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The efficient monitoring of foliar nutrient such as the C:N ratio is important for sustainable rangeland productivity. Recent advances in Earth observation technology provide multispectral sensors with high spatial, spectral and temporal resolution suitable for modelling foliar nutrients. This study explored the fusion of Sentinel-2 and PlanetScope datasets using the pixel fusion technique and the random forest algorithm. The result in this study is vital in understanding the value of image fusion in enhancing rangeland nutrient estimation for optimal productivity. The study concludes that the fusion of optical sensors requires further investigation. [ABSTRACT FROM AUTHOR]

Rice is one of the most essential food crops in the world and accurate estimation of rice yield is a major content of agricultural research. Recently, scholars have used machine learning algorithms for rice yield estimation. However, there are few studies on rice yield prediction based on rice phenological stages. In this study, a method for rice yield prediction on the basis of rice phenology analysis was proposed. For this study, the cumulative NDVI and EVI based Logistic regression curves were carried out to determine the phenological period. Comparing several regression models, the results of the random forest regression model developed using phenology-based regression analysis performed better. The R2 of training and validation samples were 0.96 and 0.95, respectively, with RMSE of 0.06 ton/ha. This method is feasible for governments to predict rice yield and make farm risk management decisions. [ABSTRACT FROM AUTHOR]