Cu<sup>2+</sup>污染下农作物叶片特征光谱探究

doi:10.3964/j.issn.1000-0593(2025)01-0264-08

Abstract
References
Related Citation (15)

Download: PDF (3782 KB)
Export: BibTeX | EndNote (RIS)

Abstract A maize pot experiment with different copper stress gradients was designed in an outdoor greenhouse to explore the sensitive leaf types and spectral ranges of crop pollution response under heavy metal stress. Taking maize leaves as the research object, the hyperspectral reflectance data and heavy metal content data of maize leaves during the heading period were measured using instruments, providing basic data for research. This paper designed the Leaf Spectral Detection Method (LSDM) from the frequency domain perspective, combined with time-frequency analysis, to obtain sensitive leaf shapes and spectral bands under heavy metal copper stress, providing technical support for heavy metal monitoring in crops. Based on the growth process of maize, this study explores the full spectrum and sub-spectrum of the old leaf (O), middle leaf (M), and new leaf (N) spectra from 350 to 1 300 nm. Firstly, the hyperspectral reflectance data of maize leaves under copper stress were subjected to double differentiation (SOD) and envelope removal (CR) and transformed into the frequency domain. The Daubechies wavelet 6-layer decomposition was performed using time-frequency analysis methods. Then, based on the signal anomaly points, wavelet high-value points, and SODCR curve high-value points, the spectral anomaly parameters SAP (Spectral Anomaly Parameters) of maize leaves are defined, namely: Abnormal Changes Reflectivity (ACR), which is the absolute value of the difference between the abnormal reflectance and the reflectance of the next adjacent band; Abnormal Wavelet Coefficients (AWC), which is the absolute value of the difference between the abnormal wavelet coefficients and the wavelet coefficients of the next adjacent band; Abnormal SODCR value (ASR), which is the absolute value of the difference between the abnormal point SODCR value and the SODCR value of the next adjacent band. Finally, by examining the correlation between spectral anomaly parameters and heavy metal content in maize leaves, we aim to explore the leaf types and spectral segments sensitive to copper pollution. The results showed that the leaf spectral detection method LSDM can efficiently enhance weak information in maize leaves and accurately locate the spectral anomaly caused by heavy metal copper stress, with the anomaly range concentrated within 350 to 800 nm; Spectral anomaly parameters can quantitatively measure the spectral anomalies of maize leaves under heavy metal copper stress; Under different copper stress gradients, maize new leaves (N) exhibit sensitive leaf types, with sensitive spectral segments including blue edges, green peaks, yellow edges, and red valleys. This paper can provide technical support for monitoring heavy metals in other cereal crops and their canopy scales.

Key words：Heavy metal stress; Maize leaves; Hyperspectral; Sensitive blade type; Sensitive spectral range

Received: 2024-06-18 Accepted: 2024-07-15

ZTFLH:

TP75

Corresponding Authors: YANG Ke-ming E-mail: ykm@163.com

Cite this article:

ZHANG Chao,WU Xuan,YANG Ke-ming, et al. Exploration of Spectral Characteristics of Crop Leaves Under Cu²⁺ Pollution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 264-271.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)01-0264-08 OR https://www.gpxygpfx.com/EN/Y2025/V45/I01/264

[1] HAN Tian-fu, LI Ya-zhen, QU Xiao-lin, et al(韩天富, 李亚贞, 曲潇林, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2022, 38(1): 100.
[2] SONG Hong-mei, LI Ting-liang, LIU Yang, et al(宋红梅, 李廷亮, 刘洋, 等). Journal of Soil and Water Conservation(水土保持学报), 2022, 37(1): 1.
[3] TIAN Wen, ZONG Da-peng, FANG Cheng-gang, et al(田稳, 宗大鹏, 方成刚, 等). China Environmental Science (中国环境科学), 2022, 42(10): 4901.
[4] SU Yan-fang, LI Ming-li, ZHAO Pei-qiang, et al(苏艳芳, 李明丽, 赵培强, 等). Applied Chemical Industry(应用化工), 2024, 53(5): 1234.
[5] Su Haifeng, Zhang Yanzhao, Lu Zichun, et al. Journal of Cleaner Production, 2022, 373: 133825.
[6] Lin T, Zheng Q X, You G L, et al. Doklady Earth Sciences, 2022, 506(1): 839.
[7] Geetha N, Sunilkumar C R, Bhavya G, et al. Environmental Research, 2022, 216(1): 114498.
[8] LI Jun-meng, ZHAI Xue-dong, YANG Zi-han, et al(李俊猛, 翟雪东, 杨子涵, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(9): 2890.
[9] Kendler S, Mano Z, Aharon R，et al. Scientific Reports, 2022, 12(1): 17580.
[10] Blumberg A, Schodlok M C. International Journal of Applied Earth Observation and Geoinformation, 2022, 114: 103034.
[11] Arif M, Qi Y C, Dong Z, et al. Journal of Cleaner Production, 2022, 374: 133922.
[12] GUO Hui, YANG Ke-ming, ZHANG Chao(郭辉，杨可明, 张超). Transactions of the Chinese Society of Agricultural Machinery (农业机械学报), 2019, 50(10): 153.
[13] Zhang C Y, Ren H Z, Qin Q M, et al. Remote Sensing Letters, 2017, 8(6): 576.
[14] YANG Ke-ming, HE Jia-le, LI Yan-ru, et al(杨可明，何家乐, 李艳茹, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2023, 54(9): 254.
[15] LI Yan, YANG Ke-ming, WANG Min, et al(李燕，杨可明, 王敏, 等). Journal of Agro-Environment Science (农业环境科学学报), 2019, 38(1): 14.
[16] QIU Xue-hong, CAO Li, HAN Ri-chou(丘雪红, 曹莉, 韩日畴). Journal of Environmental Entomology(环境昆虫学报), 2020, 42(2): 267.