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基于地基多光谱的不同水氮处理条件下春玉米叶片信息采集

Study on leaf information collection of spring maize under different water nitrogen treatment conditions based on ground-based multispectrum
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摘要 【目的】研究不同水氮处理对多光谱采集春玉米叶片信息的影响。【方法】设置3个水平灌水定额(75%、100%、125%作物需水量ETc)和4个水平的施氮量(0、200、400、600 kg/hm^(2))处理,采用地基多光谱拍摄的方法获取春玉米叶片的光谱信息,选取5个植被指数分析不同水氮处理对多光谱信息采集的影响。结合实测数据处理,分析相关性、粒子群优化的BP神经网络变化,研究实测值与植被指数的变化趋势。【结果】植被指数NDVI在植株发育中期对SPAD值的反演效果较好,灌水量和施氮量均会影响植被指数对于SPAD值的反演。在中灌水处理(W_(2))条件下植被指数OSAVI与SAVI对表层土壤水分的反演较优,且OSAVI与0~20 cm土壤水分数据的PSO-BP神经网络建模优于SAVI对于10~30 cm土壤水分的PSO-BP神经网络建模。【结论】在100%ETc灌水水平、施氮400 kg/hm^(2)条件下,使用NDVI与OSAVI进行SPAD值和地表0~20 cm的土壤水分的反演较为准确。 【Objective】To investigate the effects of different water and nitrogen treatments on multispectral collection of leaf information from spring maize.【Methods】Three levels of irrigation quotas(75%,100%,125%ETc)and four levels of N application(0,200,400,600 kg/hm^(2))were set,Ground-based multispectral photography was used to obtain spectral information of spring maize leaves,and five vegetation indices were selected to study the effects of different water and nitrogen treatments on multispectral information acquisition.Combined with the measured data,the BP neural network with correlation analysis and particle swarm optimization was used to analyze the trend of the measured values and vegetation index.【Results】The results showed that the inversion of vegetation index NDVI was better for SPAD values at the middle of vegetation development,and both irrigation and nitrogen application affected the inversion of vegetation index for SPAD values.The inversions of vegetation indices OSAVI and SAVI for surface soil moisture under medium irrigation treatment(W_(2))were superior,and the PSO-BP neural network modeling results of OSAVI with soil moisture data from 0 to 20 cm were better than those of SAVI for soil moisture from 10 to 30 cm.【Conclusion】In summary,it is more accurate to use NDVI with OSAVI for inversion of SPAD values and soil moisture at the surface 0-20 cm at 100%ETc irrigation level and above 400 kg/hm^(2)N application level.
作者 李池 陈刚 杨继革 杨庭瑞 赵经华 马明杰 LI Chi;CHEN Gang;YANG Jige;YANG Tingrui;ZHAO Jinghua;MA Mingjie(College of Water Conservancy and Civil Engineering,Xinjiang Agricultural University/Key Laboratory of Water Resources Engineering Safety and Water Disaster Prevention and Control,Urumqi 830052,China;Karamay Lüchen Agricultural Development Co.,Ltd,Karamay Xinjiang 834000,China)
出处 《新疆农业科学》 CAS CSCD 北大核心 2024年第10期2374-2387,共14页 Xinjiang Agricultural Sciences
基金 新疆维吾尔自治区重大科技专项项目(2020A01003-4)。
关键词 春玉米 多光谱 水氮 神经网络 spring maize multispectral water nitrogen neural network
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