Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

ABSTRACT

A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R² = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R² = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.

KEYWORDS:

• hyperspectra
• chlorophyll content
• inversion
• PROSAIL
• random forest regression (RFR)

Acknowledgements

This research is partially supported by the National Natural Science Foundation of China (No. 41401473); the Postdoctoral Science Foundation of China (2013M531329); the Natural Science Foundation of Jiangsu, China (BK2012145); and the National Natural Science Foundation of China (No. 31560130 and No. 41401397). The authors extend particular thanks to the ESA for providing the validation data (including the hyperspectral remote-sensing images and ground measurement data). The authors also wish to thank Miss Qin Sun and Miss Meng Wang from the School of Geodesy and Geomatics, Jiangsu Normal University, China, for their assistance in processing the CHRIS data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is partially supported by the National Natural Science Foundation of China [No. 41401473]; the Postdoctoral Science Foundation of China [2013M531329]; the Natural Science Foundation of Jiangsu, China [BK2012145]; and the National Natural Science Foundation of China [No. 31560130 and No. 41401397].