基于遥感数据构建冬小麦孕穗期变量施氮模型

doi:10.6048/j.issn.1001-4330.2024.11.011

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (11): 2705-2712.DOI: 10.6048/j.issn.1001-4330.2024.11.011

• 作物遗传育种·耕作栽培 • 上一篇下一篇

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

收稿日期:2024-05-20 出版日期:2024-11-20 发布日期:2025-01-08
通信作者: 陈署晃(1973-),女,湖南人,研究员,硕士,研究方向为土壤肥料与农业信息技术,(E-mail)chensh66@163.com
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)

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 Published:2024-11-20 Online:2025-01-08
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)

摘要/Abstract

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

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

中图分类号:

S512

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.

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.

图/表 8

表1 冬小麦氮浓度梯度

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

表2 植被指数及其公式和引用

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]

表2 植被指数及其公式和引用

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]

图1 不同氮肥水平处理SPAD变化

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

图2 不同氮肥水平处理反射率变化

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

表3 植被指数与SPAD值的相关性

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^**

图3 冬小麦产量与施氮量关系

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

图4 冬小麦产量与指数关系

Fig.4 Relationship between winter wheat yield and index

图5 冬小麦氮肥变量施氮

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

参考文献 33

[1]	杜佳囝, 李广, 马维伟, 等. 黄土丘陵区4种典型植被土壤可溶性氮组分特征[J]. 水土保持学报, 2021, 35(6): 251-257.
	DU Jianan, LI Guang, MA Weiwei, et al. Characteristics of soil soluble nitrogen components under four typical vegetations in the loess hilly areas[J]. Journal of Soil and Water Conservation, 2021, 35(6): 251-257.
[2]	李铁成, 张忠学, 张作合, 等. 减施氮肥对节水灌溉稻田NH₃、N₂O排放与氮肥利用的影响[J/OL]. 农业机械学报:1-12.
	Li Tiecheng, Zhang Zhongxue, Zhang Zuohe, et al. Effects of reduced nitrogen fertilizer application on NH₃ and N₂O emissions and nitrogen fertilizer utilization in water-saving irrigated rice fields[J/OL]. Journal of Agricultural Machinery: 1-12.
[3]	陈检锋, 尹梅, 陈华, 等. 云南莴笋高产养分临界值施肥模型研究[J]. 中国农学通报, 2023, 39(25): 89-93. DOI
	CHEN Jianfeng, YIN Mei, CHEN Hua, et al. Study on fertilization system of nutrient critical value for high yield of Asparagus lettuce in Yunnan[J]. Chinese Agricultural Science Bulletin, 2023, 39(25): 89-93. DOI
[4]	白如霄, 崔瑜, 和海秀, 等. 新疆塔额盆地滴灌甜菜临界氮浓度模型的构建与检验[J]. 核农学报, 2023, 37(5): 1048-1055. DOI
	BAI Ruxiao, CUI Yu, HE Haixiu, et al. Development and verification of critical nitrogen concentration model for drip-irrigated sugar beet in Ta’e Basin, Xinjiang[J]. Journal of Nuclear Agricultural Sciences, 2023, 37(5): 1048-1055. DOI
[5]	边立丽, 艾栋, 张云贵, 等. 基于植被指数的烤烟精准追肥研究[J]. 中国土壤与肥料, 2023,(1): 126-132.
	BIAN Lili, AI Dong, ZHANG Yungui, et al. Precise top dressing of flue-cured tobacco based on vegetation index[J]. Soil and Fertilizer Sciences in China, 2023,(1): 126-132.
[6]	李志博, 李亚芹, 赵浣旻, 等. 基于GNDVI指数的土壤—水稻冠层变量施氮决策方案研究[J]. 中国农机化学报, 2022, 43(4): 160-165.
	LI Zhibo, LI Yaqin, ZHAO Huanmin, et al. Study on the decision-making scheme of soil-rice canopy variable nitrogen application based on GNDVI index[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(4): 160-165.
[7]	赖宁, 耿庆龙, 李青军, 等. 基于RapidSCAN CS-45的新疆滴灌冬小麦氮肥推荐研究[J]. 麦类作物学报, 2021, 41(1): 96-104.
	LAI Ning, GENG Qinglong, LI Qingjun, et al. Study on nitrogen fertilizer recommendation based on RapidSCAN CS-45 for drip-irrigated winter wheat in Xinjiang[J]. Journal of Triticeae Crops, 2021, 41(1): 96-104.
[8]	董超, 赵庚星, 宿宝巍, 等. 基于无人机多光谱影像的冬小麦返青期变量施氮决策模型研究[J]. 光谱学与光谱分析, 2019, 39(11): 3599.
	DONG Chao, ZHAO Gengxing, SU Baowei, et al. Decision model of variable nitrogen fertilizer in winter wheat returning green stage based on UAV multi-spectral images[J]. Spectroscopy and Spectral Analysis, 2019, 39(11): 3599.
[9]	汪旭, 邓裕帅, 练雪萌, 等. 基于无人机多光谱技术的甜菜冠层叶绿素含量反演[J]. 中国糖料, 2022, 44(4): 36-42.
	WANG Xu, DENG Yushuai, LIAN Xuemeng, et al. Inversion of chlorophyll content in sugar beet canopy based on UAV multispectral technique[J]. Sugar Crops of China, 2022, 44(4): 36-42.
[10]	李艳大, 曹中盛, 舒时富, 等. 基于作物生长监测诊断仪的双季稻叶干重监测模型[J]. 作物学报, 2021, 47(10): 2028-2035. DOI
	LI Yanda, CAO Zhongsheng, SHU Shifu, et al. Model for monitoring leaf dry weight of double cropping rice based on crop growth monitoring and diagnosis apparatus[J]. Acta Agronomica Sinica, 2021, 47(10): 2028-2035. DOI
[11]	张秋阳. 基于多源遥感数据的冬小麦氮肥推荐的研究[D]. 哈尔滨: 东北农业大学, 2015.
	ZHANG Qiuyang. Study on N Fertilization Recommendation in Winter Wheat Based on Multi-sources Remote Sensing Data[D]. Harbin:Northeast Agricultural University, 2015.
[12]	张秋阳, 杨贵军, 徐新刚, 等. 基于GeoEye-1高分遥感影像的冬小麦氮肥推荐应用[J]. 麦类作物学报, 2014, 34(12): 1685-1693.
	ZHANG Qiuyang, YANG Guijun, XU Xingang, et al. Recommended application of N rate for winter wheat based on GeoEye-1 image[J]. Journal of Triticeae Crops, 2014, 34(12): 1685-1693.
[13]	程千, 徐洪刚, 曹引波, 等. 基于无人机多时相植被指数的冬小麦产量估测[J]. 农业机械学报, 2021, 52(3): 160-167.
	CHENG Qian, XU Honggang, CAO Yinbo, et al. Grain yield prediction of winter wheat using multi-temporal UAV based on multispectral vegetation index[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(3): 160-167.
[14]	Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150.
[15]	白燕英, 高聚林, 张宝林. 基于NDVI与EVI的作物长势监测研究[J]. 农业机械学报, 2019, 50(9): 153-161.
	BAI Yanying, GAO Julin, ZHANG Baolin. Monitoring of crops growth based on NDVI and EVI[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(9): 153-161.
[16]	PEARSON R L, MILLER L D. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie, Pawnee National Grasslands, Colorado[C]. Proceedings of the 8th International Symposium on Remote Sensing of Environment, 1972:1355-1379.
[17]	Chen J M. Evaluation of vegetation indices and a modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996, 22(3): 229-242.
[18]	ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring the vernal advancements and retro gradation of natural vegetation[M]. Greenbelt: Texas A and M University, Remote Sensing Center, 1974:371.
[19]	Gong P, Pu R L, Biging G S, et al. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1355-1362.
[20]	HUETE A R. Soil influences in remotely sensed vegetation-canopy spectra[M]. ASRAR G.Theory and applications of optical remote sensing.New York: Wiley, 1989:107-141.
[21]	黄婷, 梁亮, 耿笛, 等. 波段宽度对利用植被指数估算小麦LAI的影响[J]. 农业工程学报, 2020, 36(4): 168-177.
	HUANG Ting, LIANG Liang, GENG Di, et al. Effects of band width on estimation of wheat LAI using vegetation index[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(4): 168-177.
[22]	Gitelson A A, Viña A, Ciganda V, et al. Remote estimation of canopy chlorophyll content in crops[J]. Geophysical Research Letters, 2005, 32(8): L08403
[23]	朱曼, 张立福, 王楠, 等. 基于Sentinel-2的UNVI植被指数及性能对比研究[J]. 遥感技术与应用, 2021, 36(4): 936-947. DOI
	ZHU Man, ZHANG Lifu, WANG Nan, et al. Comparative study on UNVI vegetation index and performance based on sentinel-2[J]. Remote Sensing Technology and Application, 2021, 36(4): 936-947.
[24]	Daughtry C S T, Walthall C L, Kim M S, et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment, 2000, 74(2): 229-239.
[25]	陶惠林, 冯海宽, 徐良骥, 等. 基于无人机高光谱遥感数据的冬小麦生物量估算[J]. 江苏农业学报, 2020, 36(5): 1154-1162.
	TAO Huilin, FENG Haikuan, XU Liangji, et al. Winter wheat biomass estimation based on hyperspectral remote sensing data of unmanned aerial vehicle(UAV)[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(5): 1154-1162.
[26]	Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment, 2001, 76(2): 156-172.
[27]	Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment, 2004, 90(3): 337-352.
[28]	李港, 孔亚聪, 代元帅, 等. 基于野外原位光谱的棉田耕层土壤全氮含量监测模型研究[J]. 干旱地区农业研究, 2023, 41(6): 273-280.
	LI Gang, KONG Yacong, DAI Yuanshuai, et al. Study on monitoring model for total nitrogen content in plow layer of cotton field based on field in situ spectroscopy[J]. Agricultural Research in the Arid Areas, 2023, 41(6): 273-280.
[29]	彭建, 徐飞雄, 邓凯, 等. 琅琊山景区不同叶绿素条件下树种叶片光谱差异分析[J]. 光谱学与光谱分析, 2018, 38(6): 1839.
	PENG Jian, XU Feixiong, DENG Kai, et al. Spectral differences of tree leaves at different chlorophyll relative content in Langya Mountain[J]. Spectroscopy and Spectral Analysis, 2018, 38(6): 1839.
[30]	谢东, 何敬, 何嘉晨, 等. 基于无人机高光谱影像的水稻叶片SPAD值反演方法研究[J/OL]. 山西农业大学学报(自然科学版),1-10.
	Xie Dong, He Jing, He Jiachen, et al. Research on SPAD value inversion method of rice leaves based on UAV hyperspectral image[J/OL]. Journal of Shanxi Agricultural University (Natural Science Edition),1-10.
[31]	王磊, 白由路. 不同氮处理春玉米叶片光谱反射率与叶片全氮和叶绿素含量的相关研究[J]. 中国农业科学, 2005, 38(11): 2268-2276.
	WANG Lei,BAI Youlu. Correlation between corn leaf spectral reflectance and leaf total nitrogen and chlorophyll content under different nitrogen level[J]. Scientia Agricultura Sinica, 2005, 38(11): 2268-2276. DOI
[32]	王建伟, 李树人, 周汉平, 等. 不同施氮量条件下烤烟叶面积系数与冠层光谱参数的相关性[J]. 烟草科技, 2011, 44(3): 77-80.
	WANG Jianwei, LI Shuren, ZHOU Hanping, et al. Correlation of leaf area index with canopy reflectance spectrum of flue-cured tobacco under different nitrogen application rate[J]. Tobacco Science & Technology, 2011, 44(3): 77-80.
[33]	李新伟, 吕新, 张泽, 等. 棉花氮素营养诊断与追肥推荐模型[J]. 农业机械学报, 2014, 45(12): 209-214.
	LI Xinwei, LYU Xin, ZHANG Ze, et al. Diagnosis of nitrogen nutrition and recommended model of topdressing for cotton[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(12): 209-214.

基于遥感数据构建冬小麦孕穗期变量施氮模型

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

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 33

相关文章 15

编辑推荐

Metrics

[1]	肖淑婷, 颜安, 王卫霞, 张青青, 侯正清, 马梦倩, 孙哲. 天山中部典型林区地上生物量时空变化及影响因素分析[J]. 新疆农业科学, 2024, 61(9): 2237-2244.
[2]	张磊, 姚梦瑶, 刘志刚, 李娟, 杨洋, 蔡大润, 陈果, 李波, 李晓荣, 陈勋基, 翟云龙. 基于无人机多光谱NDVI值估测玉米产量[J]. 新疆农业科学, 2024, 61(4): 845-851.
[3]	郭正宇, 孙倩, 胡馨月, 黄瑾依. 基于遥感主要环境影响因子与春尺蠖羽化的相关性分析[J]. 新疆农业科学, 2024, 61(4): 954-963.
[4]	党旭伟, 林馨园, 贺正, 陈燕, 慈宝霞, 马学花, 郭晨荔, 贺亚星, 刘扬, 马富裕. 基于无人机热红外遥感图像提取滴灌棉花冠层温度及精度评价[J]. 新疆农业科学, 2024, 61(3): 565-575.
[5]	侯正清, 颜安, 谢开云, 袁以琳, 夏雯秋, 肖淑婷, 张振飞, 孙哲. 基于植被指数融合天山假狼毒地上生物量的估测[J]. 新疆农业科学, 2024, 61(11): 2787-2796.
[6]	赵昊楠, 马海燕, 阿斯娅·曼力克, 田聪, 徐俊, 潘竟, 孙宗玖, 郑逢令. 基于深度学习和GEE的作物遥感分类[J]. 新疆农业科学, 2024, 61(11): 2807-2814.
[7]	秦叶康阳, 李嘉欣, 靳瑰丽, 刘文昊, 马建, 李文雄, 陈梦甜. 基于UAV和CNN ResNet 18参数调节的伊犁绢蒿荒漠草地植物识别性能分析[J]. 新疆农业科学, 2024, 61(10): 2547-2556.
[8]	刘亮, 彭建, 李刚勇, 韩万强, 刘玉佳, 关靖云, 刘程才, 郑江华. 2010~2021年新疆库鲁斯台草原动态遥感监测变化分析[J]. 新疆农业科学, 2024, 61(1): 230-240.
[9]	才仁加甫, 曹彪, 白云岗, 刘旭辉, 余其鹰, 刘敏杰. 和田河沙漠段生态输水植被恢复遥感评价和植被变化驱动因素分析[J]. 新疆农业科学, 2022, 59(8): 2041-2050.
[10]	杨勇强, 王振锡, 师玉霞, 连玲, 高亚利. 基于无人机影像的天山云杉林单木树冠信息提取[J]. 新疆农业科学, 2020, 57(8): 1484-1492.
[11]	石志钰, 裴雅琨, 朱玉涛, 贾玉姣, 胡晓倩, 侯士聪, 侯玉霞. 基于数字图像棉花黄萎病诊断与防治的管理远程监测[J]. 新疆农业科学, 2020, 57(6): 1166-1174.
[12]	魏青, 张宝忠, 魏征. 基于无人机多光谱影像的地物识别[J]. 新疆农业科学, 2020, 57(5): 932-939.
[13]	韩万强, 靳瑰丽, 岳永寰, 王惠宁, 宫珂, 吴雪儿, 吾鲁帕·阿得尔卡里. 伊犁绢蒿荒漠草地3种主要植物光谱及植被指数改进[J]. 新疆农业科学, 2020, 57(5): 950-957.
[14]	武文丽, 袁也, 李园园, 楚光明, 张昊. 荒漠林高光谱与光学影像植被指数(VI)的比较[J]. 新疆农业科学, 2020, 57(1): 149-156.
[15]	葛元梅, 陈翔宇, 洪帅, 马露露, 吕新, 张泽. 基于红边参数不同品种的估算模型[J]. 新疆农业科学, 2019, 56(6): 1032-1040.