Earth observation metadata ontology model for spatiotemporal-spectral semantic-enhanced satellite observation discovery: a case study of soil moisture monitoring

Monitoring soil moisture with satellite sensors is an effective approach for agricultural drought assessment. Currently, large quantities of sensor-derived observation data with different observation metadata models exist, and they require efficient and accurate methods of discovery. In this study, an earth observation (EO) metadata ontology with a spatiotemporal-spectral-enhanced structure is designed to solve this problem. The ontology is based on the proposed EO metadata model, which is composed of nonfunctional and functional sub-modules and supports the Open Geospatial Consortium EO profile of observations and measurements. Using EO metadata ontology, an application for soil moisture monitoring in Hubei Province in China is tested. The results indicate that metadata retrieval with a spatiotemporal-spectral-enhanced method can efficiently achieve fine-grained discovery of qualified EO metadata and obtain soil moisture monitoring information from sensor images. In summary, the spatiotemporal-spectral semantics in the proposed ontology demonstrate the use of EO metadata in the context of a soil moisture monitoring application, improving the efficiency and accuracy of EO metadata discovery.