Distributed Aperture Radar Tomographic Sensors (DARTS) to Map Three-Dimensional Vegetation Structure and Surface Topography

We propose to mature and demonstrate a set of relevant technologies that, when coupled with recent developments in miniaturized spaceborne radars, will enable formations of satellites to perform disruptive global vegetation structure and surface topography measurements. The recent report from the 2017-2027 US Decadal Survey for Earth Science and Applications from Space recommended the global mapping of surface topography and three-dimensional vegetation structure as one of the highest priority "incubator" measurements to undertake in the next decade. Despite its extremely high relevance, significant technological developments are needed to enable the implementation of a feasible and affordable space mission that can demonstrably retrieve vegetation structural characteristics, including the underlying ground topography, with the desired vertical and horizontal resolutions, accuracy and temporal sampling. Our concept, named Distributed Aperture Radar Tomographic Sensors (DARTS), is based on multistatic tomographic SAR (TomoSAR) observations. A notional architecture for measuring vegetation structure and surface topography globally every ~10-15 days includes a formation of ~5-10 spacecraft equipped with full-polarimetric, bistatic, L-band distributed radar instruments. One or more of the spacecrafts, the hub(s), transmits the radar signal while all remaining satellites receive the echo simultaneously. The relative position, attitude, timing, and clock are synchronized between all platforms to enable coherent, multistatic SAR observations of common scenes with nominally 50 m horizontal resolution and 3-5 m vertical resolution after multi-looking. The formation's relative orbital control requirement is <10 m but the knowledge of the relative positions is required to <1 cm to maintain coherent phase between the distributed radar receivers for tomographic reconstruction. Data from each spacecraft is transferred to the hub spacecraft(s) via inter-satellite communications for data reduction and downlink to Earth for tomographic processing and digital elevation model generation. The objectives of our investigation are to (1) Design, build and test a distributed system to synchronized timing, clock, relative position and sensor data for all of the distributed DARTS elements; (2) Miniaturize the distributed phase-coherent radar system by leveraging recent RF system-on-chip (RFSoC) technologies and implement compact L-band radars in order to achieve time-synchronization and phase coherence across the distributed elements. (3) Design the optimal distributed system architecture given the scientific requirements for surface topography and vegetation tomographic imaging, and analyze the tomographic signals acquired by both fixed-position and mobile small Unmanned Aerial System (sUAS) synchronized radars; (4) Design a SmallSat-compatible, L-band foldable patch antenna, including the electrical backend and the mechanical structure required to enable a cost-effective formation of satellites for the distributed DARTS system. A multistatic interferometric system enabling single-pass SAR tomography constitutes a disruptive measurement for ecosystem science because it can provide global, high-quality year-round measurements of 3D vegetation structure while being insensitive to dynamic changes in vegetation, soil and atmosphere. Coupled with fast repeat periods, such a measurement would provide a consistent 4D view of vegetation (3D in space + 1D in time) complementing and potentially overcoming the limitations of spaceborne lidar missions and other planned missions such as TANDEM-L and BIOMASS. This investigation responds directly to the Instrument Development and Demonstration sub-element of the IIP solicitation. The proposed period of performance is 3 years. The entry Technology Readiness Level (TRL) is 2, which we plan to increase by 1 every year, aiming for a TRL equal to 5 at the end of the investigation.

Increase scientific understanding of natural phenomena using remote sensing

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