Understanding habitat connectivity is critical for managing nonindigenous aquatic species (NAS) spread. Dams and watershed boundaries can be impassable to NAS during typical conditions but may become temporarily passable during flooding. The goal of our project was to develop an approach for identifying locations of aquatic connectivity at a fine spatial scale along watershed boundaries using readily available data. To develop this approach, we focused on the potential for range expansion of invasive fish in the United States via possible cross-boundary habitat connections. First, we developed an index using metrics of elevation, watershed size, and geology at regular points along a watershed boundary to stratify points by likelihood of connectivity during high precipitation (>20 mm of precipitation in a 3-day period). We then used a subset of points across a gradient of connectivity likelihoods to gather Landsat-derived observed surface water data and developed a statistical model to predict surface water presence from landscape characteristics. We applied the model throughout the entire watershed boundary to identify locations of hydrologic connectivity during high-water events. The presence of surface water on watershed boundaries was predicted by the interactions between watershed boundary point elevation relative to the minimum adjacent HUC-12 elevations and watershed boundary point elevation relative to neighboring point elevations (marginal R2 = 0.94). Our approach can be used to identify potential areas of surface water connectivity between watersheds quickly and easily at a fine spatial scale using readily available, remotely sensed data that can inform conservation and management actions across disciplines.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation Center of Excellence Team assesses and calibrates Landsat remote-sensing data to ensure high-quality data products are publicly available. These data products are used to make informed decisions about natural resources and the environment. This report is part of a series of quarterly reports intended to provide updated observed geometric and radiometric analysis results for Landsats 8 and 9.
","publicationDate":"2025-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Haque, Md Obaidul 0000-0002-0914-1446","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":290335,"corporation":false,"usgs":false,"family":"Haque","given":"Md Obaidul","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":947607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":947608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shrestha, Ashish 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in Blue Mesa Reservoir, Colorado","interactions":[],"lastModifiedDate":"2025-08-13T13:31:47.444734","indexId":"70270202","displayToPublicDate":"2025-08-11T08:26:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Remote sensing of chlorophyll a and temperature to support algal bloom monitoring in Blue Mesa Reservoir, Colorado","title":"Remote sensing of chlorophyll a and temperature to support algal bloom monitoring in Blue Mesa Reservoir, Colorado","docAbstract":"We present methods to reconstruct historical chlorophyll a and surface water temperatures from satellite-based remote sensing products for Blue Mesa Reservoir, Colorado, to support algal bloom monitoring. A machine learning model was trained to construct chlorophyll a concentrations from Sentinel-2 satellite imagery and in situ measurements of chlorophyll a concentrations (out of bag RMSE = 1.9 μg/L, R2 = 0.63) and reconstruct summertime chlorophyll a concentrations over the entire reservoir from 2016 through 2023. Concurrently, we developed an approach to retrieve remotely sensed water temperatures from the Landsat collection 2 provisional surface temperature product (MAE = 0.6°C) and reconstructed summertime surface water temperature records from 2000 through 2023. Finally, we demonstrate how the reconstructed chlorophyll a and temperature records can yield insight on reservoir dynamics. The chlorophyll a records indicate that algal blooms have a consistent spatial pattern across multiple years, initiating in the eastern end of the reservoir and spreading to the west over time. 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","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70038","usgsCitation":"King, T.V., Bean, R., Walton-Day, K., Mast, M.A., Gohring, E.J., Gidley, R.G., Day, N.K., and Gibney, N., 2025, Remote sensing of chlorophyll a and temperature to support algal bloom monitoring in Blue Mesa Reservoir, Colorado: Journal of the American Water Resources Association, v. 61, no. 4, e70038, 19 p., https://doi.org/10.1111/1752-1688.70038.","productDescription":"e70038, 19 p.","ipdsId":"IP-157284","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":494445,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.70038","text":"Publisher Index Page"},{"id":494016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Gunnison County","otherGeospatial":"Blue Mesa Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.35295276247045,\n 38.535074315629544\n ],\n [\n -107.35295276247045,\n 38.430806876675575\n ],\n [\n -107.03469816287091,\n 38.430806876675575\n ],\n [\n -107.03469816287091,\n 38.535074315629544\n ],\n [\n -107.35295276247045,\n 38.535074315629544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bean, Robert Allen 0000-0001-5940-9757","orcid":"https://orcid.org/0000-0001-5940-9757","contributorId":344328,"corporation":false,"usgs":true,"family":"Bean","given":"Robert Allen","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":336569,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mast, M. 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The upcoming NASA/USGS Landsat Next mission, with an increase in spatial and spectral resolution over previous Landsat missions, is no exception. Landsat Next will produce nearly six times the amount of image data per day over either of the current Landsat 8 or Landsat 9 observatories. Near-lossless compression, where the image after compression is not identical to the original image, allows for the efficient storage and transmission of all image data while meeting the mission’s global coverage, temporal revisit frequency, and science measurement and performance requirements. Although the Landsat user community is understandably cautious about lossy compression, it is possible to constrain the maximum loss, or error, introduced during compression, ensuring that any added error remains within the intrinsic noise level of the instrument. The Consultative Committee for Space Data Systems image compression standard, CCSDS 123.0-B-2, was chosen for the Landsat Next mission because it is an internationally supported standard suited for integration with space hardware, and it allows control over the magnitude and distribution of compression error. Using several proxy datasets as a surrogate for Landsat Next image data, an investigation was performed to determine a preliminary set of parameter values that would keep the added compression error within acceptable limits. The results of these studies demonstrate that near-lossless image compression can be utilized by the Landsat Next instruments to store and downlink all science data without compromising image quality or mission requirements.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2025.114929","usgsCitation":"Eon, R.S., De Groot, C., Pedelty, J., Gerace, A., Montanaro, M., Covington, R.K., DeLisa, A.S., Hsieh, W., Hengear-leon, J.M., Daniels, D.J., Engebretson, C., Crawford, C., Holmes, T.R., Dabney, P., and Cook, B.D., 2025, Toward a near-lossless image compression strategy for the NASA/USGS Landsat Next mission: Remote Sensing of Environment, v. 329, 114929, 11 p., https://doi.org/10.1016/j.rse.2025.114929.","productDescription":"114929, 11 p.","ipdsId":"IP-175687","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":496331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2025.114929","text":"Publisher Index 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Calibration and Validation Report—Quarter 4, 2024","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2024","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The Earth Resources Observation and Science Cal/Val Center of Excellence Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 4 (October–December) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251036","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Steinwand, D., Bresnahan, P., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Kaita, E., Levy, R., Miller, J., Ding, L., and Teixeira Pinto, C., 2025, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2024 (ver. 1.1, June 2026): U.S. Geological Survey Open-File Report 2025–1036, 56 p., https://doi.org/10.3133/ofr20251036.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-175052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":505284,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1036/versionHist.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"}},{"id":505283,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251036/full","description":"OFR 2026-1036 HTML"},{"id":505281,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1036/ofr20251036.pdf","text":"Report","size":"6.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1036"},{"id":505273,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1036/coverthb2.jpg"},{"id":491624,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":491619,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1036/ofr20251036.XML"},{"id":491620,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1036/images/"}],"edition":"Version 1.0: July 2025; Version 1.1 June 2026","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Introduction: Rangeland ponds are vital to the livelihoods of pastoral and agropastoral communities in Africa, providing an important source of water for livestock. However, sparse instrumentation across much of Africa makes it extremely challenging to monitor surface water availability in these areas. Model estimates of surface water, for example, as used by the Famine Early Warning Systems Network (FEWS NET) Water Point Viewer, are one of the few operational tools available to monitor surface water stress across pastoral areas of the Sahel and East Africa.
Methods: Water availability data from these models are difficult to validate. New methods using satellite data to classify surface water provide an opportunity to assess the performance of these tools. This study compares water availability estimates derived from Landsat and Sentinel 1 satellite imagery to in situ observations and model simulations of water availability in 22 ephemeral ponds located in the Ferlo region of Senegal.
Results and discussion: The Active-Passive Water Classification (APWC) algorithm detected surface water at each location. Over 2022 and 2023, water was detected in pond locations annually at a frequency of 68.2% for all ponds and at a frequency of 43.8% for ponds with a surface area <10,000 square meters (m2). The APWC results outperform global and continental surface water datasets in the Ferlo region. Seasonal water availability was captured in 12 ponds over the 2022 and 2023 seasons. The 12 locations can function as sentinel ponds to monitor local water availability. Study results demonstrate the viability of satellite methods to assess water availability in the region, as well as the challenges to using satellite-based methods to estimate water availability in small ponds.
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NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":942096,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Funk, Chris","contributorId":302160,"corporation":false,"usgs":false,"family":"Funk","given":"Chris","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":942097,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoell, Andrew","contributorId":331301,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":79182,"text":"NOAA ESRL","active":true,"usgs":false}],"preferred":false,"id":942098,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jasinski, Michael","contributorId":357641,"corporation":false,"usgs":false,"family":"Jasinski","given":"Michael","affiliations":[{"id":85485,"text":"NASA, Goddard Space Flight Center Department, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":942099,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70270412,"text":"70270412 - 2025 - Modeling daily ice cover in northern hemisphere lakes with a long short‐term memory neural network","interactions":[],"lastModifiedDate":"2025-08-19T15:16:54.496662","indexId":"70270412","displayToPublicDate":"2025-06-17T10:15:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Modeling daily ice cover in northern hemisphere lakes with a long short‐term memory neural network","docAbstract":"Quantifying lake ice loss is crucial for understanding the impact of climate change on lake ecosystems. In this study, we trained a deep learning model (Long-Short Term Memory with Landsat observations, 1984–2012) to simulate Northern Hemisphere lake ice changes at a fine spatial scale (> 0.1 km2) from 1980 to 2022. The model achieved good performance overall during the test period (2013–2022), and the derived ice-on and ice-off matched well with two independent ice phenology data sets. Results reveal a 76.8% increase in intermittently ice-covered lakes from the 1980s to the 2010s, alongside a 10.7-day shorter ice duration and a 3.9 percentage-points reduction in annual mean ice cover fractions. The model can track daily partial ice cover changes, providing a novel contribution to understanding shifts in lake ice cover with climate change. These findings can provide valuable insights for future limnology studies, such as improving estimates of greenhouse gas emissions from lakes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024gl113544","usgsCitation":"He, X., Andreadis, K.M., Roy, A.H., Langhorst, T., Kumar, A., and Butler, C.S., 2025, Modeling daily ice cover in northern hemisphere lakes with a long short‐term memory neural network: Geophysical Research Letters, v. 52, no. 12, e2024GL113544,10 p., https://doi.org/10.1029/2024gl113544.","productDescription":"e2024GL113544,10 p.","ipdsId":"IP-165442","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494970,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZGUGE","text":"USGS data release","linkHelpText":"A Long Short Term Memory model for predicting daily lake ice cover changes in the Northern Hemisphere"},{"id":494456,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl113544","text":"Publisher Index Page"},{"id":494313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andreadis, Konstantinos M.","contributorId":359867,"corporation":false,"usgs":false,"family":"Andreadis","given":"Konstantinos","middleInitial":"M.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langhorst, Theodore","contributorId":292528,"corporation":false,"usgs":false,"family":"Langhorst","given":"Theodore","email":"","affiliations":[{"id":27517,"text":"University of North Carolina - Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":946358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Abhishek","contributorId":316778,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhishek","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butler, Caitlyn S.","contributorId":359869,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946360,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267945,"text":"70267945 - 2025 - Resiliency of land change monitoring efforts to input data resampling","interactions":[],"lastModifiedDate":"2025-06-09T15:10:26.407318","indexId":"70267945","displayToPublicDate":"2025-06-06T10:04:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Resiliency of land change monitoring efforts to input data resampling","docAbstract":"The geometric transformation of remotely sensed imagery from one map projection to another necessitates a data resampling operation which alters the recorded values. The global Landsat archive is made available in the Universal Transverse Mercator (UTM) projection system which preserves geographic shape across small area but introduces small errors in distance and area. As remote sensing-based studies develop from local scales to regional and global, they need to adopt more appropriate map projections from which accurate area measurements can be made. While effects of resampling on recorded values have been studied in the past, the impacts on higher-level results such as land cover have not been widely reported. This study investigates an approach for monitoring land cover and land change using two input datasets derived from identical source Landsat data, where one input dataset is transformed to an equal-area map projection and thereby resampled. Recorded surface reflectance values are changed through the reprojection/resampling process, and our study highlights observed differences in derived land cover from these two different input datasets throughout the various stages of deriving land cover and related characteristics. Our findings suggest that large-scale analyses of land cover will not be substantially impacted by reprojection of input data, but small-scale analyses should exercise caution when interpreting timing and magnitude of pixel-level change and classification dynamics.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2025.1570580","usgsCitation":"Healey, N.C., Barber, C., Smith, K., Mital, R., Brown, J.F., and Robison, C., 2025, Resiliency of land change monitoring efforts to input data resampling: Frontiers in Remote Sensing, v. 6, 1570580, 11 p., https://doi.org/10.3389/frsen.2025.1570580.","productDescription":"1570580, 11 p.","ipdsId":"IP-175401","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":490669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2025.1570580","text":"Publisher Index Page"},{"id":490264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.333,\n 34.667\n ],\n [\n -84.333,\n 33.333\n ],\n [\n -83,\n 33.333\n ],\n [\n -83,\n 34.667\n ],\n [\n -84.333,\n 34.667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2025-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Healey, Nathan C. 0000-0002-8516-2636","orcid":"https://orcid.org/0000-0002-8516-2636","contributorId":280023,"corporation":false,"usgs":false,"family":"Healey","given":"Nathan","email":"","middleInitial":"C.","affiliations":[{"id":57411,"text":"KBR, Inc.","active":true,"usgs":false}],"preferred":false,"id":939736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber, Christopher P. 0000-0003-0570-1140","orcid":"https://orcid.org/0000-0003-0570-1140","contributorId":223102,"corporation":false,"usgs":true,"family":"Barber","given":"Christopher","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":939737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":939738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mital, Rohan 0009-0001-3241-756X","orcid":"https://orcid.org/0009-0001-3241-756X","contributorId":356687,"corporation":false,"usgs":false,"family":"Mital","given":"Rohan","affiliations":[{"id":85186,"text":"KBR, Inc. contractor to the USGS EROS Center","active":true,"usgs":false}],"preferred":false,"id":939739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":939740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robison, Charles 0000-0002-7623-2380","orcid":"https://orcid.org/0000-0002-7623-2380","contributorId":217916,"corporation":false,"usgs":false,"family":"Robison","given":"Charles","email":"","affiliations":[{"id":39714,"text":"SGT Inc. (USGS Contractor)","active":true,"usgs":false}],"preferred":false,"id":939741,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267839,"text":"70267839 - 2025 - Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets","interactions":[],"lastModifiedDate":"2025-06-23T15:25:59.175515","indexId":"70267839","displayToPublicDate":"2025-06-02T09:11:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets","docAbstract":"Landsat Analysis Ready Data (ARD)-based time-series present challenges in monitoring surface urban heat islands (SUHI) due to rapid changes in land surface temperature (LST) compared to cloud-free satellite observations. This research investigates the use of a spatiotemporal gap-filling model as a feasible and cost-effective solution to produce Landsat time-series LST products with both high spatial resolution and temporal frequency. The study identified and filled Landsat ARD thermal times-series data gaps due to missing data, cloud and shadow effects, and data quality. The accuracy of Landsat gap-filled products was assessed using randomly selected clear observations of Landsat and uncertainty products from the gap-filling model and was evaluated using various existing temperature datasets, including climate data from NOAA Global Historical Climate Network station observations, Daily Surface Weather and Climatological Summaries (DAYMET), and LST including MODIS, VIIRS and ECOSTRESS. The result suggests that the gap-filled Landsat LST has significant correlations with existing datasets including field observation and remote sensing data derived from other sensors that have similar monthly and seasonal variation patterns. The uncertainty maps show spatial distributions of uncertainty for gap-filled pixels that have high or low uncertainties. The Landsat gap-filled time-series datasets can be used to measure annual, seasonal, or even monthly landscape thermal conditions, which are useful for SUHI and relevant research, and to perform multi-decade time-series LST change analysis under climate change conditions.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2025.2505254","usgsCitation":"Shi, H., and Xian, G.Z., 2025, Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets: International Journal of Remote Sensing, v. 46, no. 12, p. 4559-4582, https://doi.org/10.1080/01431161.2025.2505254.","productDescription":"24 p.","startPage":"4559","endPage":"4582","ipdsId":"IP-160289","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":939092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":939093,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","interactions":[{"subject":{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"70261584","publicationYear":"2024","noYear":false,"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"predicate":"SUPERSEDED_BY","object":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"id":1}],"lastModifiedDate":"2025-05-20T13:49:59.506555","indexId":"ofr20251007","displayToPublicDate":"2025-05-16T07:54:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1007","displayTitle":"Mapping Eelgrass (Zostera marina) Cover and Biomass at Izembek Lagoon, Alaska, Using In-Situ Field Data and Sentinel-2 Satellite Imagery","title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","docAbstract":"The U.S. Geological Survey and the U.S. Fish and Wildlife Service have developed a three-tiered strategy for monitoring eelgrass (Zostera marina) beds at Izembek Lagoon, Alaska, that targets different spatial and temporal scales. The broadest-scale monitoring (tier-1) uses satellite imagery about every 5 years to delineate the spatial extent of eelgrass beds throughout the lagoon. This report describes the most recent (mid-2020s) tier-1 eelgrass monitoring at Izembek Lagoon. The monitoring effort began by canvasing all satellite imagery collected during summer, under clear daytime skies and at low-tide, since the last tier-1 effort in 2006. Two eelgrass maps of Izembek Lagoon were generated by first creating maps of spectrally unique classes from two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing those spectral classes with information about eelgrass conditions based on field data. Specifically, maps depicting various eelgrass metrics, such as percentage of cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons of the 2016 and 2020 Sentinel-2 maps showing eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined during 2006–20 were most prevalent in the central part of Izembek Lagoon. More recently, during 2016-20, areas of possible biomass decline were more prevalent in the southern part of the lagoon. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data allows conditions to be compared over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251007","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","programNote":"Land Management Research Program","usgsCitation":"Douglas, D.C., Fleming, M.D., Patil, V.P., and Ward, D.H., 2025, Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery: U.S. Geological Survey Open-File Report 2025–1007, 30 p., https://doi.org/10.3133/ofr20251007. [Supersedes preprint https://doi.org/10.1101/2024.08.07.607047.]","productDescription":"Report: vii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169599","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":485960,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1007/coverthb2.jpg"},{"id":485963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HLTAHD","text":"USGS data release","description":"USGS data release","linkHelpText":"Eelgrass (Zostera marina) maps from 2016 and 2020, at Izembek Lagoon, Alaska"},{"id":485961,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1007"},{"id":485962,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251007/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1007"},{"id":485964,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1007/images"},{"id":485965,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.XML"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.098854980302,\n 55.171004418891414\n ],\n [\n -162.87580882559715,\n 55.150219377165826\n ],\n [\n -162.79513255687405,\n 55.2819754305454\n ],\n [\n -162.63219813180595,\n 55.3494888427272\n ],\n [\n -162.52937543637472,\n 55.342292888511395\n ],\n [\n -162.47875503247008,\n 55.40162041992025\n ],\n [\n -162.50248334680037,\n 55.47879257840398\n ],\n [\n -162.74767592913727,\n 55.39443394016618\n ],\n [\n -162.92959300566955,\n 55.31259575418295\n ],\n [\n -163.098854980302,\n 55.171004418891414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km2 in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less).
","language":"English","publisher":"MDPI","doi":"10.3390/rs17101704","usgsCitation":"Sexstone, G., Akie, G.A., Selkowitz, D.J., Barnhart, T., Rey, D., León-Salazar, C., Carbone, E., and Bearup, L.A., 2025, Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time: Remote Sensing, v. 17, no. 10, 1704, 24 p., https://doi.org/10.3390/rs17101704.","productDescription":"1704, 24 p.","ipdsId":"IP-174585","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":490140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17101704","text":"Publisher Index Page"},{"id":486286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.33,\n 40.85\n ],\n [\n -106.33,\n 39.65\n ],\n [\n -105.17,\n 39.65\n ],\n [\n -105.17,\n 40.85\n ],\n [\n -106.33,\n 40.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":938013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":938015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"León-Salazar, Claudia","contributorId":355707,"corporation":false,"usgs":false,"family":"León-Salazar","given":"Claudia","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":938016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carbone, Emily","contributorId":355708,"corporation":false,"usgs":false,"family":"Carbone","given":"Emily","affiliations":[{"id":84819,"text":"Northern Water","active":true,"usgs":false}],"preferred":false,"id":938017,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bearup, Lindsay A.","contributorId":139257,"corporation":false,"usgs":false,"family":"Bearup","given":"Lindsay","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":938018,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","interactions":[{"subject":{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","indexId":"ofr20211030V","publicationYear":"2025","noYear":false,"chapter":"V","displayTitle":"System Characterization Report on Resourcesat-2A Advanced Wide Field Sensor","title":"System characterization report on Resourcesat-2A Advanced Wide Field Sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-05-01T13:42:31.433279","indexId":"ofr20211030V","displayToPublicDate":"2025-04-29T08:31:59","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"V","displayTitle":"System Characterization Report on Resourcesat-2A Advanced Wide Field Sensor","title":"System characterization report on Resourcesat-2A Advanced Wide Field Sensor","docAbstract":"This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resourcesat-2 and -2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about Indian Space Research Organisation satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium and from the Indian Space Research Organisation at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performance of the Resourcesat-2A AWiFS sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had offsets in the range of −1.10 meters (m; −0.020 pixel) to 3.67 m (0.066 pixel) in easting and −5.68 m (−0.101 pixel) to 10.38 m (0.185 pixel) in northing with root mean square error values from 5.60 m (0.100 pixel) to 11.31 m (0.202 pixel) in easting and from 3.00 m (0.054 pixel) to 13.52 m (0.241 pixel) in northing.
The exterior geometric performance had mean offsets of −25.29 m in easting and 16.22 m northing with root mean square error values of 26.07 m in easting and 17.60 m in northing compared to the Landsat 8 Operational Land Imager sensor. The radiometric performance had offsets from −0.002 to 0.029 and slopes from 0.733 to 1.012. Spatial performance was in the range of 1.354 to 1.639 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.108 to 0.174.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030V","usgsCitation":"Shrestha, M., Kim, M., Sampath, A., and Clausen, J., 2025, System characterization report on Resourcesat-2A Advanced Wide Field Sensor, chap. V of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 18 p., https://doi.org/10.3133/ofr20211030V.","productDescription":"v, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170096","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":485174,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030V/full"},{"id":485170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/v/coverthb.jpg"},{"id":485171,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-V"},{"id":485172,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.XML"},{"id":485173,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/v/images/"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","interactions":[{"subject":{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","indexId":"ofr20211030T","publicationYear":"2025","noYear":false,"chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-04-24T14:11:03.291909","indexId":"ofr20211030T","displayToPublicDate":"2025-04-23T12:23:04","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","docAbstract":"This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances.
To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing.
We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager.
The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030T","usgsCitation":"Park, S., Shrestha, M., Kim, M., Sampath, A., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor, chap. T of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030T.","productDescription":"v, 17 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170097","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":484900,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/t/images/"},{"id":484899,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.XML"},{"id":484898,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-T"},{"id":484897,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/t/coverthb.jpg"},{"id":484901,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030T/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Climate-driven forest mortality events have been extensively observed in recent decades, prompting the question of how quickly these affected forests can recover their functionality following such events. Here we assessed forest recovery in vegetation greenness (normalized difference vegetation index) and canopy water content (normalized difference infrared index) for 1,699 well-documented forest mortality events across 1,600 sites worldwide. By analysing 158,427 Landsat surface reflectance images sampled from these sites, we provided a global assessment on the time required for impacted forests to return to their pre-mortality state (recovery time). Our findings reveal a consistent decline in global forest recovery rate over the past decades indicated by both greenness and canopy water content. This decline is particularly noticeable since the 1990s. Further analysis on underlying mechanisms suggests that this reduction in global forest recovery rates is primarily associated with rising temperatures and increased water scarcity, while the escalation in the severity of forest mortality contributes only partially to this reduction. Moreover, our global-scale analysis reveals that the recovery of forest canopy water content lags significantly behind that of vegetation greenness, implying that vegetation indices based solely on greenness can overestimate post-mortality recovery rates globally. Our findings underscore the increasing vulnerability of forest ecosystems to future warming and water insufficiency, accentuating the need to prioritize forest conservation and restoration as an integral component of efforts to mitigate climate change impacts.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41477-025-01948-4","usgsCitation":"Yan, Y., Hong, S., Chen, A., Peñuelas, J., Allen, C.D., Hammond, W.M., Munson, S.M., Myneni, R.B., and Piao, S., 2025, Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events: Nature Plants, v. 11, p. 731-742, https://doi.org/10.1038/s41477-025-01948-4.","productDescription":"12 p.","startPage":"731","endPage":"742","ipdsId":"IP-166805","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2025-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Yan, Yuchao","contributorId":344981,"corporation":false,"usgs":false,"family":"Yan","given":"Yuchao","email":"","affiliations":[{"id":65605,"text":"Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China","active":true,"usgs":false}],"preferred":false,"id":948719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hong, Songbai","contributorId":344984,"corporation":false,"usgs":false,"family":"Hong","given":"Songbai","email":"","affiliations":[{"id":65605,"text":"Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China","active":true,"usgs":false}],"preferred":false,"id":948720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Anping","contributorId":303015,"corporation":false,"usgs":false,"family":"Chen","given":"Anping","email":"","affiliations":[{"id":37774,"text":"Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":948721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peñuelas, Josep","contributorId":361384,"corporation":false,"usgs":false,"family":"Peñuelas","given":"Josep","affiliations":[{"id":86261,"text":"CREAF, Cerdanyola del Valles, Barcelona, Spain; CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":948722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Craig D.","contributorId":361385,"corporation":false,"usgs":false,"family":"Allen","given":"Craig","middleInitial":"D.","affiliations":[{"id":86262,"text":"Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":948723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hammond, William M.","contributorId":361386,"corporation":false,"usgs":false,"family":"Hammond","given":"William","middleInitial":"M.","affiliations":[{"id":86263,"text":"Institute of Food and Agricultural Sciences, Agronomy Department, University of Florida, Gainesville, FL, USA","active":true,"usgs":false}],"preferred":false,"id":948724,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":220026,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948725,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Myneni, Ranga B.","contributorId":361387,"corporation":false,"usgs":false,"family":"Myneni","given":"Ranga","middleInitial":"B.","affiliations":[{"id":86266,"text":"Department of Earth and Environment, Boston University, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":948726,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Piao, Shilong","contributorId":288837,"corporation":false,"usgs":false,"family":"Piao","given":"Shilong","affiliations":[{"id":61843,"text":"College of Urban and Environmental Sciences, Sino‐French Institute for Earth System Science, Peking University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":948727,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70266349,"text":"70266349 - 2025 - The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","interactions":[],"lastModifiedDate":"2025-05-07T13:11:44.831779","indexId":"70266349","displayToPublicDate":"2025-04-16T08:13:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","docAbstract":"Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2025.114723","usgsCitation":"Ju, J., Zhou, Q., Freitag, B., Roy, D., Zhang, H., Sridhar, M., Mandel, J., Arab, S., Schmidt, G.L., Crawford, C., Gascon, F., Strobl, P., Masek, J.G., and Neigh, C., 2025, The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset: Remote Sensing of Environment, v. 324, 114723, 17 p., https://doi.org/10.1016/j.rse.2025.114723.","productDescription":"114723, 17 p.","ipdsId":"IP-178601","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488127,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2025.114723","text":"Publisher Index Page"},{"id":485453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ju, Junchang","contributorId":354466,"corporation":false,"usgs":false,"family":"Ju","given":"Junchang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Qiang","contributorId":354468,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freitag, Brian","contributorId":354470,"corporation":false,"usgs":false,"family":"Freitag","given":"Brian","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, David P.","contributorId":294404,"corporation":false,"usgs":false,"family":"Roy","given":"David P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":935739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hankui","contributorId":354472,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":935740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sridhar, Madhu","contributorId":350383,"corporation":false,"usgs":false,"family":"Sridhar","given":"Madhu","affiliations":[{"id":83729,"text":"University of Alabama Huntsville","active":true,"usgs":false}],"preferred":false,"id":935741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mandel, John","contributorId":354474,"corporation":false,"usgs":false,"family":"Mandel","given":"John","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":354476,"corporation":false,"usgs":true,"family":"Arab","given":"Saeed","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935743,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":935744,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935745,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gascon, Ferran","contributorId":173965,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","email":"","affiliations":[{"id":27013,"text":"European Space Agency, Belgium","active":true,"usgs":false}],"preferred":false,"id":935746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Strobl, Peter A.","contributorId":354478,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter A.","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":935747,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":935748,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Neigh, Christopher S.R.","contributorId":354481,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher S.R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935749,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70265931,"text":"70265931 - 2025 - Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery","interactions":[],"lastModifiedDate":"2025-04-22T16:13:44.477123","indexId":"70265931","displayToPublicDate":"2025-04-03T11:10:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5012,"text":"Soil and Tillage Research","active":true,"publicationSubtype":{"id":10}},"title":"Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery","docAbstract":"Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (fR) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify fR at scale has been limited. In this study we produced annual maps of fR for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs (n = 895) classified to fR using SamplePoint software. Univariate linear regressions were used to compare photograph fR to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in fR estimation in terms of coefficient of determination (R2 = 0.869) and root mean square error (RMSE = 0.111), when R2 and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest fR estimation performance (R2 = 0.795; RMSE = 0.141), suggesting that SINDRI can map fR accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of fR estimation significantly, particularly for SWIRA (R2 = 0.767; RMSE = 0.144), but also NDTI (R2 = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for fR estimation, SWIRA has the potential to improve fR estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global fR dating back to 1982.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.still.2025.106524","usgsCitation":"Lamb, B.T., Hively, W.D., Jennewein, J., Thieme, A., Soroka, A.M., Santos, L., Jones, D., and Mirsky, S., 2025, Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery: Soil and Tillage Research, v. 251, 106524, 19 p., https://doi.org/10.1016/j.still.2025.106524.","productDescription":"106524, 19 p.","ipdsId":"IP-170664","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":488482,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.still.2025.106524","text":"Publisher Index Page"},{"id":484843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"251","noUsgsAuthors":false,"publicationDate":"2025-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":934058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":934059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934060,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santos, Leticia","contributorId":353598,"corporation":false,"usgs":false,"family":"Santos","given":"Leticia","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":934061,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Daniela","contributorId":353599,"corporation":false,"usgs":false,"family":"Jones","given":"Daniela","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":934062,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mirsky, Steven","contributorId":292000,"corporation":false,"usgs":false,"family":"Mirsky","given":"Steven","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":934063,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268307,"text":"70268307 - 2025 - Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments","interactions":[],"lastModifiedDate":"2025-06-20T14:02:06.25149","indexId":"70268307","displayToPublicDate":"2025-03-28T08:57:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments","docAbstract":"Coastal erosion, intensified by sea level rise, poses significant threats to coastal communities in Hawaiʻi and similar island communities. This study projects long-term shoreline change on the Hawaiian Island of O‘ahu using the data-assimilated CoSMoS-COAST shoreline change model. CoSMoS-COAST models four key shoreline processes: (1) Alongshore transport, (2) Recession due to sea level rise, (3) Cross-shore transport due to waves, and (4) Residual processes represented by a linear trend term. This study marks the first application of CoSMoS-COAST for an oceanic equatorial island with narrow beaches and a dynamic wave climate. The model is informed with a novel combination of shoreline data derived from high-resolution imagery from Planet, Sentinel-2, and Landsat satellites, wave-climate hindcasts specific to Hawai‘i, and regional beach-slope surveys. On a dynamic northern Oʻahu beach, the model achieved a root mean square error of 9.4 m between observations and model output. CoSMoS-COAST predicts that 81% of O‘ahu’s sandy beach coastline could experience beach loss by 2100; with 39.8% of this loss happening by 2030. This represents an increase, 43.3%, in net landward shoreline change compared to previous erosion forecasts, for 0.3 m of sea level rise (2050). Additionally, dynamic processes such as cross-shore equilibrium processes and alongshore sediment transport, play a large contribution to gross shoreline change within the next decade, particularly on O ‘ahu’s north and west shores. In the long term, we find that recession due to sea level rise and residual processes dominate, but dynamic, wave-driven processes (longshore and cross-shore transport) still account for 34% of shoreline change between present and 2100. We assert dynamic, wave-driven processes are a crucial addition for accurate modeling of island sandy beach environments. These findings have implications for O‘ahu’s coastal planning and development, suggesting updates to shoreline policies that rely upon erosion forecasting, and highlights the importance of incorporating wave and alongshore transport in erosion models for other Pacific islands.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-95074-y","usgsCitation":"Moskvichev, R., Mikkelsen, A., Anderson, T., Vitousek, S., Joel Nicolow, and Fletcher, C., 2025, Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments: Scientific Reports, v. 15, 10794, 23 p., https://doi.org/10.1038/s41598-025-95074-y.","productDescription":"10794, 23 p.","ipdsId":"IP-176072","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-95074-y","text":"Publisher Index Page"},{"id":491019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.97415529522343,\n 21.740805505081653\n ],\n [\n -158.15139260242358,\n 21.600579083024826\n ],\n [\n -158.2903083296884,\n 21.599465629948895\n ],\n [\n -158.24240635476954,\n 21.479162346928234\n ],\n [\n -158.11187347311537,\n 21.27955194162557\n ],\n [\n -157.87475869726666,\n 21.277320121849456\n ],\n [\n -157.78853514241246,\n 21.229327812614713\n ],\n [\n -157.6687802051152,\n 21.253883970723265\n ],\n [\n -157.62447087831504,\n 21.30744683110042\n ],\n [\n -157.70231158755843,\n 21.41228417679669\n ],\n [\n -157.71069443316924,\n 21.478047962139442\n ],\n [\n -157.79811553739634,\n 21.456873031193922\n ],\n [\n -157.82446162360165,\n 21.49476283830066\n ],\n [\n -157.8172763273639,\n 21.53152880555119\n ],\n [\n -157.97415529522343,\n 21.740805505081653\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Moskvichev, Richelle","contributorId":357155,"corporation":false,"usgs":false,"family":"Moskvichev","given":"Richelle","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":940768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mikkelsen, Anna","contributorId":357158,"corporation":false,"usgs":false,"family":"Mikkelsen","given":"Anna","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":940769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Tiffany","contributorId":357161,"corporation":false,"usgs":false,"family":"Anderson","given":"Tiffany","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":940770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Joel Nicolow","contributorId":357164,"corporation":false,"usgs":false,"family":"Joel Nicolow","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":940772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fletcher, Charles","contributorId":357167,"corporation":false,"usgs":false,"family":"Fletcher","given":"Charles","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":940773,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264292,"text":"ofr20211030S - 2025 - System characterization report on the Environmental Mapping and Analysis Program (EnMAP)","interactions":[{"subject":{"id":70264292,"text":"ofr20211030S - 2025 - System characterization report on the Environmental Mapping and Analysis Program (EnMAP)","indexId":"ofr20211030S","publicationYear":"2025","noYear":false,"chapter":"S","displayTitle":"System Characterization Report on the Environmental Mapping and Analysis Program (EnMAP)","title":"System characterization report on the Environmental Mapping and Analysis Program (EnMAP)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-03-13T13:49:42.501372","indexId":"ofr20211030S","displayToPublicDate":"2025-03-12T08:56:51","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"S","displayTitle":"System Characterization Report on the Environmental Mapping and Analysis Program (EnMAP)","title":"System characterization report on the Environmental Mapping and Analysis Program (EnMAP)","docAbstract":"This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030S","usgsCitation":"Kim, M., Park, S., and Anderson, C., 2025, System characterization report on the Environmental Mapping and Analysis Program (EnMAP), chap. S of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors:U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030S.","productDescription":"vi, 28 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167720","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":483138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/s/coverthb.jpg"},{"id":483141,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/s/images/"},{"id":483142,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030S/full"},{"id":483139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–S"},{"id":483140,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 3 (July–September) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website at https://earthexplorer.usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251006","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Levy, R., Miller, J., and Ding, L., 2025, ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024 (ver. 1.1, June 2026): U.S. Geological Survey Open-File Report 2025–1006, 56 p., https://doi.org/10.3133/ofr20251006.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-172164","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":482584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1006/coverthb2.jpg"},{"id":505313,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1006/versionHist.txt","text":"Version History","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":505312,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1006 PDF"},{"id":482589,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251006/full","description":"OFR 2025–1006 HTML"},{"id":482588,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":482587,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1006/images/"},{"id":482586,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.XML","description":"OFR 2025–1006 XML"}],"edition":"Version 1.0: February 28, 2025; Version 1.1: June 11, 2026","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.
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Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of HLS V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes (>~75°N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or sub-weekly time series applications.
","language":"English","publisher":"IEEE Xplore","doi":"10.1109/LGRS.2025.3533923","usgsCitation":"Zhou, Q., Neigh, C., Ju, J., Dabney, P., Cook, B., Zhu, Z., Crawford, C., Gascon, F., Strobl, P., and Sridhar, M., 2025, Towards seamless global 30-meter terrestrial monitoring: Evaluating 2022 cloud free coverage of harmonized Landsat and Sentinel-2 (HLS) V2.0: IEEE Geoscience and Remote Sensing Letters, v. 22, 5000505, 5 p., https://doi.org/10.1109/LGRS.2025.3533923.","productDescription":"5000505, 5 p.","ipdsId":"IP-164632","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488360,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/lgrs.2025.3533923","text":"Publisher Index Page"},{"id":481614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang","contributorId":350371,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":63570,"text":"Science Systems and Applications Inc","active":true,"usgs":false}],"preferred":false,"id":925890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neigh, Christopher","contributorId":350372,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ju, Junchang","contributorId":350375,"corporation":false,"usgs":false,"family":"Ju","given":"Junchang","affiliations":[{"id":83726,"text":"University of Maryland College Park","active":true,"usgs":false}],"preferred":false,"id":925892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dabney, Philip","contributorId":350376,"corporation":false,"usgs":false,"family":"Dabney","given":"Philip","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Bruce","contributorId":350378,"corporation":false,"usgs":false,"family":"Cook","given":"Bruce","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhe","contributorId":350380,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":925895,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":925896,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gascon, Ferran","contributorId":350381,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":925897,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Strobl, Peter","contributorId":350382,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":925898,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sridhar, Madhu","contributorId":350383,"corporation":false,"usgs":false,"family":"Sridhar","given":"Madhu","affiliations":[{"id":83729,"text":"University of Alabama Huntsville","active":true,"usgs":false}],"preferred":false,"id":925899,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70262229,"text":"fs20253001 - 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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.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Email: custserv@usgs.gov
","tableOfContents":"The southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region.
We developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns.
We used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region.
The burn severity model had a root mean square error (RMSE) of 0.48 (R2 = 0.70) and 0.50 (R2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires.
Although regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems.
More complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.