# Sentinel-2 for Crop Yield Estimation: A Systematic Review

arXiv: https://arxiv.org/abs/2603.23779 · HTML: https://mlsanity.com/paper/2603.23779/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2026.

## Built on by (most cited first, 6 of 6)

- [Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement](https://mlsanity.com/paper/2607.21881/) (2026)
- [Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis](https://mlsanity.com/paper/2605.21804/) (2026)
- [From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California](https://mlsanity.com/paper/2608.13856/) (2026)
- [The spatial anatomy of urban wildfire vulnerability: a spatially validated GeoAI framework reveals the roles of building density and vegetation moisture in structure loss during the 2025 Palisades Fire](https://mlsanity.com/paper/2608.22293/) (2026)
- [Multisource Remote Sensing and Geospatial Analysis of Vineyard Wildfire Impacts and Resilience: The 2019 Kincade Fire](https://mlsanity.com/paper/2609.16168/) (2026)
- [A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires](https://mlsanity.com/paper/2609.16199/) (2026)

## Abstract (arXiv)

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.