SPCTOR: Sensing-Policy Controller and OptimizeR (SPCTOR)

This proposal outlines new technology concepts for multi-sensor infusion under the current AISTs New Observation Strategies (NOS) thrust with specific focus on (a) Integrated operation of different types of instruments or at different vantage points, (b) Evaluation/comparison of alternative observing [sensing] strategies, and (c) Estimation of science value to enable comparison of observing strategies. Specifically, we propose to develop technologies that coordinate the operations of networks of ground-based and Unmanned Aerial Vehicle (UAV)-based sensors, jointly referred to as Agents. When sensing Agents are coordinated, they can deliver ground-truth information to multiple end-uses that may have different application requirements, especially for different NASA remote sensing science products. We call the overall system achieving this technology vision "Sensing Policy Controller and OptimizeR (SPCTOR)." This technology concept is particularly relevant when considering the fact that existing ground networks have limited capabilities in terms of spatiotemporal sampling flexibility, multi-user coordination, and multi-sensor integration. These limitations are further compounded when multiple users, each with different application requirements, seek access to sensing networks that may have limited resources. Earth-looking remote sensing observatories, in particular, almost always require ground based in-situ networks for geophysical product validation and calibration. In the domain of soil moisture remote sensing, which is our target observable, current (e.g., SMAP and SMOS) and future (e.g., NISAR) missions have different spatial (100 m – 10s km) and temporal (daily to weekly) mapping requirements, resulting in complex and potentially conflicting demands on in-situ networks for validating their products. This proposal will directly address the aforementioned limitations. Building upon elements of existing high-TRL (up to 7) technology heritage of the Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE), we propose a new framework to coordinate soil moisture sensing strategies across multiple battery-operated Agents by means of a new machine-learning-based entity called the Sensing-Policy Controller (SPC). In essence, the PC considers Agents' energy constraints, recent data and observations, and respective application (or science) based performance metrics, and then makes the determination on whether to update an Agent's observation strategy. We will further expand in situ sensor network capabilities to enable integration, interaction, and interoperability between ground sensor networks and UAV-based software-defined radars (SDRadars) for field-scale operations. UAVs have reached a level of technological maturity such that their inclusion within existing sensor networks with potential science applications can be feasibly considered. We therefore pursue two principal technology development and research objectives: Objective 1: Develop a Sensing-Policy Controller (SPC) for multi-Agent observation strategy coordination and optimization. (NOS elements b and c). Objective 2: Develop and demonstrate integrated operations between in-situ wireless sensors networks and networks of UAV-SDRadars (NOS element a) Our technology development plan includes analysis, computations, laboratory experiments, and field demonstrations. The entry Technology Readiness Levels (TRLs) for the above two objectives are 2 and 4 and exit TRLs are 4 and 6, respectively.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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