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.

Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the US National Ecological Observatory Network (NEON), there is increasing need for methods that are both flexible and automatable across numerous images with diverse land cover. FlexBRDF corrects for BRDF effects in groups of flightlines, with key user-selectable features including kernel selection, land cover stratification (we employ NDVI), and use of a reference solar zenith angle (SZA). We demonstrate FlexBRDF using a series of nine long (150-400 km) AVIRIS-Classic flightlines collected on 22 May 2013 over Southern California, where rough terrain, diverse land cover, and a wide range of solar illumination yield significant BRDF effects, and then test the approach on additional AVIRIS-Classic data from California, AVIRIS-Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Based on comparisons of overlap areas between adjacent flightlines, correction algorithms built from multiple flightlines concurrently performed better than corrections built for single images (RMSE improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common SZA among a group of flightlines also improved performance. While BRDF corrections tailored to individual sites may be preferred for local studies, FlexBRDF is compatible with bulk processing of large datasets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.