Preprocessed Ground Truth Maize Yield Samples for Subfield Prediction

Preprocessed ground truth maize yield samples from a study using Sentinel-2 satellite data and machine learning for subfield yield prediction. The dataset, created by Dorijan Radocaj, was last updated on May 4, 2026. It is a small dataset of 368.7 KB, distributed under a CC-BY-4.0 license.

Use Cases

• Training yield prediction models based on ground truth maize samples.
• Validating remote sensing-derived phenology metrics against field measurements.
• Developing interpretable ensemble models for agricultural applications.

Strengths

• Data is explicitly preprocessed and ready for analysis.
• Includes ground truth samples, which are essential for model validation.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's small size (368.7 KB) indicates a limited scope.

Provenance

Source

figshare

Collection Method

Derived from a study on subfield maize yield prediction using Sentinel-2 land surface phenology metrics.

Time Range

null

Freshness

Last updated 2026-05-04 15:52:49; freshness should be verified.

Geography

null