Monitoring Crop Growth During the Period of the Rapid Spread of COVID-19 in China by Remote Sensing
文献类型： 外文期刊
作者： Wang, Yan 1 ; Peng, Dailiang 1 ; Yu, Le 2 ; Zhang, Yaqiong 3 ; Yin, Jie 4 ; Zhou, Leilei 4 ; Zheng, Shijun 1 ; Wang, Fumi 1 ;
作者机构： 1.Chinese Acad Sci, Key Lab Digital Earth Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
2.Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China
3.Minist Ecol & Environm, Ctr Satellite Applicat Ecol & Environm, Beijing 100006, Peoples R China
4.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Henan, Peoples R China
5.Zhejiang Univ, Inst Remote Sensing & Informat Technol Applicat, Hangzhou 310058, Peoples R China
6.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： Agriculture; Meteorology; Remote sensing; Monitoring; Spatial resolution; Vegetation mapping; Earth; Coronavirus disease 2019 (COVID-19); crop growth; remote sensing
期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）
ISSN： 1939-1404
年卷期： 2020 年 13 卷
页码：
收录情况： SCI
摘要： The status of crop growth under the influence of COVID-19 is an important information for evaluating the current food security in China. This article used the cloud computing platform of Google Earth Engine, to access and analyze Sentinel-2, MODIS, and other multisource remote sensing data in the last five years to monitor the growth of crops in China, especially in Hubei province, during the period of the rapid spread of COVID-19 (i.e., from late January to mid-March 2020), and compared with the growth over the same period under similar climate conditions in the past four years. We further analyzed the indirect effects of COVID-19 on crop growth. The results showed that: the area of the crops with better growth (51%) was much more than that with worse growth (22%); the crops with better and worse growth were mainly distributed in the North China Plain (the main planting areas of winter wheat in China) and the South China regions (such as Guangxi, Guangdong province), respectively. The area of the crops with a similar growth occupied 27%. In Hubei province, the area of the crops with better growth (61%) was also more than that with worse growth (27%). It was found that there was no obvious effect from COVID-19 on the overall growth of crops in China during the period from late January to mid-March 2020 and the growth of crops was much better than that during the same period in previous years. The findings in this study are helpful in evaluating the impact of the COVID-19 on Chinas agriculture, which are conducive to serve the relevant agricultural policy formulation and to ensure food security.
- 相关文献
作者其他论文 更多>>
-
Extraction of the upright maize straw by integrating UAV multispectral and DSM data
作者：Chao, Aosheng;Xing, Enguang;Gao, Yunbing;Li, Cunjun;Qin, Yuan;Zhu, Chengyang;Liu, Yu;Chao, Aosheng;Zhu, Chengyang;Zhu, Qingwei
关键词：Upright maize straw; UAV; New straw index; Spectral characteristics; Digital surface model
-
Insights into Phylogeny, Taxonomy, Origins and Evolution of Crataegus and Mespilus, Based on Comparative Chloroplast Genome Analysis
作者：Meng, Jiaxin;Wang, Yan;Dong, Ningguang;Song, Han;Dong, Wenxuan
关键词：
Crataegus ; chloroplast genome; phylogeny; molecular dating; biogeography inference -
Advancing grape breeding through an in vitro embryo germination technique without cold stratification
作者：Liu, Zhenhua;Wang, Yan;Wang, Huiling;Sun, Lei;Yan, Ailing;Ren, Jiancheng;Wang, Xiaoyue;Xu, Haiying;Song, Jing;Guan, Pingyin
关键词：Grape; Seed dormancy; Germination; Embryo culture
-
Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage
作者：Zou, Mengxi;Zhou, Zixiang;Zou, Mengxi;Liu, Yu;Li, Cunjun;Meng, Haoran;Xing, Enguang;Ren, Yanmin;Fu, Maodong;Li, Cunjun
关键词：plant height; feature fusion; machine learning; deep learning; UAV; LAI; winter wheat
-
Genome-Wide Identification of the ClpB Gene Family in Tomato and Expression Analysis Under Heat Stress
作者：Zhang, Yuemei;Yang, Tailai;Han, Jiaxi;Su, Xiao;Cong, Yanqing;Wang, Yan;Lin, Tao;Zhou, Ming
关键词：Caseinolytic Protease B;
Solanum lycopersicum ; high temperature; expression pattern; AAA plus -
Emerging Pollutants
作者：Wang, Yawei;Zhang, Qiurui;Shi, Yali;Shi, Jianbo;Qu, Guangbo;Qiao, Min;Liu, Mei;Liu, Guorui;Liu, Yanna;Yang, Lili;Yang, Xiaoxi;Yang, Ruiqiang;Zheng, Minghui;Jiang, Lu;Yao, Linlin;He, Yong;Cai, Yaqi;Wei, Dongbin;Liao, Chunyang;Jiang, Guibin;Yu, Nanyang;Wei, Si;Ji, Rong;Pan, Bingcai;Liu, Jianguo;Qiu, Xinghua;Lu, Rongjing;Chen, Jiazhe;Cheng, Zhen;Zhu, Chuhong;Jiang, Longfei;Mai, Bixian;Zhang, Gan;Luo, Xiaojun;Luo, Chunling;Jin, Biao;Zhao, Shizhen;Zeng, Yanhong;Zhu, Yumin;Yang, Rongyan;Zhang, Ying;Chen, Xin;Zhu, Lingyan;Hua, Jianghuan;Yang, Lihua;Zhou, Bingsheng;Guo, Yongyong;Liang, Chengqian;Han, Jian;Wang, Yan;Zhang, Quan;Zhao, Meirong;Mo, Xunjie;Zhu, Ying;Sheng, Nan;Dai, Jiayin;Ying, Guangguo;Zhang, Qianqian;Gao, Chuanzi;Qiu, Wenhui;Tang, Shuqin;Chen, Da;Chen, Jingwen;Cui, Yunhan;Tian, Sinuo;Li, Cheng;Fang, Mingliang;Wang, Yuan;Liu, Nannan;Yang, Pan;Chen, Hexia;Zhang, Zhen;Liu, Guorui;Yang, Ruiqiang;Lin, Bingcheng;Zheng, Minghui;Jiang, Guibin;Zhao, Fanrong;Yao, Jingzhi;Tan, Hongli;Liu, Guorui;Yang, Lili;Yang, Ruiqiang;Zheng, Minghui;Jiang, Guibin;Yang, Lihua;Zhou, Bingsheng;Guo, Yongyong;Han, Jian;Hua, Jianghuan;Liang, Chengqian;Zhao, Jincai;Shi, Jianbo
关键词：emerging pollutants; occurrence level; environmental behavior; ecological risks; control strategies
-
Application of APSIM model in winter wheat growth monitoring
作者：Tan, Yunlong;Chen, Junjie;Tan, Yunlong;Cheng, Enhui;Peng, Dailiang;Feng, Xuxiang;Zhang, Bing;Cheng, Enhui;Peng, Hao;Cheng, Enhui;Peng, Dailiang;Zhao, Bin;Xie, Qiaoyun;Peng, Hao;Li, Cunjun;Lu, Chuang;Li, Yong;Li, Yong
关键词：winter wheat; vegetation index; remote sensing; growth monitoring; cultivated land management
