STATEMENT OF WORK.pdf

Statement of Work

Project Title: Center for Soil Dynamics Technologies (NSF IUCRC Award ID#2231659)

Requirement: NASA seeks to work with the University of Southern California (USC) for a membership in the Center for Soil Dynamics Technologies (SoilTech) which is part of the National Science Foundation (NSF) Industry‐University Cooperative Research Centers (I/UCRC) Program. This membership will provide NASA access to research within SoilTech to identify technology‐based solutions to develop next‐ generation sensor systems capable of in‐situ and remote measurement of dynamic variables in managed and unmanaged soils relevant to the New Observing Strategies (NOS) thrust of the Advanced Information Systems Technology (AIST) Program.

Proposed Work:

See attached below a summary description of the proposal that was selected by NSF (Award ID 2231659; see https://www.nsf.gov/awardsearch/showAward?) and that describes the work that will be performed by the SoilTech Center.

NASA's AIST Program supports the development and adoption of advanced software and information systems technologies expected to be needed by future Earth Science missions and programs. One of the three main thrusts of AIST is focused on New Observing Strategies (NOS) to design future observing systems through intelligent, timely, dynamic, and coordinated distributed sensing, especially using diverse nodes in space, in the air and in‐situ. NOS‐type observing systems will also be essential to provide continuous data streams to Earth System Digital Twins (ESDT), another AIST thrust. The projects carried out by the proposed SoilTech Center directly benefit ESTO AIST, particularly addressing the needs and gaps of the NOS thrust. For example, SoilTech proposes to develop and coordinate in‐situ and remote sensing observations at multiple spatiotemporal scales, using multi‐mode sensors including not just microwave radars but also potentially optical and chemical sensors. Research will be conducted by the Center in early‐stage technology development into autonomous sensing networks that can collect and ingest data from multiple disparate observations for near‐real‐time decision support, i.e., also directly applicable to continuously updating digital twins running what‐if scenarios. The heterogeneous observational data will be harmonized via machine learning, and the AI tools and predictive models that will be developed may be used in natural hazard mitigation and response (e.g., wildfires and landslides), but also more generally for improving the understanding, prediction and response to Earth system processes, natural phenomena and human activities as well as their many interactions. By developing instruments and methods quickly and in a cost‐effective fashion that complement NASA space‐based datasets, SoilTech projects will benefit both fundamental and applied NASA Earth Science research.