Accurate representation of the interplay between ecosystems and climate in Earth system models (ESMs) is important for understanding and predicting changes in carbon, water, and energy cycles. The traditional land components of ESMs often rely on the categorization of plant functional types (PFTs), a method that may overlook the ecological variations within plant groups. Using the CliMA Land model with surface hyperspectral data primarily sourced from Manuscript draft submitted to Ecosphere-SHIFT collection 2 AVIRIS-NG (Airborne Visible/Infrared Imaging Spectrometer-Next Generation) during the NASA's SHIFT (Surface Biology and Geology High-Frequency Time Series) campaign, we derive leaf biogeochemical traits, and compare a trait-based land modeling approach to the commonly used PFT one. Our findings reveal significant spatial and temporal variations in traits and other related optical and photosynthetic parameters, not only within the same PFT but also across different seasons. Validation against TROPOMI Solar-Induced Fluorescence (SIF) at 740nm shows that the trait-based approach enhanced model accuracy due to better representation of phenology and spatial heterogeneity. Our findings highlight the limitations of traditional PFTbased approaches in ESMs and underscore the importance of incorporating detailed, trait-based data for a more accurate and comprehensive understanding of ecosystem functions.

The retrival algorithms used for optical remote sensing satellite data to estimate Earth’s geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal to noise ratio (SNR) performance to meet science objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8 day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.