Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

Naichen Xing, Wenjiang Huang, Huichun Ye, Yingying Dong, Weiping Kong, Yu Ren, Qiaoyun Xie

Research output: Contribution to journalArticlepeer-review

2 Citations (Web of Science)


Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.

Original languageEnglish
Pages (from-to)580-602
Number of pages23
JournalBig Earth Data
Issue number4
Publication statusPublished - 2022
Externally publishedYes


Dive into the research topics of 'Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages'. Together they form a unique fingerprint.

Cite this