The leaf chlorophyll concentration is an important biophysical parameter of plant physiological state since it plays crucial roles in photosynthesis, physiology, and other biological functions in plants. It can be estimated directly using destructive sampling or indirectly using handheld devices. Leaves were collected from the Bhitarkanika mangrove ecosystem from the upper canopy of the trees for three species such as Heritiera fomes, Excoecaria agallocha, and Avicennia officinalis for laboratory analysis, while Soil Plant Analysis Development (SPAD)-502 was used to get an indirect estimation. Regression analysis was done to investigate the relationship between SPAD-based chlorophyll and lab-based Leaf Chlorophyll Content (LCC) using 15 samples for each three species H. fomes, E. agallocha, and A. officinalis. Two-pigment (chlorophyll a and b) indices such as PSNDa and PSNDb images and two soil line indices such as SAVI and (g) OSAVI images were derived using Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS NG) Hyperspectral images. Classified image of three dominant mangrove species using six indices (red edge, chlorophyll, and soil line-based properties), and three red edge bands were derived from AVIRIS NG data using Random Forest (RF) algorithm. Different spectra of H. fomes and E. agallocha, generated from AVIRIS, exhibit no significant variation in the pattern of reflectance around NIR region. Two mangrove species such as H. fomes and E. agallocha were classified as major or dominant species. In RF model, red edge-based indices exhibit highest contribution for generation of species distribution map.