{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:26:12Z","timestamp":1776176772433,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world\u2019s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM\/ETM+\/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999\u20132019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth\u2122 online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (\u03b2 = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999\u20132019 with overall accuracies ranging from 82% to 99% and the user\u2019s\/producer\u2019s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 \u00d7 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p &lt; 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.<\/jats:p>","DOI":"10.3390\/rs13061170","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T03:41:21Z","timestamp":1616125281000},"page":"1170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)"],"prefix":"10.3390","volume":"13","author":[{"given":"Wenmin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Brandt","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, 1350 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2375-1651","authenticated-orcid":false,"given":"Alexander V.","family":"Prishchepov","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, 1350 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0287-8197","authenticated-orcid":false,"given":"Zhaofu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0245-9179","authenticated-orcid":false,"given":"Chunguang","family":"Lyu","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3067-4527","authenticated-orcid":false,"given":"Rasmus","family":"Fensholt","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, 1350 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20260","DOI":"10.1073\/pnas.1116437108","article-title":"Global food demand and the sustainable intensification of agriculture","volume":"108","author":"Tilman","year":"2011","journal-title":"Proc. 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