Boosting Crop Yield Predictions: Combining SIF with Other Remote Sensing Indices and Machine Learning

Furthermore, incorporating machine learning algorithms with SIF and other remote sensing indices can enhance the accuracy of crop yield prediction models. A study used random forest regression with SIF, NDVI, and temperature data to predict maize yield in the United States, and the results showed a significant improvement in prediction accuracy compared to using only SIF or other indices. Therefore, combining SIF with other indices and machine learning algorithms can lead to more accurate and reliable crop yield predictions, which can ultimately aid in food security and agricultural management.