Leaf Area Index (LAI) is an important measure of how dense and structured plant leaves are, crucial for studying climate change, plant growth models, and carbon/water cycles. Remote sensing is key for getting LAI data, but current methods often overlook how terrain affects accuracy, especially in mountainous areas. Improving accuracy in mountainous LAI remote sensing requires considering how terrain impacts leaf reflectance rates, with models and data corrections being crucial. This review discusses progress in studying leaf reflectance models and terrain corrections for mountainous LAI remote sensing, highlighting existing issues and future research trends.