Multi-path fusion: a hierarchical machine learning approach for combining diverse data sets for a forest monitoring new observing system

New Observing Systems (NOS) will be NASA's next generation approach for Earth remote sensing, utilizing many diverse observing capabilities to produce optimized measurements integrated from multiple vantage points and in multiple dimensions. NOS will require strong data fusion foundations to be able to intelligently combine, and retrieve information from, data coming from assets differing in characteristics like instrument type, spectral domain, and spatial and temporal resolution. We are developing an end-to-end data fusion framework employing advanced Artificial Intelligence (AI) Machine Learning (ML) techniques with the primary purpose to drive the design and operation of multi-sensor NOS for Earth sciences and beyond. This work requires building ML-enabled analytic tools and advanced environments to take advantage of high-performance computing systems for the creation of a NOS workflow that utilizes large amounts of diverse airborne and satellite observations along with ancillary information including climate and drought time series and soil properties. We are demonstrating the framework using a forest productivity and degradation use case, but the framework is designed to be applicable to a wide variety of NOS scientific objectives.