Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach

Abstract

Accurate crop yield estimation is crucial for food security and effective crop management in precision agriculture. Previous studies have shown the correlation between remotely sensed data and crop yield, emphasizing the need for continuous time series of radiometric indices from satellite imagery. However, passive sensors are limited by cloud cover, restricting valid image acquisition. This study explored the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data to enhance NDVI estimation and yield prediction of spinach. Random Forest Regression models were developed to predict NDVI from SAR data at two scales: (i) a general crop-scale model and (ii) specific plot-scale models. Both scales achieved R2 values above 0.9 for NDVI estimation, with better results at the plot scale. Integrating NDVI values derived from Sentinel-1 significantly improved yield estimation accuracy using NDVI time series compared to using NDVI from Sentinel-2 alone. The results indicated that plot-scale NDVI estimation had the lowest error rates (1.4%) and the highest R2 (0.89), outperforming the crop-scale model. The integration of SAR-based NDVI reduced data gaps caused by cloud cover and enabled earlier, more informed crop management decisions. These findings underscore the importance of SAR-based NDVI estimation for enhancing yield predictions in precision agriculture.

Plant phenotyping relevance

SARと光学データを融合し、作物・圃場プロット単位のNDVIを推定する手法を開発・検証しており、植物状態の測定法が中心的です。

Code and data availability

The supplied blocks describe Sentinel-1/2 data processing and Random Forest modeling for spinach NDVI/yield estimation, but contain no data availability statement, no public deposit of the plot-level radiometric time series, yield measurements, or author analysis code, and no trained model checkpoints. Sentinel-1/2 and

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