{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:54:22Z","timestamp":1770915262863,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T00:00:00Z","timestamp":1550707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX13AJ24A"],"award-info":[{"award-number":["NNX13AJ24A"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["G12PC00069"],"award-info":[{"award-number":["G12PC00069"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 \u00d7 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed.<\/jats:p>","DOI":"10.3390\/rs11040447","type":"journal-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T03:49:44Z","timestamp":1550807384000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0856-9659","authenticated-orcid":false,"given":"Alexey","family":"Egorov","sequence":"first","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-0250","authenticated-orcid":false,"given":"David","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment &amp; Spatial Sciences and Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-3616","authenticated-orcid":false,"given":"Hankui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"given":"Zhongbin","family":"Li","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8628-4395","authenticated-orcid":false,"given":"Lin","family":"Yan","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-890X","authenticated-orcid":false,"given":"Haiyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., and Cohen, W.B. (2019). Current status of Landsat program, science, and applications. Remote Sens. Environ., In Press.","DOI":"10.1016\/j.rse.2019.02.015"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.3390\/rs10091363","article-title":"Analysis Ready Data: Enabling analysis of the Landsat archive","volume":"10","author":"Dwyer","year":"2018","journal-title":"Remote Sens."},{"key":"ref_3","unstructured":"(2018, December 14). U.S. Landsat Analysis Ready Data, Available online: https:\/\/landsat.usgs.gov\/ard."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Goward, S., Williams, D., Arvidson, T., Rocchio, L., Irons, J.R., Russell, C., and Johnston, S. (2017). Landsat\u2019s Enduring Legacy: Pioneering Global Land Observations from Space, American Society for Photogrammetry and Remote Sensing.","DOI":"10.14358\/ASPRS.1.57083.101.7"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud cover in Landsat observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The Availability of Cloud-free Landsat ETM+ data over the Conterminous United States and Globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1080\/17445647.2015.1125308","article-title":"Spatiotemporal distribution of Landsat imagery of Europe using cloud cover-weighted metadata","volume":"12","author":"Tolnai","year":"2016","journal-title":"J. Maps"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.3390\/rs70201482","article-title":"Meeting earth observation requirements for global agricultural monitoring: An evaluation of the revisit capabilities of current and planned moderate resolution optical earth observing missions","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"902","DOI":"10.3390\/rs9090902","article-title":"A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring","volume":"9","author":"Li","year":"2017","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"564","DOI":"10.3390\/rs70100564","article-title":"A one year Landsat 8 conterminous United States study of cirrus and non-cirrus clouds","volume":"7","author":"Kovalskyy","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.14358\/PERS.72.10.1155","article-title":"Historical record of Landsat global coverage","volume":"72","author":"Goward","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.14358\/PERS.72.10.1137","article-title":"Landsat-7 long-term acquisition plan: Development and validation","volume":"72","author":"Arvidson","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S0034-4257(01)00248-6","article-title":"Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets","volume":"78","author":"Teillet","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"(2018, December 14). Food and Agriculture Organization of the United Nations, Global Administrative Unit Layers. Available online: http:\/\/www.fao.org\/geonetwork\/srv\/en\/main.home."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Williams, R.S., and Ferrigno, J.G. (2002). Glaciers of the conterminous United States\u2014Glaciers of the western United States, Satellite Images of Glaciers of the World, Professional Paper 1386\u2013J\u20132.","DOI":"10.3133\/pp1386J"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2015.04.022","article-title":"Image interpretation-guided supervised classification using nested segmentation","volume":"165","author":"Egorov","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1029\/WR017i005p01480","article-title":"Areal distribution of snow water equivalent evaluated by snow cover monitoring","volume":"17","author":"Martinec","year":"1981","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1016\/j.rse.2007.12.013","article-title":"A 20-year Landsat water clarity census of Minnesota\u2019s 10,000 lakes","volume":"112","author":"Olmanson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Egorov, A.V., Roy, D.P., Zhang, H.K., Hansen, M.C., and Kommareddy, A. (2018). Demonstration of percent tree cover classification using Landsat analysis ready data (ARD) and sensitivity analysis with respect to Landsat ARD processing level. Remote Sens., 10.","DOI":"10.3390\/rs10020209"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1109\/TGRS.2004.836769","article-title":"Four years of Landsat-7 on-orbit geometric calibration and performance","volume":"42","author":"Lee","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ Composited Mosaics of the Conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1109\/TGRS.2004.840720","article-title":"Landsat sensor performance: History and current status","volume":"42","author":"Markham","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2016.08.022","article-title":"Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change","volume":"186","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., and Zhu, Z. (2019). Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sens., 11.","DOI":"10.3390\/rs11010051"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.01.001","article-title":"The vegetation greenness trend in Canada and US Alaska from 1984\u20132012 Landsat data","volume":"176","author":"Ju","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1080\/01431160600967128","article-title":"Stabilizing high-order, non-classical harmonic analysis of NDVI data for average annual models by damping model roughness","volume":"28","author":"Hermance","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1109\/TGRS.2012.2183137","article-title":"Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis","volume":"50","author":"Brooks","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"Roy, D.P., and Yan, L. (2018). Robust Landsat-based crop time series modelling. Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/0143116031000115265","article-title":"VEGETATION\/SPOT: An operational mission for the Earth monitoring; presentation of new standard products","volume":"25","author":"Maisongrande","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/LGRS.2006.875433","article-title":"The global impact of clouds on the production of MODIS bidirectional reflectance model-based composites for terrestrial monitoring","volume":"3","author":"Roy","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Helder, D., Markham, B., Morfitt, R., Storey, J., Barsi, J., Gascon, F., Clerc, S., LaFrance, B., Masek, J., and Roy, D.P. (2018). Observations and recommendations for the calibration of Landsat 8 OLI and Sentinel 2 MSI for improved data interoperability. Remote Sens., 10.","DOI":"10.3390\/rs10091340"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yan, L., and Roy, D.P. (2018). Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS). Remote Sens., 10.","DOI":"10.3390\/rs10040609"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Brooks, E., Wynne, R., and Thomas, V. (2018). Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data. Remote Sens., 10.","DOI":"10.3390\/rs10101502"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0034-4257(02)00085-8","article-title":"Achieving sub-pixel geolocation accuracy in support of MODIS land science","volume":"83","author":"Wolfe","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.rse.2018.04.021","article-title":"Sentinel-2A multi-temporal misregistration characterization and an orbit-based sub-pixel registration methodology","volume":"215","author":"Yan","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:33:48Z","timestamp":1760186028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,21]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11040447"],"URL":"https:\/\/doi.org\/10.3390\/rs11040447","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,21]]}}}