不同光谱指数反演马铃薯叶片氮累积量的研究

doi:10.3724/SP.J.1006.2020.04023

Abstract

Abstract:

As an important derivative parameter of optical spectrum, spectral index could reflect the leaf nitrogen accumulation of crops. However, the sensitive spectral index varies with different environments and crops. In order to obtain the sensitive spectral index for potato in Inner Mongolia, field experiments were conducted in Chayouzhongqi and Hangjinqi of Inner Mongolia from 2016 to 2018, and during the potato growth period, the canopy spectrum information of potato cultivars Kexin 1 and Shepody was obtained using a handheld spectrometer (SVC HR-1024i). Based on the previous spectral indices algorithm, the correlation coefficients between the leaves nitrogen accumulation of potato (LNA) and each of the 22 spectral indices were compared, and the nitrogen nutrition diagnosis models of potato at critical growth stages were established using linear and nonlinear regression analysis. The results were as follows: (1) the red edge area was the main spectral band for inverting the LNA of potato, and Vogelmann red edge index 2 (VOG2), Vogelmann red edge index 3 (VOG3) were the sensitive spectral indices for potato LNA in Inner Mongolia, which composed of 715, 720, 726, 734, and 747 nm of spectral bands. (2) At the seedling stage, tuber formation stage or whole growth stage, the quadratic regression models (R ² > 0.75) between VOG3 and LNA could estimate better the LNA of potato under different nitrogen levels using VOG3. (3) The root mean square error (RMSE) of the models was 4.04-6.69, 9.45-10.89, 9.17-13.45 kg hm ^-2, indicating the accuracy of using the models to predict potato LNA varies with potato growth stage, and it was lower at late growth stages, while it is higher for whole growth duration. In summary, the staged modeling for potato early growth period and the unified modeling for potato later growth period could accurately estimate the potato LNA, which provides a theoretical basis and method for the application of spectral indices in the nitrogen nutrition diagnosis of potato.

Key words: potato, leaf nitrogen accumulation, spectral index, monitoring models

HAN Kang, YU Jing, SHI Xiao-Hua, CUI Shi-Xin, FAN Ming-Shou. Inversion of nitrogen accumulation in potato leaf with different spectral indices[J].Acta Agronomica Sinica, 2020, 46(12): 1979-1990.

Figures/Tables 13

Table 1

Table 2

Table 3

Table 4

Spectral index formula"

	光谱指数 Spectral index	计算公式 Calculation formula	参考文献 References
1	Simple Ratio Index (SRI)	R₈₀₀ ? R₆₈₀	Sim et al.^[13]
2	Normalized Difference Vegetation (NDVI)	(R₈₀₀ - R₆₈₀) ? (R₈₀₀+ R₆₈₀)	Rouse et al. ^[14]
3	Difference Vegetation Index (DVI)	R₈₀₀ - R₆₈₀	Richardson et al. ^[15]
4	Structure Insensitive Pigment Index (SIPI)	(R₈₀₀- R₄₄₅) ? (R₈₀₀ + R₆₈₀)	Penuelas et al. ^[16]
5	Modified Red Edge Normalized Difference Vegetation (mND₇₀₅)	(R₇₅₀ - R₇₀₅) ? (R₇₅₀ + R₇₀₅ - 2 × R₄₄₅)	Sims et al. ^[13]
6	Modified Simple Ratio Index (mSR₇₀₅)	(R₇₅₀ - R₄₄₅) ? (R₇₀₅ - R₄₄₅)	Sims et al. ^[13]
7	Photochemical Reflectance Index (PRI)	(R₅₃₁ - R₅₇₀) ? (R₅₇₀ + R₅₃₁)	Gamon et al. ^[17]
8	Plant Senescence Reflectance Index (PSRI)	(R₆₈₀ - R₅₀₀) ? R₇₅₀	Merzlyak et al.^[18]
9	The MERIS terrestrial Chlorophyll Index (MTCI)	(R₇₅₀ - R₇₁₀) ? (R₇₁₀ - R₆₈₀)	Dash et al. ^[19]
10	Modified Chlorophyll Absorption in Refectance Index (MCARI)	[(R₇₀₀ - R₆₇₀) - 0.2 × (R₇₀₀ - R₅₅₀)] × (R₇₀₀ ? R₆₇₀)	Dash et al.^[19]
11	Optimized Soil-adjusted Vegetation Index (OSAVI)	(1 + 0.16) × (R₈₀₀ - R₆₇₀) ? (R₈₀₀ + R₆₇₀ + 0.16)	Rondeaux et al.^[20]
12	Transformed Chlorophyll Absorption In Reflectance Index (TCARI)	3 × [(R₇₀₀ - R₆₇₀) - 0.2 × (R700 - R₅₅₀)] × (R₇₀₀ ? R₆₇₀)	Haboudane et al. ^[21]
13	Enhanced Vegetation Index (EVI)	2.5 × (R₈₀₀ - R₆₈₀) ? (R₈₀₀ + 6 × R₆₈₀ - 7.5 × R₄₅₀ + 1)	Huete et al.^[22]
14	Atmospherically Resistant Vegetation Index (ARVI)	(R₈₀₀ - 2 × R₆₈₀ + R₄₅₀) ? (R₈₀₀ + 2 × R₆₈₀ - R₄₅₀)	Kaufman et al.^[23]
15	705nm Normalized Difference Vegetation (NDVI₇₀₅₎	(R₇₅₀ - R₇₀₅) ? (R₇₅₀ + R₇₀₅)	Gitelson et al. ^[24]
16	Vogelmann Red Edge Index 1 (VOG1)	R₇₄₀ ? R₇₂₀	Zarco-Tejada et al. ^[25]
17	Vogelmann Red Edge Index 2 (VOG2)	(R₇₃₄ - R₇₄₇) ? (R₇₁₅ + R₇₂₆)	Zarco-Tejada et al. ^[25]
18	Vogelmann Red Edge Index 3 (VOG3)	(R₇₃₄ - R₇₄₇) ? (R₇₁₅ + R₇₂₀)	Zarco-Tejada et al. ^[25]
19	Carotenoid Reflectance Index 1 (CRI1)	1 ? R₅₁₀ - 1 ? R₅₅₀	Gitelson et al. ^[26]
20	Carotenoid Reflectance Index 2 (CRI2)	1 ? R₅₁₀ - 1 ? R₇₀₀	Gitelson et al. ^[26]
21	Anthocyanin Reflectance Index 1 (ARI1)	1 ? R₅₅₀ - 1 ? R₇₀₀	Gitelson et al. ^[27]
22	Anthocyanin Reflectance Index 2 (ARI2)	R₈₀₀ × (1 ? R₅₅₀ - 1 ? R₇₀₀)	Gitelson et al.^[28]

Table 4

Table 5

Fig. 2

Fig. 3

Table 6

Fig. 5

Table 7

Fig. 6

Fig. 4

Fig. 1

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处理 Treatment	基肥 Base fertilizer	追肥Top dressing
处理 Treatment	基肥 Base fertilizer	出苗后10 d 10 days after emergence	出苗后25 d 25 days after emergence
N0	0	0	0
N1	75	15	60
N2	75	45	180
N3	75	118.5	256.5
N4	75	150	375

试验 Experiment	年份 Year	地点 Location	品种 Variety	播种日期 Sowing date (month/day)	收获日期 Harvest date (month/day)	出苗日期 Emergence date (month/day)	数据采集日期 Data collection date (month/day)
Exp.1	2016	察右中旗 Chayouzhongqi	克新1号 Kexin 1	5/27	9/24	6/18	7/8, 7/23, 8/7, 8/22
Exp.2	2016	察右中旗 Chayouzhongqi	夏波蒂 Shepody	5/27	9/24	6/18	7/8, 7/23, 8/7, 8/22
Exp.3	2016	杭锦旗 Hangjinqi	克新1号 Kexin 1	5/27	9/24	6/20	7/10, 7/25, 8/9, 8/24
Exp.4	2017	察右中旗 Chayouzhongqi	克新1号 Kexin 1	5/11	9/20	6/13	7/3, 7/18, 8/2, 8/17
Exp.5	2018	察右中旗 Chayouzhongqi	克新1号 Kexin 1	5/3	9/28	6/10	6/30, 7/15, 7/30, 8/14

波长范围 Wavelength range (nm)	光谱分辨率 Spectral resolution (nm)	光谱采样间隔 Spectral sampling interval (nm)
350-1000	≤3.3	≤1.5
1000-1890	≤9.5	≤3.8
1890-2500	≤6.5	≤2.5

生育时期 Growth stage	样本数量Number of samples
生育时期 Growth stage	建模集Modeling set	验证集Validation set
苗期Seedling stage	45	30
块茎形成期Tuber formation stage	45	30
块茎膨大期Tuber expansion stage	45	30
淀粉积累期Starch accumulation stage	45	30
全生育时期Total growth stages	180	120

生育时期 Growth stage	监测模型方程式 Monitoring model equation	R²
苗期Seedling stage	LNA = - 269.271 × VOG3 - 16.5	0.882^**
苗期Seedling stage	LNA = 1976.733 × VOG3² + 211.143 × VOG3 + 11.236	0.913^**
	LNA = 1.712 × e^{-17.519 × VOG3}	0.908^**
块茎形成期Tuber formation stage	LNA = - 323.22 × VOG3 - 21.257	0.771^**
块茎形成期Tuber formation stage	LNA = 1393.778 × VOG3² + 162.368×VOG3 + 17.484	0.790^**
	LNA = 4.841 × e^{-10.411 × VOG3}	0.740^**
块茎膨大期Tuber expansion stage	LNA = - 403.678 × VOG3 - 34.233	0.723^**
块茎膨大期Tuber expansion stage	LNA = 1020.908 × VOG3² - 20.441 × VOG3 - 0.711	0.741^**
	LNA = 4.249 × e^{-11.184 × VOG3}	0.741^**
淀粉积累期Starch accumulation stage	LNA = - 279.991 × VOG2 - 8.249	0.550^**
淀粉积累期Starch accumulation stage	LNA = - 654.51 × VOG2² - 464.961 × VOG2 - 20.102	0.555^**
	LNA = 4.894 × e^{-11.837 × VOG2}	0.570^**
全生育时期Total growth stages	LNA = - 328.711 × VOG3 - 21.683	0.765^**
全生育时期Total growth stages	LNA = 832.343 × VOG3² - 46.576 × VOG3 - 0.242	0.778^**
	LNA = 3.867 × e^{-11.534 × VOG3}	0.773^**

Inversion of nitrogen accumulation in potato leaf with different spectral indices

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