Leaf Area Index (LAI) is a key variable for the monitoring the ability of crops to intercept solar energy for biomass production. In addition, LAI has the high seasonal variability and differ among phenophases. Therefore, accurately monitoring LAI at the vegetative, reproductive and ripening stage is central for improving yield. The aim of the study is to identify the performance of different retrieval methods on the estimation of LAI at different phenophases of rice from sentinel 2 simulated bands. To achieve this, the research seeks to answer the following research objectives (i) compare the performance of piecewise model based on the specific phenophases with single models generated from the entire active season, (ii) to determine whether phenophase models are advantageous for LAI estimation in rice. The retrieval models were developed and tested on proximal hyperspectral bands, with focus on the sentinel 2 spectral bands. The machine learning regression models (MLRAs) presented the highest retrieval accuracy during the entire growing season (R2=0.65-0.74). The hybrid model’s retrieval performance was similar to vegetation indices but with much higher errors during the entire growing season. The phenophases estimation of LAI saw a decline in the overall retrieval performance of MLRA, hybrid and VI models. The models performed much better during the vegetative stage compared to the reproductive and ripening stages. The study shows MLRA as the best model for estimating LAI during the entire growing and vegetative stages of rice growth. The hybrid models were only suited for the entire growing season (R2>0.5) but generally low when estimating for different phenohases. Finally, VI models were identified to be the best for estimating LAI during the ripening stages of irrigated rice.