Ice jams occur regularly at the southern outlet of Osoyoos Lake, which spans the border between the State of Washington and British Columbia, Canada. In recent winters, ice jams caused (1) decreases in downstream discharge that may adversely affect salmon spawning habitat and (2) short-duration lake-level rise that can interfere with lake level management agreements. In response, water managers sought to understand the environmental conditions associated with the historical ice-jam occurrences on Osoyoos Lake. Researchers compiled datasets of discharge, lake level, and air temperature from four meteorological and three hydrologic stations near Oroville, Washington, to determine “ice-jam” or “non-ice-jam” days from 1942 to 2024.
After confirming known ice jams since 1994 using Landsat 8–9 and Sentinel–2 satellite imagery along with discharge, lake level, and air temperature data, researchers designated ice-jam days. They conducted statistical analyses to examine environmental conditions associated with ice-jam occurrences on Osoyoos Lake. Statistical tests indicated significant differences in wind speed, wind direction, and air temperature between ice-jam and non-ice-jam days. A linear discriminant-analysis model correctly predicted 12 of 13 historical ice-jam days since 1994 and determined that ice jams are more likely under westerly and northwesterly winds near or above 10 kilometers per hour (km/h) and minimum temperatures near or below –9.4 degrees Celsius (°C). An analysis of historical discharge suggests that ice jams have occurred since at least the 1940s, but 13 ice jam days occurred in the past decade (2014–2024), exceeding any previous decade. The daily minimum air temperature in the Osoyoos Lake region has increased at a rate of 0.021 °C per year since the 1940s, but ice jams usually occur in winters with colder average temperatures.
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Ice jams are accumulations of ice that partially block water from flowing downstream in rivers and lakes. Ice jams form at the shallow outlet of Osoyoos Lake, which drains into the Okanogan River at the border of the United States and Canada. These ice jams can temporarily reduce river flow downstream, which can harm salmon habitat and cause short lived increases in lake levels that complicate international agreements for managing water levels.
To better understand when and why these ice jams form, researchers from the U.S. Geological Survey examined historical records of river flow, lake level, and air temperature data from stations near Oroville, Washington (located just south of the lake outlet), for the years 1942–2024. Researchers used satellite images and environmental data during 1994–2024 to confirm known ice jams and then identified “ice jam days” for that period.
The team compared weather conditions on ice jam days and non-ice-jam days. They found that ice jams are more likely to form when winds blow from the west or northwest at speeds of about 10 kilometers per hour or more, and when minimum temperatures drop to –9.4 degrees Celsius or lower. A statistical model based on air temperature, wind speed, and wind direction correctly identified nearly all known ice jam days since 1994. While the statistical model identified some days without ice jams as ice-jam days, no ice-jam days occurred outside of the range of wind and temperature conditions identified.
Although ice jams have occurred since at least the 1940s, they have become more frequent in recent years: 13 ice jam days occurred during 2014–2024, more than in any previous decade. Even though winter temperatures in the Osoyoos Lake region have risen slightly over time, ice jams tend to occur during colder than average winters.
Understanding the conditions that lead to ice jams can help decision-makers better anticipate when ice jams may occur and plan for their potential effects on salmon habitat and lake level management.
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47914 252nd Street
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The Landsat Cal/Val team, comanaged by the Earth Resources Observation and Science Cal/Val Center of Excellence and the National Aeronautics and Space Administration Landsat Science Project, 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, ensuring its reliability for scientific research. Landsat data quality is often referred to as the “gold standard” and gives other civil and commercial satellite programs a trusted reference point for measuring their own data quality.
The Landsat program started more than 50 years ago. Since then, Landsat missions have gone through multiple technological advances, which, together with improved calibration and validation techniques, have led to higher data quality over time. The Cal/Val team also maintains consistency in data calibration across the multiple generations of sensors, which is vital to many scientists for time-series analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20263001","usgsCitation":"Anderson, C., Choate, M.J., Micijevic, E., and Shaw, J.L., 2026, Landsat 8–9 geometric and radiometric calibration and characterization: U.S. Geological Survey Fact Sheet 2026–3001, 4 p., https://doi.org/10.3133/fs20263001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-177245","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":500745,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20263001/full"},{"id":500741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2026/3001/coverthb.jpg"},{"id":500742,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2026/3001/fs20263001.pdf","text":"Report","size":"8.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2026-3001"},{"id":500743,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2026/3001/fs20263001.XML"},{"id":500744,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2026/3001/images/"}],"contact":"Project team, Earth Resources Observation and Science (EROS) Cal/Val Center of Excellence (ECCOE)
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70405","usgsCitation":"Bromley, F., Borxton, P., Zhang, J., van Leeuwen, W.J., Nagler, P., and Hu, J., 2026, Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States: Hydrological Processes, v. 40, no. 3, e70405, 18 p., https://doi.org/10.1002/hyp.70405.","productDescription":"e70405, 18 p.","ipdsId":"IP-180542","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70405","text":"Publisher Index Page"},{"id":501145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Upper San Pedro watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.31987279028372,\n 31.82228360728554\n ],\n [\n -110.31987279028372,\n 31.379497469636988\n ],\n [\n -109.98150823782808,\n 31.379497469636988\n ],\n [\n -109.98150823782808,\n 31.82228360728554\n ],\n [\n -110.31987279028372,\n 31.82228360728554\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bromley, Fern 0000-0003-0596-1487","orcid":"https://orcid.org/0000-0003-0596-1487","contributorId":367222,"corporation":false,"usgs":false,"family":"Bromley","given":"Fern","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":957082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borxton, Patrick 0000-0002-2665-6820","orcid":"https://orcid.org/0000-0002-2665-6820","contributorId":248510,"corporation":false,"usgs":false,"family":"Borxton","given":"Patrick","email":"","affiliations":[{"id":49935,"text":"2University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":957083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Jiaqi","contributorId":202467,"corporation":false,"usgs":false,"family":"Zhang","given":"Jiaqi","email":"","affiliations":[{"id":36453,"text":"University of Texas, Arlington, TX, USA","active":true,"usgs":false}],"preferred":false,"id":957084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Leeuwen, Willem J.D. 0000-0002-3188-7172","orcid":"https://orcid.org/0000-0002-3188-7172","contributorId":191856,"corporation":false,"usgs":false,"family":"van Leeuwen","given":"Willem","middleInitial":"J.D.","affiliations":[],"preferred":false,"id":957085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nagler, Pamela L. 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363777,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L.","affiliations":[],"preferred":true,"id":957086,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hu, Jia","contributorId":367226,"corporation":false,"usgs":false,"family":"Hu","given":"Jia","affiliations":[],"preferred":false,"id":957087,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275084,"text":"70275084 - 2026 - Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","interactions":[],"lastModifiedDate":"2026-04-15T15:02:05.992722","indexId":"70275084","displayToPublicDate":"2026-02-26T07:54:26","publicationYear":"2026","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":"Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","docAbstract":"Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115326","usgsCitation":"Jung, J., Fattahi, H., Jeong, S., Bonnema, M.G., Jones, J.W., Bekaert, D., Chan, S.K., and Handweger, A.L., 2026, Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data: Remote Sensing of Environment, v. 337, 115326, 21 p., https://doi.org/10.1016/j.rse.2026.115326.","productDescription":"115326, 21 p.","ipdsId":"IP-183308","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":503010,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2026.115326","text":"Publisher Index Page"},{"id":502816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Jungkyo","contributorId":369929,"corporation":false,"usgs":false,"family":"Jung","given":"Jungkyo","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":959402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeong, Seongsu","contributorId":369930,"corporation":false,"usgs":false,"family":"Jeong","given":"Seongsu","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew G.","contributorId":369931,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","middleInitial":"G.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":959405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bekaert, David","contributorId":267754,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":959406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chan, Steven K.","contributorId":369933,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","middleInitial":"K.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handweger, Alexander L.","contributorId":369934,"corporation":false,"usgs":false,"family":"Handweger","given":"Alexander","middleInitial":"L.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959408,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274647,"text":"70274647 - 2026 - Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird","interactions":[],"lastModifiedDate":"2026-04-02T17:00:56.237927","indexId":"70274647","displayToPublicDate":"2026-02-25T09:47:45","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird","docAbstract":"1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood.
2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet (Brachyramphus marmoratus)—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions.
3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change.
4. Synthesis and applications: We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.70317","usgsCitation":"Cerullo, G., Gannon, D., Bailey Guerrero, J.A., Conklin, E., Kohlberg, A., Nelson, K., Rivers, J.W., Valente, J., Yang, Z., and Betts, M.G., 2026, Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird: Journal of Applied Ecology, v. 63, no. 2, e70317, 15 p., https://doi.org/10.1111/1365-2664.70317.","productDescription":"e70317, 15 p.","ipdsId":"IP-181232","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.70317","text":"Publisher Index Page"},{"id":502015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126.62346834330545,\n 49.423390089555795\n ],\n [\n -125.05731376141374,\n 37.49095069699514\n ],\n [\n -119.79362574221338,\n 38.47443712695113\n ],\n [\n -119.85861425224797,\n 41.78784262090305\n ],\n [\n -116.86760646031209,\n 41.957392910252224\n ],\n [\n -117.25973955716492,\n 49.423390089555795\n ],\n [\n -126.62346834330545,\n 49.423390089555795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"63","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Cerullo, Gianluca","contributorId":369152,"corporation":false,"usgs":false,"family":"Cerullo","given":"Gianluca","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannon, Dusty","contributorId":369153,"corporation":false,"usgs":false,"family":"Gannon","given":"Dusty","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey Guerrero, Jennifer A.","contributorId":369154,"corporation":false,"usgs":false,"family":"Bailey Guerrero","given":"Jennifer","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conklin, Emily","contributorId":369155,"corporation":false,"usgs":false,"family":"Conklin","given":"Emily","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kohlberg, Anna Bloch","contributorId":369156,"corporation":false,"usgs":false,"family":"Kohlberg","given":"Anna Bloch","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958548,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Kim","contributorId":92810,"corporation":false,"usgs":false,"family":"Nelson","given":"Kim","affiliations":[],"preferred":false,"id":958549,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rivers, James W.","contributorId":369162,"corporation":false,"usgs":false,"family":"Rivers","given":"James","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958551,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yang, Zhiqiang","contributorId":219468,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","affiliations":[{"id":27560,"text":"PNNL","active":true,"usgs":false}],"preferred":false,"id":958552,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":" Betts, Matthew G.","contributorId":369163,"corporation":false,"usgs":false,"family":" Betts","given":"Matthew","middleInitial":"G.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958553,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274155,"text":"70274155 - 2026 - Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues","interactions":[],"lastModifiedDate":"2026-03-03T14:25:13.74987","indexId":"70274155","displayToPublicDate":"2026-02-12T08:04:54","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues","docAbstract":"Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18040590","usgsCitation":"Shi, H., Barber, C.P., Sayler, K.L., Smith, K., and Hussain, R., 2026, Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues: Remote Sensing, v. 18, no. 4, 590, 25 p., https://doi.org/10.3390/rs18040590.","productDescription":"590, 25 p.","ipdsId":"IP-184356","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":500820,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18040590","text":"Publisher Index Page"},{"id":500672,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -48.23543379168905,\n -15.481214838053802\n ],\n [\n -48.23543379168905,\n -16.0688507831632\n ],\n [\n -47.30997066679669,\n -16.0688507831632\n ],\n [\n -47.30997066679669,\n -15.481214838053802\n ],\n [\n -48.23543379168905,\n -15.481214838053802\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 114.01001566832986,\n 30.818553311801224\n ],\n [\n 114.01001566832986,\n 30.334258663173557\n ],\n [\n 114.57676947004,\n 30.334258663173557\n ],\n [\n 114.57676947004,\n 30.818553311801224\n ],\n [\n 114.01001566832986,\n 30.818553311801224\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-02-13","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":956714,"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":956715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":956716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":956717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hussain, Reza 0000-0002-5445-3027","orcid":"https://orcid.org/0000-0002-5445-3027","contributorId":301245,"corporation":false,"usgs":false,"family":"Hussain","given":"Reza","affiliations":[{"id":65343,"text":"KBR, Contractor to U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":956718,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273806,"text":"70273806 - 2026 - Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","interactions":[],"lastModifiedDate":"2026-02-03T14:43:09.95163","indexId":"70273806","displayToPublicDate":"2026-02-02T08:37:01","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","docAbstract":"In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl a) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-2025-317","usgsCitation":"Benjamin Page, Crawford, C., Arab, S., Gail Schmidt, Barnes, C., and Wellington, D., 2026, Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations: Earth System Science Data, v. 18, no. 2, p. 779-800, https://doi.org/10.5194/essd-2025-317.","productDescription":"22 p.","startPage":"779","endPage":"800","ipdsId":"IP-170237","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin Page 0000-0002-9871-2406","orcid":"https://orcid.org/0000-0002-9871-2406","contributorId":359007,"corporation":false,"usgs":false,"family":"Benjamin Page","affiliations":[{"id":85733,"text":"Earth Space Technology Services (ESTS)","active":true,"usgs":false}],"preferred":false,"id":954888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":954889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":954890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gail Schmidt 0000-0002-9684-8158","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":359008,"corporation":false,"usgs":false,"family":"Gail Schmidt","affiliations":[{"id":57411,"text":"KBR, Inc.","active":true,"usgs":false}],"preferred":false,"id":954891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnes, Christopher 0000-0002-4608-4364","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":359949,"corporation":false,"usgs":false,"family":"Barnes","given":"Christopher","affiliations":[{"id":68993,"text":"KBR Inc., Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":954892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":954893,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273736,"text":"ofr20261059 - 2026 - ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 2, 2025","interactions":[],"lastModifiedDate":"2026-04-16T17:30:49.458717","indexId":"ofr20261059","displayToPublicDate":"2026-01-27T08:18:26","publicationYear":"2026","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":"2026-1059","title":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 2, 2025","docAbstract":"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 2 (April–June) of 2025. 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/ofr20261059","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., Angal, A., Levy, R., Miller, J., Ding, L., and Teixeira Pinto, C., 2026, ECCOE Landsat quarterly calibration and validation report—Quarter 2, 2025 (ver. 1.1, March 2026): U.S. Geological Survey Open-File Report 2026–1059, 56 p., https://doi.org/10.3133/ofr20261059.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-181128","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499062,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1059/ofr20261059.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1059 XML"},{"id":500674,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1059/coverthb3.jpg"},{"id":500673,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1059/ofr20261059.pdf","text":"Report","size":"5.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1059"},{"id":500521,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2026/1059/version-history_ofr20261059.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"}},{"id":499065,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261059/full","description":"OFR 2026-1059 HTML"},{"id":499064,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":499063,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1059/images"}],"edition":"Version 1.0: January 2026; Version 1.1: February 2026","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 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":"2026-01-27","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":954468,"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":954469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shrestha, Ashish 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chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":954479,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Clauson, Jeff 0000-0003-3406-4988 jclauson@usgs.gov","orcid":"https://orcid.org/0000-0003-3406-4988","contributorId":5230,"corporation":false,"usgs":true,"family":"Clauson","given":"Jeff","email":"jclauson@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":954480,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thome, Kurt","contributorId":140792,"corporation":false,"usgs":false,"family":"Thome","given":"Kurt","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":954481,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Angal, Amit","contributorId":360771,"corporation":false,"usgs":false,"family":"Angal","given":"Amit","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":954482,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":954483,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Miller, Jeff","contributorId":204570,"corporation":false,"usgs":false,"family":"Miller","given":"Jeff","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":954484,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Ding, Leibo","contributorId":330182,"corporation":false,"usgs":false,"family":"Ding","given":"Leibo","email":"","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":954485,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Teixeira Pinto, Cibele","contributorId":357558,"corporation":false,"usgs":false,"family":"Teixeira Pinto","given":"Cibele","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":954486,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70273871,"text":"70273871 - 2026 - The surface is not superficial: Utilizing hyper-local thermal photogrammetry for pedestrian thermal comfort inquiry","interactions":[],"lastModifiedDate":"2026-02-11T15:13:43.569738","indexId":"70273871","displayToPublicDate":"2026-01-19T08:07:19","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The surface is not superficial: Utilizing hyper-local thermal photogrammetry for pedestrian thermal comfort inquiry","docAbstract":"The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (TS) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution TS collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR TS averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR TS strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR TS explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18020348","usgsCitation":"Steinharter, L., Ibsen, P.C., deSouza, P., and McHale, M.R., 2026, The surface is not superficial: Utilizing hyper-local thermal photogrammetry for pedestrian thermal comfort inquiry: Remote Sensing, v. 18, no. 2, 348, 25 p., https://doi.org/10.3390/rs18020348.","productDescription":"348, 25 p.","ipdsId":"IP-183417","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18020348","text":"Publisher Index Page"},{"id":499747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Denver","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.29210252650385,\n 39.926551113261525\n ],\n [\n -105.29210252650385,\n 39.49581897348219\n ],\n [\n -104.64227323476742,\n 39.49581897348219\n ],\n [\n -104.64227323476742,\n 39.926551113261525\n ],\n [\n -105.29210252650385,\n 39.926551113261525\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Steinharter, Logan","contributorId":366132,"corporation":false,"usgs":false,"family":"Steinharter","given":"Logan","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":955339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ibsen, Peter Christian 0000-0002-3436-9100","orcid":"https://orcid.org/0000-0002-3436-9100","contributorId":260735,"corporation":false,"usgs":true,"family":"Ibsen","given":"Peter","email":"","middleInitial":"Christian","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"deSouza, Priyanka","contributorId":366133,"corporation":false,"usgs":false,"family":"deSouza","given":"Priyanka","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":955341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McHale, Melissa R.","contributorId":366135,"corporation":false,"usgs":false,"family":"McHale","given":"Melissa","middleInitial":"R.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":955342,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274099,"text":"70274099 - 2026 - ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest","interactions":[],"lastModifiedDate":"2026-02-25T15:35:31.584463","indexId":"70274099","displayToPublicDate":"2026-01-09T08:26:28","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10942,"text":"PNAS Nexus","active":true,"publicationSubtype":{"id":10}},"title":"ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest","docAbstract":"Sandy beaches act as buffers against various coastal hazards but are vulnerable to episodic (seasonal) and chronic (interannual) erosion. Understanding the variation of shoreline position, a key metric in coastal morphology, over a spectrum of time scales is therefore crucial in assessing hazard vulnerability. Long-standing research has investigated the role of El Niño-Southern Oscillation (ENSO), the dominant mode of climate variability in the Pacific Basin, in seasonal shoreline variability. Yet, ENSO’s chronic influence—and that of another Pacific climate mode, the Pacific Decadal Oscillation (PDO)—on shoreline anomalies remains poorly understood. Here, we examine the variability of sandy beaches in the US Pacific Northwest, a ∼750 km long coastal region on the US West Coast. We leverage 40 years (1984–2024) of shoreline data from publicly available Earth-observing (Landsat) satellite imagery at a high spatial resolution (>10,000 shore-normal transects at 50-m alongshore spacing) and employ Convergent Cross Mapping (CCM), a methodology for inferring causality in dynamical systems. We discover that strong El Niño years are signified by erosion (75.1% of transects), and strong La Niña years exhibit accretional behavior (73.4% of transects). Furthermore, we establish, for the first time, that both ENSO and PDO exert a statistically significant control on interannual shoreline variability, particularly on the alongshore component (in 95 and 100% of littoral cells, respectively), with water level fluctuations playing a critical role. This effort advances our understanding of the seasonal-to-interannual interactions between Pacific Basin climate variability and the PNW’s coastal morphodynamics, with implications for sediment management and coastal adaptation.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/pnasnexus/pgaf404","usgsCitation":"Taherkhani, M., Vitousek, S., Graffin, M., Vos, K., Allan, J.C., Kaminsky, G.M., Ruggiero, P., 2026, ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest: PNAS Nexus, v. 5, no. 1, pgaf404, 15 p., https://doi.org/10.1093/pnasnexus/pgaf404.","productDescription":"pgaf404, 15 p.","ipdsId":"IP-176083","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":500608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/pnasnexus/pgaf404","text":"Publisher Index Page"},{"id":500510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.83369555506572,\n 48.57459950593827\n ],\n [\n -126.00388302759357,\n 39.14100845126933\n ],\n [\n -122.98418105660868,\n 39.14100845126933\n ],\n [\n -122.61669284602645,\n 48.33915875055985\n ],\n [\n -125.83369555506572,\n 48.57459950593827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Taherkhani, Mohsen","contributorId":366984,"corporation":false,"usgs":false,"family":"Taherkhani","given":"Mohsen","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":956529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":956530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graffin, Marcan","contributorId":366985,"corporation":false,"usgs":false,"family":"Graffin","given":"Marcan","affiliations":[{"id":47711,"text":"University of Toulouse","active":true,"usgs":false}],"preferred":false,"id":956531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vos, Kilian","contributorId":366986,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","affiliations":[{"id":87519,"text":"OHB Digital Services","active":true,"usgs":false}],"preferred":false,"id":956532,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allan, Jonathan C.","contributorId":118007,"corporation":false,"usgs":false,"family":"Allan","given":"Jonathan","email":"","middleInitial":"C.","affiliations":[{"id":7198,"text":"Oregon Department Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":956533,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaminsky, George M.","contributorId":366988,"corporation":false,"usgs":false,"family":"Kaminsky","given":"George","middleInitial":"M.","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":956534,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ruggiero, Peter","contributorId":366989,"corporation":false,"usgs":false,"family":"Ruggiero","given":"Peter","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":956535,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274286,"text":"70274286 - 2026 - Climate change has increased crop water consumption in Central Asia despite less water-intensive cropping","interactions":[],"lastModifiedDate":"2026-03-24T14:18:48.133425","indexId":"70274286","displayToPublicDate":"2026-01-08T09:11:32","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Climate change has increased crop water consumption in Central Asia despite less water-intensive cropping","docAbstract":"Climate change and land use change are crucial determinants of crop water consumption, particularly in drylands where water scarcity limits crop production. In Central Asia, the effects of land use and climate changes on crop water consumption remain unknown. We estimated the dynamics of crop water consumption by mapping annual actual evapotranspiration from Landsat imagery from 1987 to 2019 for all irrigated croplands in the Amu Darya Basin, the largest transboundary river in Central Asia. Total crop water consumption increased by 10%, while average consumption per unit area increased by 18%. Climate change was the main driver of the rising crop water consumption; land use changes towards less water-intensive cropping practices offset only 3% of this increase. Our findings underscore that crop production will become increasingly challenging amidst accelerating climatic changes and that changing cropping practices alone will be insufficient to curb the growing water scarcity without a global commitment to reducing emissions.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-025-03142-y","usgsCitation":"Peña-Guerrero, M.D., Senay, G., Umirbekov, A., Tarasova, L., Rufin, P., Pulatov, B., and Müller, D., 2026, Climate change has increased crop water consumption in Central Asia despite less water-intensive cropping: Communications Earth & Environment, v. 7, 122, 9 p., https://doi.org/10.1038/s43247-025-03142-y.","productDescription":"122, 9 p.","ipdsId":"IP-180332","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501667,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-025-03142-y","text":"Publisher Index Page"},{"id":501444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tajikistan, Turkmenistan, Uzbekistan","otherGeospatial":"Amu Darya basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 55.14649185414618,\n 42.425007460866794\n ],\n [\n 55.14649185414618,\n 37.25183592908982\n ],\n [\n 69.68848488936624,\n 37.25183592908982\n ],\n [\n 69.68848488936624,\n 42.425007460866794\n ],\n [\n 55.14649185414618,\n 42.425007460866794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2026-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Peña-Guerrero, M. Daniela","contributorId":367745,"corporation":false,"usgs":false,"family":"Peña-Guerrero","given":"M.","middleInitial":"Daniela","affiliations":[{"id":87619,"text":"Department of Catchment Hydrology, Helmholtz Centre for Environmental Research-UFZ, Halle (Saale), Germany","active":true,"usgs":false}],"preferred":false,"id":957617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":957618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Umirbekov, A.","contributorId":367746,"corporation":false,"usgs":false,"family":"Umirbekov","given":"A.","affiliations":[{"id":87622,"text":"Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale), Germany Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":957619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tarasova, L.","contributorId":367747,"corporation":false,"usgs":false,"family":"Tarasova","given":"L.","affiliations":[{"id":87623,"text":"Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Tashkent, Uzbekistan","active":true,"usgs":false}],"preferred":false,"id":957620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rufin, P.","contributorId":367748,"corporation":false,"usgs":false,"family":"Rufin","given":"P.","affiliations":[{"id":87624,"text":"Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":957621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pulatov, B.","contributorId":367749,"corporation":false,"usgs":false,"family":"Pulatov","given":"B.","affiliations":[{"id":87625,"text":"Research Institute of Environment and Nature Conservation Technologies, Tashkent, Uzbekistan","active":true,"usgs":false}],"preferred":false,"id":957622,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Müller, D.","contributorId":367750,"corporation":false,"usgs":false,"family":"Müller","given":"D.","affiliations":[{"id":87622,"text":"Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale), Germany Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":957623,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273442,"text":"70273442 - 2026 - Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?","interactions":[],"lastModifiedDate":"2026-01-14T15:23:53.929478","indexId":"70273442","displayToPublicDate":"2026-01-05T08:18:13","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?","docAbstract":"This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18020195","usgsCitation":"Palaseanu-Lovejoy, M., Danielson, J.J., Kim, M., Eder, B., Imahori, G., and Storlazzi, C.D., 2026, Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?: Remote Sensing, v. 18, no. 2, 195, 30 p., https://doi.org/10.3390/rs18020195.","productDescription":"195, 30 p.","ipdsId":"IP-183102","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":498700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18020195","text":"Publisher Index Page"},{"id":498609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Guam, Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.29886149831057,\n 31.035877030602435\n ],\n [\n -88.29886149831057,\n 24.818271329127427\n ],\n [\n -79.18887863828726,\n 24.818271329127427\n ],\n [\n -79.18887863828726,\n 31.035877030602435\n ],\n [\n -88.29886149831057,\n 31.035877030602435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.91979115320188,\n 18.80997482085145\n ],\n [\n -67.91979115320188,\n 17.365111187758373\n ],\n [\n -64.61443502140921,\n 17.365111187758373\n ],\n [\n -64.61443502140921,\n 18.80997482085145\n ],\n [\n -67.91979115320188,\n 18.80997482085145\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.5400287592969,\n 13.788590651628851\n ],\n [\n 144.5400287592969,\n 13.156374228238235\n ],\n [\n 145.08326832494907,\n 13.156374228238235\n ],\n [\n 145.08326832494907,\n 13.788590651628851\n ],\n [\n 144.5400287592969,\n 13.788590651628851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":305576,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","affiliations":[],"preferred":true,"id":953719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","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":953720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":953721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eder, Bryan","contributorId":365118,"corporation":false,"usgs":false,"family":"Eder","given":"Bryan","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":953722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Imahori, Gretchen","contributorId":365119,"corporation":false,"usgs":false,"family":"Imahori","given":"Gretchen","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":953723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":953724,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272019,"text":"gip263 - 2025 - USGS—An Unparalleled Scientific Asset","interactions":[],"lastModifiedDate":"2026-03-05T18:22:22.613234","indexId":"gip263","displayToPublicDate":"2025-12-09T16:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"263","displayTitle":"USGS: An Unparalleled Scientific Asset","title":"USGS—An Unparalleled Scientific Asset","docAbstract":"The U.S. Geological Survey (USGS) delivers information critical to powering our economy, managing our natural resources, and keeping Americans safe and healthy.1
$21B
Geologic maps save users an estimated 15% in annual costs: a value of between $14B and $21B.
$25.6B
in annual value to users of imagery from Landsat satellites, which were codeveloped by NASA and the USGS and operated through their lifespans by the USGS.
$13.5B
in annual benefits is generated by the USGS's 3D Elevation Program.
44%
USGS-identified undiscovered geothermal energy is equal to 44% of current U.S. electricity generation.
29.4B
barrels of oil and 391.6 trillion cubic feet of gas in recoverable resources are available on U.S. public lands based on USGS assessments.
$424B
in recent wildland fire damages highlight the need for USGS fire science, which supports efforts to protect communities and reduce risk.
USGS earthquake, volcano, landslide, and coastal hazard monitoring and information save lives and minimize costs; for example, $2.8M can be saved because of USGS enhanced information about a Mauna Loa eruption.
$4.5B
is the estimated cost of annual flooding. Through a network of over 11,885 streamgages, the USGS supports public safety and enables forecasts, early warning systems, and management actions that protect lives and property.
$3.1B
The USGS identified a $3.1B risk to the American economy if China restricts gallium imports. This is one example underscoring the importance of the USGS mapping critical minerals, investigating supply chains, and producing the Nation’s critical minerals list.
$21B
in estimated annual costs results from invasive species. The USGS’s invasive species research informs approaches used to reduce their effects on agriculture, water infrastructure, disease transmission, fisheries, and outdoor recreation.
USGS innovations support early warnings for harmful algal blooms—over $2M in yearly benefits are provided to Kansas alone.
$45B
USGS science informs the management of big game (such as deer and elk). The big-game hunting industry contributes $45B to the U.S. economy.
$4.1T
Mineral commodities are necessary for the $4.1T in value added to the GDP by major industries that consume processed mineral materials and employ 1 million workers. Because of this, USGS data on mineral supply, demand, and trade are highly valued.
45,000 metric tons
Rare earths power the growing technology economy, including cell phones, electric vehicles, and medical devices. For over 70 years, USGS work has supported the discovery of rare earth resources in California’s Mountain Pass area, which produced 45,000 metric tons of rare earth concentrates in 2024—over 11% of the global supply.
$70.2B
USGS science informs early warning systems and management strategies to mitigate disease outbreaks in agriculture—critical research on highly pathogenic avian influenza, for example, helps safeguard the $70B value in poultry and egg production.
$11.8B
USGS groundwater tools are vital for agriculture; for example, in the Mississippi Alluvial Plain, 65% of farming relies on groundwater to support its $11.8B annual industry.
1Values throughout are given in billions (B), millions (M), and trillions (T) of U.S. dollars. GDP is “Gross Domestic Product.” Percentages are shown as %.
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Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km2 of permanent waters and 8 million km2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ae137b","usgsCitation":"Khare, A., Gupta, B.C., Rajib, A., Vanderhoof, M.K., Wu, Q., 2025, Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities: Environmental Research Letters, v. 20, no. 12, 124042, 17 p., https://doi.org/10.1088/1748-9326/ae137b.","productDescription":"124042, 17 p.","ipdsId":"IP-164592","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ae137b","text":"Publisher Index Page"},{"id":500503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Khare, Arushi","contributorId":366982,"corporation":false,"usgs":false,"family":"Khare","given":"Arushi","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gupta, Bikas C.","contributorId":366983,"corporation":false,"usgs":false,"family":"Gupta","given":"Bikas","middleInitial":"C.","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rajib, Adnan","contributorId":365158,"corporation":false,"usgs":false,"family":"Rajib","given":"Adnan","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":956527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wu, Qiusheng","contributorId":208272,"corporation":false,"usgs":false,"family":"Wu","given":"Qiusheng","email":"","affiliations":[{"id":37769,"text":"Binghamton University","active":true,"usgs":false}],"preferred":false,"id":956528,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273274,"text":"70273274 - 2025 - Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud","interactions":[],"lastModifiedDate":"2025-12-29T16:30:45.746731","indexId":"70273274","displayToPublicDate":"2025-11-01T10:22:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5987,"text":"Photogrammetric Engineering & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud","docAbstract":"Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.
","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.25-00081R3","usgsCitation":"Teluguntla, P., Thenkabail, P., Oliphant, A., Aneece, I., Biggs, T., Murali Krishna Gumma, Foley, D., McCormick, R.L., Rohitha, N., Long, E., and Lawton, J., 2025, Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud: Photogrammetric Engineering & Remote Sensing, v. 91, no. 11, p. 703-714, https://doi.org/10.14358/PERS.25-00081R3.","productDescription":"12 p.","startPage":"703","endPage":"714","ipdsId":"IP-179081","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":498274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.25-00081r3","text":"Publisher Index 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production","interactions":[],"lastModifiedDate":"2026-03-31T20:41:18.801907","indexId":"70274551","displayToPublicDate":"2025-10-24T15:36:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"An automated compositing method for producing annual clear images from Landsat Collection 2 for annual NLCD production","docAbstract":"Quality image input is fundamental to the quality of derived land cover products. Substantial time and effort are usually required to prepare images. Here, we present a novel and streamlined compositing algorithm that ingests Landsat Collection 2 Analysis Ready Data (ARD) and outputs cloud-free and gap-free composite imagery, which can be directly used for classification. This method leverages and improves the previous National Land Cover Database (NLCD) Virtual Median Value Point (VMVP) compositing method, the first part of the image preparation for NLCD 2019 operational production. The NLCD 2019 image preparation approach includes a second part, a residual cloud and cloud shadow detection and gap-filling method, to produce final cloud-free and gap-free composite imagery. The second part requires one clear reference image for each target year. Additional reference images are needed for producing reasonable observations for perennial ice/snow areas because Pixel QA (Quality Assessment) from ARD has difficulties differentiating ice/snow areas from clouds. Unlike the NLCD 2019 image preparation approach, our new compositing method, which is referred to as Automated VMVP (AVMVP), uses Landsat ARD as the only input and does not require reference images and extra steps. In this method, we developed new spectral filter criteria coupled with counts of clear observations using Pixel QA to identify potential cloud and cloud shadow observations on initially selected observations from the NLCD VMVP compositing algorithm. We also automate “gap-filling” using clear observations retrieved from a maximum of ±2 years around the target year when needed. Finally, a percentile-filtered compositing method was developed for the perennial ice/snow areas. All these steps are streamlined, pixel-based, and directly run on Landsat Collection 2 ARD. We have run successful tests on the conterminous United States (CONUS). Composite images derived from our innovative method were used to produce the CONUS Annual NLCD Collection 1 product suite that covers the period from 1985 to 2023.
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increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long-Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no-cost Harmonized Landsat and Sentinel-2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super-spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7-year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.
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2025 - Thirty years of the U.S. National Land Cover Database: Impacts and future direction","interactions":[],"lastModifiedDate":"2026-03-19T20:00:20.373012","indexId":"70274252","displayToPublicDate":"2025-10-01T14:33:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5987,"text":"Photogrammetric Engineering & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Thirty years of the U.S. National Land Cover Database: Impacts and future direction","docAbstract":"The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based landcover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, long-term land-cover and land-change dataset for the United States, ensuring continued support for broad national applications while complementing higher-resolution and global-mapping efforts.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.25-00121R2","usgsCitation":"Sohl, T.L., Jin, S., Dewitz, J., Wickham, J., Brown, J.F., Stehman, S., Herold, N., Schleeweis, K., Tollerud, H.J., and Deering, C., 2025, Thirty years of the U.S. National Land Cover Database: Impacts and future direction: Photogrammetric Engineering & Remote Sensing, v. 91, no. 10, p. 647-659, https://doi.org/10.14358/PERS.25-00121R2.","productDescription":"13 p.","startPage":"647","endPage":"659","ipdsId":"IP-180474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":957183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":957184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":222454,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wickham, James","contributorId":140259,"corporation":false,"usgs":false,"family":"Wickham","given":"James","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":957186,"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":957187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stehman, Stephen","contributorId":39747,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","affiliations":[],"preferred":false,"id":957188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herold, Nathaniel","contributorId":140258,"corporation":false,"usgs":false,"family":"Herold","given":"Nathaniel","email":"","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":957189,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schleeweis, Karen","contributorId":169308,"corporation":false,"usgs":false,"family":"Schleeweis","given":"Karen","email":"","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":957190,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957191,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957192,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70273022,"text":"70273022 - 2025 - Predicting aquatic habitat connectivity across watershed boundaries: Implications for interbasin spread of nonindigenous aquatic species.","interactions":[],"lastModifiedDate":"2025-12-12T15:14:04.925076","indexId":"70273022","displayToPublicDate":"2025-09-11T08:08:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Predicting aquatic habitat connectivity across watershed boundaries: Implications for interbasin spread of nonindigenous aquatic species.","docAbstract":"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.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2025.1646017","usgsCitation":"Pfaff, P.J., Coulter, A.A., Schall, B.J., Davis, T., Chipps, S.R., and Coulter, D.P., 2025, Predicting aquatic habitat connectivity across watershed boundaries: Implications for interbasin spread of nonindigenous aquatic species.: Frontiers in Environmental Science, v. 113, 1646017, 8 p., https://doi.org/10.3389/fenvs.2025.1646017.","productDescription":"1646017, 8 p.","ipdsId":"IP-168696","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497698,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2025.1646017","text":"Publisher Index Page"},{"id":497465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.5860882678942,\n 47.0340515938843\n ],\n [\n -98.5860882678942,\n 42.75965927049364\n ],\n [\n -96.3287308953151,\n 42.75965927049364\n ],\n [\n -97.02339781306394,\n 45.96566324768915\n ],\n [\n -97.13756830247006,\n 47.16863340208883\n ],\n [\n -98.5860882678942,\n 47.0340515938843\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","noUsgsAuthors":false,"publicationDate":"2025-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Pfaff, Peter J.","contributorId":363920,"corporation":false,"usgs":false,"family":"Pfaff","given":"Peter","middleInitial":"J.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":952106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coulter, Alison A.","contributorId":363922,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":952107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schall, Benjamin J.","contributorId":363925,"corporation":false,"usgs":false,"family":"Schall","given":"Benjamin","middleInitial":"J.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":952108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Tanner","contributorId":348518,"corporation":false,"usgs":false,"family":"Davis","given":"Tanner","affiliations":[{"id":83369,"text":"South Dakota Game, Fish, and Parks","active":true,"usgs":false}],"preferred":false,"id":952109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coulter, David P.","contributorId":363929,"corporation":false,"usgs":false,"family":"Coulter","given":"David","middleInitial":"P.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":952111,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271160,"text":"ofr20251048 - 2025 - ECCOE Landsat quarterly calibration and validation report—Quarter 1, 2025","interactions":[],"lastModifiedDate":"2026-02-03T15:18:16.668024","indexId":"ofr20251048","displayToPublicDate":"2025-09-02T08:01:24","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":"2025-1048","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2025","title":"ECCOE Landsat quarterly calibration and validation report—Quarter 1, 2025","docAbstract":"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 1 (January–March) of 2025. 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.
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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. Water temperatures increased at a linearized rate of 0.3°C per decade from 2000 through 2023 and were inversely proportional to reservoir water surface elevation. Finally, mean summer remotely sensed chlorophyll a concentration had a moderately positive correlation with mean summer remotely sensed water temperature.
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