Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis

Plant Methods

Renshan Zhu¹  Xianting Wu¹  Yan Gong²  Yi Ma²  Bo Duan²  Shenghui Fang²  Yi Peng² 

[1] College of Life Sciences, Wuhan University;School of Remote Sensing and Information Engineering, Wuhan University;

关键词: Yield estimation;    Rapeseed;    Unmanned aerial vehicle;    Canopy reflectance;    Spectral mixture analysis;    Abundance;   

DOI  :  10.1186/s13007-018-0338-z

来源: DOAJ

【 摘 要 】

Abstract Background The accurate quantification of yield in rapeseed is important for evaluating the supply of vegetable oil, especially at regional scales. Methods This study developed an approach to estimate rapeseed yield with remotely sensed canopy spectra and abundance data by spectral mixture analysis. A six-band image of the studied rapeseed plots was obtained by an unmanned aerial vehicle (UAV) system during the rapeseed flowering stage. Several widely used vegetation indices (VIs) were calculated from canopy reflectance derived from the UAV image. And the plot-level abundance of flower, leaf and soil, indicating the fraction of different components within the plot, was retrieved based on spectral mixture analysis on the six-band image and endmember spectra collected in situ for different components. Results The results showed that for all tested indices VI multiplied by leaf-related abundance closely related to rapeseed yield. The product of Normalized Difference Vegetation Index and short-stalk-leaf abundance was the most accurate for estimating yield in rapeseed under different nitrogen treatments with the estimation errors below 13%. Conclusion This study gives an important indication that spectral mixture analysis needs to be considered when estimating yield by remotely sensed VI, especially for the image containing obviously spectral different components or for crops which have conspicuous flowers or fruits with significantly different spectra from their leave.

【 授权许可】

Unknown