Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/Agro-Geoinformatics.2017.8047054 | 2017 6th International Conference on Agro-Geoinformatics |
Keywords | Field | DocType |
cyberinfrastructure,agricultural drought,web service,workflow,remote sensing | Data science,Geospatial analysis,Computer science,Upload,Data pre-processing,Cyberinfrastructure,Agriculture,Information extraction,Web service,Workflow | Conference |
ISSN | ISBN | Citations |
2334-3168 | 978-1-5386-3885-9 | 2 |
PageRank | References | Authors |
0.41 | 7 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziheng Sun | 1 | 82 | 15.31 |
| Liping Di | 2 | 811 | 98.92 |
| Chen Zhang | 3 | 8 | 3.31 |
| Hui Fang | 4 | 23 | 4.02 |
| Eugene Yu | 5 | 21 | 6.77 |
| Li Lin | 6 | 44 | 23.07 |
| Xicheng Tan | 7 | 14 | 3.78 |
| Liying Guo | 8 | 2 | 0.41 |
| Chen Zhongxin | 9 | 23 | 3.99 |
| Peng Yue | 10 | 48 | 9.99 |
| Lili Jiang | 11 | 17 | 10.70 |
| Ziao Liu | 12 | 4 | 0.83 |