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At least 307 records · Page 17Linked to original sources
USGS2016

Applying the collective impact approach to address non-native species: A case study of the Great Lakes Phragmites Collaborative

To address the invasion of non-native Phragmites in the Great Lakes, researchers at the U.S. Geological Survey—Great Lakes Science Center partnered with the Great Lakes Commission in 2012 to establish the Great Lakes Phragmites Collaborative (GLPC). The GLPC is a regional-scale partnership established to improve collaboration among stakeholders and increase the effectiveness of non-native Phragmites management and research. Rather than forming a traditional partnership with a narrowly defined goal, the GLPC follows the principles of collective impact to engage stakeholders, guide progress, and align resources to address this complex, regional challenge. In this paper, the concept and tenets of collective impact are described, the GLPC is offered as a model for other natural resource-focused collective impact efforts, and steps for establishing collaboratives are presented. Capitalizing on the interactive collective impact approach, the GLPC is moving toward a broadly accepted common agenda around which agencies and individuals will be able to better align their actions and generate measureable progress in the regional campaign to protect healthy, diverse ecosystems from damage caused by non-native Phragmites .

Biological Invasions↗
USGS2023

Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis

Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large-scale water-quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset ( N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal-aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time-series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

Limnology and Oceanography Letters↗
USGS2018

Spatial spectroscopic models for remote exploration

Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional “science traceability matrices” into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science—Imaging spectroscopy—Alteration mineralogy—Field geology—Cuprite—AVIRIS-NG—Robotic exploration. Astrobiology 18, 934–954.

Astrobiology↗
USGS2019

Measuring suspended sediment in sand-bedded rivers using down-looking acoustic doppler current profilers

The use of side-looking acoustic Doppler velocity meters (ADVMs) to estimate fluvial suspended-sediment concentrations (SSC) has become more operational by the U.S. Geological Survey in recent years; however, direct transfer of these techniques to down-looking acoustic Doppler current profilers (ADCPs) currently is not widely feasible. Key assumptions in the sidelooking ADVM method related to sediment homogeneity within the acoustic measurement volume are almost never met in wide, sand-bedded rivers because SSC and particle size commonly vary with depth and location in the river cross section. The use of ADCPs to estimate SSC has been investigated by researchers, but the requirements and limitations of an operational method that could be successfully applied at many locations are not well defined. If an operational method could be developed, the use of ADCPs, which are routinely used for flow measurements, would revolutionize sediment science by providing rapid measurements of sediment flux and spatial distribution. We collected detailed datasets in six sand-bedded rivers in the U.S. in 2016-2018, to evaluate the efficacy of using down-looking ADCPs of multiple frequencies to estimate SSC. The datasets included replicate sets of point and depth-integrated suspended-sediment samples and stationary and cross-sectional backscatter profiles using multiple ADCPs with differing frequencies. Reasonable calibrations were developed at all sites measured when calibrating to the coarse fraction (R2 0.66 to 0.98 with slopes close to 0.1 using 1200kHz ADCPs). Calibrations to the fines fraction were problematic because acoustic backscatter response was dominated by coarse particles when present, and substantial attenuation was contributed by coarse particles at some sites. A sensitivity analysis on minimum datasets showed that good calibrations could be developed using two verticals of data collected over a range of backscatter and sediment conditions, with a minimum of three points sampled for sediment within each vertical. Overall, results to date show great promise in using ADCPs to rapidly estimate and visualize SSC with high spatial resolution, and a new beta software tool called Sediment Transect Acoustics simplifies data processing. Improvements are underway to the beta software used in processing to allow incorporation of more acoustic and sediment characteristics and to estimate SSC in areas of the river cross section unmeasured by the ADCP.

Conference Paper↗
USGS2014

Advances in a distributed approach for ocean model data interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

Journal of Marine Science and Engineering↗
USGS2021

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

Remote Sensing of Environment↗
USGS2007

Mars reconnaissance orbiter's high resolution imaging science experiment (HiRISE)

The HiRISE camera features a 0.5 m diameter primary mirror, 12 m effective focal length, and a focal plane system that can acquire images containing up to 28 Gb (gigabits) of data in as little as 6 seconds. HiRISE will provide detailed images (0.25 to 1.3 m/pixel) covering ∼1% of the Martian surface during the 2‐year Primary Science Phase (PSP) beginning November 2006. Most images will include color data covering 20% of the potential field of view. A top priority is to acquire ∼1000 stereo pairs and apply precision geometric corrections to enable topographic measurements to better than 25 cm vertical precision. We expect to return more than 12 Tb of HiRISE data during the 2‐year PSP, and use pixel binning, conversion from 14 to 8 bit values, and a lossless compression system to increase coverage. HiRISE images are acquired via 14 CCD detectors, each with 2 output channels, and with multiple choices for pixel binning and number of Time Delay and Integration lines. HiRISE will support Mars exploration by locating and characterizing past, present, and future landing sites, unsuccessful landing sites, and past and potentially future rover traverses. We will investigate cratering, volcanism, tectonism, hydrology, sedimentary processes, stratigraphy, aeolian processes, mass wasting, landscape evolution, seasonal processes, climate change, spectrophotometry, glacial and periglacial processes, polar geology, and regolith properties. An Internet Web site (HiWeb) will enable anyone in the world to suggest HiRISE targets on Mars and to easily locate, view, and download HiRISE data products.

Journal of Geophysical Research E: Planets↗
USGS2004

Fort Collins Science Center: Aquatic Systems and Technology Applications

Land and water management agencies are responsible for restoring and conserving our nation's natural resources. However, they face increasing, often competing demands for those resources, which can result in alteration or loss of critical riverine, riparian, wetland, and terrestrial habitats. Land and resource managers may be in federal, state, or local government, but all have the same need for quantitative, objective, science-based information that helps them plan, manage, and conserve the natural resources within their purview. The Aquatic Systems and Technology Applications Branch (ASTA) of the Fort Collins Science Center (FORT) encompasses a wide variety of studies, investigations, and activities that are related to providing tools and capabilities for natural resource managers. ASTA's mission is to provide managers with credible science-based information on the interrelationships among the physical, chemical, aquatic, and biological natural resources in river basins for resource management decision-making. Branch goals are to: develop and apply specific models and analysis tools for resource management issues, identify habitat and biological linkages in river corridor environments, design and evaluate specific water quality improvement features, define economic measures for natural resource benefits, investigate altered flow regime effects on native fish populations, characterize sediment transport effects in river corridor environments, and utilize advanced technology to evaluate landscape-scale changes in river basins.

Fact Sheet↗
USGS2004

Advanced systems data for mapping Emperor Penguin habitats in Antarctica

Commercial orbital sensor systems combined with other resource data from the U.S. Geological Survey National Civil Applications Program (NCAP) may offer an effective way of mapping Emperor penguin habitats and their response to regional climate change in Antarctica. This project examined these resources to determine their applicability for mapping Emperor penguin habitats to support the National Science Foundation. This work is especially significant to investigate satellite-based imaging as an alternative to intrusive in-the-field enumeration of Emperor penguins and the potential of applying these procedures to support The National Map (TNP).

Open-File Report↗
USGS2010

Using a distribution and conservation status weighted hotspot approach to identify areas in need of conservation action to benefit Idaho bird species

Identification of biodiversity hotspots (hereafter, hotspots) has become a common strategy to delineate important areas for wildlife conservation. However, the use of hotspots has not often incorporated important habitat types, ecosystem services, anthropogenic activity, or consistency in identifying important conservation areas. The purpose of this study was to identify hotspots to improve avian conservation efforts for Species of Greatest Conservation Need (SGCN) in the state of Idaho, United States. We evaluated multiple approaches to define hotspots and used a unique approach based on weighting species by their distribution size and conservation status to identify hotspot areas. All hotspot approaches identified bodies of water (Bear Lake, Grays Lake, and American Falls Reservoir) as important hotspots for Idaho avian SGCN, but we found that the weighted approach produced more congruent hotspot areas when compared to other hotspot approaches. To incorporate anthropogenic activity into hotspot analysis, we grouped species based on their sensitivity to specific human threats (i.e., urban development, agriculture, fire suppression, grazing, roads, and logging) and identified ecological sections within Idaho that may require specific conservation actions to address these human threats using the weighted approach. The Snake River Basalts and Overthrust Mountains ecological sections were important areas for potential implementation of conservation actions to conserve biodiversity. Our approach to identifying hotspots may be useful as part of a larger conservation strategy to aid land managers or local governments in applying conservation actions on the ground.

Idaho↗
USGS2020

Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico

An understanding of the applicability and utility of hydrologic models is critical to support the effective management of water resources throughout the Southeastern United States (SEUS) and Puerto Rico (PR). Hydrologic models have the capacity to provide an estimate of the quantity of available water at ungauged locations (i.e., areas of the country where a U.S. Geological Survey [USGS] continuous record gauge is not installed) and provide the baseline flow information necessary to develop the linkages between water availability and characteristics of streamflow that support ecological communities (i.e., support the development of flow-ecology response models). This report inventories and then directly examines and compares a subset of hydrologic models used to estimate streamflow at a number of gauged basins across the SEUS and PR. This effort was designed to evaluate, quantify, and compare the magnitude of error and to investigate the potential causes of error associated with predicted streamflows from seven hydrologic models of varying complexity and calibration strategy. This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination [R2], root mean squared error [RMSE], Nash-Sutcliffe Efficiency [NSE]) and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFMs). Additionally, streamflow predictions from a larger regional-scale hydrologic model were compared to those of several fine-scale hydrologic models under a range of hypothetical climate change scenarios to determine the range of predicted streamflow responses to fixed climate perturbations. A pilot study was conducted using predicted streamflow and boosted regression trees to develop a set of predictive flow-ecology response models to assess the potential change in fish species richness in the North Carolina Piedmont under several scenarios of water availability change. This report is intended to provide a general assessment of all the tools and techniques available to support hydrologic modeling for flow-ecology science in the SEUS and PR. It is our hope that the approach used herein to understand differences in streamflow predictions among a subset of hydrologic models that have been applied in the SEUS for developing flow-ecology response models will provide water resource managers and stakeholders with an informed pathway for developing the capacity to link streamflow and ecological response and an understanding of some of the limitations associated with these type of modeling efforts.

Alabama, Florida, Georgia, Mississippi, North Caro↗
USGS2022

Near-surface geophysics perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Pointing to the Integrated, Coordinated, Open, Networked Findability, Accessibility, Interoperability, and Reusability (ICON-FAIR) principles, we have determined several opportunities for implementation within the realm of near-surface geophysics (NSG), representing a broad range of data acquisition and processing technologies. Our work explores the multifaceted community-driven nature of NSG and, by applying ICON-FAIR principles, we identify three key strategic objectives: (i) the development of an approach to integrating NSG into other geoscience data collection projects, (ii) the creation of coordinated and open standardized NSG data, and (iii) the networking of post-secondary institutions to foster an equipment sharing consortium. The precedence within the geoscientific community demonstrates that there are significant opportunities for advancing interdisciplinary applications of NSG through the implementation of structural change within the ICON-FAIR framework.

Earth and Space Science↗
USGS2023

Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales

Monitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency-collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons ( Gavia immer ) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency-collected data, if appropriate frameworks are developed to integrate these data sources.

Ecosphere↗
USGS2007

Catch-and-release science and its application to conservation and management of recreational fisheries

Catch-and-release angling is a well-established practice in recreational angler behaviour and fisheries management. Accompanying this is a growing body of catch-and-release research that can be applied to reduce injury, mortality and sublethal alterations in behaviour and physiology. Here, the status of catch-and-release research from a symposium on the topic is summarised. Several general themes emerged including the need to: (1) better connect sublethal assessments to population-level processes; (2) enhance understanding of the variation in fish, fishing practices and gear and their role in catch and release; (3) better understand animal welfare issues related to catch and release; (4) increase the exchange of information on fishing-induced stress, injury and mortality between the recreational and commercial fishing sectors; and (5) improve procedures for measuring and understanding the effect of catch-and-release angling. Through design of better catch-and-release studies, strategies could be developed to further minimise stress, injury and mortality arising from catch-and-release angling. These strategies, when integrated with other fish population and fishery characteristics, can be used by anglers and managers to sustain or enhance recreational fishing resources. ?? 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd.

Fisheries Management and Ecology↗
USGS2022

Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.

western United States↗
USGS1997

Herbicides and their metabolites in rainfall: Origin, transport, and deposition patterns across the midwestern and northeastern United States, 1990-1991

Herbicides were detected in rainfall throughout the midwestern and northeastern United States during late spring and summer of 1990 and 1991. Herbicide concentrations exhibited distinct geographic and seasonal patterns. The highest concentrations occurred in midwestern cornbelt states following herbicide application to cropland. Volume-weighted concentrations of 0.2−0.4 μg/L for atrazine and alachlor were typical in this area during mid-April through mid-July, and weighted concentrations as large as 0.6−0.9 μg/L occurred at several sites. Concentrations of 1−3 μg/L were measured in a few individual samples. Atrazine was detected most often followed by alachlor, deethylatrazine, metolachlor, cyanazine, and deisopropylatrazine. The high ratio (∼0.5) of deethylatrazine to atrazine in rainfall suggests atmospheric degradation of atrazine. Mass deposition of herbicides was greatest in areas where herbicide use was high and decreased with distance from the cornbelt. Estimated deposition rates for both atrazine and alachlor ranged from more than 240 μg m - 2 yr - 1 for some areas in the midwestern states to less than 10 μg m - 2 yr - 1 for the New England states. The estimated annual deposition of atrazine on the Great Lakes ranged from about 12 to 63 μg m - 2 yr - 1 . The total amounts of atrazine and alachlor deposited annually in rainfall in the study area represent about 0.6% of the atrazine and 0.4% of the alachlor applied annually to crops in the study area.

Environmental Science & Technology↗
USGS2002

Groundwater contamination downstream of a contaminant penetration site. I. Extension-expansion of the contaminant plume

This study concerns the possible use of boundary layer (BL) approach for the analysis and evaluation of contaminant transport in groundwater due to contaminant penetration into the groundwater aquifer through a site of limited size. The contaminant penetration may occur through either the upper (surface) or lower (bedrock) boundary of the aquifer. Two general cases of contaminant penetration mechanisms are considered: (1) the contaminant is transferred through an interface between a contaminating and freshwater fluid phases, and (2) the contaminant arrives at groundwater by leakage and percolation. For the purpose of BL evaluation the contaminant plume is divided into three different sections: (1) the penetration section, (2) the extension-expansion section, and (3) the spearhead section. In each section a different BL method approach yields simple analytical expressions for the description of the contaminant plume migration and contaminant transport. Previous studies of the BL method can be directly applied to the evaluation of contaminant transport at the contaminant penetration section. The present study extends those studies and concerns the contaminant transport in the two other sections, which are located downstream of the penetration section. This study shows that the contaminant concentration profiles in sections 2 and 3 incorporate two BLs: (1) an inner BL adjacent to the aquifer bottom or surface boundary, and (2) an outer BL, which develops above or below the inner one. The method developed in the present study has been applied to practical issues concerning salinity penetration into groundwater in south central Kansas.

Journal of Environmental Science and Health - Part↗
USGS2026

Remote compositional analyses of space-weathered lunar maria

Visible-to-shortwave infrared (VSWIR) reflectance spectroscopy has revolutionized our understanding of planetary surface compositions. However, space-weathering processes on airless bodies complicate quantitative compositional analyses. Here, we present a framework to isolate the signatures of space weathering in VSWIR spectra of lunar maria by leveraging radiative transfer modeling under the assumptions that (i) a space-weathered target can be expressed as a mixture of fresh and fully space-weathered components and (ii) remaining signatures can be modeled by including agglutinates as an end-member component. We first validate this approach against laboratory spectra of space-weathered Apollo mare soils of known mineral compositions using a probabilistic Markov Chain Monte Carlo implementation of the Hapke radiative transfer model. Second, we illustrate how this approach can be applied to orbital Moon Mineralogy Mapper data. The proposed space-weathering correction workflow for lunar maria could be expanded to other lunar lithologies and applied to existing and future data sets.

Planetary Science Journal↗