Study on variable nitrogen fertilization model of winter wheat during booting period based on remote sensing data

doi:10.6048/j.issn.1001-4330.2024.11.011

Xinjiang Agricultural Sciences ›› 2024, Vol. 61 ›› Issue (11): 2705-2712.DOI: 10.6048/j.issn.1001-4330.2024.11.011

• Crop Genetics and Breeding·Cultivation Physiology • Previous Articles Next Articles

Study on variable nitrogen fertilization model of winter wheat during booting period based on remote sensing data

CHEN Rong¹(), LAI Ning², GENG Qinglong², LI Yongfu², XIN Huinan², LYU Caixia², LI Na², CHEN Shuhuang²()

1. College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
2. Institute of Soil, Fertilizer and Agricultural Water Conservation, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China

Received:2024-05-20 Online:2024-11-20 Published:2025-01-08
Correspondence author: CHEN Shuhuang
Supported by:
Xinjiang Wheat Industry Agriculture System(XJARS-01-21);General Project of the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01A95);Steadily Stable Support to Agricultural Science and Technology Innovation(xjnkywdzc-2023002);Steadily Stable Support to Agricultural Science and Technology Innovation(xjnkywdzc-2023007-3);Major R & D Project of Xinjiang Uygur Autonomous Region(2022A02011-2);Xinjiang Academy of Agricultural Sciences independent breedingspecial project(xjnkycxzx-2022004)

基于遥感数据构建冬小麦孕穗期变量施氮模型

陈荣¹(), 赖宁², 耿庆龙², 李永福², 信会男², 吕彩霞², 李娜², 陈署晃²()

1.新疆农业大学资源与环境学院,乌鲁木齐 830052
2.新疆农业科学院土壤肥料与农业节水研究所,乌鲁木齐 830091

通讯作者: 陈署晃
作者简介:陈荣(1997-),男,贵州纳雍人,硕士研究生,研究方向为农业信息化,(E-mail)chenrong2581@163.com
基金资助:
新疆小麦产业技术体系(XJARS-01-21);新疆维吾尔自治区自然科学基金面上基金(2023D01A95);农业科技创新稳定支持专项(xjnkywdzc-2023002);农业科技创新稳定支持专项(xjnkywdzc-2023007-3);新疆维吾尔自治区重大专项(2022A02011-2);新疆农业科学院自主培育专项(xjnkycxzx-2022004)

Abstract

Abstract:

【Objective】 The objective of this study is to identify and quantitatively analyze nitrogen abundance and deficiency according to the growth requirements of winter wheat during the key growth periods, so as to realize quantitative nitrogen fertilization, which is the key technology that should be solved in precise fertilization and intelligent planting.【Methods】 ASD HH2 hyperspectrometer was used to obtain the reflectance of winter wheat canopy with different nitrogen application rates, and the chlorophyll concentration and yield were recorded.According to the reflectance of the winter wheat canopy, 14 vegetation indices were extracted, and the vegetation index that best characterized the nitrogen status of plants was selected, and a nitrogen application model based on vegetation index variables was constructed according to the relationship between nitrogen application rate, vegetation index and winter wheat yield.【Results】 (1) Chlorophyll concentration could accurately reflect the growth status and nitrogen application effect of winter wheat, and there were significant differences in the canopy reflectance data of winter wheat under different nitrogen application rates.(2) Correlation analysis showed that the normalized vegetation index NDVI had a good correlation coefficient of 0.705, which better reflected the nitrogen effect of winter wheat canopy.(3) A variable nitrogen fertilization model based on NDVI was constructed at booting stage, i.e., Nr=700.59×NDVI²-1 693.46×NDVI+955.92, which provided a scientific basis for precise nitrogen fertilization of winter wheat.【Conclusion】 The hyperspectral data obtained by ASD HH2 were used to construct a model and method of nitrogen fertilization in winter wheat, which provided an important reference for the in-depth study of precision fertilization of winter wheat.

Key words: remote sensing; vegetation index; nitrogen application model

摘要：

【目的】研究根据冬小麦关键生育期生长需求解析氮丰缺识别和精准定量施氮模型,实现量化施氮,为冬小麦智慧施肥种植的关键技术提供参考。【方法】利用ASD HH2高光谱仪获取不同施氮量的冬小麦冠层反射率,记录叶绿素浓度和产量等信息。通过冬小麦冠层反射率提取14种植被指数,选择最优表征植株氮状况的植被指数,分析施氮量、植被指数和冬小麦的产量之间的关系,构建基于植被指数变量施氮模型。【结果】叶绿素浓度可以准确反映冬小麦的生长状况和施氮效果,不同的施氮量下冬小麦的冠层反射率数据差异显著;归一化植被指数NDVI的相关性好,相关系数达到0.705,较好反映了冬小麦冠层氮素效应的情况;构建了基于NDVI的冬小麦孕穗期变量施氮模型,即N_r=700.59×NDVI²-1693.46×NDVI+955.92。【结论】利用ASD HH2获取高光谱数据构建冬小麦施氮模型与方法。

关键词: 遥感, 植被指数, 施氮模型

CLC Number:

S512

CHEN Rong, LAI Ning, GENG Qinglong, LI Yongfu, XIN Huinan, LYU Caixia, LI Na, CHEN Shuhuang. Study on variable nitrogen fertilization model of winter wheat during booting period based on remote sensing data[J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2705-2712.

陈荣, 赖宁, 耿庆龙, 李永福, 信会男, 吕彩霞, 李娜, 陈署晃. 基于遥感数据构建冬小麦孕穗期变量施氮模型[J]. 新疆农业科学, 2024, 61(11): 2705-2712.

Figures/Tables 8

Tab.1 Nitrogen concentration gradient of winter wheat

年份 Year	处理 Treatments	N (kg/hm²)	P₂O₅ (kg/hm²)	K₂O (kg/hm²)
2021	N₀	0	120	20
	N₁	180	120	20
	N₂	210	120	20
	N₃	240	120	20
	N₄	270	120	20
	N₅	315	120	20
2022	N₀	0	120	20
	N₁	180	120	20
	N₂	210	120	20
	N₃	240	120	20
	N₄	315	120	20
2023	N₀	0	150	45
	N₁	210	150	45
	N₂	240	150	45
	N₃	270	150	45
	N₄	300	150	45
	N₅	330	150	45

Tab.2 Vegetation index and its formulas and references

编号 Num bering	植被指数 Vegetation index	公式 Formula	引用文献 Reference
1	DVI	$ρ_{N I R} - ρ_{R}$	[14]
2	EVI	$2.5 \frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + 6 ρ_{R} - 7.5 ρ_{B} + 1}$	[15]
3	RVI	$ρ_{N I R} / ρ_{R}$	[16]
4	MSR	$\frac{ρ_{N I R} / ρ_{R} - 1}{\sqrt{ρ_{N I R} / ρ_{R}} + 1}$	[17]
5	NDVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + ρ_{R}}$	[18]
6	MNVI	$\frac{1.5 (ρ_{N I R}^{2} - ρ_{R})}{ρ_{N I R}^{2} + ρ_{R} + 0.5}$	[19]
7	SAVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + ρ_{R} + L} (1 + L) (L = 0.5)$	[20]
8	OSAVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} - ρ_{R} + L} (L = 0.16)$	[21]
9	CI_RE	$\frac{ρ_{N I R}}{ρ_{R E}} - 1$	[22]
10	NDVI_RE	$\frac{ρ_{N I R} - ρ_{R E}}{ρ_{N I R} + ρ_{R E}}$	[23]
11	MCARI	$[ρ_{R E} - ρ_{R} - 0.2 (ρ_{R E} - ρ_{C})] ρ_{R E} / ρ_{R}$	[24]
12	TCARI	$3 [ρ_{R E} - ρ_{R} - 0.2 (ρ_{R E} - ρ_{C}) ρ_{R E} / ρ_{R}]$	[25]
13	TVI	$60 (ρ_{N I R} - ρ_{G}) - 100 (ρ_{R} - ρ_{G})$	[26]
14	MTVI2	$\frac{1.5 [1.2 (ρ_{N I R} - ρ_{G}) - 2.5 (ρ_{R} - ρ_{G})]}{\sqrt{{(2 ρ_{N I R} + 1)}^{2} - (6 ρ_{N I R} - 5 \sqrt{ρ_{R}}) - 0.5}}$	[27]

Tab.2 Vegetation index and its formulas and references

编号 Num bering	植被指数 Vegetation index	公式 Formula	引用文献 Reference
1	DVI	$ρ_{N I R} - ρ_{R}$	[14]
2	EVI	$2.5 \frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + 6 ρ_{R} - 7.5 ρ_{B} + 1}$	[15]
3	RVI	$ρ_{N I R} / ρ_{R}$	[16]
4	MSR	$\frac{ρ_{N I R} / ρ_{R} - 1}{\sqrt{ρ_{N I R} / ρ_{R}} + 1}$	[17]
5	NDVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + ρ_{R}}$	[18]
6	MNVI	$\frac{1.5 (ρ_{N I R}^{2} - ρ_{R})}{ρ_{N I R}^{2} + ρ_{R} + 0.5}$	[19]
7	SAVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} + ρ_{R} + L} (1 + L) (L = 0.5)$	[20]
8	OSAVI	$\frac{ρ_{N I R} - ρ_{R}}{ρ_{N I R} - ρ_{R} + L} (L = 0.16)$	[21]
9	CI_RE	$\frac{ρ_{N I R}}{ρ_{R E}} - 1$	[22]
10	NDVI_RE	$\frac{ρ_{N I R} - ρ_{R E}}{ρ_{N I R} + ρ_{R E}}$	[23]
11	MCARI	$[ρ_{R E} - ρ_{R} - 0.2 (ρ_{R E} - ρ_{C})] ρ_{R E} / ρ_{R}$	[24]
12	TCARI	$3 [ρ_{R E} - ρ_{R} - 0.2 (ρ_{R E} - ρ_{C}) ρ_{R E} / ρ_{R}]$	[25]
13	TVI	$60 (ρ_{N I R} - ρ_{G}) - 100 (ρ_{R} - ρ_{G})$	[26]
14	MTVI2	$\frac{1.5 [1.2 (ρ_{N I R} - ρ_{G}) - 2.5 (ρ_{R} - ρ_{G})]}{\sqrt{{(2 ρ_{N I R} + 1)}^{2} - (6 ρ_{N I R} - 5 \sqrt{ρ_{R}}) - 0.5}}$	[27]

Fig.1 Changes of SPAD treatment statistics at different nitrogen levels

Fig.2 Changes of reflectivity statistics of different nitrogen fertilizer levels

Tab.3 Correlation analysis between vegetation index and SPAD value

植被指数 Vegetation index	相关性 Correlation
DVI	0.606^**
EVI	0.637^**
RVI	0.473^**
MSR	0.500^**
NDVI	0.705^**
MNVI	0.629^**
SAVI	0.633^**
OSAVI	0.612^**
CI_RE	0.673^**
NDVI_RE	0.704^**
MCARI	0.412^**
TCARI	0.412^**
TVI	0.573^**
MTVI2	0.585^**

Fig.3 Relationship between winter wheat yield and nitrogen application rate

Fig.4 Relationship between winter wheat yield and index

Fig.5 Nitrogen application chart of nitrogen fertilizer variable in winter wheat

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Study on variable nitrogen fertilization model of winter wheat during booting period based on remote sensing data

基于遥感数据构建冬小麦孕穗期变量施氮模型

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Metrics

[1]	XIAO Shuting, YAN An, WANG Weixia, ZHANG Qingqing, HOU Zhengqing, MA Mengqian, SUN Zhe. Analysis of spatial and temporal variations of aboveground biomass and the factors affecting it in a typical forest area in the central Tianshan Mountains [J]. Xinjiang Agricultural Sciences, 2024, 61(9): 2237-2244.
[2]	ZHANG Lei, YAO Mengyao, LIU Zhigang, LI Juan, YANG Yang, CAI Darun, CHEN Guo, LI Bo, LI Xiaorong, CHEN Xunji, ZHAI Yunlong. Research of maize yield estimation based on unmanned aerial vehicle multispectral NDVI [J]. Xinjiang Agricultural Sciences, 2024, 61(4): 845-851.
[3]	GUO Zhengyu, SUN Qian, HU Xinyue, HUANG Jinyi. Correlation analysis of main environmental impact factors based on remote sensing and Apocheima cinerarius Erschoff emergence [J]. Xinjiang Agricultural Sciences, 2024, 61(4): 954-963.
[4]	HOU Zhengqing, YAN An, XIE Kaiyun, YUAN Yilin, XIA Wenqiu, XIAO Shuting, ZHANG Zhenfei, SUN Zhe. Estimation of aboveground biomass of Diarthron tianschanicum based on vegetation index fusion [J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2787-2796.
[5]	ZHAO Haonan, MA Haiyan, Asiya Manlike, TIAN Cong, XU Jun, PAN Jing, SUN Zongjiu, ZHENG Fengling. Crop remote sensing classification study in Qitai County based on deep learning and Google earth engine (GEE) [J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2807-2814.
[6]	QIN Yekangyang, LI Jiaxin, JIN Guili, LIU Wenhao, MA Jian, LI Wenxiong, CHEN Mentian. Identification of main plants in desert grassland Seriphidium transiliense based on UAV remote sensing and CNN ResNet 18 [J]. Xinjiang Agricultural Sciences, 2024, 61(10): 2547-2556.
[7]	LIU Liang, PENG Jian, LI Gangyong, HAN Wanqiang, LIU Yujia, GUAN Jingyun, LIU Chengcai, ZHENG Jianghua. Analysis of dynamic remote sensing monitoring changes in Kulusitai grassland in Xinjiang from 2010 to 2021 [J]. Xinjiang Agricultural Sciences, 2024, 61(1): 230-240.
[8]	YANG Yongqiang, WANG Zhenxi, SHI Yuxia, LIAN Ling, GAO Yali. The Extraction of Single Wood Canopy of Picea Schrenkiana var tianshanica Forest Based on UAV Image [J]. Xinjiang Agricultural Sciences, 2020, 57(8): 1484-1492.
[9]	SHI Zhiyu, PEI Yakun, ZHU Yutao, JIA Yujiao, HU Xiaoqian, HOU Shicong, HOU Yuxia. Study on Remote Monitoring of Cotton Verticillium wilt Diagnosis and Control Management Based on Digital Image [J]. Xinjiang Agricultural Sciences, 2020, 57(6): 1166-1174.
[10]	WEI Qing, ZHANG Baozhong, WEI Zheng. Research on Object Recognition Based on UAV Multispectral Image [J]. Xinjiang Agricultural Sciences, 2020, 57(5): 932-939.
[11]	HAN Wanqiang, JIN Guili, YUE Yonghuan, WANG Huining, GONG Ke, WU Xueer, Wulupa Ardererkali. Spectral Characteristics and Modified Vegetation Index of ihree Main Plants of Seriphidium transiliense Desert Grassland [J]. Xinjiang Agricultural Sciences, 2020, 57(5): 950-957.
[12]	WU Wenli, YUAN Ye, LI Yuanyuan, CHU Guangming, ZHANG Hao. Comparison of Hyperspectral and Optical Image Vegetation Index (VI) in Desert Forest [J]. Xinjiang Agricultural Sciences, 2020, 57(1): 149-156.
[13]	GE Yuan-mei, CHEN Xiang-yu, HONG Shuai, MA Lu-lu, L Xin , ZHANG Ze. Establishment of Estimation Model for Different Varieties Based on Red Edge Parameters [J]. Xinjiang Agricultural Sciences, 2019, 56(6): 1032-1040.
[14]	LI Tian-sheng, CUI Jing, WANG Hai-jiang, YANG Jin. Study on Water Content Estimation Model of Winter Wheat Based on Hyperspectral Characteristic Wavelength [J]. Xinjiang Agricultural Sciences, 2019, 56(10): 1772-1782.
[15]	LI Na, WANG Xin-jun, CHANG Meng-di, YAN Li-nan, XU Xiao-long. Response of Sparse Vegetation Patch Pattern to Precipitation Based on Landsat Image [J]. Xinjiang Agricultural Sciences, 2019, 56(10): 1895-1903.