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Results for “Applied Optics”

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USGS2012

The high-pressure electronic structure of magnesiowustite (Mg, Fe)O: applications to the physics and chemistry of the lower mantle

The electronic structure of magnesiowustite is investigated using self-consistent field X α scattered wave (SCF- X α-SW) molecular orbital calculations on (FeO 6 ) 10− and (FeMg 12 O 14 ) 2− clusters. Calculated one-electron transition energies are used to interpret the optical spectrum of (Mg, Fe)O. The results are applied to the electrical and thermal conductivity of the lower mantle. The spin pairing of Fe 2+ and the effect of pressure on bonding in magnesiowustite, with some inferences regarding the incorporation of oxygen in the outer core, is also addressed. The approach used here appears to give a reliable description of the energy and pressure dependence of the spin-allowed 5 T 2 g → 5 E g ligand field transition and the spin-pairing transition of Fe 2+ in (Mg, Fe)O. However, the oxygen to metal charge transfer transitions in (Mg, Fe)O are not as reliably determined insofar as the p - d band gap varies with cluster size and the energies of the charge transfer states cannot be found without including configurational interaction. Nevertheless, it is argued that the charge transfer transitions that are intrinsic to (Fe, Mg)O are of a sufficiently high energy to be irrelevant to the electrical and thermal conductivity of the lower mantle. This is especially true if Fe 2+ adopts the low-spin configuration. The geophysically significant properties of (Fe, Mg)O probably result from defect Fe 3+ .

Journal of Geophysical Research↗

USGS2021

Using bottom trawls to monitor subsurface water clarity in marine ecosystems

Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, K d , and optical depth, ζ – apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted K d from tags to the multi-sensor K d (490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag K d was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in K d occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.

Alaska↗

USGS2003

Continuous measurement of suspended-sediment discharge in rivers by use of optical backscatterance sensors

Optical sensors have been used to measure turbidity and suspended-sediment concentration by many marine and estuarine studies, and optical sensors can provide automated, continuous time series of suspended-sediment concentration and discharge in rivers. Three potential problems with using optical sensors are biological fouling, particle-size variability, and particle-reflectivity variability. Despite varying particle size, output from an optical backscatterance sensor in the Sacramento River at Freeport, California, USA, was calibrated successfully to discharge-weighted, cross-sectionally averaged suspended-sediment concentration, which was measured with the equal discharge-, or width-increment, methods and an isokinetic sampler. A correction for sensor drift was applied to the 3-year time series. However, the calibration of an optical backscatterance sensor used in the Colorado River at Cisco, Utah, USA, was affected by particle-size variability. The adjusted time series at Freeport was used to calculate hourly suspended-sediment discharge that compared well with daily values from a sediment station at Freeport. The appropriateness of using optical sensors in rivers should be evaluated on a site-specific basis and measurement objectives, potential particle size effects, and potential fouling should be considered.

California, Utah↗

USGS2023

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Alaska, California, Delaware, Florida, Maine, Mary↗

USGS2024

Physics-based satellite-derived bathymetry (SDB) using Landsat OLI images

The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.

Remote Sensing↗

USGS2021

A review of spatially resolved techniques and applications of organic petrography in shale petroleum systems

This review examines new techniques and applications of organic petrography in source-rock reservoir petroleum systems that have occurred along with development of the global ‘shale revolution’ in energy resources. The review is limited to techniques and instrumentation that provide spatially resolved information, typically at or below microscales, for dispersed organic matter occurring in situ in samples of shale and mudrock. A brief summary of ion beam sample preparation is followed by discussion of the most common analytical techniques and applications. Advantages and limitations of each technique, including requisite sample preparation, types of information generated [e.g., molecular or elemental (isotopic) abundance], sensitivity, and resolution are discussed. In a few cases, techniques not yet applied to organic petrology of shale or mudrock are described (e.g., X-ray photoelectron spectroscopy imaging), in anticipation of near-term future application. The most common in situ techniques applied for characterization of organic matter in shale and mudrock include optical (including fluorescence) and electron microscopies, Raman, fluorescence, and infrared spectroscopies, and surficial measurements via force microscopy. Techniques that show growing application to organic petrography of shale include tip-enhanced photothermal infrared spectroscopy, mass spectrometry imaging, and synchrotron-based spectroscopies, among others. It is anticipated that the future of dispersed organic matter petrography will hold continued development of integrated instrument techniques (e.g., simultaneous or sequential correlative microscopies and/or spectroscopies of the same location), increased instrumental resolution, increased use of multiscale and multimodal organic petrography investigations, and three-dimensional imaging and chemical speciation mapping applications via multiple analytical approaches.

International Journal of Coal Geology↗

USGS2003

Optical dating of tufa via in situ aeolian sand grains: A case example from the Southern High Plains, USA

Precipitated carbonates (commonly termed tufas or travertines) maybe of considerable utility for palaeoenvironmental reconstruction. Their potential, however, for such reconstruction is commonly limited by difficulties associated with their absolute age control. Attempts to date such deposits via uranium series techniques have been complicated by their chemically open behaviour. Here we describe an alternative approach to date tufa deposits associated with ephemeral saline lake basins from the Southern High Plains, USA. We have optically dated sand grains of a mixed aeolian/fluvial (spring fed) origin as the integrating dosimeter. We assume that the grains are fully resetting prior to their incorporation into the tufa deposits and employ a time-dependent disequilibrium dosimetric model to account for the build-up of uranium series daughter products. The approach was applied to a set of four samples with known stratigraphic association. We obtained stratigraphically sensible optical ages ranging from 78??8 to 56??4ka. These data are consistent with existing palaeoenvironmental models of regional recharge. ?? 2003 Elsevier Science Ltd. All rights reserved.

Conference Paper↗

USGS2007

Loess sedimentation in Tibet: provenance, processes, and link with Quaternary glaciations

Well-preserved loess deposits are found on the foothills of mountains along the middle reaches of the Yarlung Zangbo River in southern Tibet. Optically stimulated luminescence (OSL) dating is used to determine loess ages by applying the single-aliquot regeneration technique. Geochemical, mineralogical, and granulometric measurements were carried out to allow a comparison between loess from Tibet and the Chinese Loess Plateau. Our results demonstrate that (i) the loess deposits have a basal age of 13-11 ka, suggesting they accumulated after the last deglaciation, (ii) loess in southern Tibet has a "glacial" origin, resulting from eolian sorting of glaciofluvial outwash deposits from braided river channels or alluvial fans by local near-surface winds, and (iii) the present loess in the interior of Tibet has accumulated since the last deglaciation when increased monsoonal circulation provided an increased vegetation cover that was sufficient for trapping eolian silt. The lack of full-glacial loess is either due to minimal vegetation cover or possibly due to the erosion of loess as glaciofluvial outwash during the beginning of each interglacial. Such processes would have been repeated during each glacial-interglacial cycle of the Quaternary. ?? 2007 Elsevier Ltd. All rights reserved.

Quaternary Science Reviews↗

USGS2018

LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface ( k ). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

Inland Waters↗

USGS2004

Biogeochemical transformation of Fe minerals in a petroleum-contaminated aquifer

The Bemidji aquifer in Minnesota, USA is a well-studied site of subsurface petroleum contamination. The site contains an anoxic groundwater plume where soluble petroleum constituents serve as an energy source for a region of methanogenesis near the source and bacterial Fe(III) reduction further down gradient. Methanogenesis apparently begins when bioavailable Fe(III) is exhausted within the sediment. Past studies indicate that Geobacter species and Geothrix fermentens -like organisms are the primary dissimilatory Fe-reducing bacteria at this site. The Fe mineralogy of the pristine aquifer sediments and samples from the methanogenic (source) and Fe(III) reducing zones were characterized in this study to identify microbiologic changes to Fe valence and mineral distribution, and to identify whether new biogenic mineral phases had formed. Methods applied included X-ray diffraction; X-ray fluorescence (XRF); and chemical extraction; optical, transmission, and scanning electron microscopy; and Mössbauer spectroscopy. All of the sediments were low in total Fe content (≈ 1%) and exhibited complex Fe-mineralogy. The bulk pristine sediment and its sand, silt, and clay-sized fractions were studied in detail. The pristine sediments contained Fe(II) and Fe(III) mineral phases. Ferrous iron represented approximately 50% of Fe TOT . The relative Fe(II) concentration increased in the sand fraction, and its primary mineralogic residence was clinochlore with minor concentrations found as a ferroan calcite grain cement in carbonate lithic fragments. Fe(III) existed in silicates (epidote, clinochlore, muscovite) and Fe(III) oxides of detrital and authigenic origin. The detrital Fe(III) oxides included hematite and goethite in the form of mm-sized nodular concretions and smaller-sized dispersed crystallites, and euhedral magnetite grains. Authigenic Fe(III) oxides increased in concentration with decreasing particle size through the silt and clay fraction. Chemical extraction and Mössbauer analysis indicated that this was a ferrihydrite like-phase. Quantitative mineralogic and Fe(II/III) ratio comparisons between the pristine and contaminated sediments were not possible because of textural differences. However, comparisons between the texturally-similar source (where bioavailable Fe(III) had been exhausted) and Fe(III) reducing zone sediments (where bioavailable Fe(III) remained) indicated that dispersed detrital, crystalline Fe(III) oxides and a portion of the authigenic, poorly crystalline Fe(III) oxide fraction had been depleted from the source zone sediment by microbiologic activity. Little or no effect of microbiologic activity was observed on silicate Fe(III). The presence of residual “ferrihydrite” in the most bioreduced, anoxic plume sediment (source) implied that a portion of the authigenic Fe(III) oxides were biologically inaccessible in weathered, lithic fragment interiors. Little evidence was found for the modern biogenesis of authigenic ferrous-containing mineral phases, perhaps with the exception of thin siderite or ferroan calcite surface precipitates on carbonate lithic fragments within source zone sediments.

Geochimica et Cosmochimica Acta↗

USGS2016

Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea

A time series of organic carbon export from Gulf of Maine (GoM) watersheds was compared to a time series of biological, chemical, bio-optical, and hydrographic properties, measured across the GoM between Yarmouth, NS, Canada, and Portland, ME, U.S. Optical proxies were used to quantify the dissolved organic carbon (DOC) and particulate organic carbon in the GoM. The Load Estimator regression model applied to river discharge data demonstrated that riverine DOC export (and its decadal variance) has increased over the last 80 years. Several extraordinarily wet years (2006–2010) resulted in a massive pulse of chromophoric dissolved organic matter (CDOM; proxy for DOC) into the western GoM along with unidentified optically scattering material (<0.2 μm diameter). A survey of DOC in the GoM and Scotian Shelf showed the strong influence of the Gulf of Saint Lawrence on the DOC that enters the GoM. A deep plume of CDOM-rich water was observed near the coast of Maine which decreased in concentration eastward. The Forel-Ule color scale was derived and compared to the same measurements made in 1912–1913 by Henry Bigelow. Results show that the GoM has yellowed in the last century, particularly in the region of the extension of the Eastern Maine Coastal Current. Time lags between DOC discharge and its appearance in the GoM increased with distance from the river mouths. Algae were also a significant source of DOC but not CDOM. Gulf-wide algal primary production has decreased. Increases in precipitation and DOC discharge to the GoM are predicted over the next century.

Gulf of Maine↗

USGS2020

Pacific Continental Shelf Environmental Assessment (PaCSEA): Characterization of Seasonal Water Masses within the Northern California Current System Using Airborne Remote Sensing off Northern California, Oregon, and Washington, 2011–2012

Here, we use ocean color measurements (Figure 1) and sea surface temperature (SST) data collected using sensors mounted on low-flying aircraft to characterize NCCS water masses and identify patterns among seasons and between years. To accomplish this, we applied k-means clustering to measured and derived ecologically-relevant physical and bio-optical variables (SST, Chla, absorbance by colored dissolved organic matter [aCDOM], proxy particle load). These classifications will be used in the future to evaluate species habitat distributions in the NCCS.

California, Oregon, Washington↗

USGS2024

Structure and morphology of chars and activated carbons obtained from thermal treatment of coal and biomass origin materials, including their wastes: Results from the ICCP Microscopy of Carbon Materials Working Group

This paper describes the evaluation of petrographic textures in char and activated carbon derived from coal, coal by-products and biomass, formed during carbonization and activation processes. This work represents the results of interlaboratory exercises from 2016 to 2022 of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology. The interlaboratory exercises were run on photomicrograph samples. For textural characterization of carbon materials, the existing American Society for Testing and Materials (ASTM) classification system for metallurgical coke was applied. Morphological differences were evaluated in 29 carbon material types, including 22 char samples, and 7 activated carbon (AC) samples obtained experimentally using conventional direct/indirect and microwave heating technologies. This approach gives an extended view on the identification of microporous carbons, and how a certain heat treatment develops a certain optical texture and structure in a raw material. The requested evaluation of carbon materials was related to their porosity, origin, extent, and characteristics, which are particular to each carbon material type. Because carbon matrices can form a wide range of optical textures during heat treatment it is important to demonstrate which carbon occurrences will have a crucial role in industrial applications dominated by adsorption phenomena. The interlaboratory exercises included 17 participants from 14 laboratories. Four sets of digital black and white and colour photomicrographs were distributed, which in total comprised 184 fields of different types of carbon material. The results were evaluated based on four levels: (i) optical texture (isotropic/anisotropic), (ii) optical type and size (punctiform, mosaic, fiber, ribbon, domain), iii) morphology (porous, non-porous/massive), and (iv) particle origin (precursor type). The statistical method applied to evaluate the results was based on “raw agreement indices”. Comparative analyses of the average values of the level of overall agreement showed homogeneity in the results, the mean value was 89%, with a minimum value of 87% and a maximum value of 91% for those who participated in at least three out of four exercises.

International Journal of Coal Geology↗

USGS2020

Reconstructing the velocity and deformation of a rapid landslide using multiview video

Noncontact measurements of spatially varied ground surface deformation during landslide motion can provide important constraints on landslide mechanics. Here, we present and test a new method for extracting measurements of rapid landslide surface displacement and velocity (accelerations of approximately 1 m/s 2 ) using sequences of stereo images obtained from a pair of inexpensive, stationary 4K video cameras with nominal frame rates of 29.97 Hz. The method combines elements of Structure from Motion with those of optical flow to extract data on 3‐D evolution of the ground surface during slope failure. We apply the method to an experiment at the U.S. Geological Survey debris‐flow flume in which a high‐speed, liquefying landslide was triggered by gradually adding water to a 6‐m 3 prism of loosely packed sediment on a 31° slope. Strip‐scanning lidar measurements made during the experiment corroborate our video‐based measurements, but the latter encompassed the entire landslide surface and were much lower in cost. Our video‐based measurements enabled computation of depth‐integrated landslide dilation/contraction rates. The range of computed rates was within the ranges inferred from independent measurements of evolving pore water pressures and reasonable estimates of the hydraulic permeability of the sediment. Dilation and contraction rates play a crucial role in landslide mechanics. The dilation and contraction we observe contradict the incompressible flow assumption used in many studies that have employed noncontact methods to infer landslide properties.

Oregon↗

USGS2018

Sampling strategies to improve passive optical remote sensing of river bathymetry

Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth ($d_{max}$) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that $d_{max}$ was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of $d_{max}$ consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than $d_{max}$, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Remote Sensing↗

USGS2010

Bayesian estimation of weak material dispersion: Theory and experiment

This work considers the estimation of dispersion in materials via an interferometric technique. At its core, the problem involves extracting the quadratic variation in phase over a range of wavelengths based on measured optical intensity. The estimation problem becomes extremely difficult for weakly dispersive materials where the quadratic nonlinearity is very small relative to the uncertainty inherent in experiment. This work provides a means of estimating dispersion in the face of such uncertainty. Specifically, we use a Markov Chain Monte Carlo implementation of Bayesian analysis to provide both the dispersion estimate and the associated confidence interval. The interplay between various system parameters and the size of the resulting confidence interval is discussed. The approach is then applied to several different experimental samples.

Optics Express↗

USGS2019

Optical wave gauging using deep neural networks

We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.

California, North Carolina↗

USGS2026

Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations

Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short-term dynamics requires high spatiotemporal resolution. Here, we applied a globally-applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel-2 MSI imagery for chlorophyll- a (Chl- a ) estimation and validated its strong performance across the northern Gulf coast of America (GoA) estuaries, including Galveston Bay (TX), Barataria-Terrebonne Estuary (LA), Apalachicola Estuary (FL) and Tampa Bay (FL). The test set showed strong performance (MAE: 1.44 mg m −3 ; RMSE: 17.7 mg m −3 ; slope: 0.86; median symmetric accuracy: 30.33%). The validated VAE was then applied to 76 Sentinel-2 MSI images to assess phytoplankton biomass responses to hurricanes Harvey (2017), Michael (2018), Ida (2021), Francine (2024), Helene (2024), and Milton (2024) in the GoA estuaries. Results showed that hurricane disturbances on Chl- a typically lasted 3–5 weeks. Estuarine waters west (left) of hurricane tracks showed a rapid decline in Chl- a (∼5 mg m −3 ) due to elevated turbidity from heavy rainfall, and wind-driven flushing in the estuary, followed by a rebound over about two weeks, with Chl- a increasing approximately 10–15 mg m −3 above pre-storm levels. In contrast, right-side waters showed a slower response, likely from oligotrophic seawater intrusion driven by the hurricane's counterclockwise rotation. Post-storm observations showed increased freshwater phytoplankton like chlorophytes and cyanobacteria dominating estuaries, while shelf-waters exhibited elevated dinoflagellates (e.g., Karenia brevis bloom after Hurricane Milton). These results highlight the spatial heterogeneity of hurricane impacts on estuarine phytoplankton dynamics, which may trigger cascading effects on biogeochemical cycling and food webs, potentially prolonging ecosystem recovery.

Alabama, Florida, Louisiana, Mississippi, Texas↗