Forest aboveground biomass in the southwestern U.S. from MISR and GEDI: Assessment with NASA Carbon Monitoring System data

Forest aboveground biomass (AGB) density mapping initiatives generally use one of three remote sensing approaches: lidar, radar, or near-nadir multispectral imaging leveraging machine learning methods, or a combination thereof. However, the active instrument record is limited and near-nadir multispectral imaging data are relatively insensitive to canopy physical structure. Multiangle imaging enables annual wall-to-wall mapping with a global record that extends back to 2000 as these data are highly sensitive to forest AGB. This paper describes work to validate estimates in a published annual, wall-to-wall record of forest AGB on a 250 m grid, derived using 672 nm imagery from the NASA, Jet Propulsion Laboratory's Multiangle Imaging Spectro-Radiometer (MISR) for 2000–2021, covering the southwestern United States. Estimates in the published MISR-derived annual forest AGB map series for the southwestern United States and the Global Ecosystem Dynamics Investigation (GEDI) L4B Gridded 1 km AGB product were both found to be highly consistent with NASA Carbon Monitoring System (CMS) airborne lidar survey (ALS) AGB data. MISR and GEDI v.2 (v.2.1) estimates yielded similar coefficients of determination (∼0.7) and Root Mean Square Error (RMSE) (∼60 Mg ha −1 ) for all ALS data used. For the large CMS Sonoma County Improved AGB dataset, MISR and GEDI v.2 (v.2.1) estimates yielded R 2 = 0.88, 0.88 (0.91); RMSE = 58, 40 (37) Mg ha −1 . Estimates from MISR thus have an accuracy similar to that of the GEDI L4B gridded AGB product, with some limitations (e.g., topographic shading, tall, dense canopies). However the published MISR maps are on a 250 m grid, wall-to-wall, and cover the period 2000–2021. These results suggest MISR is able to provide a means to investigate trajectories of forest AGB change in the southwestern U.S. from 2000 onwards—over a substantial period of accelerating environmental and human- and climate-driven change– with reasonable precision. • Forest biomass data from MISR and GEDI v2 (v2.1) were evaluated with ALS estimates. • For Sonoma Co., R 2 = 0.88, 0.88 (0.91), RMSE = 58, 40 (37) Mg ha −1 , respectively. • For all survey sites, R 2 = ∼0.73, 0.70 (0.69) and RMSE = 58, 57 (62) Mg ha −1 . • Spatial differences in the AGB estimates reveal topography as the main driver. • Precision is similar in the MISR and GEDI L4B gridded AGB data, with some caveats.