干旱区生态环境知识资源中心

Precise monitoring of the yield of crops is critical for growth diagnosis and precision management. Many spectral indices have been developed for yield estimation. The objective of this study was to determine the most suitable model for the rational estimation of spring wheat yield using canopy hyperspectral reflectance data of spring wheat. Canopy hyperspectral datasets were obtained during the whole growth stage from the seeding to mature stage in semi-arid rained region of Loess Plateau in Northwest China. On the basis of the correlation analysis of the hyperspectral data and the yield data, the study compared potentials and limitations of hyperspectral indices from the canopy level, and analyzed the relationship between hyperspectral indices and yield in different grown stage. The spectral indices REP in booting stage and heading stage are sensitive to the yield. We proposed a compound regression equation from the spectral index of booting and heading stage, the R-2 is 0.875, the combined multiphase phase spectral index is better for yield estimation. The results from field experiment are limited to large-scale experiments and would provide a good basis for remote sensing of yield estimation in a wide range of vegetation.