Sentinel-2 for crop yield estimation: A systematic review
- 1Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Abstract
Accurate and timely crop yield estimation is fundamental for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 constellation has catalyzed a paradigm shift in Earth observation for agriculture, enabling field and sub-field scale monitoring. This review synthesizes recent advances in crop yield estimation that leverage Sentinel-2 data. A dominant theme is the transition from regional-scale to high-resolution field-level assessments, driven by three approaches: (i) empirical models using vegetation indices coupled with machine and deep learning (e.g., Random Forest, Convolutional Neural Networks); (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) through data assimilation of Sentinel-2 derived biophysical variables such as Leaf Area Index; and (iii) data fusion of Sentinel-2 with Sentinel-1 Synthetic Aperture Radar to overcome cloud cover. The synthesis shows that Sentinel-2-based frameworks can explain a large fraction of within-field yield variability, while performance remains constrained by limited ground-truth data, cloud gaps, and model transferability. Looking ahead, knowledge-guided models, self-supervised foundation-model pre-training, lightweight edge workflows, improved ground observations, and multi-sensor fusion are key pathways toward robust, operational decision-support tools for precision agriculture.
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BibTeX
@article{narimani2026sentinel2,
title = {{Sentinel-2 for crop yield estimation: A systematic review}},
author = {Mohammadreza Narimani and Alireza Pourreza and Ali Moghimi and Parastoo Farajpoor},
journal = {Smart Agricultural Technology},
year = {2026},
month = jul,
doi = {10.1016/j.atech.2026.102405},
url = {https://doi.org/10.1016/j.atech.2026.102405},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Sentinel-2 for crop yield estimation: A systematic review
AU - Mohammadreza Narimani
AU - Alireza Pourreza
AU - Ali Moghimi
AU - Parastoo Farajpoor
PY - 2026
DA - 2026/07/17
JO - Smart Agricultural Technology
DO - 10.1016/j.atech.2026.102405
UR - https://doi.org/10.1016/j.atech.2026.102405
AB - Accurate and timely crop yield estimation is fundamental for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 constellation has catalyzed a paradigm shift in Earth observation for agriculture, enabling field and sub-field scale monitoring. This review synthesizes recent advances in crop yield estimation that leverage Sentinel-2 data. A dominant theme is the transition from regional-scale to high-resolution field-level assessments, driven by three approaches: (i) empirical models using vegetation indices coupled with machine and deep learning (e.g., Random Forest, Convolutional Neural Networks); (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) through data assimilation of Sentinel-2 derived biophysical variables such as Leaf Area Index; and (iii) data fusion of Sentinel-2 with Sentinel-1 Synthetic Aperture Radar to overcome cloud cover. The synthesis shows that Sentinel-2-based frameworks can explain a large fraction of within-field yield variability, while performance remains constrained by limited ground-truth data, cloud gaps, and model transferability. Looking ahead, knowledge-guided models, self-supervised foundation-model pre-training, lightweight edge workflows, improved ground observations, and multi-sensor fusion are key pathways toward robust, operational decision-support tools for precision agriculture.
SN - 2772-3755
ER -