Monitoring Carbon Stock Change at the Individual-Plant Scale: A Methodological Review and Integrative Framework

Abstract

With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO2 exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems.

Plant phenotyping relevance

個体植物スケールの炭素蓄積変化を推定するための測定・センシング・統合手法を体系的に比較し、セグメンテーションや不確実性伝播を含む統合枠組みを提案する方法論レビューであり、植物状態の取得・推定が中心である。

Code and data availability

This is a methodological review proposing a conceptual framework; the supplied blocks contain no public phenotype datasets, plant images/sensor data, author analysis code, or trained models. All cited DOIs in the reference block refer to prior external works, not paper-specific assets.

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