摘要
Fraction of absorbed photosynthetically active radiation （FPAR） is one of the important variables in many vegetation productivity and biomass estimation models. Therefore, it is significant to retrieve FPAR accurately for the improvement of model precision. On the basis of the field experiment, this article analyzed the correlations between corn canopy FPAR and spectral reflectance, and reflectance derivative. Discussion about the mechanism of FPAR estimation with different empirical models is based upon corn canopy reflectance, reflectance derivative, NDVI （normalized difference vegetation index） and RVI （ratio vegetation index）. The reflectance of visible bands showed much better correlations with FPAR than near-infrared bands. The correlation between FPAR and reflectance derivative varied more frequently and greatly than that between FPAR and reflectance, and with preferable correlation only around 520, 570, 670, 805, 950, and 1 010 nm. Reflectance and reflectance derivative both had intimate correlation with FPAR at some typical single band, with the maximum R^2 of 0.791 and 0.882, respectively. In a word, reflectance derivative and vegetation index were much effective in the estimation of corn FPAR than reflectance, and the stepwise regression of multibands with reflectance derivative showed the best regression with R^2 of 0.944. Reflectance at 375 and 950 nm with absorption characteristics caused by water showed prodigious potential for FPAR precisely estimating model establishment. On the whole, vegetation index and reflectance derivative had good relationships with FPAR, and could be used for FAPR estimation. It would be effective for choosing right bands and excavating the hyperspectral data to improve FPAR estimating precision.
Fraction of absorbed photosynthetically active radiation （FPAR） is one of the important variables in many vegetation productivity and biomass estimation models. Therefore, it is significant to retrieve FPAR accurately for the improvement of model precision. On the basis of the field experiment, this article analyzed the correlations between corn canopy FPAR and spectral reflectance, and reflectance derivative. Discussion about the mechanism of FPAR estimation with different empirical models is based upon corn canopy reflectance, reflectance derivative, NDVI （normalized difference vegetation index） and RVI （ratio vegetation index）. The reflectance of visible bands showed much better correlations with FPAR than near-infrared bands. The correlation between FPAR and reflectance derivative varied more frequently and greatly than that between FPAR and reflectance, and with preferable correlation only around 520, 570, 670, 805, 950, and 1 010 nm. Reflectance and reflectance derivative both had intimate correlation with FPAR at some typical single band, with the maximum R^2 of 0.791 and 0.882, respectively. In a word, reflectance derivative and vegetation index were much effective in the estimation of corn FPAR than reflectance, and the stepwise regression of multibands with reflectance derivative showed the best regression with R^2 of 0.944. Reflectance at 375 and 950 nm with absorption characteristics caused by water showed prodigious potential for FPAR precisely estimating model establishment. On the whole, vegetation index and reflectance derivative had good relationships with FPAR, and could be used for FAPR estimation. It would be effective for choosing right bands and excavating the hyperspectral data to improve FPAR estimating precision.
基金
Many thanks are given to the Knowledge Innovation Program of the Chinese Academy of Sciences （KZCX-SW-356）
the National Natural Science Foundation of China （40401003）, and the Project of Field Station on Resources and Environment of the Chinese Academy of Sciences.
