数字农科院2.0

CHINA CROP GROWTH MONITORING SYSTEM-METHODOLOGY AND OPERATIONAL ACTIVITIES OVERVIEW

文献类型： 外文期刊

作者： Huang Qing;Chen Zhongxin;Wu Wenbin;Teng Fei;Li Dandan;de Wit, Allard

作者机构：

关键词： China-CGMS;CHARMS;crop growth simulation model;GIS;yield prediction

期刊名称： 2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)

ISSN： 2153-6996

年卷期： 2011 年

页码：

收录情况： SCI

摘要： China Agriculture Remote Sensing Monitoring System (CHARMS) was established in 2001 by the Ministry of Agriculture of China. The main tasks of CHARMS are to monitor agricultural condition with remote sensing and other techniques and to provide basic information for government decision-making and agricultural production management. The European crop growth monitoring system (EU-CGMS) has been studied, introduced, digested and absorbed into CHARMS since last year, and a new China-CGMS was built. The system is made of weather data, crop characteristics data, soil information data, field surveys data, administrative region vector data as well as agro-meteorological modelling and statistical analysis tools etc.. The China-CGMS has been providing information of crop growth condition at three different levels of aggregation: counties, provinces, and regions. This paper gives an overview of China-CGMS, including basic principles, methods and regular operation of China-CGMS.

分类号：

  • 相关文献

[1]The Application of China-CGMS in the Main Crop Growth Monitoring in Northeast China. Huang Qing,Wu Wenbin,Teng Fei,Li Dandan,Ren Jianqiang,Deng Hui. 2012

[2]REGIONAL YIELD PREDICTION OF WINTER WHEAT BASED ON RETRIEVAL OF LEAF AREA INDEX BY REMOTE SENSING TECHNOLOGY. Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Yang, Xiaomei,Liu, Xingren. 2009

[3]Yield estimation of winter wheat in North China Plain by using crop growth monitoring system (CGMS). Teng Fei,Chen Zhongxin,Wu Wenbin,Huang Qing,Li Dandan,Xia Tian. 2012

[4]Methodological evolution of potato yield prediction: a comprehensive review. Yongxin Lin,Shuang Li,Shaoguang Duan,Yanran Ye,Bo Li,Guangcun Li,Dianqiu Lyv,Liping Jin,Chunsong Bian,Jiangang Liu. 2023

[5]Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Liangliang Zhang,Zhao Zhang,Yuchuan Luo,Juan Cao,Ruizhi Xie,Shaokun Li. 2021

[6]Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Minghan Cheng,Josep Penuelas,Matthew F. McCabe,Clement Atzberger,Xiyun Jiao,Wenbin Wu,Xiuliang Jin. 2022

[7]Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods. Crusiol, L. G. T.,Sun, Liang,Sibaldelli, R. N. R.,Felipe Junior, V,Furlaneti, W. X.,Chen, R.,Sun, Z.,Wuyun, D.,Chen, Z.,Nanni, M. R.,Furlanetto, R. H.,Cezar, E.,Nepomuceno, A. L.,Farias, J. R. B.. 2022

[8]Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. Zhenwang Li,Lei Ding,Dawei Xu. 2022

[9]Combining multi-indicators with machine-learning algorithms for maize at the-level in China. Cheng, Minghan,Penuelas, Josep,McCabe, Matthew F.,Atzberger, Clement,Jiao, Xiyun,Wu, Wenbin,Jin, Xiuliang. 2022

[10]Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery. Juncheng Ma,Binhui Liu,Lin Ji,Zhicheng Zhu,Yongfeng Wu,Weihua Jiao. 2023

[11]Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning. Jianzheng Li,Ganqiong Li,Ligang Wang,Denghua Li,Hu Li,Chao Gao,Minghao Zhuang,Jiayu Zhuang,Han Zhou,Shiwei Xu,Zhengjiang Hu,Enli Wang. 2023

[12]Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data. Shurong Yang,Lei Li,Shuaipeng Fei,Mengjiao Yang,Zhiqiang Tao,Yaxiong Meng,Yonggui Xiao. 2024

[13]Deep learning-based association analysis of root image data and cucumber yield. Zhu, Cuifang,Yu, Hongjun,Lu, Tao,Li, Yang,Jiang, Weijie,Li, Qiang. 2024

[14]Multi-random ensemble on Partial Least Squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing. Bohan Mao,Qian Cheng,Li Chen,Fuyi Duan,Xiaoxiao Sun,Yafeng Li,Zongpeng Li,Weiguang Zhai,Fan Ding,Hao Li,Zhen Chen. 2024

[15]Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing. Fei S.,Xiao S.,Zhu J.,Xiao Y.,Ma Y.. 2024

[16]Peanut yield prediction using remote sensing and machine learning approaches based on phenological characteristics. Xuehui Hou,Junyong Zhang,Xiubin Luo,Shiwei Zeng,Yan Lu,Qinggang Wei,Jia Liu,Wenjie Feng,Qiaoyu Li. 2025

[17]Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments. Zhu, Cuifang,Yu, Hongjun,Zhao, Caili,Wu, Hongyang,Wan, Xiaoyang,Lu, Tao,Li, Yang,Jiang, Weijie,Li, Qiang. 2026

[18]Esophageal cancer spatial and correlation analyses: Water pollution, mortality rates, and safe buffer distances in China. Zhang Xueyan,Zhuang Dafang,Jiang Dong,Ma Xin. 2014

[19]Temperature and Precipitation Suitability Evaluation for the Winter Wheat and Summer Maize Cropping System in the Huang-Huai-Hai Plain of China. Qiu Jian-jun,Li Hu,Nguyen Thanh Tuan,Verdoodt, Ann,Van Ranst, Eric. 2011

[20]The timing variations and spatial pattern of winter wheat in China. Zhuang Jiayu,Li Zhemin,Li Yanni,Zhuang Jiayu,Li Zhemin,Li Yanni,Yong Xi,Yong Xi. 2016

作者其他论文 更多>>