TY - GEN
T1 - Establish cyberinfrastructure to facilitate agricultural drought monitoring
AU - Sun, Ziheng
AU - Di, Liping
AU - Zhang, Chen
AU - Fang, Hui
AU - Yu, Eugene
AU - Lin, Li
AU - Tan, Xicheng
AU - Guo, Liying
AU - Chen, Zhongxin
AU - Yue, Peng
AU - Jiang, Lili
AU - Liu, Ziao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Agricultural drought greatly impacts the crop yield. Monitoring agricultural drought can deliver critical information to farmers on when, where and how much to irrigate. However, precisely monitoring which requires many kinds of data sources and data fusion and mining is still a huge challenge for scientists. In recent years, many data sources like remote sensed hyperspectral images are released online and open to the public. Agricultural scientists need spend a lot of time on downloading, preprocessing and interpreting the data manually which delayed the valuable information being discovered. This paper aims to establish a Cyberinfrastructure (CI) to facilitate the agricultural drought monitoring. The CI is composed of web services and workflow module. The CI can help agricultural scientists to easily retrieve and pre-process the multi-source datasets with minimum efforts. In real-world scenarios, CI can automatically stream the related data into the ready-to-analyze form and deliver them to the information consumers and stakeholders. We developed and experimented in the operational GADMFS (Global Agricultural Drought Monitoring and Forecasting System). The result shows that our approach can truly decrease the time cost of data preprocessing and accelerate the speed of information extraction and delivery.
AB - Agricultural drought greatly impacts the crop yield. Monitoring agricultural drought can deliver critical information to farmers on when, where and how much to irrigate. However, precisely monitoring which requires many kinds of data sources and data fusion and mining is still a huge challenge for scientists. In recent years, many data sources like remote sensed hyperspectral images are released online and open to the public. Agricultural scientists need spend a lot of time on downloading, preprocessing and interpreting the data manually which delayed the valuable information being discovered. This paper aims to establish a Cyberinfrastructure (CI) to facilitate the agricultural drought monitoring. The CI is composed of web services and workflow module. The CI can help agricultural scientists to easily retrieve and pre-process the multi-source datasets with minimum efforts. In real-world scenarios, CI can automatically stream the related data into the ready-to-analyze form and deliver them to the information consumers and stakeholders. We developed and experimented in the operational GADMFS (Global Agricultural Drought Monitoring and Forecasting System). The result shows that our approach can truly decrease the time cost of data preprocessing and accelerate the speed of information extraction and delivery.
KW - agricultural drought
KW - Cyberinfrastructure
KW - remote sensing
KW - web service
KW - workflow
UR - http://www.scopus.com/inward/record.url?scp=85032835068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032835068&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2017.8047054
DO - 10.1109/Agro-Geoinformatics.2017.8047054
M3 - Conference contribution
AN - SCOPUS:85032835068
T3 - 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
BT - 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
Y2 - 7 August 2017 through 10 August 2017
ER -