Impact of leaf phenology on estimates of aboveground biomass density in a deciduous broadleaf forest from simulated GEDI lidar

Abstract The Global Ecosystem Dynamics Investigation (GEDI) is a waveform lidar instrument on the International Space Station used to estimate aboveground biomass density (AGBD) in temperate and tropical forests. Algorithms predict footprint AGBD from GEDI relative height (RH) metrics were developed simulated waveforms with leaf-on (growing season) conditions. Leaf-off data lower canopy cover are expected have shorter RH metrics, therefore excluded GEDI’s gridded products. However, effects of leaf phenology metric heights, implications for models that can include multiple nonlinear predictors, not been quantified. Here, we test sensitivity predictions phenology. We using high-density drone collected mountain forest Czech Republic under leaf-off conditions, 51 d apart. compared footprint-level Level 4 A datasets. Mean increased by 31% 57% 88%. < RH50 more sensitive changes than ⩾ RH50. Candidate deciduous-broadleaf-trees prediction stratum Europe trained measurements exhibited systematic difference 0.6%–19% when applied data, as predictions. Models least contained only highest or predictor terms both positive negative coefficients, such systematically was partially offset among terms. These results suggest that, consideration model choice, be suitable prediction, which could increase availability reduce sampling error some