文献类型： 外文期刊

作者： Peng, Yufeng ¹ ; Qiu, Bingwen ¹ ; Tang, Zhenghong ² ; Xu, Weiming ¹ ; Yang, Peng ³ ; Wu, Wenbin ³ ; Chen, Xuehong ⁴ ; Zhu, Xiaolin ⁵ ; Zhu, Peng ⁶ ; Zhang, Xin ⁸ ; Wang, Xinshuang ⁹ ; Zhang, Chengming ¹⁰ ; Wang, Laigang ¹¹ ; Li, Mengmeng ¹ ; Liang, Juanzhu ¹ ; Huang, Yingze ¹ ; Cheng, Feifei ¹ ; Chen, Jianfeng ¹ ; Wu, Fangzheng ¹ ; Jian, Zeyu ¹ ; Li, Zhengrong ¹ ;

作者机构： 1.Fuzhou Univ, Sch Phys & Informat Engn, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350116, Peoples R China

2.Univ Nebraska Lincoln, Community & Reg Planning Program, Lincoln, NE 68588 USA

3.Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Beijing, Peoples R China

4.Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China

5.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China

6.Univ Hong Kong, Dept Geog, Hong Kong, Peoples R China

7.Univ Hong Kong, Inst Climate & Carbon Neutral, Hong Kong, Peoples R China

8.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China

9.Shaanxi Geomatics Ctr Minist Nat Resources, Xian, Peoples R China

10.Shandong Agr Univ, Coll Informat Sci & Engn, Tai An, Peoples R China

11.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Peoples R China

12.Fuzhou Univ, Acad Digital China Fujian, Yangguang Keji Bldg,floor 8th,Xueyuan Rd 2, Fuzhou 350116, Fujian, Peoples R China

关键词： Special cash crop; Agroforestry crop mapping; Tea plantation; Phenology-based algorithm; Sentinel-1/2

期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：13.5； 五年影响因子：14.2 ）

ISSN： 0034-4257

年卷期： 2024 年 303 卷

页码：

收录情况： SCI

摘要： Tea 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 degrees to 18 degrees. 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.