A Robust Wireless Sensor Network Architecture for the Large-scale Deployment of the Soil Moisture Sensing Controller and Optimal Estimator (SoilSCaPE)

We develop energy-efficient wireless sensor network technologies and data analysis techniques for dynamic and near-real-time validation of space-borne soil moisture measurements, in particular those from the Soil Moisture Active and Passive (SMAP) mission. Soil moisture fields are functions of variables that change over time across the range of scales from a few meters to several kilometers, necessitating the deployment of an extensive in-situ network for validation of coarse-resolution retrievals of soil moisture from SMAP and other remote sensing data. Previously we have reported on the scheduling and placement strategies for achieving optimal spatial and temporal sampling by the network. This work focuses on the latest developments of the large-scale wireless sensor network architecture that we have termed the Ripple architecture, and in particular, its latest version Ripple-2. The new network architecture solves many of the previous problems encountered during field deployments of the SoilSCAPE network, including reliability and scalability. The new architecture will be described, along with the results of the latest field deployments at the University of Michigan Matthaei botanical gardens and at the representative field site in Canton, Oklahoma. The status of the large-scale deployment at the Tonzi Ranch in central California will also be given. Additionally, the latest results of hydrologic and radar landscape simulators will also be presented, highlighting the connection between the SoilSCAPE network data, remote sensing retrievals, and the target science application of SMAP validation.