| The purpose of this study is to develop a method of modeling crop leaf chlorophyll content (Chlab) using remote sensing data with a physically-based modelling approach. During the 2013 growing season, ground data were collected at 28 corn and wheat sites near Stratford, Ontario. Effective leaf area index, hyperspectral leaf reflectance and transmittance, and chemically extracted Chlab were acquired at each site. A two-step inversion process was developed to model crop Chlab using Landsat-8. In this process, a look-up-table (LUT) was developed using SAIL, a bidirectional radiative-transfer model, to simulate canopy reflectance. The LUT was then utilized to calculate leaf-level reflectance and input into PROSPECT, a leaf-level radiative transfer model, to estimate Chlab. Validation of PROSPECT with ground-based Chlab using simulated Landsat-8 bands shows an R2= 0.83 and RMSE=8.48 mug/cm 2. Validation using the LUT shows an R2=0.64 at the leaf level and R2= 0.87 at the canopy level. |
