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.
Highlights
Fraction of photosynthetically active radiation (FPAR) absorbed by crops, as the fraction of incoming solar radiation in the spectral range of 400–700 nm absorbed by crop canopies (Moreau and Li, 1996; Ma et al, 2007), was critical to understanding and quantifying the exchange of mass, energy and momentum between atmosphere and land surface, which played an important role in most ecosystem productivity including crop biomass models (Muñoz et al, 2010)
Models based on linear FPAR-normalized difference vegetation index (NDVI) relationships suffered from a major flaw with the saturation of NDVI at the higher leaf area index (∼3.5) (Samanta et al, 2012), thereby resulting in the lower sensitivity to FPAR change using a linear model in such case (Myneni and Williams, 1994; Zhang et al, 2009)
From blooming stage to milk stage, FPAR revealed a tendency of slow increase or reached the saturation status
Summary
Fraction of photosynthetically active radiation (FPAR) absorbed by crops, as the fraction of incoming solar radiation in the spectral range of 400–700 nm absorbed by crop canopies (Moreau and Li, 1996; Ma et al, 2007), was critical to understanding and quantifying the exchange of mass, energy and momentum between atmosphere and land surface, which played an important role in most ecosystem productivity including crop biomass models (Muñoz et al, 2010). Hyperspectral remote sensing was an important technique to fulfill real-time monitoring for growth status of crops based on its superior performance in acquiring vegetation canopy information rapidly and non-destructively. It still had the statistical uncertainty using regression analysis based on only five points. Models based on linear FPAR-NDVI relationships suffered from a major flaw with the saturation of NDVI at the higher leaf area index (∼3.5) (Samanta et al, 2012), thereby resulting in the lower sensitivity to FPAR change using a linear model in such case (Myneni and Williams, 1994; Zhang et al, 2009) Another issue was the limited data for boreal ecosystems. In order to develop a practical methodology for assessing FPAR of wheat canopies, exhaustive statistical analysis of FPAR-VI relationships for wheat canopies was conducted by using ground-measured hyperspectral data collected from a series of field experiments
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