Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR) for Wheat Canopies Based on Hyperspectral Vegetation Indexes

Abstract

Fraction of photosynthetically active radiation (FPAR), as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from concurrent ground-measured hyperspectral data were explored. FPAR revealed the most strongly correlation with normalized difference index (NDI), and scaled difference index (N*). Both NDI and N* revealed the increase as the increase of FPAR; however, NDI value presented the stagnation as FPAR value beyond 0.70. On the other hand, N* showed a decreasing tendency when FPAR value was higher than 0.70. This special relationship between FPAR and vegetation index could be employed to establish a piecewise FPAR assessment model with NDI as a regression variable during FPAR value lower than 0.70, or N* as the regression variable during FPAR value higher than 0.70. The model revealed higher assessment accuracy up to 16% when compared with FPAR assessment models based on a single vegetation index. In summary, it is feasible to apply NDI and N* for accomplishing wheat canopy FPAR assessment, and establish an FPAR assessment model to overcome the limitations from vegetation index saturation under the condition with high FPAR value.