Vegetation carbon stock is a key component of the terrestrial carbon cycle and supports climate-change mitigation and carbon-neutrality strategies. While field inventories provide accurate references, they are constrained by cost and limited scalability, motivating the rapid adoption of remote sensing for large-scale spatial estimation and mapping. However, the literature lacks a consolidated bibliometric and critical synthesis focused on above-ground vegetation carbon stock estimation. Therefore, this review aims to provide a quantitative overview of publication trends, synthesise methodological developments, and identify key research gaps in remote-sensing-based above-ground vegetation carbon stock estimation. A total of 1825 Web of Science records (2015–2024) were retrieved, of which 763 were included for bibliometric mapping using VOSviewer version 1.6.20 and CiteSpace version 6.3.R2, complemented by a critical review of 32 high-quality studies. Results indicate a shift from passive optical and single-index approaches toward active sensing and multi-sensor, multi-platform integration, alongside broad uptake of machine learning and an emerging dominance of deep learning for nonlinear modelling and feature learning. Research attention is expanding beyond forests to non-forest ecosystems, yet challenges persist in spatial resolution, validation data availability, and cross-biome generalizability. This review summarizes methodological trajectories and identifies priorities for robust, transferable above-ground carbon estimation.
Min et al. (Sat,) studied this question.