Tan Changwei, Wang Dunliang, Zhou Jian, Du Ying, Luo Ming, Zhang Yongjian, Guo Wenshan
Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Yangzhou University, Yangzhou, China.
Front Plant Sci. 2018 Jun 7;9:776. doi: 10.3389/fpls.2018.00776. eCollection 2018.
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.
光合有效辐射比例（FPAR）作为评估产量和生物量生产的重要指标，是为作物管理提供指导的关键。然而，缺乏良好的高光谱数据常常会阻碍准确可靠的FPAR评估，尤其是对于小麦而言。在本研究中，为了制定准确的FPAR评估策略，探讨了小麦冠层FPAR与从同步地面测量的高光谱数据得出的植被指数之间的关系。FPAR与归一化差异指数（NDI）和尺度差异指数（N）的相关性最强。NDI和N均随FPAR的增加而增加；然而，当FPAR值超过0.70时，NDI值出现停滞。另一方面，当FPAR值高于0.70时，N呈下降趋势。FPAR与植被指数之间的这种特殊关系可用于建立分段FPAR评估模型，当FPAR值低于0.70时，以NDI作为回归变量；当FPAR值高于0.70时，以N作为回归变量。与基于单一植被指数的FPAR评估模型相比，该模型的评估精度提高了16%。总之，应用NDI和N来完成小麦冠层FPAR评估并建立FPAR评估模型以克服高FPAR值条件下植被指数饱和的局限性是可行的。
