<p>This paper aims to review state-of-the-art studies on forest degradation using remote-sensing techniques. By analyzing the most commonly used remote-sensing products and techniques, our study provides valuable insights into current trends in forest degradation research. A hybrid approach combining literature review, bibliometric analysis, and systematic review was used. Research studies from more than 70 countries were reviewed. The four countries with the highest number of publications were the United States (21.9%), China (13.7%), India (7.3%), and Spain (6%). It was observed that the number of publications has steadily increased, averaging 300 articles annually. Moreover, they present current perspectives, introducing Landsat (52% of the total) for the detection of disturbances and mapping of long-term degradation. Additionally, Sentinel-2 (6.4%) is used to analyze sudden degradations, such as forest fires or storms. The results highlighted the importance of various processing algorithms, including Spectral Mixture Analysis (20%) and Random Forests (16%), among others. In terms of application scale, most models and remote-sensing index applications are conducted at the regional scale, allowing for an understanding of the diversity and impacts of degradation. For the validation metrics, a non-comprehensive use of these metrics is identified for both classification and regression tasks. By examining current remote-sensing applications and emerging innovations in forest degradation monitoring, this work provides comprehensive insights by highlighting key trends in data sources, analytical techniques, and assessment methods, while also revealing untapped opportunities in advanced Artificial Intelligence models, multi-sensor synergies, and automated change detection. Indeed, this review provides valuable insights to guide future research directions, optimize methodological choices, and advance the development of more effective forest degradation monitoring protocols.</p>