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This research aims to estimate rice yield at the village level using a machine learning approach based on multi-source satellite data.

Multisource Satellite Data-Driven Machine Learning Approach for Rice Yield Prediction

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This research aims to estimate rice yield at the village level using a machine learning approach based on multi-source satellite data.
Utilized optical and SAR satellite data alongside climatic and land surface model parameters.
Evaluated four machine learning models including Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Gradient Boosting.
Conducted Leave-One-Year-Out cross-validation to assess temporal robustness using data from 2017 to 2023.
Random Forest model achieved an R2 of 0.465, RMSE of 415.34 kg ha−1 for independent test year 2022.
In 2023, RF improved with an R2 of 0.838, RMSE of 325.75 kg ha−1.
LOYO validation showed RF's robustness with a mean R2 of 0.702 and mean RMSE of 384.73 kg ha−1.

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Randomized trial shows improved rice yield prediction in villages, suggesting enhanced agricultural planning support.

Tiwari et al. (2026) studied this question.

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