Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation
The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical parameter of vegetation. It is often estimated using vegetation indices (VIs) derived from remote sensing data, such as the normalized difference VI (NDVI) Ideally a linear relationship is used for the estimation; however, most conventional VIs are affected by canopy background reflectance and their sensitivity to FPAR declines at high biomass. In this study, a multiplier, the ratio of the green to the red reflectance, was introduced to improve the linear relationship between VIs and crop FPAR. Three widely used VIs NDVI, the green normalized difference VI (GNDVI) and the renormalized difference VI (RDVI) were modified this way and were called modified NDVI (MNDVI) modified GNDVI (MGNDVI) and modified RDVI (MRDVI) respectively. A sensitivity study was applied to analyse the correlation between the three modified indices and the leaf area index (LAI) using the reflectance data simulated by the combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model (PROSAIL model) The results revealed that these new indices reduced the saturation trend at high LAI and achieved better linearity with crop LAI at low to medium biomass when compared with their corresponding original versions. This has also been validated using in situ FPAR measurements over wheat and maize crops. In particular, estimation using MNDVI achieved a coefficient of determination (R2) of 0.97 for wheat and 0.86 for maize compared to 0.90 and 0.82 for NDVI, respectively, while MGNDVI achieved 0.97 for wheat and 0.88 for maize, compared to 0.90 and 0.81 for GNDVI, respectively. Algorithms based on the VIs when applied to both wheat and maize showed that MNDVI and MGNDVI achieved a better linearity relationship with FPAR (R2 0.92) in comparison with NDVI (R2 0.85) and GNDVI (R2 0.82) The study demonstrated that applying the green to red reflectance ratio can improve the accuracy of FPAR estimation.