High-throughput phenotyping for climate-resilient forests: integrating multi-sensor fusion and root-shoot dynamics.

Abstract

Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.

Plant phenotyping relevance

植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。

Code and data availability

This is a semi-systematic review of high-throughput forest phenotyping. The supplied blocks contain no paper-specific phenotype datasets, sensor/image data, analysis code, trained models, or supplements with such assets; figures are described as conceptual schematics, and no data or code availability statements with a

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