A Cloud Computing-Enabled Spatio-Temporal Cyber-Physical Information Infrastructure for Efficient Soil Moisture Monitoring

Clicks: 212
ID: 76499
2016
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #32 of 51 articles by views in isprs international journal of geo-information

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Comprehensive surface soil moisture (SM) monitoring is a vital task in precision agriculture applications. SM monitoring includes remote sensing imagery monitoring and in situ sensor-based observational monitoring. Cloud computing can increase computational efficiency enormously. A geographical web service was developed to assist in agronomic decision making, and this tool can be scaled to any location and crop. By integrating cloud computing and the web service-enabled information infrastructure, this study uses the cloud computing-enabled spatio-temporal cyber-physical infrastructure (CESCI) to provide an efficient solution for soil moisture monitoring in precision agriculture. On the server side of CESCI, diverse Open Geospatial Consortium web services work closely with each other. Hubei Province, located on the Jianghan Plain in central China, is selected as the remote sensing study area in the experiment. The Baoxie scientific experimental field in Wuhan City is selected as the in situ sensor study area. The results show that the proposed method enhances the efficiency of remote sensing imagery mapping and in situ soil moisture interpolation. In addition, the proposed method is compared to other existing precision agriculture infrastructures. In this comparison, the proposed infrastructure performs soil moisture mapping in Hubei Province in 1.4 min and near real-time in situ soil moisture interpolation in an efficient manner. Moreover, an enhanced performance monitoring method can help to reduce costs in precision agriculture monitoring, as well as increasing agricultural productivity and farmers’ net-income.
Reference Key
zhou2016aisprs Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhou, Lianjie;Chen, Nengcheng;Chen, Zeqiang;
Journal isprs international journal of geo-information
Year 2016
DOI
DOI not found
URL
http://www.mdpi.com/2220-9964/5/6/81
Keywords
geography (general) electronic computers. computer science geology

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.