# Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction

arXiv: https://arxiv.org/abs/2401.11844 · HTML: https://mlsanity.com/paper/2401.11844/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Self-attention for raw optical Satellite Time Series Classification](https://mlsanity.com/paper/1910.10536/) (2019)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks](https://mlsanity.com/paper/1711.08681/) (2017)
- [Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series](https://mlsanity.com/paper/2007.00586/) (2020)
- [Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series](https://mlsanity.com/paper/2112.07558/) (2021)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level](https://mlsanity.com/paper/2308.08948/) (2023)
- [A Comparative Assessment of Multi-view fusion learning for Crop Classification](https://mlsanity.com/paper/2308.05407/) (2023)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Exploring Self-Attention for Crop-type Classification Explainability](https://mlsanity.com/paper/2210.13167/) (2022)

## Built on by (most cited first, 8 of 8)

- [Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach](https://mlsanity.com/paper/2501.01132/) (2025)
- [Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration](https://mlsanity.com/paper/2510.19579/) (2025)
- [In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data](https://mlsanity.com/paper/2403.16582/) (2024)
- [Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction](https://mlsanity.com/paper/2510.18648/) (2025)
- [YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction](https://mlsanity.com/paper/2604.00940/) (2026)
- [Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing](https://mlsanity.com/paper/2407.08274/) (2024)
- [PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction](https://mlsanity.com/paper/2605.23478/) (2026)
- [Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function](https://mlsanity.com/paper/2606.05759/) (2026)

## Abstract (arXiv)

Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, and industry stakeholders. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Combining heterogeneous data views poses a fusion challenge, like identifying the view-specific contribution to the predictive task. We present a novel multi-view learning approach to predict crop yield for different crops (soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our multi-view input data includes multi-spectral optical images from Sentinel-2 satellites and weather data as dynamic features during the crop growing season, complemented by static features like soil properties and topographic information. To effectively fuse the data, we introduce a Multi-view Gated Fusion (MVGF) model, comprising dedicated view-encoders and a Gated Unit (GU) module. The view-encoders handle the heterogeneity of data sources with varying temporal resolutions by learning a view-specific representation. These representations are adaptively fused via a weighted sum. The fusion weights are computed for each sample by the GU using a concatenation of the view-representations. The MVGF model is trained at sub-field level with 10 m resolution pixels. Our evaluations show that the MVGF outperforms conventional models on the same task, achieving the best results by incorporating all the data sources, unlike the usual fusion results in the literature. For Argentina, the MVGF model achieves an R2 value of 0.68 at sub-field yield prediction, while at field level evaluation (comparing field averages), it reaches around 0.80 across different countries. The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.