Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108071
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorPeng, Yen_US
dc.creatorQiu, Ben_US
dc.creatorTang, Zen_US
dc.creatorXu, Wen_US
dc.creatorYang, Pen_US
dc.creatorWu, Wen_US
dc.creatorChen, Xen_US
dc.creatorZhu, Xen_US
dc.creatorZhu, Pen_US
dc.creatorZhang, Xen_US
dc.creatorWang, Xen_US
dc.creatorZhang, Cen_US
dc.creatorWang, Len_US
dc.creatorLi, Men_US
dc.creatorLiang, Jen_US
dc.creatorHuang, Yen_US
dc.creatorCheng, Fen_US
dc.creatorChen, Jen_US
dc.creatorWu, Fen_US
dc.creatorJian, Zen_US
dc.creatorLi, Zen_US
dc.date.accessioned2024-07-23T04:08:17Z-
dc.date.available2024-07-23T04:08:17Z-
dc.identifier.issn0034-4257en_US
dc.identifier.urihttp://hdl.handle.net/10397/108071-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2024 Elsevier Inc. All rights reserved.en_US
dc.rights© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Peng, Y., Qiu, B., Tang, Z., Xu, W., Yang, P., Wu, W., Chen, X., Zhu, X., Zhu, P., Zhang, X., Wang, X., Zhang, C., Wang, L., Li, M., Liang, J., Huang, Y., Cheng, F., Chen, J., Wu, F., . . . Li, Z. (2024). Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images. Remote Sensing of Environment, 303, 114016 is available at https://doi.org/10.1016/j.rse.2024.114016.en_US
dc.subjectAgroforestry crop mappingen_US
dc.subjectPhenology-based algorithmen_US
dc.subjectSentinel-1/2en_US
dc.subjectSpecial cash cropen_US
dc.subjectTea plantationen_US
dc.titleWhere is tea grown in the world : a robust mapping framework for agroforestry crop with knowledge graph and sentinels imagesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume303en_US
dc.identifier.doi10.1016/j.rse.2024.114016en_US
dcterms.abstractTea trees (Camellia sinensis), a quintessential homestead agroforestry crop cultivated in over 60 countries, hold significant economic and social importance as a vital specialty cash crop. Accurate nationwide crop data is imperative for effective agricultural management and resource regulation. However, many regions grapple with a lack of agroforestry cash crop data, impeding sustainable development and poverty eradication, especially in economically underdeveloped countries. The large-scale mapping of tea plantations faces substantial limitations and challenges due to their sparse distribution compared to field crops, unfamiliar characteristics, and spectral confusion among various land cover types (e.g., forests, orchards, and farmlands). To address these challenges, we developed the Manual management And Phenolics substance-based Tea mapping (MAP-Tea) framework by harnessing Sentinel-1/2 time series images for automated tea plantation mapping. Tea trees, exhibiting higher phenolic content, evergreen characteristics, and multiple shoot sprouting, result in extensive canopy coverage, stable soil exposure, and radar backscatter signal interference from frequent picking activities. We developed three phenology-based indicators focusing on phenolic content, vegetation coverage, and canopy texture leveraging the temporal features of vegetation, pigments, soil, and radar backscattering. Characteristics of biochemical substance content and manual management measures were applied to tea mapping for the first time. The MAP-Tea framework successfully generated China's first updated 10 m resolution tea plantation map in 2022. It achieved an overall accuracy of 94.87% based on 16,712 reference samples, with a kappa coefficient of 0.83 and an F1 score of 85.63%. The tea trees are typically cultivated in mountainous and hilly areas with a relatively low planting density (averaging about 10%). Alpine tea trees exhibited a notably dense concentration and dominance, mainly found in regions with elevations ranging from 700 m to 2000 m and slopes between 2° to 18°. The areas with low altitudes and slopes hold the largest tea plantation area and output. As the slope increased, there was a gradual decline in the dominance of tea areas. The results suggest a good potential for the knowledge-based approaches, combining biochemical substance content and human activities, for national-scale tea plantation mapping in complex environment conditions and challenging landscapes, providing important reference significance for mapping other agroforestry crops. This study contributes significantly to advancing the achievement of the Sustainable Development Goals (SDGs) considering the crucial role that agroforestry crops play in fostering economic growth and alleviating poverty. The first 10m national Tea tree data products in China with good accuracy (ChinaTea10m) are publicly accessed at https://doi.org/10.6084/m9.figshare .25047308.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing of environment, 15 Mar. 2024, v. 303, 114016en_US
dcterms.isPartOfRemote sensing of environmenten_US
dcterms.issued2024-03-15-
dc.identifier.scopus2-s2.0-85183324362-
dc.identifier.eissn1879-0704en_US
dc.identifier.artn114016en_US
dc.description.validate202407 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera3070-
dc.identifier.SubFormID49357-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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