Estimation of corn chlorophyll content using different red edge position algorithms
This research was based on the combined planting model of corn-soybean strip intercropping and the corns under different nitrogen levels were used as the test materials. The reflectance spectrum and chlorophyll content of leaves and canopies of corns were measured at the jointing stage, tasseling st...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2021-08-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2020.10.201 |
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| Summary: | This research was based on the combined planting model of corn-soybean strip intercropping and the corns under different nitrogen levels were used as the test materials. The reflectance spectrum and chlorophyll content of leaves and canopies of corns were measured at the jointing stage, tasseling stage and filling stage. Red edge position (REP) was extracted by continuous wavelet transform (CWT) and other algorithms [maximum first derivative method (FD), four-point interpolation method (FPI) and linear extrapolation method (LEM)]. The quantitative relationships between REP and chlorophyll contents were systematically analyzed to compare the accuracy and stability of the REP extracted by each red edge algorithm on the two scales of leaf and canopy. The results showed that, based on the REP-CWT, the estimation accuracy of chlorophyll content was higher on leaf and canopy scales, and the stability was the strongest, which indicated that REP-CWT was feasible in extracting the REP of corn reflectance spectrum. The quantitative estimation models of corn leaf chlorophyll content and canopy chlorophyll content base on REP-LEM and REP-FPI, respectively, were the best. This study provides a new method for extracting the REP of corn reflectance spectrum, and then constructs the best quantitative estimation model of corn chlorophyll content on different observation scales (leaf and canopy), and offers an effective way to monitor the nitrogen nutrition status of corn. |
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| ISSN: | 1008-9209 2097-5155 |