Spatiotemporal fusion for crop model assimilation: Linfen Basin case study, Chinese Loess Plateau

Abstract

Abstract Spatiotemporal fusion addresses challenges in crop growth monitoring: namely, spatial discreteness of high‐resolution imagery and spectral mixing in high‐temporal‐frequency data. These can hinder accurate yield and phenology estimates, especially in fragmented semi‐arid landscapes. To improve winter wheat monitoring, we quantitatively and qualitatively evaluated two normalized differential vegetation index (NDVI) fusion methods: the spatial and temporal nonlocal filter‐based fusion model (STNLFFM) and enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Our primary focus was to address the limitations of MODIS data in crop phenology monitoring, specifically to resolve the challenges of spatial and temporal resolution. STNLFFM, incorporating inter‐image coefficients and temporal variation, outperformed ESTARFM by eliminating stripe artifacts from prolonged high‐resolution data gaps. Additionally, assimilating NDVI data via the four‐dimensional variational (4DVAR) method resulted in improved accuracy, with a 6.001% mean absolute percentage error in leaf area index (LAI) estimation, compared with 6.285% using the CERES‐Wheat model. Yield estimation accuracy was enhanced by 2.734% through the 4DVAR‐assimilated LAI, particularly in addressing inaccuracies in mountain‐cropland transition zones. This study addresses this gap by providing an integrated spatiotemporal fusion framework that mitigates data quality and scale mismatches, thereby improving the accuracy of crop growth monitoring and yield predictions. This study highlights the following: (1) the operational advantages of STNLFFM in fragmented landscapes and (2) the potential of variational assimilation to reduce model uncertainties. The proposed approach is applicable to precision agriculture in topographically complex regions and provides a scalable solution for addressing mixed‐pixel challenges in the Earth's observational data.

Plant phenotyping relevance

冬小麦のフェノロジー・LAI・収量を推定する時空間融合と4DVAR同化フレームワークを開発・比較・評価しており、植物形質推定手法が研究の中心である。

Code and data availability

The article reports a spatiotemporal fusion and crop model assimilation study (NDVI/LAI fusion, CERES-Wheat assimilation) but provides no public repository, dataset, code, or model deposit. The data availability statement only offers inquiries to the corresponding author, so any paper-specific data or analysis assets (

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