Delong Wang , Pingping Yang , Jiong Fu

Hainan Provincial Key Laboratory of Crop Molecular Breeding, Sanya, 572025, Hainan, China
Author    Correspondence author
Field Crop, 2025, Vol. 8, No. 6   
Received: 13 Nov., 2025    Accepted: 24 Dec., 2025    Published: 13 Dec., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wheat is one of the most important cereal crops globally, and accurate yield prediction is critical for ensuring food security and supporting precision agriculture. Traditional estimation methods relying on field surveys are often time-consuming, labor-intensive, and limited in spatial coverage. This study integrates remote sensing technologies with crop modeling approaches to establish a real-time and scalable framework for wheat yield prediction. Specifically, we utilized satellite and UAV-based remote sensing data-including NDVI, LAI, and chlorophyll indices-combined with process-based crop models such as WOFOST and APSIM to simulate crop growth dynamics. The integration was achieved through data assimilation techniques that continuously feed remote observations into crop models, enabling dynamic calibration and validation across growth stages. A case study in the Indo-Gangetic Plains demonstrated that assimilating Sentinel-2 data with the WOFOST model significantly improved yield prediction accuracy and provided timely forecasts beneficial for regional decision-making. This integrated approach enhances both spatial and temporal resolution in yield monitoring, offering a more reliable foundation for precision management, early warning systems, and policy development. Future research should focus on incorporating artificial intelligence and machine learning algorithms to refine model performance and expanding open-access platforms for wider application in climate-resilient wheat production.

Remote sensing; Crop modeling; Wheat yield prediction; Data assimilation; Precision agriculture