Leaf area index (LAI) is identified as a Level 2b product to be derived from the Sentinel-2 (S2) Multispectral Imager (MSI) in support of user services [1]. The Validation of Sentinel 2 (VALSE2) project conducted a review, implementation and validation of LAI algorithms suitable for the MSI. Validation was performed using simulated MSI imagery co-located with in-situ LAI over 7 ESA Campaigns. Here we describe two implemented algorithms, the INRA Neural Network algorithm (NNET) and the CCRS Red-Edge algorithm (CCRS), and report on their verification using the PROSAILH radiative transfer model as well as validation both over the BARRAX ESA Campaign as well as prior campaigns. Results indicate both algorithms can provide reasonably unbiased LAI estimates with acceptable error (<1 unit) over prior validation sites but with larger (>1 unit) error over BARRAX. The larger error may be due to a combination of noisy input image data as well as the combination of sparse canopies and bright soils at that site.