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

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.