Fusion: A f̲ully u̲ltraportable s̲ystem for i̲maging o̲bjects in n̲ature

To improve satellite-derived estimates of terrestrial plant production and exchange of CO
₂
, water, and energy with the atmosphere, scientists need to consider ecosystem composition, structure, function, and health. This can be accomplished through the fusion of Light Detection And Ranging (LiDAR) data, which can provide 3D information about the vertical and horizontal distribution of vegetation and hyperspectral remote sensing, which can inform us about variations in biophysical variables (e.g., photosynthetic pigments) and responses to environmental stressors (e.g., heat, moisture loss). Satellite observations from upcoming Decadal Survey missions will provide NASA with the unique opportunity to fuse LiDAR data from ICESat-II, DESDynI, and LIST with hyperspectral and thermal imagery from HyspIRI and GEO-CAPE. This synergy will augment and enhance the individual science objectives of decadal survey missions, and will allow scientists the opportunity to develop 3D models of plant canopies that better describe global cycling of carbon, water and energy. Multiple NASA's Earth Science Focus Areas are served by this science, including carbon cycle and ecosystems; water and energy cycle; and climate variability and change (i.e., ecosystem responses and feedbacks to climate change). One of the major obstacles to the development of data fusion algorithms is the availability of accurately co-registered data of similar grain size. This is often the case when instruments are flown on different platforms and at different times during a field campaign. We believe that “instrument fusion” is a prerequisite to “data fusion”, and we have developed a system the integrates a full-waveform LiDAR, narrow band hyperspectral imager, and broad band thermal imager in a single, compact and portable instrument package that could be readily deployed on a number of observation platforms. FUSION will provide accurate co aligned datasets that are needed for: (i) calibration and validation of satellite-derived land products; (ii) development of data fusion algorithms; and (iii) combine observations from multiple sensors to characterize ecosystem composition, structure, function, and health.