Development of a Multi-Scale Spectrum Phenotyping Framework for High-Throughput Screening of Salt-Tolerant Rice Varieties

Abstract

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands.

Plant phenotyping relevance

植物の塩ストレス耐性をスペクトルデータから定量評価するマルチスケール表現型解析フレームワークの開発であり、特徴選択、分類検証、指標構築、センサー間融合が中心的な方法論的貢献である。

Code and data availability

The supplied blocks describe the multi-scale spectral phenotyping framework, data acquisition (FieldSpec 4 hyperspectral, DJI M3M UAV, Sentinel-2), and analysis methods, but contain no public phenotype/trait dataset, image/sensor data deposit, author analysis code, trained model, or supplement with an explicit public-­

No evidence-backed public reproduction asset is currently recorded.