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A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images

文献类型：期刊论文

作者Di, Yuanyuan1,2; Dong, Jinwei2; Zhu, Fangfang1; Fu, Ping1
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2022-07-01
卷号198页码:13
关键词Ginger mapping Phenology-based algorithm Sentinel-2 Google Earth Engine
ISSN号0168-1699
DOI10.1016/j.compag.2022.107066
通讯作者Dong, Jinwei(dongjw@igsnrr.ac.cn) ; Fu, Ping(ping.fu@nottingham.edu.cn)
英文摘要Ginger (Zingiber officinale), a commonly used spice, dietary supplement, and medical plant, is expanding rapidly in China due to its increasing demands and price in the international market. However, the existing information on its area and spatial distribution can only be derived from statistical data, and spatially explicit maps of ginger planting areas are unavailable. We proposed a simple but robust ginger planting area mapping algorithm using the time-series Sentinel-2 images and Google Earth Engine (GEE) platform. Changyi city, the hotspot city with the highest ginger production in China, was selected as our study area. Two ginger indices (Ginger phenological features index and Ginger cultivating management index) were generated based on the four unique phenology features: 1) high reflectance at the start of the growing season due to white agricultural mulching films, 2) a slower green-up feature in the early season, 3) low reflectance in summer due to black sunshade nets cover, 4) a unique green feature at the end of the growing season. The accuracy assessment based on the ground truth data showed fairly reasonable overall accuracies of 96.7%-97.3%, while the Matthews correlation coefficients (MCC) were 0.871-0.924. We found a rapid ginger expansion from 63.0 km(2) in 2019 to 101.5 km(2) in 2021 in the study area, by 61% from 2019 to 2021. Moreover, about 78% of ginger land in the study area had crop rotation every two years to boost yield and prevent bacterial diseases in the soil. Our study identified four critical features of ginger and demonstrated the potential of the phenology-based approach, Sentinel-2 data, and GEE platform for ginger mapping. This study is expected to apply to regional or national ginger mapping to provide valuable information for the ginger planting plan and guide the international ginger trade.
WOS关键词PERFORMANCE ; DROUGHT
资助项目National Key Research and Development Program of China[2018YFA0606100] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19040301] ; National Natural Science Foundation of China[41871349] ; University of Nottingham Ningbo China ; Institute for Geographic Science and Natural Resources Research (IGSNRR), CAS
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:000880649800002
出版者ELSEVIER SCI LTD
资助机构National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; University of Nottingham Ningbo China ; Institute for Geographic Science and Natural Resources Research (IGSNRR), CAS
源URL[http://ir.igsnrr.ac.cn/handle/311030/186837]  
专题中国科学院地理科学与资源研究所
通讯作者Dong, Jinwei; Fu, Ping
作者单位1.Univ Nottingham Ningbo China, Fac Sci & Engn, Ningbo 315100, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Di, Yuanyuan,Dong, Jinwei,Zhu, Fangfang,et al. A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,198:13.
APA Di, Yuanyuan,Dong, Jinwei,Zhu, Fangfang,&Fu, Ping.(2022).A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images.COMPUTERS AND ELECTRONICS IN AGRICULTURE,198,13.
MLA Di, Yuanyuan,et al."A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images".COMPUTERS AND ELECTRONICS IN AGRICULTURE 198(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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