Abstract
Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.
Highlights
As winter wheat is one of the most important food crops in China, the timely and accurate monitoring of the growth and nutrition of this crop contributes to proper field management
This study focused on testing a remote sensing method to accurately estimate the leaf chlorophyll content (LCC) of winter wheat over crop canopies with minimum effects from leaf area index (LAI)
The results showed that the newly proposed RECAI/triangular vegetation index (TVI) performed excellently for LCC estimation, with R2 values that increased by more than 13.09% and root mean square error (RMSE) values that decreased by more than 6.22%
Summary
As winter wheat is one of the most important food crops in China, the timely and accurate monitoring of the growth and nutrition of this crop contributes to proper field management. Zillmann et al [16] concluded that the normalized difference red edge (NDRE) index was strongly linearly related to the winter wheat chlorophyll content at the canopy level based on RapidEye images. Gitelson et al [20] found that the reciprocal reflectance in the range from 695–705 nm is closely related to LCC and proposed a new index called CIred-edge that obviously improved the accuracy of chlorophyll content prediction. Based on the band settings of medium resolution imaging spectrometer (MERIS) data, Dash and Curran [23] proposed the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index (MTCI), which is strongly related to the red-edge position and has been used to successfully predict vegetation chlorophyll contents at the canopy level. Jin et al [25]
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