## 1 Introduction  
LiDAR, hyperspectral and thermal remote sensing are core areas of current and planned NASA remote sensing capability (e.g., Landsat Enhanced Thematic Mapper Plus, ETM\({}^{+}\), and Operational Land Imager, OLI; Earth Observing-1, Hyperion; Earth Observing System's Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER, Multi-angle Imaging SpectroRadiometer, MISR, and Moderate Resolution Imaging Spectroradiometer, MODIS; Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite, NPP VIIRS; Ice, Cloud, and Land Elevation Satellite-2 Advanced Topographic Laser Altimeter System, ICESat-2; and Hyperspectral Infrared Imager, HyspIRI), and fusion of complementary data from different sensors offers the potential for improved global remote sensing of terrestrial ecosystems. Formation flying missions, such as NASA's A-Train satellite constellation, offer near-simultaneous coverage from a number of Earth-observing systems; however, the spatial resolution of the current generation of NASA sensors limits some terrestrial ecology applications. Furthermore, efforts to merge 3D structure information from LiDAR with imaging spectrometer and thermal data is currently hindered by the absence of a LiDAR designed for vegetation in space [1] and an EO-1 satellite that is approaching the end of its useful life. Airborne platforms are more flexible than satellite missions for developing and testing data fusion at fine spatial (1 to 10 m) and spectral resolutions, which have prompted a new generation of LiDAR and imaging spectrometer instrument packages [2, 3, 4]. Airborne platforms offer specific advantages for the study of terrestrial ecosystem, including targeted acquisitions of seasonal and diurnal processes (e.g., wetland inundation, plant phenology, drought and fire impacts) and coordinated field and remote sensing data collection needed to scale process-level understanding to the scale of airborne and satellite remote sensing observations [5]. Furthermore, the addition of coincident thermal data to LiDAR and imaging spectrometer data broadens the range of potential terrestrial ecology applications to include research on evapotranspiration, hydrology, forest health and urban applications [6, 7]. Here, we describe the development of a unique multi-sensor instrument, Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager, which advances previous concepts for data fusion by integrating LiDAR, hyperspectral and thermal sensors in a lightweight and portable system for worldwide research applications. G-LiHT's single-solution GPS-INS (Global Positioning System and Inertial Navigation System) avoids multi-dimensional data effects that are introduced when data is collected at different time and observational scales [2]. Imaging spectroscopy provides quantitative information on vegetation cover, species composition and biophysical and chemical properties that can be derived from measurements of reflected sunlight in the visible through shortwave infrared wavelengths [8, 9, 10, 11, 12]. Light detection and ranging provides quantitative, 3D information on terrain and vegetation cover, height and distribution of canopy elements, which can be used to characterize biodiversity and habitat [13] and estimate light interception in plant photosynthesis and production models [14]. Land surface temperature provides data needed to estimate evapotranspiration and other surface energy fluxes [6], and can be an indicator of soil or vegetation moisture status [15]. By using commercial off-the-shelf instrumentation and general aviation aircraft, G-LiHT also reduces development and operational costs. Low-cost deployment of the G-LiHT system opens a wide range of applications for targeted, airborne remote sensing, including diurnal and seasonal processes in terrestrial ecosystems at \(\sim\)1 m spatial resolution (Figure 1).