A Hybrid RTM-Informed Machine Learning Framework with Crop-Specific Canopy Structural Parameterization for Crop Fractional Vegetation Cover Estimation

Abstract

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE

Plant phenotyping relevance

作物の葉面積被覆率という明示的な植物キャノピー形質を推定するハイブリッドモデルを開発し、多数のサンプルと既存プロダクトで検証しており、測定・推定手法が研究の中心である。

Code and data availability

The paper's core phenotyping asset is a database of 43,343 10 m CropFVC validation samples (UAV/Jilin-1-derived) constructed by the authors; it is paper-specific but only available from the corresponding author upon request. Sentinel-2 imagery and GEOV3 FVC products are generic public data sources, not paper-specific,

No evidence-backed public reproduction asset is currently recorded.