{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:26:10Z","timestamp":1776176770769,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Provincial Department of Science and Technology Research Project","award":["212102310019"],"award-info":[{"award-number":["212102310019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8\/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders.<\/jats:p>","DOI":"10.3390\/rs14184476","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T07:05:56Z","timestamp":1662620756000},"page":"4476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8\/9 Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Yan","family":"Guo","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0106-6709","authenticated-orcid":false,"given":"Haoming","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"},{"name":"Key Research Institute of Yellow River Civilization and Sustainable Development Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-3494","authenticated-orcid":false,"given":"Xiaoyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxin","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6962-8838","authenticated-orcid":false,"given":"Yaochen","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"},{"name":"Key Research Institute of Yellow River Civilization and Sustainable Development Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/17538947.2017.1356388","article-title":"Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data","volume":"11","author":"Liu","year":"2018","journal-title":"Int. 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