In addition combination SIF with other remote sensing index to predicts crops yield can obtain better performance than using only index For example used high-resolution SIF and EVI to predict the rice

Furthermore, incorporating machine learning algorithms with SIF and other remote sensing indices can further improve the accuracy of yield prediction. A study used a combination of SIF, NDVI, and LST with Random Forest algorithm to predict maize yield in the United States, and the results showed that the model had a high accuracy of 86%. Another study used SIF, NDVI, and meteorological data with Support Vector Machine algorithm to predict wheat yield in India, and the results showed that the model had an accuracy of 89%.

Overall, the combination of SIF with other remote sensing indices and machine learning algorithms provides a promising approach for predicting crop yield. This approach can help farmers and policymakers to make informed decisions regarding crop management, food security, and economic planning