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USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗

USGS2011

Nanoparticles formed from bacterial oxyanion reduction of toxic Group 15 and 16 metalloids

This chapter presents some examples of nanoparticles formed by only a few microbial species that are cultivated in only a handful of laboratories worldwide. The investigations so far have just scratched the surface of the potential of the natural world to yield bionanomineral producers. While future research should involve screening surveys of the prokaryotes for this biomineralizing phenomenon, more detailed investigations are justified. The chapter discusses microbial Interaction with Group 15 and 16 Toxic Metalloids. The toxicity of the metalloids Se, Te, and As is due to the disruption of thiol intracellular biochemistry through the formation of stable, long-lived sulfur complexes. Selenium oxyanion reduction occurs in a wide range of microbes, including representatives of the Wolinella , Pseudomonas , Sulfurospirillum , Enterobacter , Thaurea , Bacillus , Shewanella , and Citrobacter genera. Technological applications of Se(0) and Te(0) nanoparticles include their use in photocopiers, microelectronic circuits, and solar cells as a result of their photo-optical and semiconducting physical properties. Nonetheless, once novel Se, Te, and As bionanoparticles are identified as having significant technical applications, applied research into their practical commercial production will without doubt ensue rapidly.

Book chapter↗

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↗

USGS2019

Vertical coseismic offsets from differential high-resolution stereogrammetric DSMs: The 2013 Baluchistan, Pakistan earthquake

The recent proliferation of high-resolution (< 3-m spatial resolution) digital topography datasets opens a spectrum of geodetic applications in differential topography, including the quantification of coseismic vertical displacement fields. Most investigations of coseismic vertical displacements to date rely, in part, on pre- or post-event lidar surveys that are intractable or non-existent in many locales. Stereogrammetric digital surface models (DSMs) derived from high-resolution satellite optical imagery provide a new avenue for the retrieval of spatially-dense vertical coseismic displacements on a global scale. In this study, we generated 2-m resolution pre- and post-seismic DSMs from satellite optical imagery spanning the 2013 Mw7.7 Baluchistan strike-slip earthquake that occurred on the Hoshab fault in southern Pakistan. We applied the Iterative Closest Point algorithm to the DSMs to quantify the coseismic vertical displacement field at a spatial resolution of 10-30 m and to generate 3D coseismic strain tensors. We found that across-fault vertical offsets alternated between uplift and subsidence and varied between ~1-3 m in a non-systematic manner along the Hoshab fault. We show that the pre-existing topography and near-fault geomorphology are variably consistent and inconsistent with the displacement kinematics of the 2013 earthquake, and we argue that these relationships highlight varied slip sense history along the Hoshab fault. Notably, topography along the southern extents of the Hoshab fault requires different surface displacement kinematics than occurred in the 2013 earthquake, suggesting that the Hoshab fault accommodates varying senses of slip (bimodal slip) through time.

Baluchistan Province↗

USGS2025

Earthquake magnitude and source parameter estimation with a distributed acoustic sensing dataset in the Gorda subduction zone

Distributed acoustic sensing (DAS) systems offer a cost‐effective way to create large‐scale strainmeter arrays for seismological applications using fiber‐optic cables. DAS‐based strain measurements are known to be influenced by various factors, bringing into question their general reliability for accurate earthquake characterization. A 15‐km‐long DAS deployment in northern California was operational within 3 days of the 2022 M w 6.4 Ferndale earthquake and ran continuously throughout the aftershock sequence. We utilize these aftershock data to validate DAS‐based strain measurements in two ways. We first test the accuracy of DAS‐based magnitude estimates from peak dynamic strains by comparing them with magnitude and attenuation scaling relations derived independently from traditional borehole strainmeter (BSM) data. We demonstrate that DAS‐based magnitudes are comparable to BSM‐based magnitudes when corrections for variations in site response along the fiber‐optic cable are properly made. Magnitude errors are spatially correlated, potentially because of factors such as finite‐fault effects (e.g., stress drop) or more complex, unmodeled path attenuation or because of wave propagation effects in heterogeneous media. We then apply more advanced source characterization methodology to the DAS data using a time‐domain empirical Green’s function (EGF) deconvolution approach to measure details of the moment rate history. The EGF approach using DAS data depends on careful treatment of distorting factors such as anthropogenic sources of noise and optical phase wrapping but successfully isolates source spectra for moderate‐magnitude earthquakes: source spectral ratios obtained from DAS data, broadband seismometer data, and BSM data in the same region show consistent results, revealing differences in directivity and spectral shape among earthquakes. Although further research is needed to refine source‐time‐function estimation techniques for DAS data, particularly for larger magnitude events, these case studies demonstrate the clear potential of DAS for earthquake source characterization.

California↗

USGS2020

High-resolution imaging of hydrothermal heat flux using optical and thermal Structure-from-Motion photogrammetry

Quantifying hydrothermal heat flux at meter-scale resolution over N0.25 km2 is required to bridge in-situ heat flux and satellite-based measurements. We advance a methodology that blends ground-based daytime optical and nighttime thermal infrared (TIR) imagery using Structure-from-Motion photogrammetry to map radiant hydrothermal heat flux over these scales at sites with low signal-to-noise ratios that would otherwise be difficult to characterize using, for example, unmanned aerial systems. The improved method uses a computerized telescopic mount to relocate and align daytime optical acquisitions with nighttime TIR imagery, thereby enabling TIR acquisition from multiple camera orientations positioned throughout a study area. This facilitates mapping of thermal features at sites of varying size and complexity and helps to ameliorate topographic occlusion effects and geometric distortions that can bias radiant hydrothermal heat flux estimates derived from the resulting orthorectified thermal maps. We assessed detection thresholds of this method at three sites across central California, which range in size, topography, and heat flux conditions. We found that blending of optical and thermal acquisitions successfully detected anomalous heat flux, even in cases where temperatures were only slightly greater than background. This approach might be applied to a variety of volcanic and hydrothermal systems to quantify the spatial distribution of heat flux, and how this may relate to factors such as the distribution of ground fractures and lava flow rheology.

Journal of Volcanology and Geothermal Research↗

USGS1987

Image restoration techniques as applied to Landsat MSS and TM data

Two factors are primarily responsible for the loss of image sharpness in processing digital Landsat images. The first factor is inherent in the data because the sensor's optics and electronics, along with other sensor elements, blur and smear the data. Digital image restoration can be used to reduce this degradation. The second factor, which further degrades by blurring or aliasing, is the resampling performed during geometric correction. An image restoration procedure, when used in place of typical resampled techniques, reduces sensor degradation without introducing the artifacts associated with resampling. The EROS Data Center (EDC) has implemented the restoration proceed for Landsat multispectral scanner (MSS) and thematic mapper (TM) data. This capability, developed at the University of Arizona by Dr. Robert Schowengerdt and Lynette Wood, combines restoration and resampling in a single step to produce geometrically corrected MSS and TM imagery. As with resampling, restoration demands a tradeoff be made between aliasing, which occurs when attempting to extract maximum sharpness from an image, and blurring, which reduces the aliasing problem but sacrifices image sharpness. The restoration procedure used at EDC minimizes these artifacts by being adaptive, tailoring the tradeoff to be optimal for individual images.

Pecora XI Symposium↗

USGS2026

Rapid seismic and infrasound assessment of large landslides: A case study from Denali National Park and Preserve (Alaska)

Large, rapid landslides are a global hazard that can occur in remote, mountainous areas. Eyewitness reports of landslides and satellite imagery can often be limited or delayed, particularly during inclement weather. However, landslide-generated seismic and infrasound (low-frequency atmospheric sound) waves can be remotely detected in near real-time. This information can significantly expedite characterization and possible landslide response activities. Here, we highlight these capabilities using a > 4 million m³ ice–rock avalanche in Denali National Park and Preserve (Alaska). This event was detected via a landslide-specific seismic location and volume estimation algorithm deployed in Alaska, and — notably — by standard earthquake monitoring systems. Following rapid detection of this event, we combined its seismic and infrasound dataset with optical, synthetic aperture radar, and oblique aerial imagery, multitemporal digital elevation models, and a numerical flow model to reconstruct its failure timeline and dynamics. We apply array processing to infrasound signals traveling > 250 km and find that two precursory events occurred minutes prior to the main failure. We use long-period seismic signals to infer the force exerted by the landslide on the Earth and constrain the rheological parameters of our numerical flow simulation with this result and deposit morphology. The main failure produced a steeply-dipping impulsive initial downward force and reached speeds exceeding 60 m/s. This impulsive force generated relatively strong seismic body waves, which contributed to the earthquake system detection. This large, remote Alaska landslide underscores the key value of seismic and infrasound analysis for rapid landslide assessment and motivates efforts to further operationalize these approaches.

Alaska↗

USGS2005

Outgassing models for Landsat-4 thematic mapper short wave infrared bands

Detector responses to the Internal Calibrator (IC) pulses in the Landsat-4 Thematic Mapper (TM) have been observed to follow an oscillatory behavior. This phenomenon is present only in the Short Wave Infrared (SWIR) bands and has been observed throughout the lifetime of the instrument, which was launched in July 1982 and imaged the Earth's surface until late 1993. These periodic changes in amplitude, which can be as large as 7.5 percent, are known as outgassing effects and are believed to be due to optical interference caused by a gradual buildup of an ice-like material on the window of the cryogenically cooled dewar containing the SWIR detectors. Similar outgassing effects in the Landsat-5 TM have been characterized using an optical thin-film model that relates detector behavior to the ice film growth rate, which was found to gradually decrease with time. A similar approach, which takes into consideration the different operational history of the instrument, has been applied in this study to three closely sampled data sets acquired throughout the lifetime of the Landsat-4 TM. Although Landsat-4 and Landsat-5 Thematic Mappers are essentially identical instruments, data generated from analyses of outgassing effects indicate subtle, but important, differences between the two. The estimated lifetime model could improve radiometric accuracy by as much as five percent.

Conference Paper↗

USGS2022

Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: II. Chemofacies from hierarchical cluster analysis

Hierarchical cluster analysis (HCA) was applied to a geochemical dataset representing the Eocene Green River Formation in the Piceance Basin of Colorado to identify chemofacies in core and outcrop samples from the basin margin and the basin center. The input dataset consisted of inductively coupled plasma optical emission spectroscopy and mass spectrometry and total organic carbon (TOC) content analyses of 186 basin margin outcrop samples and 190 basin center core samples discussed in Part 1 of this study (this volume). TOC values and twenty-five major and trace elements were used as variables to define statistical clusters of samples for the overall dataset, for the two basin center cores, and for each separate core or outcrop dataset by HCA applying Euclidean distance and Ward’s method algorithms. For each dataset, five cluster-defined chemofacies were identified. The chemofacies for each dataset show chemical affinities with five informally defined rock types– mudstone, marlstone, carbonate-rich mudstone, siliciclastic-rich mudstone/siltstone/sandstone, and Na-rich (saline) mudstone, with each showing variations in TOC content and abundance of redox sensitive minor and trace elements. A close relationship between enrichment of redox sensitive elements, particularly As and Mo, and TOC is identified in the basin center. Whereas enrichment factors (relative to average shale) are relatively low for many Period IV (PIV) transition metals, as discussed in Part 1 of this study, their consistent coherence in enrichment or depletion in HCA-defined chemofacies demonstrates the expected relationship between redox state and organic richness. Enrichment in PIV transition metals also shows a correlation to enrichment in elements with affinity for siliciclastic sediment. Enrichment/depletion among several groups of redox indicators is not everywhere consistent, with some chemofacies showing, for example, enrichment of PIV transition metals and depletion of sulfur and arsenic. Early timing of saline conditions in the basin margin is clearly displayed in the chemofacies log display, consistent with observations based on geochemical interpretations of concentrations and elemental ratios discussed in Part 1. Overall, the chemofacies are consistent with major mineralogical units and lake history stages defined in previous work, but provide more detail on the fluctuations in lake chemistry that occurred during deposition of Green River oil shale in the Piceance Basin.

Colorado↗

USGS1980

The influences of land use and land cover on climate; an analysis of the Washington-Baltimore area that couples remote sensing with numerical simulation

The Sun drives the atmospheric heat engine by warming the terrestrial surface which in turn warms the atmosphere above. Climate, therefore, is significantly controlled by complex interaction of energy flows near and at the terrestrial surface. When man alters this delicate energy balance by his use of the land, he may alter his climatic environment as well. Land use climatology has emerged as a discipline in which these energy interactions are studied; first, by viewing the spatial distributions of their surface manifestations, and second, by analyzing the energy exchange processes involved. Two new tools for accomplishing this study are presented: one that can interpret surface energy exchange processes from space, and another that can simulate the complex of energy transfers by a numerical simulation model. Use of a satellite-borne multispectral scanner as an imaging radiometer was made feasible by devising a gray-window model that corrects measurements made in space for the effects of the atmosphere in the optical path. The simulation model is a combination of mathematical models of energy transfer processes at or near the surface. Integration of these two analytical approaches was applied to the Washington-Baltimore area to coincide with the August 5, 1973, Skylab 3 overpass which provided data for constructing maps of the energy characteristics of the Earth's surface. The use of the two techniques provides insights into the relationship of climate to land use and land cover and in predicting alterations of climate that may result from alterations of the land surface.

Professional Paper↗

USGS2024

Tracking surface changes caused by volcanic activity

Introduction Dynamic volcanic landscapes produce various changes at the surface of volcanic edifices. For example, rising magma can induce thermal emissions, formation of ground cracks, and variations in glacier and edifice morphology; volcanic deposits from eruptions can transform the land surface with tephra fall, pyroclastic flows, lava flows and domes, and lahars; and geomorphic changes from landslides and lahars can occur in the absence of unrest or eruption. The best way to detect these changes is with imagery obtained via satellite, aircraft (including unoccupied aircraft systems, or UAS), and ground-based imaging. Rapid advances in imaging technologies have been leveraged by the U.S. Geological Survey (USGS) Volcano Hazards Program to improve the ability to monitor volcanoes. To this end, the guidance outlined here provides a framework for tracking volcanic unrest and the emplacement and evolution of volcanic deposits, further elucidating the processes associated with volcanic eruptions. The techniques currently used include (1) various telemetered and non-telemetered cameras, (2) high-resolution ground-based optical (visible to short-wave infrared wavelengths) and thermal infrared photography, (3) satellite and airborne thermal, optical, and synthetic aperture radar (SAR) imagery, and (4) light detection and ranging (lidar) surveys from airborne and ground-based platforms. Given that similar or overlapping techniques are applied to meet the capabilities listed in this chapter, we first provide an overview of remote sensing techniques. The use of UAS in monitoring surface change is briefly mentioned in this chapter and described in more detail in the dedicated UAS chapter ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–L, 5 p., https://doi.org/10.3133/sir20245062l.">chapter L , this volume; Diefenbach, 2024).

Scientific Investigations Report↗

USGS2017

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

Massachusetts↗

USGS2015

Microscopical characterization of carbon materials derived from coal and petroleum and their interaction phenomena in making steel electrodes, anodes and cathode blocks for the Microscopy of Carbon Materials Working Group of the ICCP

This paper describes the evaluation of petrographic textures representing the structural organization of the organic matter derived from coal and petroleum and their interaction phenomena in the making of steel electrodes, anodes and cathode blocks. This work represents the results of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology between the years 2009 and 2013. The round robin exercises were run on photomicrograph samples. For textural characterization of carbon materials the existing ASTM classification system for metallurgical coke was applied. These round robin exercises involved 15 active participants from 12 laboratories who were asked to assess the coal and petroleum based carbons and to identify the morphological differences, as optical texture (isotropic/anisotropic), optical type (punctiform, mosaic, fibre, ribbon, domain), and size. Four sets of digital black and white microphotographs comprising 151 photos containing 372 fields of different types of organic matter were examined. Based on the unique ability of carbon to form a wide range of textures, the results showed an increased number of carbon occurrences which have crucial role in the chosen industrial applications. The statistical method used to evaluate the results was based on the “raw agreement indices”. It gave a new and original view on the analysts' opinion by not only counting the correct answers, but also all of the knowledge and experience of the participants. Comparative analyses of the average values of the level of overall agreement performed by each analyst in the exercises during 2009–2013 showed a great homogeneity in the results, the mean value being 90.36%, with a minimum value of 83% and a maximum value of 95%.

International Journal of Coal Geology↗

USGS2023

Rapid pre-explosion increase in dome extrusion rate at La Soufrière, St. Vincent quantified from synthetic aperture radar backscatter

The extrusion rate of a lava dome is a critical parameter for monitoring silicic eruptions and forecasting their development. Satellite radar backscatter can provide unique information about dome growth during a volcanic eruption when other datasets (e.g., optical, thermal, ground-based measurements, etc.) may be limited. Here, we present an approach for estimating volcanic topography from individual backscatter images. Using data from multiple SAR sensors we apply the method to the dome growth during the 2021 eruption at La Soufrière, St. Vincent. We measure an average extrusion rate of 1.8 m 3 s −1 between December 2020 and March 2021 before an acceleration in extrusion rate to 17.5 m 3 s −1 in the 2 days prior to the explosive eruption on 9 April 2021. We estimate a final dome volume of 19.4 million m 3 , extrapolated from the SAR sensors, with approximately 15% of the total extruded volume emplaced in the last 2 days. A possible explanation for the acceleration in extrusion rate could be the combined emptying of a conduit and reservoir of older material before the ascent of gas-rich magma in April 2021.

La Soufrière, Saint Vincent↗

USGS2022

Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption

Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

Alaska↗

USGS2022

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll- a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon , and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella , a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

New York, Oregon, Texas↗

USGS2026

Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber-optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying for earthquake monitoring. Here, we develop a machine learning method to distinguish large (M≥5.4) earthquakes from smaller ones within the first 4 seconds of a strain waveform after a P-wave arrival without determining location. Using ensemble decision tree models trained on borehole strainmeter data (3.5≤M≤7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these findings valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification.

California↗