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Acta Agronomica Sinica ›› 2026, Vol. 52 ›› Issue (10): 3054-3068.doi: 10.3724/SP.J.1006.2026.61026

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Estimation of leaf nitrogen concentration in winter wheat based on an improved successive projections algorithm and continuous wavelet transform

Fu Yuan-Yuan1,2(), Li Shuai-Feng1, Chen Ke1, Xia Tian1, Feng Hai-Kuan3, Shu Mei-Yan1,2, Guo Wei1,2, Qiao Hong-Bo1, Yue Ji-Bo1,2,*()   

  1. 1 College of Artificial Intelligence, Henan Agricultural University, Zhengzhou 450046, Henan, China
    2 Henan International Joint Laboratory of Agricultural Big Data and Artificial Intelligence, Zhengzhou 450046, Henan, China
    3 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
  • Received:2026-03-25 Accepted:2026-07-21 Online:2026-10-12 Published:2026-07-22
  • Contact: * Yue Ji-Bo, E-mail: yuejibo@henau.edu.cn
  • Supported by:
    General Program of the Natural Science Foundation of Henan Province(252300421853)

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Abstract:

Remote sensing-based diagnosis of crop leaf nitrogen concentration (LNC) is of great importance for guiding precise nitrogen application, improving nitrogen use efficiency, and increasing crop yield. To address the limitations of the traditional successive projections algorithm (SPA) in handling nonlinear relationships among spectral features, this study proposes an improved successive projections algorithm, namely the kernel successive projections algorithm (KSPA). Continuous wavelet transform (CWT) was used for multi-scale spectral decomposition, and KSPA was subsequently applied to extract key features associated with LNC. For comparison, vegetation indices, SPA, and partial least squares-variable importance in projection (PLS-VIP) were also used for feature selection. Models based on partial least squares regression (PLSR), random forest (RF), and categorical boosting (CatBoost) were constructed to estimate winter wheat LNC, and Shapley additive explanations (SHAP) were used for model interpretability analysis. The results showed that intermediate CWT decomposition scales performed well in estimating winter wheat LNC, and the features selected by KSPA significantly improved model performance. The CatBoost model combined with KSPA at the seventh CWT scale achieved the best performance (R2 = 0.829, RMSE = 0.322%, RPD = 2.425). Compared with the best models based on PLS-VIP (RF: R2 = 0.729, RMSE = 0.405%) and SPA (CatBoost: R2 = 0.731, RMSE = 0.403%), this model increased R2 by 0.100 and 0.098 and reduced RMSE by 0.083% and 0.081%, respectively. Leave-one-year-out cross-validation further confirmed the robustness and generalization ability of the proposed method. Overall, the improved algorithm effectively overcomes the linear limitations of traditional SPA in feature selection and provides a useful reference for precision monitoring of nitrogen nutrition in winter wheat.

Key words: winter wheat leaf nitrogen concentration, continuous wavelet transform, kernel successive projections algorithm, categorical boosting, SHAP analysis

Table 1

Descriptive statistics of winter wheat leaf nitrogen concentration"

指标Index 所有数据All data 建模集Calibration set 验证集Validation set
样本数Sample 533 408 125
最大值Maximum value (%) 5.07 5.07 4.84
最小值Minimum value (%) 1.09 1.09 1.59
平均值 Mean (%) 3.11 3.08 3.18
标准差Standard deviation (%) 0.87 0.89 0.78
变异系数Coefficient of variation (%) 27.97 28.90 24.53
峰值Kurtosis (%) 2.13 2.09 2.14

Table 2

Vegetation indices and calculation formulas"

植被指数Vegetation index 计算公式Calculation formula
三角植被指数Triangular vegetation index (TVI) [17] R705/(R717+R491)
角度不敏感植被指数Angle insensitive vegetation index (AIVI) [18] [R445× (R720+R735)-R573× (R720-R735)]/[R720× (R573+R445)]
LIC指数Lightness index for chlorophyll (LIC) [19] R440/R690
改进型归一化差分705指数
Modified normalized difference 705 index (mND705) [20]		 (R750-R705)/(R750+R705-2×R445)
归一化色素叶绿素指数
Normalized pigment chlorophyll index (NPCI) [21]		 (R680-R430)/(R680+R430)
卡特指数Carter index (CTR) [18] R695/R420
归一化差分植被指数
Normalized difference vegetation index (NDVI) [22]		 (R731-R709)/(R731+R709)
比值指数Ratio index (RI) [21] R735/R720
归一化差分705指数Normalized difference 705 index (ND705) [21] (R750-R705)/(R750+R705)
光化学反射指数Photochemical reflectance index (PRI) [21] (R570-R531)/(R570+R531)
陆地叶绿素指数MERIS terrestrial chlorophyll index (MTCI) [23] (R754-R709)/(R709-R681)
沃格尔曼红边指数Vogelmann red edge index (VOG) [21] R740/R720
简单比值色素指数Simple ratio pigment index (SRPI) [21] R430/R680
改进型红边简单比值指数
Modified simple ratio 705 index (mSR705) [23]		 (R750-R445)/(R705-R445)
简单比值指数Simple ratio index (SR) [24] R712/R758

Table 3

Accuracy of winter wheat LNC prediction models based on raw spectral data and vegetation indices"

特征
Feature		 模型
Model		 建模集Calibration set 验证集Validation set
R2 RMSE (%) RPD R2 RMSE (%) RPD
原始光谱Original spectrum PLSR 0.712 0.477 1.864 0.600 0.491 1.588
RF 0.799 0.399 2.231 0.504 0.547 1.425
CatBoost 0.730 0.462 1.926 0.592 0.496 1.573
植被指数Vegetation index PLSR 0.624 0.545 1.633 0.627 0.475 1.643
RF 0.735 0.457 1.946 0.661 0.453 1.723
CatBoost 0.777 0.419 2.120 0.695 0.429 1.817

Fig. 1

Correlation coefficient matrix between wavelet coefficients and LNC at different CWT decomposition scales LNC: leaf nitrogen concentration; CWT: continuous wavelet transform."

Fig. 2

Wavelet coefficient curves at different CWT decomposition scales CWT: continuous wavelet transform."

Table 4

Number of wavelet coefficients selected by PLS-VIP, SPA, and KSPA at different CWT decomposition scales"

方法
Method		 尺度4
Scale 4		 尺度5
Scale 5		 尺度6
Scale 6		 尺度7
Scale 7		 尺度8
Scale 8
原始光谱Original spectrum 951 951 951 951 951
偏最小二乘-变量投影重要性PLS-VIP 32 34 32 26 44
连续投影算法SPA 40 37 26 39 32
核化连续投影算法KSPA 52 49 63 68 56

Fig. 3

Accuracy of winter wheat LNC estimation models integrating different CWT scales and PLS-VIP A-C show the variation curves of R2, RMSE, and RPD for the calibration set, and D-F show those for the validation set. Abbreviations are the same as those given in Table 3 and Table 4."

Fig. 4

Accuracy of winter wheat LNC estimation models integrating different CWT scales and SPA A-C show the variation curves of R2, RMSE, and RPD for the calibration set, and D-F show those for the validation set. Abbreviations are the same as those given in Table 3 and Table 4."

Fig. 5

Performance of winter wheat LNC estimation models based on three machine learning methods using optimal-scale CWT and SPA Abbreviations are the same as those given in Table 3 and Table 4."

Fig. 6

Accuracy of winter wheat LNC estimation models integrating different CWT scales and KSPA A-C show the variation curves of R2, RMSE, and RPD for the calibration set, and D-F show those for the validation set. Abbreviations are the same as those given in Table 3 and Table 4."

Fig. 7

Performance of winter wheat LNC estimation models with three machine learning methods using optimal-scale CWT and KSPA Abbreviations are the same as those given in Table 3 and Table 4."

Table 5

Results of the CWT and KSPA ablation experiments"

特征
Feature		 最佳尺度
Optimal scale		 最佳模型
Optimal model		 建模集Calibration set 验证集Validation set
R2 RMSE (%) RPD R2 RMSE (%) RPD
CWT 5 CatBoost 0.817 0.380 2.341 0.740 0.396 1.968
KSPA CatBoost 0.758 0.437 2.036 0.655 0.456 1.710

Fig. 8

SHAP summary plot for the CatBoost model based on CWT scale 7 and KSPA feature selection Abbreviations are the same as those given in Table 3 and Table 4. SHAP: shapley additive explanations."

Table 6

Descriptive statistics of winter wheat LNC in each year"

指标
Index		 试验1
Trial 1		 试验2
Trial 2		 试验3
Trial 3		 试验4
Trial 4
样本数Sample 128 128 125 152
最大值Maximum value (%) 4.48 5.07 4.84 4.46
最小值Minimum value (%) 1.66 1.20 1.59 1.09
平均值Mean (%) 3.30 3.40 3.18 2.64
标准差Standard deviation (%) 0.71 0.98 0.78 0.75
变异系数Coefficient of variation (%) 21.52 28.82 24.53 28.41
峰值Kurtosis (%) 2.11 2.37 2.14 2.13

Table 7

Optimal performance of winter wheat LNC models in leave-one-year-out cross-validation using trials 2, 3, and 4 as the calibration set and trial 1 as the validation set"

特征
Feature		 最佳尺度
Optimal scale		 最佳模型
Optimal model		 建模集Calibration set 验证集Validation set
R2 RMSE (%) RPD R2 RMSE (%) RPD
原始光谱Original spectrum PLSR 0.641 0.538 1.672 0.457 0.523 1.363
KSPA CatBoost 0.685 0.504 1.784 0.511 0.497 1.435
CWT 7 CatBoost 0.669 0.517 1.742 0.560 0.471 1.514
CWT+PLS-VIP 5 RF 0.742 0.456 1.971 0.602 0.448 1.591
CWT+SPA 6 CatBoost 0.752 0.448 2.009 0.624 0.467 1.527
CWT+KSPA 6 CatBoost 0.758 0.442 2.036 0.675 0.405 1.761

Table 8

Optimal performance of winter wheat LNC models in leave-one-year-out cross-validation using trials 1, 3, and 4 as the calibration set and trial 2 as the validation set"

特征
Feature		 最佳尺度
Optimal scale		 最佳模型
Optimal model		 建模集Calibration set 验证集Validation set
R2 RMSE (%) RPD R2 RMSE (%) RPD
原始光谱Original spectrum PLSR 0.577 0.522 1.540 0.467 0.714 1.375
KSPA PLSR 0.693 0.445 1.807 0.529 0.671 1.464
CWT 7 CatBoost 0.776 0.380 2.114 0.544 0.660 1.487
CWT+PLS-VIP 7 CatBoost 0.675 0.458 1.756 0.523 0.675 1.454
CWT+SPA 7 CatBoost 0.750 0.402 2.001 0.582 0.632 1.553
CWT+KSPA 7 CatBoost 0.785 0.372 2.162 0.611 0.610 1.609

Table 9

Optimal performance of winter wheat LNC models in leave-one-year-out cross-validation using trials 1, 2, and 3 as the calibration set and trial 4 as the validation set"

特征
Feature		 最佳尺度
Optimal scale		 最佳模型
Optimal model		 建模集Calibration set 验证集Validation set
R2 RMSE (%) RPD R2 RMSE (%) RPD
原始光谱Original spectrum CatBoost 0.699 0.458 1.826 0.437 0.562 1.338
KSPA RF 0.680 0.472 1.770 0.466 0.548 1.373
CWT 7 CatBoost 0.687 0.467 1.789 0.471 0.546 1.379
CWT+PLS-VIP 7 CatBoost 0.672 0.478 1.749 0.483 0.539 1.396
CWT+SPA 8 RF 0.661 0.486 1.719 0.478 0.542 1.389
CWT+KSPA 7 CatBoost 0.701 0.456 1.833 0.521 0.502 1.498
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