Leaf Area Index (LAI) governs canopy processes. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of Sentinel-2 for assessing LAI. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The continuous data were resampled to Sentinel-2 resolution. The LAI prediction abilities by Partial Least Squares (PLS) models were compared and evaluated. For the continuous and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93 and 0.92, respectively. According to the Variable Importance in Projection (VIP) analysis, the red-edge spectral region was found to be highly important for LAI assessment. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed. The prediction abilities of these indices were compared, peaking for wheat, with REIP r values of 0.91 for both data formations. Therefore, it is concluded that Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat and therefore can be potentially implemented by sensors containing four red-edge bands.