Retrieving the Leaf Area Index of Dense and Highly Clumped Moso Bamboo Canopies from Sentinel-2 MSI Data

The effects of leaf clumping on leaf area index (LAI, m2·m−2) retrieval have been proved by several studies. For dense and highly clumped Moso bamboo canopies, LAI is usually retrieved using the SAIL-series models that do not account for leaf clumping, although these retrievals are subsequently successfully validated by indirect ground-based methods that do account for leaf clumping. In order to explore these two seemingly contradictory results, LAIs of 21 Moso bamboo canopies retrieved by the GOST2 model (incorporating leaf clumping), the 4SAIL model and the SNAP tool (both without leaf clumping), respectively, were validated against ground-based LAI estimations, including the direct allometric method and indirect digital hemispherical photograph (DHP) methods. LAIs retrieved by GOST2 show strong agreement with the surrogate truth estimated by the allometric method (R2 = 0.79, RMSE = 3.03), but underestimations of retrieved LAIs by 4SAIL and the SNAP tool reach up to 27.6 and 28.8, respectively, due to lack of consideration of leaf clumping. These results indicate the following: (1) Depending on gap analysis-based clumping index (Ω) algorithms, leaf clumping corrections in indirect ground-based LAI estimations are unsuccessful for highly clumped Moso bamboo canopies due to heavy overlapped leaves; (2) LAIs of dense and highly clumped Moso bamboo canopies can be retrieved from satellite remote sensing data through canopy reflectance models with leaf clumping consideration; (3) The misunderstanding of LAI ranges of Moso bamboo canopies by previous studies (2.2–6.5) can be attributed to the application of gap analysis-based Ω for indirect ground-based LAI estimations; and (4) Effective leaf area index (Le) derived from satellite remote sensing data, and validated using gap analysis-based Le/Ω, could be erroneously interpreted as LAI.