光谱学与光谱分析, 2018, 38 (10): 3221, 网络出版: 2018-11-25   

基于分数阶微分算法的大豆冠层氮素含量估测研究

Estimation of Canopy Nitrogen Content of Soybean Crops Based on Fractional Differential Algorithm
张亚坤 1,2,3,*罗斌 2,3潘大宇 2,3宋鹏 2,3路文超 2,3王成 2,3赵春江 1,2,3
作者单位
1 东北农业大学电气与信息学院, 黑龙江 哈尔滨 150030
2 北京农业智能装备技术研究中心, 北京 100097
3 国家农业智能装备工程技术研究中心, 北京 100097
摘要
氮素与作物的生长发育、 产量和品质密切相关。 作物冠层氮素含量的快速、 准确、 无损检测对于作物营养诊断和长势评估具有重要意义。 传统的氮素检测方法检测周期长、 操作复杂, 同时具有破坏性, 无法实现作物氮素含量在时间和空间上的连续动态监测。 基于光谱遥感技术快速、 无损地获取作物氮素含量是近年来作物组分快速检测研究的热点。 当前的研究大多基于原始光谱或整数阶微分(一阶、 二阶)预处理后的光谱进行氮素含量预测, 原始光谱或整数阶微分预处理后的光谱会忽略光谱曲线间的渐变信息, 影响氮素含量的预测准确度。 与原始光谱和整数阶微分方法相比, 分数阶微分算法在背景噪声去除、 有效信息提取等方面较有优势。 为研究分数阶微分预处理算法在作物氮素检测中的应用, 本文以不同施肥处理下的盆栽大豆作物为研究对象, 获取大豆苗期、 花期、 结荚期和鼓粒期四个生育期共256组冠层高光谱及对应的大豆冠层氮素含量(CNC)数据, 运用分数阶微分算法对光谱数据进行0~2阶微分预处理, 微分间隔为0.1, 分别采用归一化光谱植被指数NDSI、 比值光谱指数RSI对预处理后的光谱数据和大豆冠层氮素含量数据进行相关性分析, 得到各阶微分预处理下NDSIα(α代表分数阶微分阶数)与大豆CNC, RSIα与大豆CNC相关系数绝对值的最大值及其对应的波段组合——最优波段组合NDSIα(opt)和RSIα(opt), 采用线性回归方法, 建立各阶微分下NDSIα(opt)与CNC, RSIα(opt)与CNC的预测模型, 并与常用植被指数(VOGII, MTCI, DCNI, NDRE)建立的氮素含量预测模型进行比较, 研究分数阶微分算法对大豆作物冠层氮素含量预测模型的效果。 结果表明: (1)在0~2阶微分范围内, 最优波段组合NDSIα(opt), RSIα(opt)与大豆CNC的相关系数随阶数增加呈现先升高后下降趋势。 其中, 0.8阶微分下NDSI0.8(R725, R769)与大豆CNC的相关系数最大, 为0.875 9; 0.7阶微分下RSI0.7(R548, R767)与大豆CNC的相关系数最大, 为0.865 1; (2)分数阶微分预处理能够细化光谱数据中的有效信息, 增强光谱数据对冠层氮素含量的敏感性, 尤其是增强红边平台波段与氮素含量的正相关性及绿波段与氮含量的负相关性; (3)与整数阶微分、 常用植被指数相比, 分数阶微分能够提高大豆CNC预测模型的准确性。 其中, 基于0.7阶微分RSI0.7(R548, R767)建立的大豆CNC预测模型与0阶微分RSI0(R725, R769)相比建模集决定系数(R2C)和预测集决定系数(R2P)分别提高了0.061 9和0.016 6, 建模集均方根误差(RMSEC)和预测集均方根误差(RMSEP)分别降低了0.552 5和0.180 9, 预测相对偏差(RPD)提高了0.110 4。 基于0.7阶微分RSI0.7(R548, R767)建立的大豆CNC预测模型与VOG II相比R2C和R2P分别提高了0.086 6和0.025 5, RMSEC和RMSEP分别降低了0.757 5和0.248 3, RPD提高了0.146 88; (4)基于0.7阶微分比值光谱指数RSI(R548, R767)建立的大豆LNC预测模型较优, 其R2C为0.748 4, R2P为0.800 3, RMSEC为4.752 9, RMSEP为3.511 1, RPD为2.253 7, 能够较好的估测大豆冠层氮素含量。 研究表明分数阶微分算法在大豆冠层氮素含量的定量预测中具有一定的优势, 为光谱遥感技术在作物氮营养检测中的应用开拓了新的思路。
Abstract
Nitrogen is one of the most important fertilizers and closely related to the growth, development, yield and quality of crops. Rapid, accurate and non-destructive assessment of nitrogen content in crops is critical for nutrition diagnosis and growth monitoring. Traditional detection methods of nitrogen content are complicated, time-consuming and destructive, which makes the continuous dynamic monitoring of nitrogen content in time and space impossible. It is a hot topic for rapid and non-destructive estimation of crop nitrogen content based on spectral remote sensing technology in recent years. Nevertheless, existing researches about the estimation of nitrogen content were mostly focused on the original or integer differential spectra (first order, second order). Some studies indicated that the original or integer differential spectra might ignore the effective information, which would influence the estimation accuracy of nitrogen content in crops. Fractional order differential algorithm has the advantages in background noise removal and effective information extraction compared with the integer differential methods. Hence, the objective of this study was to explore the feasibility of detecting nitrogen in crops by fractional order differential algorithm. 256 datasets, which were consisted of canopy spectral data and the relevant canopy nitrogen content (CNC) data, were collected during seedling, flowering, pod and drum stages in soybean plants. The plants were treated with different fertilizer components under pot conditions. 0~2 order differentials of spectral data were computed by Grünwald-Letnikov fractional differential equation with differential interval of 0.1. Afterwards, the correlation between the preprocessed spectra and soybean CNC under different fractional order differential were analyzed using the normalized difference spectral index (NDSI) and ratio spectral index (RSI). The maximums of correlation coefficient between soybean CNC and NDSIα (α is the fractional differential order), and between soybean CNC and RSIα were determined under each fractional order differentials. Simultaneously, the corresponding optimal band combinations of NDSIα(opt) and RSIα(opt) were obtained respectively. Eventually, the estimation models of soybean CNC based on NDSIα(opt) and RSIα(opt) under different fractional order differential were established and compared using linear regression method. The estimation models of soybean CNC based on five common vegetation indices including VOG II, MTCI, DCNI, NDRE and TCARI were also established and compared. The results showed that: (1) With the increasing of differential order, the correlation coefficients between soybean CNC and NDSIα(opt), soybean CNC and RSIα(opt) increased firstly and then decreased in the fractional differential range of 0~2. For NDSIα, the maximum correlation coefficient was obtained between soybean CNC and NDSI0.8(R725, R769) under 0.8 order differential, and the relevant value was 0.875 9. For RSIα, the maximum correlation coefficient was obtained between soybean CNC and RSI0.7 (R548, R767) under 0.7 order differential, and the relevant value was 0.865 1; (2) The useful information in spectral data could be extracted and refined using fractional differential algorithm. Therefore, the sensitivity of spectra to soybean CNC could be enhanced. Specifically, the positive correlation between soybean CNC and the band near red edge platform, and the negative correlation between soybean CNC and near the band near green region were enhanced; (3) Compared with the models developed by integer differential and common vegetation indices, the estimation models based on fractional differential were more accurate. For integer differential, the determination coefficients of calibration (R2C) and prediction (R2P) based on RSI0.7(R548, R767) under 0.7 order differential improved 0.061 9 and 0.016 6 compared with the model based on RSI0 (R725, R769) under 0 order differential, respectively. The relevant root mean square errors of the calibration (RMSEC) and prediction (RMSEP) were reduced 0.552 5 and 0.180 9, respectively. The relevant ratio of prediction to deviation (RPD) increased 0.110 4. For common vegetation indices, the R2C and R2P based on RSI0.7(R548, R767) under 0.7 order differential improved 0.086 6 and 0.025 5 compared with the model based on VOG II, respectively. The relevant RMSEC and RMSEP were reduced 0.757 5 and 0.248 3, respectively. The relevant RPD increased 0.146 88; (4) The model based on the ratio spectral index RSI0.7(R548, R767) under 0.7 order differential had the best performance in estimation of soybean CNC with R2C of 0.748 4, R2P of 0.800 3, RMSEC of 4.752 9, RMSEP of 3.511 1 and RPD of 2.253 7 in this study. The results indicated that fractional differential algorithm had the advantages in the quantitative estimation of soybean CNC, which provided a new view for the estimation of crop nitrogen content based on spectral remote sensing technology.

张亚坤, 罗斌, 潘大宇, 宋鹏, 路文超, 王成, 赵春江. 基于分数阶微分算法的大豆冠层氮素含量估测研究[J]. 光谱学与光谱分析, 2018, 38(10): 3221. ZHANG Ya-kun, LUO Bin, PAN Da-yu, SONG Peng, LU Wen-chao, WANG Cheng, ZHAO Chun-jiang. Estimation of Canopy Nitrogen Content of Soybean Crops Based on Fractional Differential Algorithm[J]. Spectroscopy and Spectral Analysis, 2018, 38(10): 3221.

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