A Comparative Review of Seven-Component Decomposition and Neural Network Approaches for Mangrove Detection Using L-Band PolSAR Data

Mangrove forests play an important role in coastal protection, biodiversity conservation and carbon sequestration by making their accurate and reliable mapping important for sustainable environmental management. However cloud cover and/or atmospheric interference limit optical remote sensing; however, Polarimetric Synthetic Aperture Radar (PolSAR) is important for all-weather, day and night observations. The objective of this review is to explore the potential for mangrove detection using L-band PolSAR and Seven-Component Decomposition (7SD) and to highlight the polarimetric scattering features and the neural-network-based classification. It critically reviews the existing research related to mangrove mapping, SAR processing, scattering decomposition techniques and machine-learning techniques with emphasis on Multilayer Perceptron (MLP), Radial Basis Function Network (RBFN) and Cascade Forward Network (CFN) classifiers. Important methodological issues such as speckle filtering, feature scaling, class imbalance, quality of the reference labels, spatial autocorrelations, and information leakage when evaluating models are also discussed. Moreover, it offers a comparable framework for assessment that is reproducible and includes consistent data partitions, spatially independent assessment, confusion matrices, overall accuracy, precision, recall, F1 score and Kappa statistics. Research opportunities are identified in the synthesis of feature ablation, multi-temporal analysis, sensor fusion, and cross-region validation. This review focuses on evidence-based model selection, transparent reporting and generalisation assessment for the support of robust and reproducible mangrove monitoring using PolSAR.