Construction of spectral index based on multi-angle spectral data for estimating cotton leaf nitrogen concentration

Abstract

Rapid, non-destructive, and accurate monitoring is of great significance for determining crop nitrogen status and improving nitrogen fertilizer management. Multiple angle spectral data contains comprehensive and abundant information of crop canopy. In this study, the spectral data of cotton leaves at different leaf inclination angles (0°, 15°, 30°, and 45°) were collected using hyperspectral imaging technology at the seedling stage, bud stage, flowering stage, and boll-forming stage of cotton from 2018 to 2020, and indoor determination of cotton leaf nitrogen concentration (LNC) was conducted simultaneously. Based on the spectral features in blue edge and green edge regions, a new multi-angle blue-edge absorption vegetation index (MBEAVI) was constructed. Then, the MBEAVI and 16 vegetation indices (VI) reported in previous studies were constructed using single-angle and multi-angle spectral data of 2018 (320 sets) and 2019 (320 sets), and the inter-annual test was conducted with the data of 2020 (320 sets). The results showed that the VI models based on multi-angle spectral data (multi-angle models) had higher accuracy than the models based on single-angle spectral data (single-angle models). Among single-angle models, the model based on the optimized red edge absorption index (OREA) had the highest accuracy in cotton LNC estimation, with R2 of 0.735. However, the accuracy of multi-angle model, MBEAVI, was significantly higher than that of the OREA model, with R2 of 0.812. Besides, the R2 of the MBEAVI model reached 0.789 in the inter-annual test. Therefore, the MBEAVI model based on multi-angle spectral data had a higher accuracy and stability in predicting cotton LNC compared with the single-angle models. This study provides theoretical support for improving the accuracy of monitoring cotton nitrogen status by using multi-angle hyperspectral data.