ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2016-12, Vol.9 (12), p.5588-5598
• Main Authors: Zhou, Lianjie, Chen, Nengcheng, Chen, Zeqiang, Xing, Chenjie
• Format: Article
• Language: English
• Subjects: Agriculture; Algorithms; Cloud computing; Distributed databases; Drought; Drought index; Earth; earth observation data (EOD) sharing enhancement; Imagery; Mapping; Moisture effects; Plant cover; precision agriculture (PA); Precision farming; Remote observing; Remote sensing; Rivers; sensor observation service (SOS); Servers; Soil moisture
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2016.2574810

The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.