-concentration conditions but more affected by scattering in optically dense plumes. These findings highlight the complementary strengths of UV and TIR remote sensing techniques and emphasize the importance of accounting for plume density and observation geometry when interpreting satellite SO₂ retrievals.

Hawaii↗

USGS2013

Geodatabase compilation of hydrogeologic, remote sensing, and water-budget-component data for the High Plains aquifer, 2011

The High Plains aquifer underlies almost 112 million acres in the central United States. It is one of the largest aquifers in the Nation in terms of annual groundwater withdrawals and provides drinking water for 2.3 million people. The High Plains aquifer has gained national and international attention as a highly stressed groundwater supply primarily because it has been appreciably depleted in some areas. The U.S. Geological Survey has an active program to monitor the changes in groundwater levels for the High Plains aquifer and has documented substantial water-level changes since predevelopment: the High Plains Groundwater Availability Study is part of a series of regional groundwater availability studies conducted to evaluate the availability and sustainability of major aquifers across the Nation. The goals of the regional groundwater studies are to quantify current groundwater resources in an aquifer system, evaluate how these resources have changed over time, and provide tools to better understand a systems response to future demands and environmental stresses. The purpose of this report is to present selected data developed and synthesized for the High Plains aquifer as part of the High Plains Groundwater Availability Study. The High Plains Groundwater Availability Study includes the development of a water-budget-component analysis for the High Plains completed in 2011 and development of a groundwater-flow model for the northern High Plains aquifer. Both of these tasks require large amounts of data about the High Plains aquifer. Data pertaining to the High Plains aquifer were collected, synthesized, and then organized into digital data containers called geodatabases. There are 8 geodatabases, 1 file geodatabase and 7 personal geodatabases, that have been grouped in three categories: hydrogeologic data, remote sensing data, and water-budget-component data. The hydrogeologic data pertaining to the northern High Plains aquifer is included in three separate geodatabases: (1) base data from a groundwater-flow model; (2) hydrogeology and hydraulic properties data; and (3) groundwater-flow model data to be used as calibration targets. The remote sensing data for this study were developed by the U. S. Geological Survey Earth Resources Observation and Science Center and include historical and predicted land-use/land-cover data and actual evapotranspiration data by using remotely sensed temperature data. The water-budget-component data contains selected raster data from maps in the “Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 Through 1949 and 2000 Through 2009” report completed in 2011 ( http://pubs.usgs.gov/sir/2011/5183/ ). Federal Geographic Data Committee compliant metadata were created for each spatial and tabular data layer in the geodatabases.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗

USGS2026

The application of state-and-transition models to remotely sensed fractional cover datasets

Assessing vegetation conditions in expansive rangeland ecosystems has long posed a persistent challenge. Recent advancements in remote sensing technologies have provided new avenues for improving this assessment process. In this study, conducted at two ranch sites in Nevada, we integrated existing line-point-intercept monitoring data with additional field observations to evaluate the vegetation condition or “State” at various monitoring points. We then employed machine learning techniques to classify gridded raster datasets, aligning them with existing state-and-transition models (STMs) specific to each study area. Leveraging Landsat-derived fractional cover datasets, along with climate and soil predictors, we aimed to predict vegetation State within specific Vegetation Great Groups (VGGs). The resulting vegetation State maps were then combined to generate a cohesive representation of vegetation conditions across the study sites. Our analysis revealed that relative functional group cover emerged as a superior predictor of vegetation state and ecological processes at the site level. However, we encountered variations in state mapping accuracy ranging from approximately 14% to 44% error. These discrepancies were influenced by factors such as study location, landscape heterogeneity, availability of training data, and species-specific challenges, all of which complicate the accurate classification of remote sensing datasets.

Nevada↗

USGS2020

Transitioning from change detection to monitoring with remote sensing: A paradigm shift

The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.

Remote Sensing of Environment↗

USGS2010

Hydrothermal alteration of the Late Eocene Caetano ash-flow caldera, north-central Nevada: A field and ASTER remote sensing study

Geologic mapping and analysis of ASTER remote sensing data were used to define the effects of a large hydrothermal system in the 12–18 by 22 km Caetano caldera. The caldera formed at ~33.8Ma during eruption of >1100km 3 of the rhyolitic Caetano Tuff that left a 1 km deep basin which was partly filled by a lake. Magma resurgence resulted in shallow(<1 km) emplacement of the Redrock Canyon granite porphyry intrusion into caldera fill in the southwestern part of the caldera. Field and petrographic studies indicate that heat from the pluton or from deeper residual Caetano magma caused a large hydrothermal system to form that altered both the pluton and intracaldera Caetano Tuff throughout the western half of the caldera. ASTER-based mineral mapping shows that the most intense alteration, including local alunite, developed primarily along the southwestern and western caldera margins. Alteration was most intense in the Redrock Canyon intrusion itself and in the upper unit of the Caetano Tuff, which is a sequence of thin ash flows interlayered with finely bedded sedimentary rocks and mesobreccia. Hydrothermally altered intracaldera tuff was domed and crosscut by the unaltered 33.78 ± 0.05 Ma Carico Lake pluton, indicating that caldera formation, magma resurgence, pluton emplacement, and hydrothermal activity all occurred in less than about 100 ka.

Nevada↗

USGS2009

Discussion on remote sensing for aquatic monitoring

The special session on Remote Sensing for Aquatic Resource Monitoring concluded with an expert panel discussion. Panel members were Jennifer Bountry (hydraulic engineer, Bureau of Reclamation), Mimi D’Iorio (GIS analyst and database manager, National Oceanic and Atmospheric Administration), Russ Faux (president, Watershed Sciences, Inc.), Steve Lanigan (team leader, Aquatic and Riparian Effectiveness Monitoring Program, U.S. Forest Service), and Amar Nayegandhi (computer scientist, Jacobs Technology, contracted to U.S. Geological Survey). The panel was moderated by Ralph Haugerud (geologist, U.S. Geological Survey) and there were significant contributions from the audience. The dialogue is summarized below in question and answer format. This summary is followed by discussion of what we learned in the course of the special session and identification of some next steps for the Pacific Northwest aquatic monitoring community.

PNAMP Special Publication↗

USGS2021

Uncertainty in remote sensing of streams using noncontact radars

Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.

Arizona, Colorado, Oklahoma, Texas↗

USGS2018

Crisis remote sensing during the 2018 lower East Rift Zone eruption of Kīlauea Volcano

Kīlauea Volcano, Hawai‘i, is renowned as one of the most active and closely monitored volcanoes on Earth. Scores of seismometers and deformation sensors form an array across the volcano to detect subsurface magmatic activity, and ground observers track eruptions on the surface. In addition to this dense ground-based monitoring, remote sensing – both airborne and spaceborne – has become a backbone tool at the U.S. Geological Survey’s (USGS) Hawaiian Volcano Observatory (HVO) for mapping activity and forecasting volcanic hazards. Remote observations were critical components of HVO’s response to the historically unprecedented 2018 eruption from Kīlauea’s lower East Rift Zone (ERZ); here we describe some of the many types of remote sensing tools that were utilized, and the specific monitoring roles they filled.

Hawaii↗