Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy

Abstract

Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages. By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R2 = 0.602, RMSE = 0.094). Our approach improves performance over single-temporal models, and successful maize density maps were generated for the three typical demonstration counties. This work represents an advancement in large-scale crop density estimation, with the potential to expand to other regions and support precision agriculture efforts, offering a foundation for future research on optimizing agricultural resource management.

Code and data availability

The paper describes field-survey maize density data (102 sites), Sentinel-1/2 imagery, and RF/SVR/KNN models, but provides no public deposit, availability statement, or authors' URL for any dataset, imagery, code, or trained model. The only URL (Google Earth Engine) is a generic processing platform, not a paper asset.

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