Advanced information systems to fill STV gaps: next-generation stereo+lidar fusion and sensor technology

Instrument fusion is the prerequisite to data fusion for the high-resolution global Surface Topography and Vegetation (STV) targeted observable – data collected by a combination of single- or multi-platform radar, lidar, and stereo instruments must be precisely aligned with known uncertainty to enable essential downstream fusion and scientific analysis. This is one of many critical measurement gaps identified by the Decadal Survey Incubator (DSI) program for the STV TO that will require new, innovative technology. Other priority gaps include 1) insufficient multi-sensor data fusion methods and algorithms, 2) insufficient measurement geolocation and vertical accuracy, 3) stereo processing uncertainty and shortcomings for vegetation, and 4) poorly defined requirements for key stereo acquisition parameters. This crossover proposal focuses on two priority technologies identified by the DSI to reduce these STV measurement gaps: "Information systems (including improved multi-sensor data fusion methods and algorithms)," and "Subsystems/components to significantly improve spaceborne high-resolution stereo photogrammetry over the current state-of-the-art." We assembled an experienced team from academia, government, and industry to address these priority gaps. Specifically, we will: 1) Develop novel information systems and on-board algorithms that can deliver the precise pointing knowledge needed for next-generation lidar and TDI linescan image sensors. These subsystems will solve the notorious "jitter" problem responsible for large residual geolocation uncertainty in commercial stereo images. We will co-develop these information systems through a long-standing partnership with Planet. 2) Develop information systems that use cutting-edge, multi-sensor deep learning fusion techniques to improve the horizontal resolution, vertical accuracy/precision, and quality of stereo+lidar datasets for priority STV targets (vegetation, ice/snow). 3) Develop stereo photogrammetry information systems with robust joint optimization routines, rigorous uncertainty metrics, and stereo+lidar fusion alignment to support next-generation stereo imaging rigs and constellations 4) Leverage state-of-the-art radiative transfer model simulations (DART) and existing/new on-orbit and airborne datasets to support development activities and evaluate key stereo acquisition parameters, which will complement STV OSSE efforts to define STV instrument requirements. We will primarily focus on fusion of lidar and stereo photogrammetry to improve lower-level elevation data products (e.g., point clouds, gridded raster DSM/DTM), as these topographic data products are fundamental for many downstream applications and "science fusion" approaches involving high-level products from other measurement approaches (e.g., SAR). These innovative algorithms will guide development of a next-generation, cloud-based processing framework that will efficiently generate enhanced, high-quality data products for STV and commercial satellite imaging constellations in the coming decades.

Maturation of observing systems, instrument technology, and measurement concepts for Planetary Boundary Layer and Surface Topography and Vegetation

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