Global mapping of forest clumping index based on GEDI canopy height and complementary data

Clumping index (CI), describing the extent of foliage grouping within canopy structures that cause nonrandomness of the leaf spatial distribution within the canopy, is a structural parameter of plant canopies needed in modelling carbon and water cycles of terrestrial ecosystems. CI has been previously retrieved based on an angular index named Normalized Difference between Hotspot and Darkspot (NDHD) derived from multi-angle satellite data. However, since the darkspot reflectance used in NDHD contains shadows influenced by topographical variations, CI retrieved from NDHD is contaminated by topographic effects on multi-angle data. Moreover, the existing global CI products are limited by their spatial resolutions, e.g. 6 km from POLDER and 500 m from Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we utilized the canopy height (H) metric produced from a spaceborne LiDAR system named Global Ecosystem Dynamic Investigation (GEDI) to implement the mapping for global forest CI at 30 m resolution in the following steps: (1) The H map from GEDI and a CI map from MODIS were employed to establish the relationships between H and CI across flat terrains for different plant functional types (PFT) and vegetation foliage densities characterized using the Simple Ratio (SR); (2) Using the relationships, a new global map of forest CI is derived from the GEDI H and SR maps at 30 m resolution; (3) Field-measured CI values by the Tracing Radiation and Architecture of Canopies (TRAC) instrument at 39 sites over three continents were used to validate the new CI map with satisfactory results at all forest sites: (R2 = 0.84, RMSE = 0.0199 for all PTFs; R2= 0.42, RMSE = 0.0180 for broadleaf forests, and R2 = 0.48, RMSE = 0.0206 for needleleaf forests). In the new global high-resolution forest CI map, CI is increased by about 10-15 % and is much less variable with topography over mountainous areas, indicating that topographical effect on CI derivation is greatly reduced. This new map would therefore be highly useful for improving terrestrial carbon and water cycle estimation.