光谱学与光谱分析 |
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Models for Estimating Foliar Fe and Mn Concentration of Armeniaca vulgaris cv. Luntaibaixing Using Spectral Reflectance |
HU Zhen-zhu, PAN Cun-de*, WANG Shi-wei, GUO Zhi-chao, WANG Qing-tao, DING Fan, LI Yuan |
College of Forestry and Horticulture,Xinjiang Agricultural University,Key Laboratory of Forestry Ecology and Industry Technology in Arid Region, Education Department of Xinjiang, Urumqi 830052, China |
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Abstract Aimed at providing technology for a rapid nutrition diagnosis system of micronutrients in Armeniaca vulgaris cv. Luntaibaixing, we established an element concentration estimation model for its foliar ferrum (Fe) and manganese (Mn) concentration based on spectrum analysis. The foliar spectrum reflectance at various phenological periods of fruit development under different soil fertility conditions was measured by Unispec-SC spectrometer. By analyzing the correlation of foliar Fe, Mn concentration at various phenological periods of fruit development, the spectrum reflectance Rλ and its first-order differential f′(Rλ), we filtered out its sensitive bands. And we established an element concentration estimation model for its foliar Fe and Mn at various phenological periods of fruit development with the linear regression model. The results showed that the spectral sensitive bands of foliar Fe in fruit setting period were 873 and 874 nm, 375 and 437 nm in fruit core-hardening period, 836 and 837 nm in maturity period and 325 and 1 054 nm in post-harvest period. However, the spectral sensitive bands of Mn were 913 and 1 129 nm, 425 and 970 nm, 390 and 466 nm, 423 and 424 nm, respectively. The Fe and Mn concentration of A. vulgaris cv. Luntaibaixing leaves were the most relevant to the first-order differential f′(Rλ) of its spectrum reflectance, whose linear spectrum estimation model fitting degree was the highest and reached to a significant or highly significant level. It showed that the spectral sensitive bands of Fe and Mn element varied with different phenological periods of fruit development. The spectrum estimation models for its foliar Fe and Mn concentration could be established with linear model according to its first-order differential f′(Rλ).
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Received: 2013-07-06
Accepted: 2013-12-05
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Corresponding Authors:
PAN Cun-de
E-mail: pancunde@163.com
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[1] YAN Fang-zhai, LI Juan-cui, XIN Zhou, et al. International Journal of Remote Sensing, 2013, 7(10):2502. [2] Raymond E, Hunt J R. International Journal of Remote Sensing, 2013, 34(7):2502. [3] Brantley S T, Zinnert J C, Yong D R. Remote Sensing of Environment, 2011, 115(2): 514. [4] PANG Zhan-jun, ZHAO Zhi-jun, GAO Yan-kui, et al(庞占军,赵志军,高彦魁,等). Anhui Agricultural Science(安徽农业科学), 2008, 36(31): 13529. [5] GAO Shu-ran, PAN Cun-de, WANG Zhen-xi, et al(高淑然,潘存德,王振锡,等). Xinjiang Agricultural Sciences(新疆农业科学), 2011, 48(11): 1961. [6] LIAO Qin-hong, WANG Ji-hua, YANG Gui-jun, et al. Journal of Applied Remote Sensing, 2013, 7(12): 1. [7] MA Chao-fei, MA Jian-wen, HAN Xiu-zhen(马超飞, 马建文, 韩秀珍). Journal of Remote Sensing(遥感学报),2001,5(5):334. [8] Pimstein A, Karnieli A, Bansal S K, et al. Field Crops Research. 2011, 121(1): 125. [9] HU Yong-guang, LI Ping-ping, MU Jian-hua, et al(胡永光,李萍萍,母建华,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2008, 28(12): 2821. [10] HU Zhen-zhu, PAN Cun-de, WANG Shi-wei, et al(胡珍珠,潘存德,王世伟,等). Xinjiang Agricultural Sciences(新疆农业科学),2013, 50(2): 238. [11] LIU Rong-yuan, HUANG Wen-jiang, REN Hua-zhong, et al(刘镕源,黄文江,任华忠,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(9):115. |
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