• ISSN 1008-505X
  • CN 11-3996/S
张银杰, 王磊, 白由路, 杨俐苹, 卢艳丽, 孙艳敏, 张静静, 李格. 基于玉米叶片光谱特征的土壤无机氮含量估算模型的建立与验证[J]. 植物营养与肥料学报, 2020, 26(7): 1206-1215. DOI: 10.11674/zwyf.19466
引用本文: 张银杰, 王磊, 白由路, 杨俐苹, 卢艳丽, 孙艳敏, 张静静, 李格. 基于玉米叶片光谱特征的土壤无机氮含量估算模型的建立与验证[J]. 植物营养与肥料学报, 2020, 26(7): 1206-1215. DOI: 10.11674/zwyf.19466
ZHANG Yin-jie, WANG Lei, BAI You-lu, YANG Li-ping, LU Yan-li, SUN Yan-min, ZHANG Jing-jing, LI Ge. Establishment and variation of soil inorganic nitrogen content estimation model based on maize leaf spectral characteristics[J]. Journal of Plant Nutrition and Fertilizers, 2020, 26(7): 1206-1215. DOI: 10.11674/zwyf.19466
Citation: ZHANG Yin-jie, WANG Lei, BAI You-lu, YANG Li-ping, LU Yan-li, SUN Yan-min, ZHANG Jing-jing, LI Ge. Establishment and variation of soil inorganic nitrogen content estimation model based on maize leaf spectral characteristics[J]. Journal of Plant Nutrition and Fertilizers, 2020, 26(7): 1206-1215. DOI: 10.11674/zwyf.19466

基于玉米叶片光谱特征的土壤无机氮含量估算模型的建立与验证

Establishment and variation of soil inorganic nitrogen content estimation model based on maize leaf spectral characteristics

  • 摘要:
    目的 作物叶片颜色反映土壤养分的供应状况。研究作物叶片氮素相关的特征光谱信息与土壤无机氮含量的关系,以建立基于叶片光谱信息的土壤无机氮含量诊断模型,实现利用高光谱技术对作物和土壤进行实时监测。
    方法 在两年(2017—2018) 的玉米 (郑单958) 田间试验中,设置6个施氮水平,施氮量分别为0、60、120、180、240、300 kg/hm2。在玉米的拔节期、大喇叭口期、开花吐丝期、灌浆期测定叶片高光谱反射率,对植株和土壤样品进行采集,分析土壤无机氮含量的变化,明确叶片光谱反射率与土壤无机氮含量的关系,利用光谱参数和偏最小二乘回归法 (partial least squares regression,PLSR) 建立诊断模型并进行模型精度的评价。
    结果 施氮处理土壤无机氮含量显著高于不施氮处理,随着生育期的推移,土壤无机氮含量呈递减趋势,追肥可显著提高土壤无机氮含量。拔节期和开花吐丝期叶片光谱反射率与土壤无机氮含量在可见光波段呈负相关关系,在近红外波段呈正相关关系;大喇叭口期两者在可见光波段呈负相关关系,灌浆期两者无明显相关关系。在光谱参数模型中,4个生育期土壤无机氮含量预测的最佳光谱指数分别为RVI-2、RSI (534,726)、RSI (567,519) 和RVI-2,其回归模型验证集的R2分别为0.642、0.749、0.696、0.540。在PLSR预测模型中,利用PLSR建立的诊断模型验证集的R2分别为0.876、0.838、0.765、0.595,RPD (ratio of percent deviation) 分别为2.140、2.077、2.002、1.369。
    结论 基于叶片光谱反射率建立的PLSR估算模型,在玉米的拔节期、大喇叭口期、开花吐丝期均能很好地预测土壤无机氮含量。因此,利用叶片光谱特征诊断土壤无机氮含量具有一定的可行性。

     

    Abstract:
    Objectives The color of crop leaves directly reflects the nutrient supply of soil. We studied the relationship between intensity of leaf chromospectrum related to N nutrition and the content of soil inorganic nitrogen, in order to establish a model to realize real-time monitoring of nitrogen contents in crops and soils by hyperspectral technology.
    Methods A maize (Zea mays L. cv Zhengdan 958) experiment was conducted in 2017 and 2018. Six nitrogen application levels were setup, they were 0, 60, 120, 180, 240 and 300 kg/hm2, respectively. At the jointing, booting, anthesis-silking and filling stages, leaves hyperspectral reflectance and soil inorganic N content were measured, and the relationship model between spectral reflectance of leaves and soil inorganic nitrogen contents was established, and the precision of the prediction by the spectral parameters model was evaluated.
    Results The soil inorganic N content decreased with the growth period, and topdressing of N fertilizer significantly improved the soil inorganic N content in the middle and late growing stage of maize. The intensity of spectral reflectance of leaves at the jointing and anthesis-silking stages was negatively correlated with the soil inorganic N content in the visible light range, and positively correlated in the near-infrared band. There was a negative correlation in the visible band at the booting stage and no obvious correlation at the filling stages. In the spectral parameter model, the optimal spectral index for the prediction of soil inorganic N content at the four growth stages was RVI-2, RSI (534, 726), RSI (567, 519) and RVI-2, respectively, and the R2 values of verification sets of the regression model were 0.642, 0.749, 0.696 and 0.540, respectively. In the PLSR prediction model, the R2 values of the verification of the diagnostic model were 0.876, 0.838, 0.765 and 0.595, and the RPD (ratio of percent deviation) were 2.140, 2.077, 2.002, 1.369, respectively.
    Conclusions Satisfactory prediction accuracy in soil inorganic nitrogen content is acquired by the PLSR prediction model based on leaf spectral reflectance at the jointing, booting and anthesis-silking stages of maize. The two-year field experiment has proved its feasibility to diagnose soil inorganic N content.

     

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