Banana plantation identification using remote sensing data in tropical and subtropical regions

Bananas are the tropical fruit with the largest global cultivation area, sales volume, and 
international trade. China is the world's second-largest producer and consumer of bananas. 
Rapid and accurate acquisition of banana planting range and spatial distribution information 
is crucial for promoting the sustainable development of the banana industry in China. 
Currently, research on banana classification and identification faces challenges such as 
insufficient mechanistic understanding, poor generalizability, and difficulties in large-scale 
application. Additionally, banana cultivation areas are often located in regions with cloudy 
and rainy climates, limiting the acquisition of optical imagery. To address this, this study 
constructs a banana identification model based on phenological characteristics: (1) Sentinel
1/2 imagery is utilized to obtain time series curves of banana spectral and scattering features, 
followed by interpolation and filtering of the time series data; (2)A phenological index based 
on optical and scattering features is developed according to banana phenological 
characteristics. By combining SAR with the index, the model's mechanistic understanding is 
enhanced while alleviating the challenges posed by cloud cover in tropical and subtropical 
regions; (3)Using the constructed phenological index alongside banana spectral, texture, and 
temporal features, a classification model is trained for banana identification in the study area. 
This banana forest identification model and the developed phenological index aim to resolve 
current issues in banana classification and provide theoretical and practical support for large
scale banana extraction and the study of tropical and subtropical economic crops.