A growing use of remote sensing, climate data, and their combination to estimate yields

The gap

Evidence profile

Sourced from the future work and stated research gap and future-work section and abstract of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 2 journals. Those papers have been cited 36 times in total.

Supporting evidence — 4 representative gaps

• Maize yield prediction using machine learning: a systematic literature review (2026) · Frontiers in Artificial Intelligence · doi

  LSTM. J. (2022). Prediction of corn yield in the USA Corn Belt using satellite data and machine learning: From an evapotranspiration perspective. Agriculture, 12(8), 1263. https://doi. org/10.3390/agriculture12081263 MDPI M. F. d. Oliveira et al.

  generalfuture work
  Keywords: corn agriculture lstm prediction yield belt using satellite machine learning evapotranspiration perspective https mdpi oliveira
• Rice Yield Estimation Based on Machine Learning Applied to UAV Remote Sensing Data (2026) · Remote Sensing · doi

  The lack of rigorous comparative benchmarking of machine learning models using multi-temporal UAV spectral data with independent temporal validation. The need for accurate in-season rice yield prediction for improved nitrogen management and climate-smart decision making.

  generalstated research gapevidence 5/5
  Keywords: lack rigorous comparative benchmarking machine learning models using
• Rice Yield Estimation Based on Machine Learning Applied to UAV Remote Sensing Data (2026) · Remote Sensing · doi

  Further research is needed to explore the application of ML models in different regions and soil types, - Investigating the use of other UAV spectral data and machine learning algorithms for rice yield prediction, - Examining the impact of climate change on rice yield and the potential of ML models to predict these changes

  generalfuture-work sectionevidence 5/5
  Keywords: further research needed explore application models different regions
• Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions (2025) · Remote Sensing · cited 36× · doi

  There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear.

  generalabstractevidence 4/5
  Keywords: there growing remote sensing climate combination estimate yields optimal indices time window wheat yield prediction

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