Towards Accurate Crop Yield Prediction: Integrating Sentinel-2 Remote Sensing with AI-Based Modelling

Abstract

Abstract. Accurate monitoring of vegetation health and canopy structure is essential for optimizing agricultural productivity and managing natural resources. Remote sensing technologies, combined with artificial intelligence (AI) and advanced satellite data, have revolutionized the capacity to assess crop conditions at large scales with high temporal and spatial resolution. This study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola. By integrating spectral reflectance data with view and solar geometry parameters, the model effectively captures the complex interactions between canopy structure and environmental factors. The methodology employs a two-layer neural network calibrated with physically based normalization to translate Sentinel-2 spectral and angular inputs into accurate LAI estimates. Validation against observed field measurements demonstrates strong agreement, underscoring the model’s robustness and reliability. Spatial analysis reveals distinct LAI patterns among the crop types, highlighting differences in canopy density and growth dynamics. Temporal profiling further illustrates crop-specific development trends, with canola showing extended canopy expansion. The results confirm that the fusion of remote sensing data with AI modelling provides a powerful tool for precision agriculture, enabling detailed monitoring of crop growth and facilitating informed decision-making. This approach offers significant potential for enhancing yield prediction, resource management, and sustainable farming practices, ultimately supporting global food security efforts.

Plant phenotyping relevance

Sentinel-2画像とニューラルネットワークにより、作物のLAIという明示的な植物形質を推定し、実測値で検証する手法が研究の中心である。

Code and data availability

The paper describes Sentinel-2 imagery, a proprietary ground-truth dataset from a private company, and a two-layer neural network for LAI estimation, but contains no public data deposit, code repository, model checkpoint, or supplement with availability language. The ground-truth shapefiles and field records were 'obta

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