Estimation of Leaf Area Index Based on Machine Learning/PROSAIL Using Optical Satellite Imagery

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias similar to 0.97 and a difference of root-mean-square-error similar to 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.