This report addresses system characterization of the Earth Surface Mineral Dust Source Investigation (EMIT) sensor, an imaging spectrometer developed by the National Aeronautics and Space Administration. This report is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior) and radiometric performances. Results of these analyses indicate that the EMIT sensor has a band-to-band geometric performance in the range of −0.355 to 0.210 pixel with a few exceptions of shortwave infrared channels. Geometric offset relative to the Landsat 8 Operational Land Imager ranged from −15.966 meters (−0.266 pixel) to 43.844 meters (0.731 pixel). Offset of a radiometric comparison ranged from −0.016 to 0.025, and slope of a radiometric comparison ranged from 0.837 to 0.985. EMIT agreed with Radiometric Calibration Network measurements within 5 percent across most of the spectral channels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030R","usgsCitation":"Shrestha, M., Sampath, A., Kim, M., and Park, S., 2024, System characterization report on the Earth Surface Mineral Dust Source Investigation (EMIT) sensor, chap. R of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 27 p., https://doi.org/10.3133/ofr20211030R.","productDescription":"vi, 27 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167707","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":464759,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030R/full"},{"id":464758,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/r/images/"},{"id":464755,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/r/coverthb.jpg"},{"id":464756,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/r/ofr20211030r.pdf","text":"Report","size":"6.35 MB","description":"OFR 2021–1030–R"},{"id":464757,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/r/ofr20211030r.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The riparian corridor along Mexico’s arid Colorado River Delta is being affected by reduction in river flow and increases in heat, drought, human infrastructure, and disturbances. These disturbances can change riparian land cover by limiting water availability for riparian plant species, increasing fire intensity and frequency, and increasing soil and water salinities. In response to these forms of degradation, restoration efforts have begun to restore riparian habitats and native plant health, but vegetation greenness and corresponding plant water use continue to decline in unrestored reaches. Researchers from the U.S. Geological Survey (USGS) Southwest Biological Science Center are monitoring riparian plant health along the Colorado River Delta to support better ecohydrological decision-making. The researchers are helping a binational team to protect, restore, and maintain native vegetation within the 150-km long riparian corridor. Researchers are using Landsat 8 Operational Land Imager (OLI) data spanning 2014–2022 to measure greenness, a proxy for plant health, and actual evapotranspiration (ETa). Using an empirical model for ETa, evapotranspiration is estimated over each 16-day Landsat 8 OLI overpass period by considering the 8 days before and after the overpass date.
In their paper, researchers noted an increase in vegetation greenness within the restoration sites over nine years, with an average increase of 41.3%, which may be partially due to targeted water deliveries at the restoration sites. Conversely, greenness in adjacent, unrestored control areas declined by 27.3%. The study showed a 22.1% increase in ETa in restored areas, compared to a 30.8% reduction in unrestored regions. Restored sites in one restored area experienced ETa increases up to 12.2%, whereas their unrestored counterparts showed a decline of 21.4%. These estimates of riparian greenness and water use may assist natural resource managers who are tasked with allocating water and managing habitats within similar riparian corridors.
","language":"English","publisher":"Department of Interior","usgsCitation":"Nagler, P.L., 2024, Colorado Delta riparian plant health improvement, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-169348","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491582,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eros.usgs.gov/doi-remote-sensing-activities/2024/usgs/colorado-delta-riparian-plant-health-improvement"},{"id":498747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10014888935096,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 34.16115185091701\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":941661,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70270587,"text":"70270587 - 2024 - A global view of remote sensing of rangelands: Evolution, applications, future pathways","interactions":[],"lastModifiedDate":"2025-08-21T14:29:51.092797","indexId":"70270587","displayToPublicDate":"2024-11-28T09:27:55","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"11","title":"A global view of remote sensing of rangelands: Evolution, applications, future pathways","docAbstract":"The application of digital remote sensing to rangelands is as long as the history of digital remote sensing itself. Before the launch of the Earth Resources Technology Satellite (ERTS) – later renamed Landsat, scientists were evaluating the use of multispectral aerial imagery to map soils and range vegetation (Yost and Wenderoth 1969). During the late 1960’s, the promise of ERTS, designed to drastically improve our ability to update maps and study earth resources, particularly in developing countries, was eagerly anticipated by a number of government agencies (Carter 1969). With the ERTS launch on July 23, 1972, a flurry of research activity aimed at the application of this new data source to map earth resources began. Practitioners who pioneered the use of satellite based digital remote sensing found the new data source a significant value for rangeland assessments (e.g., Rouse et al., 1973, Rouse et al., 1974, Bauer 1976). This early work established many of the basic techniques still in use today to assess and monitor global rangelands. The following sub-sections discuss the evolution of remote sensing data, methods, and approaches in various decades.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Remote sensing handbook","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9781003541165-14","usgsCitation":"Reeves, M., Washington-Allen, R.A., Angerer, J., Hunt, E.R., Kulawardhana, W., Kumar, L., Loboda, T., Loveland, T., Metternicht, G., Ramsey, R.D., Hall, J.V., Benedict, T.D., Millikan, P., Retallack, A., Meddens, A.J., Smith, W.K., and Zhang, W., 2024, A global view of remote sensing of rangelands: Evolution, applications, future pathways, chap. 11 of Remote sensing handbook, v. III, p. 361-418, https://doi.org/10.1201/9781003541165-14.","productDescription":"58 p.","startPage":"361","endPage":"418","ipdsId":"IP-158984","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"III","edition":"2nd edition","noUsgsAuthors":false,"publicationDate":"2024-11-28","publicationStatus":"PW","contributors":{"editors":[{"text":"Thenkabail, Prasad 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":211472,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":946743,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Reeves, Matthew","contributorId":95437,"corporation":false,"usgs":true,"family":"Reeves","given":"Matthew","affiliations":[],"preferred":false,"id":946744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Washington-Allen, Robert A.","contributorId":172793,"corporation":false,"usgs":false,"family":"Washington-Allen","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":946745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angerer, Jay","contributorId":172794,"corporation":false,"usgs":false,"family":"Angerer","given":"Jay","email":"","affiliations":[],"preferred":false,"id":946746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunt, E. Raymond","contributorId":360066,"corporation":false,"usgs":false,"family":"Hunt","given":"E.","middleInitial":"Raymond","affiliations":[],"preferred":false,"id":946747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulawardhana, Wasantha","contributorId":360067,"corporation":false,"usgs":false,"family":"Kulawardhana","given":"Wasantha","affiliations":[],"preferred":false,"id":946748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Lalit","contributorId":172796,"corporation":false,"usgs":false,"family":"Kumar","given":"Lalit","email":"","affiliations":[],"preferred":false,"id":946749,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loboda, Tatiana","contributorId":172797,"corporation":false,"usgs":false,"family":"Loboda","given":"Tatiana","email":"","affiliations":[],"preferred":false,"id":946750,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":946751,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Metternicht, Graciela","contributorId":172798,"corporation":false,"usgs":false,"family":"Metternicht","given":"Graciela","email":"","affiliations":[],"preferred":false,"id":946752,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ramsey, R. Douglas","contributorId":172799,"corporation":false,"usgs":false,"family":"Ramsey","given":"R.","email":"","middleInitial":"Douglas","affiliations":[],"preferred":false,"id":946753,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hall, Joanne V.","contributorId":360069,"corporation":false,"usgs":false,"family":"Hall","given":"Joanne","middleInitial":"V.","affiliations":[],"preferred":false,"id":946754,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Benedict, Trenton David 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":346111,"corporation":false,"usgs":true,"family":"Benedict","given":"Trenton","middleInitial":"David","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":946610,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Millikan, Pedro","contributorId":360070,"corporation":false,"usgs":false,"family":"Millikan","given":"Pedro","affiliations":[],"preferred":false,"id":946755,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Retallack, Angus","contributorId":360071,"corporation":false,"usgs":false,"family":"Retallack","given":"Angus","affiliations":[],"preferred":false,"id":946756,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Meddens, Arjan J.H.","contributorId":260476,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","middleInitial":"J.H.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":946757,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":946758,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zhang, Wen","contributorId":356791,"corporation":false,"usgs":false,"family":"Zhang","given":"Wen","affiliations":[],"preferred":false,"id":946759,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70270062,"text":"70270062 - 2024 - Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","interactions":[],"lastModifiedDate":"2025-08-08T15:24:31.376369","indexId":"70270062","displayToPublicDate":"2024-11-15T10:19:32","publicationYear":"2024","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":"Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","docAbstract":"Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16224258","usgsCitation":"Benedict, T.D., Boyte, S., and Dahal, D., 2024, Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands: Remote Sensing, v. 16, no. 22, 4258, 21 p., https://doi.org/10.3390/rs16224258.","productDescription":"4258, 21 p.","ipdsId":"IP-171996","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494184,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16224258","text":"Publisher Index Page"},{"id":493848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.28187929406397,\n 48.82060636906533\n ],\n [\n -124.99361768794509,\n 48.88697493321598\n ],\n [\n -124.99361768794509,\n 30.85327470627726\n ],\n [\n -99.69664006714606,\n 30.849197853937767\n ],\n [\n -100.28187929406397,\n 48.82060636906533\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"22","noUsgsAuthors":false,"publicationDate":"2024-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton David 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":346111,"corporation":false,"usgs":true,"family":"Benedict","given":"Trenton","middleInitial":"David","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":945270,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260810,"text":"70260810 - 2024 - Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","interactions":[],"lastModifiedDate":"2024-12-10T15:33:45.478559","indexId":"70260810","displayToPublicDate":"2024-10-31T06:56:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","docAbstract":"Many freshwater forested wetlands along the southeastern United States coastline are rapidly transitioning from forest to marsh or open water, due to climate change-related disturbances. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands. In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula (APP), North Carolina, U.S.A. We used the Landsat record (1985–2021) to examine trends of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts. We found that while 77% of the APP was either stable or revegetating, 22% of the landscape underwent a decrease in NDVI that would indicate a transition from forest to marsh or open water. Of the areas that transitioned, about half (11%) experienced an abrupt decrease in NDVI and 10% experienced a gradual decline. Increasing standard deviation and skewness of time series could serve as EWS of abrupt transitions, but can also provide false negative and positives. Our results suggest that ecosystem transitions from a forest to a marsh or open water can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify EWS for some areas, but not all. Our results allow for prioritization of conservation/restoration of coastlines which will become important in the face of climate change and sea level rise.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-024-00938-5","usgsCitation":"Martinez, M., Ardon, M.L., and Gray, J., 2024, Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing: Ecosystems, v. 27, p. 1060-1075, https://doi.org/10.1007/s10021-024-00938-5.","productDescription":"16 p.","startPage":"1060","endPage":"1075","ipdsId":"IP-133828","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":463847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Albemarle Pamlico Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.63541246515713,\n 36.06271479689754\n ],\n [\n -77.19335586830498,\n 36.06271479689754\n ],\n [\n -77.19335586830498,\n 35.278844140439915\n ],\n [\n -75.63541246515713,\n 35.278844140439915\n ],\n [\n -75.63541246515713,\n 36.06271479689754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2024-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Martinez, Melinda 0000-0001-6652-9220","orcid":"https://orcid.org/0000-0001-6652-9220","contributorId":290467,"corporation":false,"usgs":true,"family":"Martinez","given":"Melinda","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ardon, Marcelo L","contributorId":346120,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Joshua","contributorId":346121,"corporation":false,"usgs":false,"family":"Gray","given":"Joshua","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918161,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259554,"text":"fs20243039 - 2024 - Landsat geometric and radiometric calibration and characterization","interactions":[],"lastModifiedDate":"2024-10-22T22:25:01.130916","indexId":"fs20243039","displayToPublicDate":"2024-10-11T16:50:46","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3039","displayTitle":"Landsat Geometric and Radiometric Calibration and Characterization","title":"Landsat geometric and radiometric calibration and characterization","docAbstract":"The U.S. Geological Survey (USGS) 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 (Haque and others, 2024).
The accuracy of the ECCOE team calibration adjustments gives other civil and commercial satellite programs around the globe a trusted criterion and reference point. The ECCOE team works with U.S. and international government agencies and commercial vendors to help harmonize data sources as more frequent, consistent views of Earth benefits scientific research.
Since the program started, more than 50 years ago, Landsat data have improved. When advances in calibration and validation today are applied to past satellite missions, researchers can consistently see how land changes over decades. To maintain this criterion, the ECCOE team continues to seek new and better ways to calibrate and validate data, which includes using the moon for calibration and drones for ground validation (fig. 1).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243039","usgsCitation":"Shaw, J., Anderson, C., Choate, M., and Micijevic, E., 2024, Landsat geometric and radiometric calibration and characterization: U.S. Geological Survey Fact Sheet 2024–3039, 4 p., https://doi.org/10.3133/fs20243039.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-170688","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":462847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3039/covrthb.jpg"},{"id":462848,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3039/fs20243039.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462849,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3039/fs20243039.xml"},{"id":462850,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3039/images"},{"id":463094,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243039/full"}],"contact":"USGS EROS Cal/Val Center of Excellence (ECCOE) Project Team
U.S. Geological Survey
Earth Resources Observation and Science
47914 252nd Street
Sioux Falls, SD 57198
Email: eccoe@usgs.gov
Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114461","usgsCitation":"Yang, X., Zhu, Z., Kroeger, K.D., Qiu, S., Covington, S., Conrad, J.R., and Zhu, Z., 2024, Tracking mangrove condition changes using dense Landsat time series: Remote Sensing of Environment, v. 315, 114461, 20 p., https://doi.org/10.1016/j.rse.2024.114461.","productDescription":"114461, 20 p.","ipdsId":"IP-171456","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":466856,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2024.114461","text":"Publisher Index Page"},{"id":464698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.7435896795151,\n 29.468105811418027\n ],\n [\n -83.7435896795151,\n 24.675167884786973\n ],\n [\n -79.34983154106673,\n 24.675167884786973\n ],\n [\n -79.34983154106673,\n 29.468105811418027\n ],\n [\n -83.7435896795151,\n 29.468105811418027\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Xiucheng","contributorId":346867,"corporation":false,"usgs":false,"family":"Yang","given":"Xiucheng","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":920060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Zhe","contributorId":346868,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":920061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":920062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qiu, Shi","contributorId":346869,"corporation":false,"usgs":false,"family":"Qiu","given":"Shi","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":920063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Covington, Scott","contributorId":346870,"corporation":false,"usgs":false,"family":"Covington","given":"Scott","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":920064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conrad, Jeremy R.","contributorId":346871,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":920065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":920066,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70258644,"text":"ofr20241058 - 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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), 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website:
https://earthexplorer.usgs.gov.
This quarterly report is the third to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
This quarterly report is the first to not include analysis results for Landsat 7 because Enhanced Thematic Mapper Plus imaging was suspended on January 19, 2024, after the satellite transitioned into full sunlight. The satellite has been drifting since early 2022 after being lowered from the nominal orbit altitude, and the transition into full sunlight is a result of the satellite operating in its extended science mission. Additional information about the imaging suspension is available at https://www.usgs.gov/landsat-missions/news/landsat-7-imaging-suspended. Additional information about the Landsat 7 extended science mission is available at https://www.usgs.gov/landsat-missions/landsat-7-extended-science-mission.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241058","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Barsi, J., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2024 (ver. 1.1, December 2024): U.S. Geological Survey Open-File Report 2024–1058, 57 p., https://doi.org/10.3133/ofr20241058.","productDescription":"Report: viii, 57 p.; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-165326","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":439140,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1058/images/"},{"id":439137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1058/coverthb2.jpg"},{"id":439138,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1058"},{"id":439139,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.XML"},{"id":439141,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241058/full"},{"id":439142,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":464902,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1058/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: September 24, 2024; Version 1.1: December 11, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C- and N-related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi- and hyperspectral missions with targeted band placement, such as the proposed 26-band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.
","language":"English","publisher":"AGU","doi":"10.1029/2024JG008089","usgsCitation":"Yan, D., Reed, S., Rutherford, W., Javadian, M., Reibold, R.H., Villarreal, M.L., Poulter, B., Song, S., and Smith, W., 2024, Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau: Journal of Geophysical Research: Biogeosciences, v. 129, no. 8, e2024JG008089, 14 p., https://doi.org/10.1029/2024JG008089.","productDescription":"e2024JG008089, 14 p.","ipdsId":"IP-153887","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008089","text":"Publisher Index 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China Renewable Energy Engineering Institute, Beijing 100120, China.","active":true,"usgs":false}],"preferred":false,"id":918762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":918763,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutherford, William A.","contributorId":346342,"corporation":false,"usgs":false,"family":"Rutherford","given":"William A.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918764,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Javadian, 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Institute","active":true,"usgs":false}],"preferred":false,"id":918769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, William K.","contributorId":346346,"corporation":false,"usgs":false,"family":"Smith","given":"William K.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918770,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257924,"text":"70257924 - 2024 - The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats","interactions":[],"lastModifiedDate":"2024-09-03T14:04:52.601735","indexId":"70257924","displayToPublicDate":"2024-08-22T08:35:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats","docAbstract":"Decadal scale lake drying in interior Alaska results in lake margin colonization by willow shrub and graminoid vegetation, but the effects of these changes on plant production, biodiversity, soil properties, and soil microbial communities are not well known. We studied changes in soil organic carbon (SOC) and nitrogen (N) storage, plant and microbial community composition, and soil microbial activities in drying and non-drying lakes in the Yukon Flats National Wildlife Refuge. Historic changes in lake area were determined using Landsat imagery. Results showed that SOC storage in drying lake margins declined by 0.13 kg C m−2 yr−1 over 30 years of exposure of lake sediments, with no significant change in soil N. Lake drying resulted in an increase in graminoid and shrub aboveground net primary production (ANPP, +3% yr−1) with little change in plant functional composition. Increases in ANPP were similar in magnitude (but opposite in sign) to losses in SOC over a 30-year drying trend. Potential decomposition rates and soil enzyme activities were lower in drying lake margins compared to stable lake margins, possibly due to high salinities in drying lake margin soils. Microbial communities shifted in response to changing plant communities, although they still retained a legacy of the previous plant community. Understanding how changing lake hydrology impacts the ecology and biogeochemistry of lake margin terrestrial ecosystems is an underexamined phenomenon with large impacts to landscape processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007819","usgsCitation":"Patil, V.P., McFarland, J., Wickland, K., Manies, K.L., Winterstein, M., Hollingsworth, T., Euskirchen, E., and Waldrop, M., 2024, The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats: JGR Biogeosciences, v. 129, no. 8, e2023JG007819, 21 p., https://doi.org/10.1029/2023JG007819.","productDescription":"e2023JG007819, 21 p.","ipdsId":"IP-139844","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439200,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007819","text":"Publisher Index Page"},{"id":433403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150.14311428675356,\n 66.58339320669828\n ],\n [\n -150.0613509618515,\n 65.52747431340518\n ],\n [\n -143.489470862569,\n 65.54657614460567\n ],\n [\n -143.48736112738945,\n 66.54807699281983\n ],\n [\n -150.14311428675356,\n 66.58339320669828\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":912009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFarland, Jack 0000-0001-9672-8597","orcid":"https://orcid.org/0000-0001-9672-8597","contributorId":214819,"corporation":false,"usgs":true,"family":"McFarland","given":"Jack","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":912012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":912011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":912013,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Winterstein, Mark","contributorId":343792,"corporation":false,"usgs":false,"family":"Winterstein","given":"Mark","email":"","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":912014,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hollingsworth, Teresa N.","contributorId":343793,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"Teresa N.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":912015,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euskirchen, Eugénie S.","contributorId":83378,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugénie S.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":912016,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":912010,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","interactions":[{"subject":{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"70261584","publicationYear":"2024","noYear":false,"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"predicate":"SUPERSEDED_BY","object":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"id":1}],"supersededBy":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"lastModifiedDate":"2025-05-20T13:24:14.978891","indexId":"70261584","displayToPublicDate":"2024-08-09T08:50:10","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","docAbstract":"Two eelgrass (Zostera marina) maps of Izembek Lagoon, Alaska, were generated by first creating maps of spectrally unique classes from each of two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing the spectral classes with information about eelgrass conditions based on field data. Maps depicting various eelgrass metrics, such as percent cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons between the 2016 and 2020 Sentinel-2 maps of eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined between 2006 and 2020 were most prevalent in the central part Izembek Lagoon, while areas of possible biomass decline were more prevalent in the southern part between 2016 and 2020. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data provides capabilities for making comparisons over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2024.08.07.607047","usgsCitation":"Douglas, D.C., Fleming, M., Patil, V.P., and Ward, D.H., 2024, Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery: BioRxiv, https://doi.org/10.1101/2024.08.07.607047.","productDescription":"35 p.","ipdsId":"IP-168423","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466967,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.08.07.607047","text":"External Repository"},{"id":465143,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -162.9274024959248,\n 55.158158818332055\n ],\n [\n -162.84978004715862,\n 55.18903118481694\n ],\n [\n -162.68483234353056,\n 55.32568974463476\n ],\n [\n -162.63354608309427,\n 55.35091614284903\n ],\n [\n -162.5670125555805,\n 55.33988157021267\n ],\n [\n -162.4880039916577,\n 55.3792766436201\n ],\n [\n -162.49632068259703,\n 55.470522299013965\n ],\n [\n -162.5891903980851,\n 55.45166118374158\n ],\n [\n -162.78047428968748,\n 55.384001418091316\n ],\n [\n -162.88304681127136,\n 55.3438228419308\n ],\n [\n -163.03274724817751,\n 55.22383284787324\n ],\n [\n -163.09789466053493,\n 55.170827297352844\n ],\n [\n -162.9274024959248,\n 55.158158818332055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":921108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleming, Michael D.","contributorId":332620,"corporation":false,"usgs":false,"family":"Fleming","given":"Michael D.","affiliations":[{"id":79518,"text":"Images Unlimited","active":true,"usgs":false}],"preferred":false,"id":921109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":921110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":921111,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256400,"text":"sir20245033 - 2024 - Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021","interactions":[],"lastModifiedDate":"2026-02-03T18:28:42.919405","indexId":"sir20245033","displayToPublicDate":"2024-07-30T12:53:26","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5033","displayTitle":"Assessment of Long-Term Changes in Surface-Water Extent Within Klamath Marsh, South-Central Oregon, 1985–2021","title":"Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021","docAbstract":"The annual maximum extent of surface water in Klamath Marsh has naturally fluctuated in response to periods of wet and dry conditions in the surrounding basin. Field observations during the 2010s indicate that the annual maximum extent of surface water has been declining and the marsh is not responding to hydrologic inputs as it had historically. This report describes the results of a hydrologic evaluation of Klamath Marsh to characterize and understand multi-year declines in the surface-water extent and increased intermittency of streamflow exiting the marsh.
Landsat imagery collected during 1985–2021 was processed to create a time series of annual maximum surface-water extent to assess changes in surface-water inundation within the marsh. A 50-percent decrease in the mean surface area of annual total open-water extent (TOWE) during the latter half of the study period (2003–21) compared to the first half (1985–2003) was observed in this 37-year time-series dataset. The change in open-water extent was offset by a corresponding increase in dry land in the marsh.
Time series of streamflow, groundwater level, total annual precipitation, annual mean temperature, and anthropogenic water use and water management were compiled and evaluated to improve understanding of the factors affecting TOWE. Statistically significant downward trends in the regional groundwater table and streamflow into and out of the marsh were identified as well as statistically significant upward trends in annual mean temperature. Statistically significant correlations among TOWE, streamflow, and groundwater level also were identified. The decreasing trends could not be attributed to changes in total annual precipitation or changing anthropogenic groundwater use within the study area.
Declines in the open-water extent of Klamath Marsh since 2000 principally are due to a decoupling of the groundwater and surface-water system beneath the marsh because of regional declines in groundwater level. Regional increases in air temperature and the reestablishment of more than 55,000 acres of forested land within the study area have likely contributed to increasing evapotranspiration, leaving less water available for groundwater recharge and stream base flow and resulting in basin-wide declines in streamflow and groundwater levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245033","collaboration":"Prepared in cooperation with the Klamath Tribes","usgsCitation":"Kennedy, J.J., Johnson, H.M., Gingerich, S.B., 2024, Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021: U.S. Geological Survey Scientific Investigations Report 2024–5033, 32 p., https://doi.org/10.3133/sir20245033.","productDescription":"Report: ix, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-153514","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499460,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117151.htm","linkFileType":{"id":5,"text":"html"}},{"id":431674,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.XML"},{"id":431673,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5033/images"},{"id":431672,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RC7RJM","text":"USGS data release","description":"USGS data release","linkHelpText":"Klamath Marsh January through June maximum surface water extent, 1985–2021"},{"id":431671,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245033/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5033"},{"id":431670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5033"},{"id":431669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27115315992094,\n 43.15707158138778\n ],\n [\n -122.27115315992094,\n 42.30\n ],\n [\n -121.15,\n 42.30\n ],\n [\n -121.15,\n 43.15707158138778\n ],\n [\n -122.27115315992094,\n 43.15707158138778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high-resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine-class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant (p less than 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16-year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/agg2.20553","usgsCitation":"Smith, B.W., Soulard, C.E., and Walker, J., 2024, Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020: Agrosystems, Geosciences & Environment, v. 7, no. 3, e20553, 16 p., https://doi.org/10.1002/agg2.20553.","productDescription":"e20553, 16 p.","ipdsId":"IP-157770","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":466976,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/agg2.20553","text":"Publisher Index Page"},{"id":462483,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.39755424898114,\n 34.420043865180986\n ],\n [\n -118.3779343983518,\n 35.235828590126644\n ],\n [\n -118.14248794924036,\n 35.84498396138433\n ],\n [\n -121.41402389397426,\n 40.45021878284146\n ],\n [\n -124.11418123355082,\n 39.15848742125334\n ],\n [\n -120.39755424898114,\n 34.420043865180986\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Britt Windsor 0000-0003-1556-2383","orcid":"https://orcid.org/0000-0003-1556-2383","contributorId":287481,"corporation":false,"usgs":true,"family":"Smith","given":"Britt","email":"","middleInitial":"Windsor","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914533,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256002,"text":"70256002 - 2024 - On connecting hydro-social parameters to vegetation greenness differences in an evolving groundwater-dependent ecosystem","interactions":[],"lastModifiedDate":"2024-07-12T11:51:49.080855","indexId":"70256002","displayToPublicDate":"2024-07-10T06:47:49","publicationYear":"2024","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":"On connecting hydro-social parameters to vegetation greenness differences in an evolving groundwater-dependent ecosystem","docAbstract":"The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.
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This is the second quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241026","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Shaw, J.L., Denevan, A., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Barsi, J., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2023 (ver. 1.1, December 2024): U.S. Geological Survey Open-File Report 2024–1026, 62 p., https://doi.org/10.3133/ofr20241026.","productDescription":"Report: ix, 62 p.; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-162286","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":427978,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1026/coverthb2.jpg"},{"id":427981,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1026/images/"},{"id":427979,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1026/ofr20241026.pdf","text":"Report","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1026"},{"id":427980,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1026/ofr20241026.XML"},{"id":427982,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":427983,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241026/full"},{"id":464893,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1026/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: April 22, 2024; Version 1.1: December 11, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.
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Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
","language":"English","publisher":"MDPI","doi":"10.3390/s24072339","usgsCitation":"Thieme, A., Prabhakara, K., Jennewein, J., Lamb, B.T., McCarty, G.T., and Hively, W.D., 2024, Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits: Sensors, v. 24, no. 7, 2339, 25 p., https://doi.org/10.3390/s24072339.","productDescription":"2339, 25 p.","ipdsId":"IP-079899","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":439911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s24072339","text":"Publisher Index Page"},{"id":427561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Beltsville","otherGeospatial":"Beltsville Agricultural Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.93347699075227,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.029631686898625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prabhakara, Kusuma","contributorId":335445,"corporation":false,"usgs":false,"family":"Prabhakara","given":"Kusuma","affiliations":[{"id":80408,"text":"University of Maryland, Department of Geographic Sciences","active":true,"usgs":false}],"preferred":false,"id":898361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":335446,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","email":"","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarty, Gregory T.","contributorId":335447,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"T.","affiliations":[{"id":65190,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":898364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hively, W. 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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 7, 8, and 9 for quarter 3 (July–September) of 2023. 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.
This is the first quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241017","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Shaw, J.L., Denevan, A., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2023 (ver. 1.1, December 2024): U.S. Geological Survey Open-File Report 2024–1017, 65 p., https://doi.org/10.3133/ofr20241017.","productDescription":"Report: ix, 65 p.; Dataset","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-159043","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":426771,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":464896,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1017/ofr20241017.XML","text":"XML","size":"184 KB","linkFileType":{"id":8,"text":"xml"}},{"id":464897,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1017/versionHist.txt","text":"Version History","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2024–1017 Version History"},{"id":464898,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241017/full"},{"id":464899,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1017/images/"},{"id":464900,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1017/coverthb1.jpg"},{"id":464904,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1017/ofr20241017.pdf","text":"Report","size":"5.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1017 (ver.1.1)"}],"edition":"Version 1.0: March 19, 2024; Version 1.1: December 11, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The number of large, high-severity wildfires has been increasing across the western United States over the last several decades. It is not fully understood how changes in the frequency of large, severe wildfires may impact the resilience of conifer forests, due to alterations in regeneration success or failure. Our research investigates 30 years of conifer recovery patterns within 34 high-severity wildfire complexes (1988–1991) of the Northern Rocky Mountains. We evaluate the capability of snow-cover Landsat to characterize conifer tree recolonization of high-severity burn patches. Snow-cover images isolate conifer-specific vegetation signals by diminishing spectral contributions from soil and deciduous vegetation. The presence of conifer regeneration was successfully classified by snow-cover Landsat at >10% canopy cover at 98% accuracy and modeled at 3-year intervals post-fire. Spectral detectability of regenerating conifer vegetation began 11–19 years post-fire, varying across forest types. Thirty years post-fire, 65% of the total high-severity burn area had been recolonized by conifer trees, with differences observed between forest types: 72% of lodgepole pine, 77% of Douglas-fir, and 44% of fir-spruce severely burned areas containing conifer regeneration. Projected recovery timelines to pre-fire conifer vegetation also differed between lodgepole pine (29.5 years), Douglas-fir (36.9 years), and fir-spruce forests (48.7 years), as estimated from snow-cover NDVI trends. Although we generally documented patterns of conifer resilience, we also identified reduced likelihoods of recovery within high-severity burn patches exhibiting greater area-to-perimeter ratios, aridity, south-facing aspects, slopes, and elevation. Snow-cover Landsat imagery was shown to improve the characterization of post-fire forest recovery and may be applied to support forest restoration decision-making following high-severity wildfire.
Customer Service, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16060947","usgsCitation":"Kumar, A., Roy, A.H., Andreadis, K., He, X., and Butler, C., 2024, A multi-sensor approach to characterize winter water-level drawdown patterns in lakes: Remote Sensing, v. 16, no. 6, 947, 23 p., https://doi.org/10.3390/rs16060947.","productDescription":"947, 23 p.","ipdsId":"IP-159744","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16060947","text":"Publisher Index Page"},{"id":433622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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