Phenology-Guided Early Prediction of Crop Damage Under Long-Duration Inundation Using Multi-Source SAR–Optical Imagery

Abstract

Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest R2 of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period R2 of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation.

Plant phenotyping relevance

SAR・光学画像とフェノロジー指標を用いて作物被害を推定する回帰手法が研究の中心であり、作物状態（洪水被害）を定量化・検証しているため。

Code and data availability

The supplied blocks describe the phenology-guided SAR–optical crop damage framework, data sources (Sentinel-1/2, Landsat-8/9, Lutan-1, Gaofen-3, ERA5-Land, ESA WorldCover), and methods, but contain no public phenotype/trait dataset, no author-deposited imagery or annotation inputs, no analysis code/scripts/workflow, no

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