{"pageNumber":"8","pageRowStart":"175","pageSize":"25","recordCount":1869,"records":[{"id":70232150,"text":"ofr20221033 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021","interactions":[],"lastModifiedDate":"2022-09-27T12:38:02.126827","indexId":"ofr20221033","displayToPublicDate":"2022-06-10T07:10:38","publicationYear":"2022","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":"2022-1033","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 4, 2021","title":"ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021","docAbstract":"<h1>Executive Summary</h1><p>The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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.</p><p>This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: <a href=\"https://earthexplorer.usgs.gov\" data-mce-href=\"https://earthexplorer.usgs.gov\">https://earthexplorer.usgs.gov</a>.</p><p>One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: <a href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\" data-mce-href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\">https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold</a>.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221033","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, M.N., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021: U.S. Geological Survey Open-File Report 2022–1033, 38 p., 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a><br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Managing Rangeland Health</li><li>Monitoring Mountain Snow and Ice</li><li>Tracking Water Use from Space</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-08","noUsgsAuthors":false,"publicationDate":"2022-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128069,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":844343,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70231900,"text":"fs20223039 - 2022 - Georgia and Landsat","interactions":[],"lastModifiedDate":"2025-03-20T14:27:39.867136","indexId":"fs20223039","displayToPublicDate":"2022-06-02T09:16:18","publicationYear":"2022","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":"2022-3039","displayTitle":"Georgia and Landsat","title":"Georgia and Landsat","docAbstract":"<p><span>Georgia’s nickname is “The Peach State” for its fruitful production, but it also could be called “The State of Abundance.” Georgia ranks in the top 10 States for population, at more than 10 million residents, and 6 million residents are in the greater Atlanta area. Georgia also ranks in the top 10 States for forest areas with 24 million acres, or about two-thirds of the State. Its trees vary from deciduous hardwoods and pines to cypress and magnolias.</span></p><p><span>Georgia has diverse ecosystems, from the Blue Ridge Mountains in the northeast to the hilly, populous Piedmont region in the middle to the vast Coastal Plains that cover the southern half of the State with farms, forests, and wetlands. The State’s 14 major river basins, including the Chattahoochee and the Savannah, ultimately drain to the Atlantic Ocean or the Gulf of America. Millions of acres of wetlands include floodplain wetlands, freshwater swamps such as the Okefenokee Swamp, and coastal marshes. More than a dozen barrier islands lie off Georgia’s coastline. Wildlife in these myriad habitats include black bears, alligators, armadillos, manatees, sea turtles, and more than 40 species of snakes.</span></p><p><span>The Landsat satellite imagery scale and its 50-year record of Earth observations make Landsat ideal for monitoring changes in these diverse environments and supporting decision-making by government officials and land managers. Here are several ways Landsat has benefited Georgia.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223039","usgsCitation":"U.S. Geological Survey, 2022, Georgia and Landsat (ver. 1.1, March 2025): U.S. Geological Survey Fact Sheet 2022–3039, 2 p., https://doi.org/10.3133/fs20223039.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-137266","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401634,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223039/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401543,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3039/coverthb2.jpg"},{"id":401545,"rank":3,"type":{"id":31,"text":"Publication 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 \"}}]}","edition":"Version 1.0: June 2, 2022; Version 1.1: March 19, 2025","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Revealing Land Cover Changes</li><li>Finding Patterns of Forest Change</li><li>Identifying Urban Hotspots</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-02","revisedDate":"2025-03-19","noUsgsAuthors":false,"publicationDate":"2022-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":152492,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":844054,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70231899,"text":"fs20223038 - 2022 - North Carolina and Landsat","interactions":[],"lastModifiedDate":"2023-01-21T15:09:45.582857","indexId":"fs20223038","displayToPublicDate":"2022-06-01T09:15:52","publicationYear":"2022","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":"2022-3038","displayTitle":"North Carolina and Landsat","title":"North Carolina and Landsat","docAbstract":"<p>North Carolina’s rich history and importance in the colonial days played a critical role in the Nation’s economic development. It was also the setting for events like the Wright Brothers’ famous first flight of a powered aircraft, called “Wright Flyer,” which took place in Kitty Hawk in 1903. Today, North Carolina license plates proudly proclaim the State as “First in Flight.”</p><p>The aerospace and defense sectors remain large players in the State’s economy to this day. Boeing, Lockheed Martin, General Electric Aviation, and others maintain a presence in the Tar Heel State, and military installations dot the landscape.</p><p>It is fitting, then, that North Carolinians rely upon a satellite system to monitor and study the health of their State’s vast and varied terrain: its coastal plain, the sweeping valleys and peaks of the Blue Ridge Mountains, and the fertile rolling plateau of the Piedmont ecoregion that separates the two. The U.S. Geological Survey Landsat satellite program’s unparalleled 50-year record of Earth surface change offers scientists, watershed and land managers, urban planners, and others insight into landscape change, both drastic and mild. Landsat observations document everything from posthurricane floods that push inland to subtle shifts in forest composition and coastal encroachment on urban and military infrastructure.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223038","usgsCitation":"U.S. Geological Survey, 2022, North Carolina and Landsat: U.S. Geological Survey Fact Sheet 2022–3038, 2 p., https://doi.org/10.3133/fs20223038.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-140421","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401532,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223038/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401531,"rank":4,"type":{"id":34,"text":"Image 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Carolina\",\"nation\":\"USA  \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Watching Over Forests</li><li>Mapping the Coastlines</li><li>Urban Growth</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-01","noUsgsAuthors":false,"publicationDate":"2022-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological 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,{"id":70231650,"text":"fs20223032 - 2022 - Oklahoma and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:12:05.638573","indexId":"fs20223032","displayToPublicDate":"2022-05-18T10:48:40","publicationYear":"2022","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":"2022-3032","displayTitle":"Oklahoma and Landsat","title":"Oklahoma and Landsat","docAbstract":"<p>Oklahoma benefits from a varied landscape abundant in resources. Mountains, grasslands, reservoirs, rivers, fields, and forests offer employment and enjoyment in a State that epitomizes the transition from north to south and east to west. Wheat grows in northern Oklahoma; cotton grows in the south. Wetter deciduous forest lands in the southeast contrast with drier mesas in the northwest. Among the many lakes and reservoirs, the Great Salt Plains Lake on the Arkansas River has thousands of acres of salt flats with unique hourglass-shaped selenite crystals.</p><p>Grassland and grazing permeate the Sooner State, which is the second largest cattle producer in the country. Oil, natural gas, and coal have long played a key role in Oklahoma’s economy and energy industry, whereas wind farms in western Oklahoma are a more recent addition.</p><p>The Landsat series of Earth observation satellites has allowed analysts to search for oil in Oklahoma from hundreds of miles above the Earth. The satellite program also helps Oklahoma’s agriculture industry keep an eye on the condition of crops and grazing land.</p><p>Here are just a few ways Landsat has benefited Oklahoma.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223032","usgsCitation":"U.S. Geological Survey, 2022, Oklahoma and Landsat: U.S. Geological Survey Fact Sheet 2022–3032, 2 p., https://doi.org/10.3133/fs20223032.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139807","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":400758,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223032/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400753,"rank":3,"type":{"id":31,"text":"Publication 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Viewing Fields</li><li>Characterizing Wildfires</li><li>Examining Tornado Damage</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-18","noUsgsAuthors":false,"publicationDate":"2022-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":127955,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":843232,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70231263,"text":"fs20223024 - 2022 - Hawaii and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:12:48.835499","indexId":"fs20223024","displayToPublicDate":"2022-05-04T11:10:31","publicationYear":"2022","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":"2022-3024","displayTitle":"Hawaii and Landsat","title":"Hawaii and Landsat","docAbstract":"<p>Hawaii stands apart from the rest of the United States, literally and figuratively. The nearest of the eight islands that make up the Hawaiian archipelago is 2,000 miles from the U.S. mainland. Like every bit of land mass within the State, it emerged from the Pacific Ocean after thousands of years of undersea volcanic activity. Kona International Airport, on the “Big Island” of Hawai‘i, was built atop 220-year-old lava flows from the Hualālai volcano.</p><p>The volcanic soils and tropical vegetation that stretch across Hawaii’s postcard-perfect peaks and valleys, as well as the trade winds that blow precipitation northeast across the islands and the warm Kona winds that move in from the south, are among the factors that make the study of the State’s landscapes distinct from the rest of the Nation.</p><p>Six active volcanoes continue to alter the land surface. Some of them, like the constantly erupting Kīlauea or Mauna Loa, the world’s largest active volcano, can threaten human life and property. Man-made changes to the islands, such as the long-term agricultural production of sugar cane or pineapple, along with the introduction of non-native species, have also affected the health of the State’s ecosystems.</p><p>Landsat satellites, with 50 years of repeat Earth observations and decades of data from infrared and thermal instruments capable of tracking lava flows, can offer unique opportunities for the study of Hawaii’s ever-changing landscapes. Here are a few examples of how Landsat benefits the State.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223024","usgsCitation":"U.S. Geological Survey, 2022, Hawaii and Landsat: U.S. Geological Survey Fact Sheet 2022–3024, 2 p., https://doi.org/10.3133/fs20223024.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-140071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":400152,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3024/images"},{"id":400121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3024/fs20223024.pdf","text":"Report","size":"3.23 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Tracking Lava Flows from Space</li><li>Monitoring Land Change Patterns</li><li>Mapping Land Conditions</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-04","noUsgsAuthors":false,"publicationDate":"2022-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":127955,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":842157,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70231237,"text":"fs20223025 - 2022 - Pennsylvania and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:13:22.17303","indexId":"fs20223025","displayToPublicDate":"2022-05-04T08:45:52","publicationYear":"2022","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":"2022-3025","displayTitle":"Pennsylvania and Landsat","title":"Pennsylvania and Landsat","docAbstract":"<p>The Commonwealth of Pennsylvania straddles an array of landscapes. From east to west, its 46,055 square miles connect the sea-level lowlands of the Atlantic seaboard with the rolling hills of the Midwest. It also acts as a bridge between regions from north to south, with the Appalachian Mountains swooping through its center from its northern border with New York to its southern borders with Maryland and West Virginia.</p><p>The Declaration of Independence and U.S. Constitution were signed in the Commonwealth’s largest city, Philadelphia, and it was the second of the original 13 colonies to ratify the Constitution. The pivotal Civil War battle of Gettysburg took place in the southern part of Pennsylvania. Western Pennsylvania was an early hub for the coal, oil, and steel industries that fueled the Nation’s growth in the 20th century and still factor into Pennsylvania’s economy. The varied natural features of Pennsylvania have been an important proving ground for the value of satellite imagery from the USGS Landsat Program. Scientists began to lean on Landsat observations early in the program’s history to map the effect of invasive moths on Commonwealth forests. That activity continues to this day. Space-based imagery has also contributed to the study and management of land change from energy development, urban growth, and shifting land use patterns.</p><p>The Landsat Program’s unparalleled 50-year archive of repeat Earth observations remains a critical public resource for Pennsylvania as climate change and land use patterns present new challenges to land managers and urban planners in the Keystone State.</p><p>Here are just a few examples of how Landsat has benefited Pennsylvania.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223025","usgsCitation":"U.S. Geological Survey, 2022, Pennsylvania and Landsat: U.S. Geological Survey Fact Sheet 2022–3025, 2 p., https://doi.org/10.3133/fs20223025.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-140045","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Watching Over the Forests</li><li>Mapping the Past, Sketching the Future</li><li>Tracking Change from the Energy Industry</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-04","noUsgsAuthors":false,"publicationDate":"2022-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":842114,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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It is the seventh-largest State, but it ranks in the bottom one-half of States for population. More than 72 percent of its 3.1 million residents live in the Las Vegas area.</p><p>In Nevada, the desert is not dull. An extraordinary variety of wildflowers bloom in the spring, and other plants include mesquite, cacti, creosote, and yucca such as Joshua trees (<i>Yucca brevifolia</i>). Sagebrush (<i>Artemisia tridentata</i>) is more than the State flower; it is a hardy, enduring shrub foundational to a vast ecosystem in the Great Basin that feeds and shelters hundreds of wildlife species. The Silver State has a strong mining tradition, and agriculture centers around livestock ranching and irrigated crops. Although mining and agriculture once formed the base of Nevada’s economy, tourism now leads the way, mostly from gambling and entertainment in Las Vegas, Reno, and other cities. Water is a critical resource for supporting residents, visitors, and industries, and Lake Mead behind Hoover Dam on the Colorado River supplies most of it for southern Nevada.</p><p>Nevada has significant natural resources, but they face threats—especially in a warming climate. Here are a few ways Landsat benefits Nevada.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223026","usgsCitation":"U.S. Geological Survey, 2022, Nevada and Landsat: U.S. Geological Survey Fact Sheet 2022–3026, 2 p., https://doi.org/10.3133/fs20223026.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-137706","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":400091,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223026/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400042,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3026/images"},{"id":400041,"rank":3,"type":{"id":31,"text":"Publication 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Watching the Water</li><li>Monitoring Threats to Sagebrush</li><li>Characterizing Urban Areas</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-03","noUsgsAuthors":false,"publicationDate":"2022-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":842075,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70231181,"text":"fs20223027 - 2022 - Wyoming and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:14:23.957794","indexId":"fs20223027","displayToPublicDate":"2022-05-03T09:59:16","publicationYear":"2022","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":"2022-3027","displayTitle":"Wyoming and Landsat","title":"Wyoming and Landsat","docAbstract":"<p>Wyoming has the smallest population of any State—fewer than 600,000&nbsp;people—but an abun­dance of wildlife. The largest number of pronghorn (<i>Antilocapra americana</i>), often called antelope, and the biggest public bison (<i>Bison bison</i>) herd in the United States live in Wyoming, which also hosts elk (<i>Cervus elaphus</i>), moose (<i>Alces americanus</i>), bighorn sheep (<i>Ovis canadensis</i>), black bears (<i>Ursus americanus</i>), and grizzly bears (<i>U. arctos</i>). The terrain of the Nation’s 10th largest State varies from the Black Hills to the Rocky Mountains, and from Great Plains grassland to Wyoming Basin desert sagebrush.</p><p>Natural resources attract millions of visitors annually, especially to the country’s first national park, Yellowstone, and the ski slopes in Grand Teton National Park and elsewhere. Natural resources account for much of the employment in the Cowboy State, from tourism and ranching to coal, natural gas, and oil mining.</p><p>To help monitor these natural resources, researchers and land managers have relied on USGS Landsat imagery and data. The scale of images from the first Landsat satellites helped geologists detect previously unknown uranium deposits in Wyoming, which is the leading State for uranium production. Today, land managers and researchers take advantage of Landsat and its 50-year archive to explore landscape change over time—and continue to make new discoveries.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223027","usgsCitation":"U.S. Geological Survey, 2022, Wyoming and Landsat: U.S. Geological Survey Fact Sheet 2022–3027, 2 p., https://doi.org/10.3133/fs20223027.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-138811","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":400058,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223027/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400038,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3027/images"},{"id":400037,"rank":3,"type":{"id":31,"text":"Publication 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Finding a New Thermal Area</li><li>Mapping the Severity of Fires</li><li>Tracking Change Over Time</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-03","noUsgsAuthors":false,"publicationDate":"2022-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128037,"corporation":true,"usgs":false,"organization":"U.S. Geological 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,{"id":70233583,"text":"70233583 - 2022 - Development and application of Landsat-based wetland vegetation cover and unvegetated-vegetated marsh ratio (UVVR) for the conterminous United States","interactions":[],"lastModifiedDate":"2022-10-17T15:42:12.550353","indexId":"70233583","displayToPublicDate":"2022-05-02T07:01:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Development and application of Landsat-based wetland vegetation cover and unvegetated-vegetated marsh ratio (UVVR) for the conterminous United States","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Effective management and restoration of salt marshes and other vegetated intertidal habitats require objective and spatially integrated metrics of geomorphic status and vulnerability. The unvegetated-vegetated marsh ratio (UVVR), a recently developed metric, can be used to establish present-day vegetative cover, identify stability thresholds, and quantify vulnerability to open-water conversion over a range of spatial scales. We developed a Landsat-based approach to quantify the within-pixel vegetated fraction and UVVR for coastal wetlands of the conterminous United States, at 30-m resolution for 2014–2018. Here we present the methodology used to generate the UVVR from spectral indices, along with calibration, validation, and spatial autocorrelation assessments. We then demonstrate multiple applications of the data across varying spatial scales: first, we aggregate the UVVR across individual states and estuaries to quantify total vegetated wetland area for the nation. On the state level, Louisiana and Florida account for over 50% of the nation’s total, while on the estuarine level, the Chesapeake Bay Estuary and selected Louisiana coastal areas each account for over 6% of the nation’s total vegetated wetland area. Second, we present cases where this dataset can be used to track wetland change (e.g., expansion due to restoration and loss due to stressors). Lastly, we propose a classification methodology that delineates areas vulnerable to open-water expansion based on the 5-year mean and standard deviation of the UVVR. Calculating the UVVR for the period-of-record back to 1985, as well as regular updating, will fill a critical gap for tracking national status of salt marshes and other vegetated habitats through time and space.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s12237-022-01081-x","usgsCitation":"Ganju, N., Couvillion, B., Defne, Z., and Ackerman, K., 2022, Development and application of Landsat-based wetland vegetation cover and unvegetated-vegetated marsh ratio (UVVR) for the conterminous United States: Estuaries and Coasts, v. 45, p. 1861-1878, https://doi.org/10.1007/s12237-022-01081-x.","productDescription":"18 p.","startPage":"1861","endPage":"1878","ipdsId":"IP-134943","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":219340,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":847478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Defne, Zafer 0000-0003-4544-4310 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,{"id":70230756,"text":"fs20223023 - 2022 - Utah and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T17:06:49.901325","indexId":"fs20223023","displayToPublicDate":"2022-04-26T13:58:01","publicationYear":"2022","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":"2022-3023","displayTitle":"Utah and Landsat","title":"Utah and Landsat","docAbstract":"<p>Utah’s list of notable features runs long, but scenery rises to the top. The Colorado River does not simply run through southeastern Utah; it meanders through steep canyons of the eroded sedimentary rock that colors the sweeping vistas of the Colorado Plateau. Stone arches, spires, hoodoos, cliffs, and bridges in hues of red enchant residents and tourists. Mountain ranges extending through the State add dynamic views—and skiing opportunities.</p><p>The Great Salt Lake in northern Utah is the largest saltwater lake in the Western Hemisphere. The western part of Utah, including the Great Salt Lake, lies in the Great Basin, a multi-State drainage area with no outlet. Because the lake has no outlet to flush out any salt, evaporation produces a higher concentration of salts in the water or soils, called salinity. The lake lacks fish but supports algae and brine shrimp, and extensive wetlands around the lake attract millions of migratory birds.</p><p>Landsat imagery is useful for showing surface changes, such as the fluctuating water levels of the shallow Great Salt Lake. The lake flooded in the 1980s, but the southern part dropped to its lowest level in recorded history in 2021. Landsat data also can take a much deeper look at land and water conditions. Here are several ways Landsat benefits Utah.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223023","usgsCitation":"U.S. Geological Survey, 2022, Utah and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3023, 2 p., https://doi.org/10.3133/fs20223023.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-138012","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412280,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3023/versionHist.txt","size":"1.85 kB","linkFileType":{"id":2,"text":"txt"}},{"id":412279,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3023/fs20223023.pdf","text":"Report","size":"3.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3023"},{"id":399695,"rank":4,"type":{"id":39,"text":"HTML 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 \"}}]}","edition":"Version 1.0: April 26, 2022; Version 1.1: January 24, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Watching Water Use</li><li>Providing Wildfire Information</li><li>Detecting Harmful Water</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-04-26","revisedDate":"2023-01-24","noUsgsAuthors":false,"publicationDate":"2022-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological 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,{"id":70230856,"text":"70230856 - 2022 - Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series","interactions":[],"lastModifiedDate":"2022-04-27T11:42:39.21789","indexId":"70230856","displayToPublicDate":"2022-04-26T06:39:47","publicationYear":"2022","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":"Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0085\"><span>Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and&nbsp;<a class=\"topic-link\" title=\"Learn more about natural resource management from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/natural-resource-management\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/natural-resource-management\">natural resource management</a>. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense&nbsp;<a class=\"topic-link\" title=\"Learn more about Landsat from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat\">Landsat</a>&nbsp;time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic&nbsp;<a class=\"topic-link\" title=\"Learn more about surface reflectance from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/surface-reflectance\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/surface-reflectance\">surface reflectance</a>&nbsp;values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating&nbsp;<a class=\"topic-link\" title=\"Learn more about coastal vegetation from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/coastal-vegetation\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/coastal-vegetation\">coastal vegetation</a>&nbsp;caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283&nbsp;km</span><sup>2</sup><span>&nbsp;</span>of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6&nbsp;km<sup>2</sup><span>&nbsp;</span>per year) in the past 35&nbsp;years, largely due to conversion to open water in the context of sea-level rise.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113047","usgsCitation":"Yang, X., Zhu, Z., Qiu, S., Kroeger, K.D., Zhu, Z., and Covington, S., 2022, Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series: Remote Sensing of Environment, v. 276, 113047, 19 p., https://doi.org/10.1016/j.rse.2022.113047.","productDescription":"113047, 19 p.","ipdsId":"IP-138964","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448012,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113047","text":"Publisher Index 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Shirley","contributorId":219845,"corporation":false,"usgs":false,"family":"Qiu","given":"Shirley","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":841486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":841487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":290659,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhiliang","affiliations":[{"id":62470,"text":"U.S. Geological Survey, Reston, VA","active":true,"usgs":false}],"preferred":false,"id":841488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Covington, Scott","contributorId":245970,"corporation":false,"usgs":false,"family":"Covington","given":"Scott","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":841489,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70230744,"text":"fs20223021 - 2022 - Washington and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T16:57:48.188297","indexId":"fs20223021","displayToPublicDate":"2022-04-25T06:53:27","publicationYear":"2022","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":"2022-3021","displayTitle":"Washington and Landsat","title":"Washington and Landsat","docAbstract":"<p>Washington is a State of untamed wonders, from its ruggedly beautiful coastline to the volcanic peaks of the Cascades. “The Evergreen State” is also a State of contrasts, home to rainforests west of the Cascades and deserts to the east. Half of Washington is forested, and its orchards grow more than one-half of the apples sold in the United States. Rivers are important to the State, particularly the Columbia River, the largest North American river that flows into the Pacific Ocean. Dozens of dams have been constructed in the river basin, including the largest hydroelectric producer in the Nation, the Grand Coulee Dam.</p><p>Washington’s diverse landscapes also change in various ways over time. Some are relatively steady, like urban expansion in the populous Puget Sound region. Others can be sudden, like the Mount Saint Helens eruption in 1980—one of the largest volcanic eruptions that caused the largest landslide in U.S. history. Although landscape change may be hard to detect at any one time, the Landsat program provides an objective view of it over decades. Landsat not only allows one to see what Mount Saint Helens looked like before and after the eruption, but also how the area has evolved since.</p><p>Here are a few examples of how Landsat benefits Washington.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223021","usgsCitation":"U.S. Geological Survey, 2022, Washington and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3021, 2 p., https://doi.org/10.3133/fs20223021.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-134319","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":399661,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 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 \"}}]}","edition":"Version 1.0: April 25, 2022; Version 1.1: January 24, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Tracking Mount Saint Helens’ Recovery</li><li>Monitoring Landscapes and Habitats</li><li>Assessing the Aftermath of Fires</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-04-25","revisedDate":"2023-01-24","noUsgsAuthors":false,"publicationDate":"2022-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":841274,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70235816,"text":"70235816 - 2022 - Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan","interactions":[],"lastModifiedDate":"2022-12-28T16:27:54.620072","indexId":"70235816","displayToPublicDate":"2022-04-20T09:47:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2375,"text":"Journal of Maps","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan","docAbstract":"<p><span>Agriculture is a key element of Afghanistan’s economy and plays an essential role supporting the expanding population and urban development of Kabul, the country’s capital. Over the past decades the urban landscape has changed substantially and agricultural land use has shifted in its extent, location, and density. Identifying trends in the amount of agricultural area, as an indication of food production, is important for city planning and humanitarian efforts. While many studies have investigated Afghanistan's agriculture, most are conducted at scales that preclude their use for local-scale decision-making. This study quantifies agricultural extent across 32 years from 1988 to 2020 at local scale using simple and repeatable Landsat multispectral image analysis. The volume of data in time-series analysis complicatesvisualization of key findings and long-term trends. This study also explored visualization methods such as zonal mapping, animations, and the isolation of key themes in a 2D static map.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17445647.2022.2063079","usgsCitation":"DeWitt, J.D., Boston, K.M., Alessi, M.A., and Chirico, P.G., 2022, Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan: Journal of Maps, v. 18, no. 2, p. 352-361, https://doi.org/10.1080/17445647.2022.2063079.","productDescription":"10 p.","startPage":"352","endPage":"361","ipdsId":"IP-113897","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":448066,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/17445647.2022.2063079","text":"Publisher Index Page"},{"id":435869,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WKTUBI","text":"USGS data release","linkHelpText":"Urban and developed areas indicated by classification of Landsat 2018 multispectral imagery"},{"id":435868,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IC4QFW","text":"USGS data release","linkHelpText":"Agricultural area by year between 1988 and 2000 in Kabul, Afghanistan"},{"id":405389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Afghanistan","city":"Kabul","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              69,\n              34.347971491244955\n            ],\n            [\n              69.43771362304686,\n              34.347971491244955\n            ],\n            [\n              69.43771362304686,\n              34.75\n            ],\n            [\n              69,\n              34.75\n            ],\n            [\n              69,\n              34.347971491244955\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":849404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boston, Kathleen M 0000-0003-1301-9651","orcid":"https://orcid.org/0000-0003-1301-9651","contributorId":264351,"corporation":false,"usgs":false,"family":"Boston","given":"Kathleen","email":"","middleInitial":"M","affiliations":[{"id":54446,"text":"Aperture Federal, LLC","active":true,"usgs":false}],"preferred":false,"id":849405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alessi, Marissa Ann 0000-0002-1251-3108","orcid":"https://orcid.org/0000-0002-1251-3108","contributorId":244628,"corporation":false,"usgs":true,"family":"Alessi","given":"Marissa","email":"","middleInitial":"Ann","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":849406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chirico, Peter G. 0000-0001-8375-5342","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":63838,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":849407,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","interactions":[{"subject":{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","indexId":"ofr20211030K","publicationYear":"2022","noYear":false,"chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2022-04-19T10:54:07.62676","indexId":"ofr20211030K","displayToPublicDate":"2022-04-18T15:29:12","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.</p><p>The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030K","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA), chap. K of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030K.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129829","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":398958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/k/coverthb.jpg"},{"id":398959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-K"},{"id":398960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.XML"},{"id":398961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/k/images"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science (EROS) Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>Purpose and Scope</li><li>System Description</li><li>Procedures</li><li>Measurements</li><li>Analysis</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-04-18","noUsgsAuthors":false,"publicationDate":"2022-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":840873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":840874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889 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":840875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":840876,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230201,"text":"sir20215128 - 2022 - Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon","interactions":[],"lastModifiedDate":"2026-04-02T20:05:12.056404","indexId":"sir20215128","displayToPublicDate":"2022-04-11T14:48:43","publicationYear":"2022","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":"2021-5128","displayTitle":"Hydrologic Budget of the Harney Basin Groundwater System, Southeastern Oregon","title":"Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon","docAbstract":"<p class=\"p1\">Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.</p><p class=\"p1\">The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.</p><p class=\"p2\">The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.</p><p class=\"p2\">Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.</p><p class=\"p1\">Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).</p><p class=\"p1\">During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.08056640625,\n              42.35854391749705\n            ],\n            [\n              -117.7734375,\n              42.35854391749705\n            ],\n            [\n              -117.7734375,\n              44.24519901522129\n            ],\n            [\n              -120.08056640625,\n              44.24519901522129\n            ],\n            [\n              -120.08056640625,\n              42.35854391749705\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/or-water target=\" data-mce-href=\"https://www.usgs.gov/centers/or-water target=\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>2130 SW 5th Avenue<br>Portland, Oregon 97201</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Groundwater Discharge</li><li>Groundwater Recharge</li><li>Summary and Discussion of Groundwater Hydrologic Budget</li><li>Limitations</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–7</li></ul>","publishedDate":"2022-04-11","revisedDate":"2022-11-07","noUsgsAuthors":false,"publicationDate":"2022-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, C. 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 \"}}]}","edition":"Version 1.0: April 5, 2022; Version 1.1: January 13, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Mapping Fire History Across the State</li><li>Keeping an Eye on the Everglades</li><li>Addressing a Changing Climate in Miami Beach</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-04-05","revisedDate":"2023-01-13","noUsgsAuthors":false,"publicationDate":"2022-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":210377,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":839404,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70230528,"text":"70230528 - 2022 - Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model","interactions":[],"lastModifiedDate":"2022-04-15T12:05:58.826449","indexId":"70230528","displayToPublicDate":"2022-04-02T07:01:22","publicationYear":"2022","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":"Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0080\"><span>The estimation and mapping of actual&nbsp;evapotranspiration&nbsp;(ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from&nbsp;Landsat&nbsp;thermal imagery-using the Operational Simplified&nbsp;Surface Energy&nbsp;Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000&nbsp;Landsat satellite&nbsp;images were used to produce 10&nbsp;years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly&nbsp;Eddy Covariance&nbsp;(EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R</span><sup>2</sup><span>&nbsp;&lt;&nbsp;0.6) correlation on sparsely vegetated surfaces such as grasslands or woody&nbsp;savanna&nbsp;and stronger correlation (R</span><sup>2</sup>&nbsp;&gt;&nbsp;0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R<sup>2</sup><span>&nbsp;of 0.82,&nbsp;root mean square error&nbsp;of 29&nbsp;mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R</span><sup>2</sup><span>&nbsp;&gt;&nbsp;0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and&nbsp;U.S.&nbsp;Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.</span></p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113011","usgsCitation":"Senay, G.B., Friedrichs, M., Morton, C., Parrish, G.E., Schauer, M., Khand, K., Kagone, S., Boiko, O., and Huntington, J., 2022, Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model: Remote Sensing of Environment, v. 275, 113011, 16 p., https://doi.org/10.1016/j.rse.2022.113011.","productDescription":"113011, 16 p.","ipdsId":"IP-128749","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448275,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113011","text":"Publisher Index Page"},{"id":435898,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZCZ78","text":"USGS data release","linkHelpText":"Actual Evapotranspiration at Landsat scale at CONUS scale for 2010-2019"},{"id":435897,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJLMAQ","text":"USGS data release","linkHelpText":"Annual SSEBop ET rasters at Landsat scale from 2010-2019 for the CONUS"},{"id":398816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n    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,{"id":70230095,"text":"70230095 - 2022 - Landsat Update March 2022","interactions":[],"lastModifiedDate":"2022-04-19T15:21:21.853606","indexId":"70230095","displayToPublicDate":"2022-03-28T10:19:39","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10566,"text":"Landsat Update","active":true,"publicationSubtype":{"id":30}},"title":"Landsat Update March 2022","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Hartpence, A., 2022, Landsat Update March 2022: Landsat Update, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-138688","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":399091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399090,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/landsat-missions/news/landsat-update-march-2022"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartpence, Anya 0000-0002-4510-3236","orcid":"https://orcid.org/0000-0002-4510-3236","contributorId":247379,"corporation":false,"usgs":false,"family":"Hartpence","given":"Anya","email":"","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":838990,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70230039,"text":"ofr20221025 - 2022 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021","interactions":[],"lastModifiedDate":"2022-04-14T15:52:36.403875","indexId":"ofr20221025","displayToPublicDate":"2022-03-25T11:52:25","publicationYear":"2022","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":"2022-1025","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 3, 2021","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021","docAbstract":"<h1>Executive Summary</h1><p>The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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.</p><p>This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 3 (July–September), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: <a href=\"https://earthexplorer.usgs.gov\" data-mce-href=\"https://earthexplorer.usgs.gov\">https://earthexplorer.usgs.gov</a>.</p><p>One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: <a href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\" data-mce-href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\">https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold</a>.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221025","usgsCitation":"Micijevic, E., Rengarajan, R., Haque, M.O., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, N., Denevan, A., Franks, S., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021: U.S. Geological Survey Open-File Report 2022–1025, 38 p., 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,{"id":70230014,"text":"fs20223017 - 2022 - Kentucky and Landsat","interactions":[],"lastModifiedDate":"2023-01-21T15:56:29.665852","indexId":"fs20223017","displayToPublicDate":"2022-03-24T14:18:40","publicationYear":"2022","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":"2022-3017","displayTitle":"Kentucky and Landsat","title":"Kentucky and Landsat","docAbstract":"<p>From its rolling pastures to its forested Appalachian peaks, Kentucky’s scenery offers beauty along with contrast. Rivers, including the Mississippi and the Ohio, border much of the State, and more rivers and hundreds of lakes are inside its borders. 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Landsat satellite data can help State and Federal governments monitor the quality and health of Kentucky’s lands and waters.</p><p>Here is a closer look at some of the many ways that Landsat benefits Kentucky.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223017","usgsCitation":"U.S. Geological Survey, 2022, Kentucky and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3017, 2 p., https://doi.org/10.3133/fs20223017.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-127727","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412029,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223017/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411899,"rank":5,"type":{"id":34,"text":"Image 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 \"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 18, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Assessing Wildfire Effects</li><li>Monitoring Potential Harm</li><li>A Bigger View of Parks and Forests</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-03-24","revisedDate":"2023-01-18","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":838672,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70230013,"text":"fs20223018 - 2022 - Massachusetts and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:52:23.276983","indexId":"fs20223018","displayToPublicDate":"2022-03-24T10:54:23","publicationYear":"2022","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":"2022-3018","displayTitle":"Massachusetts and Landsat","title":"Massachusetts and Landsat","docAbstract":"<p>Massachusetts is the seventh smallest U.S. State in land area, but its size is surpassed by its contributions to U.S. history and the economy, its academic and medical expertise, and its natural features. The Atlantic Ocean to the east gives the “Bay State” more than 1,500 miles of coastline that were important in past fishing and maritime trade industries and in the tourism industry of today for destinations such as Boston, Cape Cod, Nantucket, and Martha’s Vineyard. Forests cover roughly 60 percent of the State, which owns 315,000 acres of forests in parklands, reserves, and woodlands.</p><p>Massachusetts celebrates many strengths, but its strengths can be vulnerable to environmental change. A robust population of 7 million translates to a density of more than 800 people per square mile; in Boston, the density rises to more than 18,000 people per square mile. Urban temperature hotspots can increase health risks to residents.</p><p>Although the whims of wind and water have long reshaped coastlines, climate change-induced sea-level rise and severe storms can amplify coastal effects. In forests, changes in temperature or precipitation can provide more favorable conditions for invasive species.</p><p>State and local governments have been taking steps to address climate change. Here are several ways Landsat has benefited the residents of Massachusetts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223018","usgsCitation":"U.S. Geological Survey, 2022, Massachusetts and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3018, 2 p., https://doi.org/10.3133/fs20223018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-137026","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412236,"rank":6,"type":{"id":39,"text":"HTML 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 \"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 23, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\" https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Tracking Invasive Species Damage</li><li>Developing a Decarbonization Plan</li><li>Identifying City Hotspots</li><li>Viewing Coastal Changes</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-03-24","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":838671,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70229682,"text":"fs20223015 - 2022 - Missouri and Landsat","interactions":[],"lastModifiedDate":"2023-01-21T15:57:42.983544","indexId":"fs20223015","displayToPublicDate":"2022-03-16T12:42:18","publicationYear":"2022","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":"2022-3015","displayTitle":"Missouri and Landsat","title":"Missouri and Landsat","docAbstract":"<p>Missouri, one of only two States that borders eight different States, lies in the heart of the United States. Distinguished by its farm fields and forests, substantial rivers and lakes, and cities filled with culture and industry, the “Show Me State” has abundant beauty and a long history of connecting the East and the West. The Pony Express, Oregon Trail, Santa Fe Trail, and California Trail all began in Missouri.</p><p>The land and the people of Missouri contribute to its resiliency. Landsat data provide important tools for Missourians to protect their landscapes and waterways and enhance their economy under a variety of circumstances, from fast-arising natural disasters to longer-term environmental phenomena.</p><p>Here are several ways that Landsat data benefit Missouri.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223015","usgsCitation":"U.S. Geological Survey, 2022, Missouri and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3015, 2 p., https://doi.org/10.3133/fs20223015.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126132","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412077,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3015/fs20223015.XML"},{"id":412078,"rank":5,"type":{"id":34,"text":"Image 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 \"}}]}","edition":"Version 1.0: March 16, 2022; Version 1.1: January 19, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\" https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Keeping an Eye on Flooding and Farming</li><li>Monitoring Forest Changes</li><li>Partnering on Geospatial Data Research</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-03-16","revisedDate":"2023-01-19","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. 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A growing population gives Idaho even more reason to use the best information available to serve the needs of its residents while wisely managing its environment and natural resources.</p><p>Soon after the first Landsat satellite launched, Idaho recognized how useful its data could be. In 1975, the Idaho Department of Water Resources started using data from Landsat 1 and has since used data from every subsequent Landsat satellite.</p><p>Here is a closer look at Landsat as a key tool for monitoring water, along with other examples of Landsat’s value to Idaho.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223013","usgsCitation":"U.S. Geological Survey, 2022, Idaho and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3013, 2 p., https://doi.org/10.3133/fs20223013.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126131","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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 \"}}]}","edition":"Version 1.0: March 8, 2022; Version 1.1: January 23, 2023","contact":"<p>Program Coordinator, <a data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Water Management in Agriculture</li><li>Rangeland Wildfires</li><li>Wildlife Habitat</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-03-08","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2022-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological 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,{"id":70231154,"text":"70231154 - 2022 - Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data","interactions":[],"lastModifiedDate":"2022-05-02T11:47:41.414819","indexId":"70231154","displayToPublicDate":"2022-03-02T06:43:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>In arid and semiarid environments, evapotranspiration (ET) is the primary discharge component in the water balance, with potential ET exceeding precipitation. For this reason, reliable estimates of ET are needed to construct accurate water budgets in these environments. Remote sensing affords the ability to provide fast, accurate, field-scale ET estimates, but these methods have largely been restricted to deep rooted (phreatophytic) plant communities underlain by shallow groundwater. We used 13 years of data from a 3-ha drainage lysimeter in a semiarid sagebrush steppe and Landsat normalized difference vegetation index (NDVI) data to calibrate a generalized least squares model capable of predicting vadose zone ET in a high elevation upland ecosystem. Annual precipitation was the best predictor of annual ET, as they were nearly balanced every year analysed (mean difference = 3&nbsp;mm). We incorporated reference crop ET and a linear combination of NDVI and precipitation to capably predict ET on a subannual, lag-determined interval of 48 days, with a mean error of only 9.92% across all observations. To our knowledge, this is the first vegetation index-ET algorithm calibrated in a semiarid upland plant community using field-scale lysimetry. Vadose zone ET is particularly important at waste disposal sites in the Desert Southwest, where accurate and spatially explicit ET estimates are needed for monitoring potential mobilization and transport of contaminants past the root zone into local aquifers and for monitoring and modelling effects of recharge on flow and transport of contaminants in underlying aquifers.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/eco.2413","usgsCitation":"Jarchow, C., Waugh, W.J., and Nagler, P.L., 2022, Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data: Ecohydrology, v. 15, no. 3, e2413, 12 p., https://doi.org/10.1002/eco.2413.","productDescription":"e2413, 12 p.","ipdsId":"IP-125318","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":399963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","county":"San Juan County","city":"Monticello","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.45678710937499,\n              37.792422407988575\n            ],\n            [\n              -109.2205810546875,\n              37.792422407988575\n            ],\n            [\n              -109.2205810546875,\n              37.97018468810549\n            ],\n            [\n              -109.45678710937499,\n              37.97018468810549\n            ],\n            [\n              -109.45678710937499,\n              37.792422407988575\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":841826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":841827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":841828,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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