Sentinel-2 for Crop Yield Estimation: A Systematic Review

TL;DR

This systematic review highlights how Sentinel-2 satellite data, combined with machine learning, process-based models, and data fusion, advances crop yield estimation at field scales, despite current limitations like data gaps and transferability issues.

Contribution

The paper synthesizes recent Sentinel-2-based crop yield estimation methods, emphasizing high-resolution, multi-modal approaches and identifying key challenges and future directions.

Findings

Machine learning and deep learning models explain within-field yield variability.

Data fusion techniques mitigate cloud-related data gaps.

Performance is limited by ground-truth data scarcity and model transferability.

Abstract

Accurate and timely crop yield estimation is critical for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 satellite constellation, with high spatial, temporal, and spectral resolution, has transformed agricultural monitoring by enabling field- and sub-field-scale analysis. This review synthesizes recent advances in Sentinel-2-based crop yield estimation. A key trend is the shift from regional models to high-resolution field-level assessments driven by three main approaches: (i) empirical models using vegetation indices combined with machine and deep learning methods such as Random Forest and Convolutional Neural Networks; (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) via data assimilation of Sentinel-2-derived variables like Leaf Area Index (LAI); and (iii) data fusion techniques combining Sentinel-2 optical data with Sentinel-1 SAR to mitigate cloud-related limitations. The review shows that machine learning, deep learning, and hybrid modeling frameworks can explain substantial within-field yield variability across crops and regions. However, performance remains constrained by limited ground-truth data, cloud-induced gaps, and challenges in model transferability across years and locations. Future directions include tighter integration of multi-modal data and improved in-season observations to support robust, operational decision-making in precision agriculture and sustainable intensification.