基于叶片反射光谱估测水稻氮营养指数

doi:10.3864/j.issn.0578-1752.2021.21.004

摘要/Abstract

摘要：

【目的】基于叶片反射光谱建立快速、无损监测水稻氮营养指数（nitrogen nutrition index,NNI）的估算模型。【方法】2018—2019年开展2个水稻品种（徽两优898和Y两优900）及5个氮肥梯度（施氮量为0、75、150、225和300 kg·hm^-2,分别记为N₀、N₁、N₂、N₃、N₄）的田间小区试验,测定关键生育期不同叶位叶片反射光谱和植株NNI,构建多种光谱指数的水稻NNI监测模型。【结果】单叶及叶位组合的敏感波段均分布在540 nm的绿光波长处,其与近红外波段构成的窄波段比值指数SR（R₉₀₀,R₅₄₀）可较好反演水稻NNI。但不同叶位叶片窄波段比值指数与水稻NNI的预测精度表现不同,顶3叶（L₃）预测精度最好（R²=0.731,RMSE =0.130,RE=11.6%）,顶2叶（L₂）次之（R²=0.707,RMSE =0.136,RE =12.2%）,顶1叶（L₁）最差（R²=0.443,RMSE =0.187,RE =14.7%）;顶2叶和顶3叶组合平均光谱（L₂₃）的预测精度优于单叶水平和其他叶位组合（R²=0.740,RMSE =0.128,RE =11.5%）。再将窄波段比值指数SR（R₉₀₀,R₅₄₀）近红外与绿光区域分别重采样50 nm和10 nm,所构建的宽波段比值指数SR[AR_{（900±50）},AR_{（540±10）}]模型精度较SR（R₉₀₀,R₅₄₀）未明显降低,且在L₂₃水平下2个模型的模型精度和预测精度基本一致（R²=0.740,RMSE =0.128,RE =11.5%）。水稻NNI小于1时与产量呈线性的正相关关系（P<0.05）,大于1时产量趋于平稳。【结论】L₂和L₃叶片反射光谱为监测水稻NNI的敏感叶位,其中叶位组合L₂₃可提高模型预测精度。基于叶片反射光谱构建的多种波段比值指数（SR（R₉₀₀,R₅₄₀）和SR[AR_{（900±50）},AR_{（540±10）}]）可快速估测水稻NNI,从而为不同传感器对水稻氮营养指数估测监测研究提供了理论依据。

关键词: 叶片, 水稻, 氮营养指数, 比值指数, 模型, 波段宽度

Abstract:

【Objective】The research aimed to analyze the relationship between rice (Oryza sativa L.) nitrogen nutrition index (NNI) and leaf spectral reflectance characteristics of leaf on different positions, so as to provide an effective method for nondestructive and timely evaluation of NNI in rice.【Method】Field experiments were conducted with different N application rates and rice cultivars across two growing seasons during 2018-2019, and the leaf hyperspectral reflectance of 350-2 500 nm of leaf on different positions and the plant NNI were measured during key fertility growth stages to construct a variety of spectral index model for rice NNI monitoring.【Result】The results indicated that green band (540 nm) at leaf level was the sensitive band for estimating NNI, and narrow band ratio index SR (R₉₀₀, R₅₄₀) composed of near infrared band and green band could be used to retrieve NNI of rice. However, the prediction accuracy of narrow band ratio index and rice NNI of leaf on different positions were different. In terms of prediction accuracy, the best single leaf position was the third leaf (L₃) from the top (R²=0.731, RMSE=0.130, RE=11.6%), the second leaf (L₂) from the top followed (R ²=0.707, RMSE=0.136, RE=12.2%), and the top one (L₁) was the worst (R ²=0.443, RMSE=0.187, RE=14.7%). The averaged spectra of L₂ and L₃ (L₂₃) was the optimum leaf spectra combination, which contributed to improving the predictability to NNI(R ²=0.740, RMSE=0.128, RE=11.5%). The samples were resampled at 50 nm and 10 nm in the near infrared region (900 nm) and green region (540 nm) respectively, and the accuracy of the wide band ratio index SR (AR_(900±50), AR_(540±10)) was not significantly lower than that of SR (R₉₀₀, R₅₄₀). The model accuracy and prediction accuracy of the two models were basically the same at L₂₃. When the NNI of rice was less than 1, there was a significant positive linear correlation with the yield, and then it tended to be stable. 【Conclusion】The results showed that the reflectance spectra of L₂ and L₃ leaves were sensitive for monitoring NNI for rice, and L₂₃ could improve the prediction accuracy of the model. Multiple band ratio indices SR (R₉₀₀, R₅₄₀) and SR (AR_(900±50), AR_(540±10)) based on leaf reflectance spectra could be used to rapidly estimate rice NNI, which provided a theoretical basis for monitoring rice NNI with various sensors.

Key words: leaf, rice(Oryza sativa L.), nitrogen nutrition index, ratio index, model, band width

徐浩聪, 姚波, 王权, 陈婷婷, 朱铁忠, 何海兵, 柯健, 尤翠翠, 吴小文, 郭爽爽, 武立权. 基于叶片反射光谱估测水稻氮营养指数[J]. 中国农业科学, 2021, 54(21): 4525-4538.

XU HaoCong, YAO Bo, WANG Quan, CHEN TingTing, ZHU TieZhong, HE HaiBing, KE Jian, YOU CuiCui, WU XiaoWen, GUO ShuangShuang, WU LiQuan. Determination of Suitable Band Width for Estimating Rice Nitrogen Nutrition Index Based on Leaf Reflectance Spectra[J]. Scientia Agricultura Sinica, 2021, 54(21): 4525-4538.

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年份 Year	有机质 Organic matter (g·kg^-1)	全氮 Total N (g·kg^-1)	有效磷 Available P (mg·kg^-1)	速效钾 Available K (mg·kg^-1)	pH
2018	32.36	2.03	24.80	211.42	5.11
2019	30.89	1.77	25.97	245.52	5.54

光谱指数 Spectral index	计算公式 Algorithm	参考文献 Reference
比值光谱指数 Simple ratio spectral index (SR)	R₁ / R₂	本研究 This study
归一化差值光谱指数 Normalized difference spectral index (ND)	(R₁ - R₂)/(R₁ + R₂)	本研究This study
修正红边归一化指数 Modified red edge normalized difference vegetation index (MSR 705)	(R₇₅₀-R₇₀₅)/(R₇₅₀+2×R₄₄₅)	[12]
红边归一化指数 Red edge normalized difference vegetation index (ND 705)	(R₇₅₀- R₇₀₅)/(R₇₅₀+ R₇₀₅)	[13]
比值指数-ldB Ratio index-1dB (RI-ldB)	R₇₃₅/R₇₂₀	[14]
Vogelmann 红边指数 Vogelman red edge index (VOG)	R₇₄₀/ R₇₂₀	[15]
双峰冠层氮指数 Double-peak canopy nitrogen index (DCNI)	(R₇₂₀-R₇₀₀)/(R₇₀₀-R₆₇₀)/(R₇₂₀-R₆₇₀+0.03)	[16]
线性内插法红边位置 Red edge position: linear interpolation method (REP_LI)	700+40×[(R₆₇₀+R₇₈₀)/2-R₇₀₀] /(R₇₄₀-R₇₀₀)	[17]
比值植被指数II Ratio Vegetation Index Ⅱ(RVI Ⅱ)	R₈₁₀/ R₅₆₀	[18]

分组因子 Grouping factor	变异来源 Variation source	平方和 Sum of squares	自由度 df	均方 Mean square	F值 F-value	P值 P-value
氮肥处理 Nitrogen rate	组间 Between groups	2.643	4	0.661	31.745	0
	组内 Within groups	1.561	75	0.021
	总计 Total	4.203	79
品种 Variety	组间 Between groups	0.231	3	0.077	1.475	0.228
	组内 Within groups	3.972	76	0.052
	总计 Total	4.203	79
生育期 Growth stage	组间 Between groups	0.094	1	0.094	1.782	0.186
	组内 Within groups	4.109	78	0.053
	总计 Total	4.203	79

时期 Stage	叶位 Leaf position	MSR705	ND705	RI-1dB	VOG	DCNI	REP_LI	RVI Ⅱ
分蘖期 Tillering stage	顶1叶（L₁）	0.483**	0.534**	0.561**	0.564**	0.607**	0.478**	0.553**
	顶2叶（L₂）	0.555**	0.479**	0.444**	0.443**	0.367**	0.436**	0.563**
	顶3叶（L₃）	0.311**	0.394**	0.406**	0.409**	0.718**	0.488**	0.398**
	顶1、2叶平均（L₂₃）	0.441**	0.456**	0.446**	0.448**	0.555**	0.488**	0.499**
拔节期 Jointing stage	顶1叶（L₁）	0.568**	0.541**	0.538**	0.536**	0.253**	0.465**	0.657**
	顶2叶（L₂）	0.717**	0.665**	0.648**	0.649**	0.770**	0.538**	0.792**
	顶3叶（L₃）	0.765**	0.742**	0.718**	0.717**	0.652**	0.669**	0.810**
	顶2、3叶平均（L₂₃）	0.791**	0.748**	0.721**	0.721**	0.721**	0.655**	0.835**
孕穗期 Booting stage	顶1叶（L₁）	0.620**	0.635**	0.711**	0.712**	0.278**	0.629**	0.686**
	顶2叶（L₂）	0.781**	0.812**	0.833**	0.835**	0.476**	0.772**	0.859**
	顶3叶（L₃）	0.730**	0.755**	0.758**	0.758**	0.485**	0.714**	0.783**
	顶2、3叶平均（L₂₃）	0.763**	0.790**	0.807**	0.808**	0.490**	0.749**	0.837**
抽穗期 Heading stage	顶1叶（L₁）	0.462**	0.511**	0.510**	0.516**	0.519**	0.557**	0.521**
	顶2叶（L₂）	0.586**	0.652**	0.684**	0.690**	0.714**	0.592**	0.730**
	顶3叶（L₃）	0.546**	0.589**	0.635**	0.643**	0.600**	0.432**	0.706**
	顶2、3叶平均（L₂₃）	0.593**	0.649**	0.696**	0.703**	0.646**	0.538**	0.734**

叶位 Leaf position	MSR705	ND705	RI-1dB	VOG	DCNI	REP_LI	RVI Ⅱ
顶1叶（L₁）	0.571**	0.576**	0.570**	0.572**	0.477**	0.584**	0.621**
顶2叶（L₂）	0.708**	0.726**	0.741**	0.745**	0.575**	0.710**	0.798**
顶3叶（L₃）	0.629**	0.660**	0.698**	0.703**	0.577**	0.543**	0.739**
顶2、3叶平均（L₂₃）	0.676**	0.702**	0.733**	0.738**	0.587**	0.642**	0.781**
样本量 160 个 Sample volume is 160