{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T18:39:29Z","timestamp":1781635169626,"version":"3.54.5"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,29]],"date-time":"2018-12-29T00:00:00Z","timestamp":1546041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["#G17PS00256"],"award-info":[{"award-number":["#G17PS00256"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["#G17AC00057"],"award-info":[{"award-number":["#G17AC00057"]}],"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>Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud\/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis.<\/jats:p>","DOI":"10.3390\/rs11010051","type":"journal-article","created":{"date-parts":[[2018,12,31]],"date-time":"2018-12-31T07:22:30Z","timestamp":1546240950000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Shi","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"},{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukun","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"},{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-4428","authenticated-orcid":false,"given":"Rong","family":"Shang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8331-7200","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8283-6407","authenticated-orcid":false,"given":"Zhe","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,29]]},"reference":[{"key":"ref_1","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. 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