Remote sensing for forage ratio estimation: a systematic review

Remote sensing has emerged as a vital technology for monitoring vegetation dynamics and ecosystem health, particularly in assessing forage quality and quantity. This study investigates the application of remote sensing tools for forage ratio estimation. A bibliometric analysis was conducted in RStudio (v4.0.4), biblioshiny using datasets obtained from the Dimensions, Scopus and Web of Science (WoS) databases, covering the period from 2005 to 02/2025 and guided by the PRISMA approach. The analysis indicates a generally low but steadily increasing research output, with notable peaks observed in 2014, 2020 and 2023. Notably, significant progress in remote sensing technologies particularly hyperspectral and multispectral imagery has enhanced the capacity to accurately estimate key forage quality parameters, including Carbon:Nitrogen (C:N) and Nitrogen:Phosphorus (N:P) ratios. Machine learning algorithms, especially random forest models, have proven effective in improving predictive accuracy for forage quality assessments. Despite these technological strides, research remains concentrated in the Northern Hemisphere, with limited contributions from developing nations, particularly in the Southern Hemisphere, highlighting a critical geographic disparity. Our keyword analysis further underscores a growing trend towards integrating machine learning with remote sensing to monitor forage ecosystems. This review calls for enhanced international collaboration and investment in underrepresented regions, emphasizing the necessity for equitable access to remote sensing technologies. As ecological challenges intensify, the continued exploration of remote sensing methodologies for forage ratio assessment is crucial for promoting sustainable land management practices and wildlife conservation efforts. Future research should focus on refining predictive models and expanding applications across diverse ecosystems, thereby maximizing the potential of remote sensing in effective forage ecosystem management.