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USGS2024

Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems

This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

Geophysical Journal International↗

USGS2026

An improved empirical model for predicting postfire debris-flow volume in the western United States

Reliable estimates of debris-flow volume can be used to help predict the magnitude of debris-flow hazards following wildfire in the western United States. In this study, we compiled and used a database of 227 postfire debris-flow volumes that were collected across the western United States to develop a multiple linear regression model for predicting postfire debris-flow volume. We explored 36 predictor variables related to rainfall, terrain, and fire characteristics, and selected the model with the combination of variables that yielded the most accurate predictions of debris-flow volume. We evaluated model performance against the entire volume database, as well as against four subsets of volume data from southern California, the Intermountain West, the Southwest, and regions with limited volume data, such as northern California and Washington. We also compared model performance against 3 existing postfire debris-flow volume models that were developed for use in southern California, the Intermountain West, and the Southwest. We demonstrate that the new volume model performs as well as the regional models in the regions for which they were developed and outperforms existing models when applied to volumes from data-limited regions in the western United States. These results indicate that the debris-flow volume model introduced in this study can be used to improve postfire hazard assessments across the western United States, especially outside of southern California.

Arizona, California, Colorado, New Mexico, Utah, W↗

USGS2024

Limited preservation of strike-slip surface displacement in the geomorphic record

Offset geomorphic markers are commonly used to interpret slip history of strike-slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike-slip surface ruptures ( n = 39), faults with geomorphic evidence of multiple strike-slip earthquakes ( n = 29), and synthetic slip distributions with added noise ( n > 10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo-slip distributions, except in specific cases with low slip variability, high slip-per-event, and semiarid climate. In cases where site-specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on-fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike-slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo-slip distributions.

Journal of Geophysical Research: Solid Earth↗

USGS2023

Slow slip detectability in seafloor pressure records offshore Alaska

In subduction zones worldwide, seafloor pressure data are used to observe tectonic deformation, particularly from megathrust earthquakes and slow slip events (SSEs). However, such measurements are also sensitive to oceanographic circulation-generated pressures over a range of frequencies that conflate with tectonic signals of interest. Using seafloor pressure and temperature data from the Alaska Amphibious Community Seismic Experiment, and sea surface height data from satellite altimetry, we evaluate the efficacy of various seasonal and oceanographic pressure signal proxy corrections and conduct synthetic tests to determine their impact on the timing and amplitude prediction of ramp-like signals typical of SSEs. We find that subtracting out the first mode of the complex empirical orthogonal functions of the pressure records on either the shelf or slope yields signal root-mean-square error (RMS) reductions up to 73% or 80%, respectively. Additional correction with proxies that exploit the depth-dependent spatial coherence of pressure records provides cumulative variance reductions up to 83% and 93%, respectively. Our detectability tests show that the timing and amplitude of synthetic SSE-like ramps can be well constrained for ramp amplitudes ≥4 cm on the shelf and ≥2 cm on the slope, using a fully automated detector. The principal limits on detectability are residual abrupt changes in pressure that occur as part of the transition to and from summer to winter conditions but are not adequately characterized by our seasonal corrections, as well as the inability to properly account for instrumental drift, which is not readily separated from the seasonal signal.

Alaska↗

USGS2026

Reconstructing the Quaternary depositional history using geologic mapping and three-dimensional modeling of the subsurface near Fort Morgan, northeastern Colorado

Centered on Fort Morgan, Colorado, this study is intended to build from previous work by adding a three-dimensional (3D) view of the subsurface to better understand the depositional history of Quaternary deposits. A 1:100,000 scale geologic map was made by combining previous geologic maps, regional soil maps, and recent field investigations. In addition to the geologic mapping, drill hole lithologic data from water wells and oil and gas exploration were compiled and lithologic units simplified to best represent the stratigraphy of the Quaternary deposits. From these subsurface data, a 3D subsurface model was constructed, trimmed at the surface by a digital elevation model, and a bedrock surface foundation gridded from drill hole data was added. The surface of the 3D model was then compared visually to the surficial geologic map. Cross sections were constructed from the 3D model and compared to site-specific drilling that was done as part of this project. Finally, the model was examined in detail to reconstruct the depositional history of the subsurface alluvial and eolian units. Alluvial and fluvial drainage basins exposed in the subsurface have a greater areal extent than the present-day narrow drainages. Older eolian sand in the subsurface tends to be interbedded with loess indicating coeval deposition. Holocene sand, both eroded from bedrock exposed at the surface north of the study area and reworked from the South Platte River, buries most of the interbedded older sand and loess.

Colorado↗

USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗

USGS2026

Color-Shaded Relief Bathymetry of Lake Chelan, Washington

In 2023, the U.S. Geological Survey (USGS) completed a 3-week field operation in Lake Chelan, Washington, the third deepest lake in the conterminous United States, to acquire sediment cores, high-resolution bathymetry data, and chirp sub-bottom profiles. The high-resolution bathymetry displayed in this map publication was collected using a Norbit Winghead i67-curved array multibeam system; raw data were cleaned and gridded (3 by 3 meters [m]) in the Teledyne CARIS software. In some locations where the slopes of Lake Chelan are near-vertical, multibeam returns were sparse, resulting in artifacts resembling holes that appear as grey dots within the color-shaded bathymetric relief. Although the 2023 bathymetric mapping of Lake Chelan resulted in similar morphometric characteristics to previous hydrographic campaigns (table 1; Kendra and Singleton, 1987; Pelletier and others, 1989), these new data provide a much more detailed understanding of the two physiographic basins that comprise Lake Chelan: the deeper (as deep as 445.8 m [fig. 1]; previously reported as 453 m) and narrower (1–2 kilometers [km] wide) Lucerne Basin to the north, and the shallower (as deep as 127.8 m [fig. 1]), and wider (2–3 km wide) Wapato Basin to the south. With this detailed imaging of Lake Chelan, we find pervasive morphological evidence of slope failure and mass-transport deposits (MTDs) in at least 89 locations within the lake and 63 subaqueous deltas (fig. 2). The deeper Lucerne Basin is dominated by steep slopes and numerous subaqueous deltas fed by steep fluvial catchments, whereas the Wapato Basin contains numerous landslide scarps, scars, and debris aprons along the more gently sloping lake margins, and abundant relict kettle structures from the Pleistocene deglaciation. Lake Chelan’s geomorphic, geophysical, and sediment data contain important records of glacial retreat and advance, regional volcanic eruptions, and earthquakes that generate ground motions severe enough to induce landslides recorded in the sediment as MTDs. The epicenter of the 1872 Washington State Earthquake (M 6.5–7.2; Bakun and others, 2002; Brocher and others, 2017; Sherrod and others, 2021) is along the Spencer Canyon Fault, near the southern end of Lake Chelan. Shaking from this earthquake was strong enough to affect local aquifers, induce liquefaction of unconsolidated sediments, produce large seiches (Brocher and others, 2018), and likely cause landslides within Lake Chelan.

Washington↗

USGS1992

Public water-supply systems and water use in Tennessee, 1988

This report summarizes the results of a study conducted by the U.S. Geological Survey, in cooperation with the Tennessee Department of Environment and Conservation (TDEC), Division of Water Supply in 1988. Data gathered during an inventory by the TDEC were collated to determine water use, supply sources, population served, and design and storage capacities of the systems. The inventory was limited to systems that were active on June 30, 1988. Results of a survey of the systems conducted by the Tennessee Department of Health and Environment during 1988 were a primary source of data for this report. Data from computer and manual files maintained by the Tennessee Department of Health and Environment and the U.S. Geological Survey also were used. The Division of Water Supply, TDEC, surveyed 541 public water-supply systems. These systems served 81 percent of the population of the State, or 3.95 million people. The gross per capita use statewide for public-supplied water was 179 gallons per day. Total water withdrawals for public supply increased about 39 percent from 510 million gallons per day (Mgal/d) in 1980, to 708 Mgalld in 1988. During the same period, the population increased about 7 percent. Surface-water withdrawals accounted for 63 percent (446 Mgal/d) of the total water withdrawn in the State. All of these withdrawals occurred in the Tennessee (56 percent or 249 Mgal/d) and the Ohio (44 percent or 197 Mgalld) hydrologic regions. Ground water supplied 262 Mgal/d or 37 percent of the total water withdrawn by public-supply systems statewide. Of that amount, 79 percent, or 208 Mgalld, was used in western Tennessee.

Tennessee↗

USGS2025

Hydrogeologic mapping and three-dimensional geologic modeling of glacial deposits in a multicounty area of southeastern Michigan, northeastern Indiana, and northwestern Ohio

The glacial deposits underlying southeastern Michigan, northeastern Indiana, and northwestern Ohio are a substantial source of water to communities, agriculture, and industry in the region. Previous efforts to characterize aquifer materials in the area cited a need for additional information about the underlying hydrogeologic characteristics and related groundwater availability as well as improved mapping of the extent and properties of the glacial deposits. Recent U.S. Geological Survey multi-State compilations of water-well drilling records have greatly increased access to high-resolution geologic data, particularly in glacial depositional environments. This study by the U.S. Geological Survey, in cooperation with the Ohio Environmental Protection Agency, uses processed data from the State-managed collections of well records to characterize the glacial deposits in the study area using two methods. The first method creates two-dimensional maps of basic hydrogeologic information commonly required for assessments of groundwater availability, including (1) total thickness of glacial deposits, (2) total thickness of coarse-grained deposits, (3) specific-capacity-based transmissivity and hydraulic conductivity, and (4) texture-based estimated equivalent horizontal and vertical hydraulic conductivity and transmissivity. The second method builds a hydrogeologic framework of the complex glacial aquifer through construction of a volumetric geologic model by using three-dimensional kriging. Results of the volumetric model indicate that aquifer materials are primarily concentrated in the western parts of the study area near the Indiana-Ohio border. Coarse-grained sediments are also present as surficial deposits in the north of the study area where intermixing glacial advances created complex distributions of unconsolidated deposits. Two-dimensional maps of hydrogeologic properties support the volumetric model, showing thicknesses of coarse-grained deposits that reach up to 250 feet in the western sections of the study area and progressively thin to near absence in the east. Visualization of the aquifer materials with a volumetric model generally shows a highly discontinuous distribution of coarse- and fine-grained materials, with no clearly defined boundaries to delineate the extent of the aquifer. Comparisons of cross sections derived from the volumetric model with existing published maps support previous near-surface hydrogeologic interpretations while filling gaps where data are sparse, particularly in deeper parts of the aquifer. Both the two-dimensional maps and the volumetric model provide data that can directly inform assessments of groundwater availability, in addition to having future applications to studies of groundwater flow and transport.

Indiana, Michigan, Ohio↗

USGS2024

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

Indiana↗

USGS2026

A review and synthesis of post-wildfire shifts in hydrologic processes and streamflow generation mechanisms

Critical water supply watersheds in the western United States (WUS) are impacted by wildfires, with potential negative effects on water quality and quantity. Scientific understanding is currently insufficient to deliver estimates of wildfire consequences for water quantity that are regionally accurate. Regional variability in the directionality and magnitude of post-wildfire shifts in streamflow generation fuels uncertainty in estimates of wildfire effects on water supply. In this work we provide a narrative review of wildfire effects on hydrologic processes and the resulting changes in streamflow generation mechanisms with a focus on the WUS, incorporating other global regions when pertinent. A conceptual model summary of wildfire effects on streamflow generation emphasizes: (1) precipitation seasonality, (2) synchrony of precipitation and potential evapotranspiration, (3) net shifts in interception, evaporation, and transpiration relative to total annual precipitation, (4) vegetation changes, including compensatory uptake and type conversion, (5) degree of overlap in rainfall rates and infiltration, (6) fire extent and severity, (7) burn scar positioning (e.g. in headwaters or proximal to watershed outlet), (8) scale-dependent groundwater leakage, (9) near-surface water storage reduction, and (10) soil to groundwater connectivity. Ongoing gaps and challenges include separating the influences of precipitation variability, water withdrawals, and post-fire land management; compound and overlapping disturbances; and lack of pre-fire data. Notable future opportunities include: harnessing ever-improving gridded and remotely sensed precipitation and fire-effects data; linking geophysical, isotopic tracer, and geochemical signatures to diagnose hydrologic changes; leveraging physically based and data-driven model advancements; and analyzing streamflow generation recovery trajectories across diverse watersheds.

western United States↗

USGS2026

Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas

In the United States, private wells are not federally regulated, and many households do not test for Arsenic (As). Chronic exposure is linked with multiple health outcomes, and risk can change sharply over short distances and with well depth. Coarse maps or sparse sampling often miss exceedances. Most existing models operate at ∼1 km resolution and use groundwater chemistry or detailed geologic logs, which limits their use in undersampled areas where improved guidance is most needed. We overcome these limitations by developing a machine learning model for Minnesota, USA, that predicts As exposure risk using only surficial variables from remote sensing and global data sets. Variables related to surface water hydrology and geomorphology are selected based on mechanistic links that control redox conditions and As mobilization. Local training was essential, and surficial geology variables that are more sensitive to local conditions were needed to maximize model accuracy. The resulting complete model was sufficiently sensitive to generate accurate and detailed risk maps and depth profiles of As concentrations above the 10 μg/L maximum contaminant level. Accuracy depended on local training data density. We identified a training data density of 0.07 wells/km 2 as a practical target for stable county-level performance. Maps of exceedance probabilities highlight priority areas for testing that are particularly important in rural communities that have received less sampling. These results support public health action by guiding where to install wells and where to test them, how much new sampling is needed, and where treatment outreach is most urgent.

Minnesota↗

USGS2025

The Long Island Sound and Watershed Metadata map application

The Long Island Sound and its watershed encompass an area of about 17,000 square miles and include the Connecticut, Housatonic, and Thames Rivers, which all drain to the sound. Dozens of organizations from government agencies, nonprofits, and Tribal Nations have developed projects and monitoring programs to analyze and protect the water resources of the watershed and sound. The abundance of data and lack of an existing searchable index require a centralized metadata repository to allow users to find water resources data more efficiently. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and the Long Island Sound Study, has created an interactive map application to visualize and search for metadata information across organizations working to monitor and protect the Long Island Sound.

Connecticut, Massachusetts, New Hampshire, New Yor↗

USGS2025

A generalized deep learning model to detect and classify volcano seismicity

Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] ( 10.1029/2024JB029194 ) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network ( VOISS-Net ) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.

Volcanica↗

USGS2026

Decadal shifts in groundwater age detected by environmental tracers across California, USA

Groundwater age offers important insight into recharge, storage, and contamination risk. Although models predict age changes can be driven by pumping and climate variability, direct observational evidence remains limited. Here, we analyzed paired environmental tracer suites (tritium, carbon-14, and tritiogenic helium-3) collected a decade apart from 268 wells across California to assess the prevalence of groundwater age transience. Travel-time distribution models and statistical tests indicated age transience at 29% of sites, occurring most often in agricultural regions, such as the San Joaquin Valley and Southern Coast Ranges, where large carbon-14 changes coincided with substantial nitrate and chloride shifts. Sites with tritiogenic helium-3 data showed more frequent age transience, underscoring the value of multi-tracer data sets. These results provide the first regional evidence of widespread groundwater age change and a method for detecting changing water balances with implications for groundwater sustainability and water quality.

California↗

USGS2025

VIPER site analysis

We needed to evaluate available orbital data of NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) mission area in order to derive a variety of maps to help the science team identify scientifically interesting places for the rover to visit and to provide scientific context for our mission. Some of these maps also fulfilled engineering and mission design needs to enable safe and efficient landing and roving. We incorporated data from the Lunar Reconnaissance Orbiter Camera, the Lunar Orbital Laser Altimeter, the Mini-RF instrument, the Chandrayaan-2 Orbital High Resolution Camera, the Korean Pathfinder Lunar Orbiter’s Shadowcam, the Kaguya Spectral Profiler and Multiband Imager, and the Chandrayaan-1 Moon Mineralogy Mapper. We used a variety of techniques to build these maps, including stereogrammetry, shape-from-shading, ice stability depth and surface temperature calculations, and the horizon method for solar illumination and direct-to-Earth communications maps. Altogether, these maps allowed us to survey for boulders, evaluate features in permanently shadowed regions that VIPER might explore, provide mineralogic context for what VIPER’s instruments may learn, estimate the ages and radar properties of craters in the VIPER mission area, and evaluate the potential for gravity traverses with the rover. These data and techniques provided a rich set of information from which both the VIPER science team and engineering teams were able to draw in order to plan a safe landing and to plan a VIPER surface mission that will be both scientifically valuable and robust from an operational perspective.

The Planetary Science Journal↗

USGS2026

Reassessing water availability in the Nile River Basin using satellite-based irrigation water use accounting

Existing estimates of irrigation areas and water use in the Nile River Basin vary widely and are based on inconsistent data and methodologies. This study leverages advances in remote sensing to provide an up-to-date and consistent basin-wide reassessment of water use in the Nile River Basin. Land cover and actual evapotranspiration data from 2013 to 2022 were used to quantify irrigated areas, irrigation water use, and the average naturalized water yield. Across the basin, 7.1 million hectares of irrigated land consume an average of 72.5 ± 2.6 billion cubic meters (BCM) of blue water annually. Egypt and Sudan together account for 92% of the irrigated area and 97% of the irrigation water use. The basin’s average naturalized yield, evaluated at Dongola station, is determined to be between 108 and 114.4 ± 3.2 BCM. Although conservative, this updated analysis indicates the basin’s average naturalized yield is 14%–20% higher than the previous naturalized yield estimate (95 BCM) and 29%–36% higher than the commonly cited observed flow of 84 BCM, which is frequently treated as the total basin-wide available water. Importantly, the study underscores that the 84 BCM represents flow at the Aswan gauging station after upstream consumptive uses, not the total water available in the basin under naturalized conditions. This distinction is critical for accurate water accounting, planning, and governance in this water-scarce basin. This study demonstrates the value of open-source remote sensing resources in data-scarce regions while emphasizing the need for region specific validation and bias correction for improved accuracy.

Nile River Basin↗

USGS2025

Annual NLCD (National Land Cover Database)—The next generation of land cover mapping

Introduction The widely used National Land Cover Database (NLCD) has long been the foundational land cover source for scientists, resource managers, and decision makers across the United States. In 2024, a reinvention as Annual NLCD added the key improvement of annual time steps to show decades of change at a higher frequency than the intervals of 2–3 years used in the legacy NLCD. Annual NLCD was derived primarily from the long Landsat satellite data record, and it includes data from other sources. The first release in 2024 of Annual NLCD provides Collection 1.0 of products encompassing land cover and land change from 1985 through 2023 for the conterminous United States (CONUS). The Annual NLCD Collection 1.0 consists of six operational products that map the unique characteristics of land cover. A map created from Annual NLCD shows 16 land cover classes for the CONUS in 2023.

conterminous United States↗