The scalloping effect represents a systemic imaging artifact inherent to scanning synthetic aperture radar (ScanSAR) systems，manifesting as periodically alternating bright-dark striping noise along the azimuth direction.This phenomenon not only significantly degrades image radiometric quality，but also adversely affects subsequent processing and downstream applications，such as ship detection and ground object classification.As a critical component of ScanSAR image processing，the scalloping effect suppression research holds substantial theoretical and practical significance.This paper presents a systematic review on recent advancements in this field.The technical characteristics and evolutionary trends of representative studies worldwide are analyzed.The existing suppression methods are categorized into three primary categories，i.e.，(i) parametric correction methods based on prior information，(ii) traditional post-processing methods without prior information，and (iii) intelligent suppression techniques based on deep learning.The performance characteristics of these methodologies are compared and evaluated，and the limitations of these methodologies in handling complex scenes and generalization are pointed out，based on which some constructive perspectives on the future research directions are put forward.