 Remote sensing technology has been used to estimate leaf area index, LA, of mangroves, but this technique has been limited due to the difficulty of capturing the entire canopy. This study used Sentinel-2, S2, imagery, airborne hyperspectral imagery, HSI, and airborne LiDAR data to estimate LAI of a multi-layered mangrove stand in Maipa, Hong Kong, China. The LiDAR data was used to stratify the overstory and understory layers, while vegetation indices as VIS and LiDAR metrics were generated as predictors to build regression models against the overstory and understory layers. The overstory layer was more accurately estimated using HSI data, while the understory layer was more accurately estimated using S2 data. Additionally, the use of LiDAR metrics and S2 VIS together produced the best results for the understory layer. This article was authored by Chao Sili, Frankie Kwan, Kit Wong, Tung Sung, and others.