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草业学报 ›› 2016, Vol. 25 ›› Issue (3): 9-21.DOI: 10.11686/cyxb2015268

• 研究论文 • 上一篇    下一篇

基于高光谱数据的高寒草甸氮磷钾含量估测方法研究--以青海省贵南县及玛沁县高寒草甸为例

高金龙1, 侯尧宸1, 白彦福1, 孟宝平1, 杨淑霞1, 胡远宁1, 冯琦胜1, 崔霞2, 梁天刚1, *   

  1. 1.草地农业生态系统国家重点实验室,兰州大学草地农业科技学院,甘肃 兰州 730020;
    2.兰州大学西部环境教育部重点实验室,甘肃 兰州 730000
  • 收稿日期:2015-05-28 出版日期:2016-03-20 发布日期:2016-03-20
  • 通讯作者: E-mail:tgliang@lzu.edu.cn
  • 作者简介:高金龙(1991-),男,甘肃永昌人,在读硕士.E-mail:rslabjinlong@163.com
  • 基金资助:
    国家自然科学基金项目(31372367,41401472)和青海省科技支撑项目(2013-N-146-4)资助

Methods for estimating nitrogen, phosphorus and potassium content based on hyperspectral data from alpine meadows in Guinan and Maqin Counties, Qinghai province

GAO Jin-Long1, HOU Yao-Chen1, BAI Yan-Fu1, MENG Bao-Ping1, YANG Shu-Xia1, HU Yuan-Ning1, FENG Qi-Sheng1, CUI Xia2, LIANG Tian-Gang1, *   

  1. 1.State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China;
    2.National Laboratory of Western China's Environmental System, Lanzhou University, Lanzhou 730000, China
  • Received:2015-05-28 Online:2016-03-20 Published:2016-03-20

摘要: 以青海省玛沁县和贵南县高寒草甸作为典型研究区,利用地物光谱仪采集了20块样地的高光谱数据,并测定了对应样地所有样方中牧草的养分含量,分析了牧草中氮磷钾素含量与冠层原始光谱反射率和一阶微分光谱反射率之间的相关关系;采用回归统计方法,基于光谱位置变量,光谱面积变量及植被指数变量构建了高寒草甸氮磷钾素的估测模型,并对模型进行了精度评价.结果表明,1)与原始光谱反射率曲线相比,一阶微分光谱反射率曲线能较好地反映牧草中N,P,K素所对应的敏感波段;2)高寒草甸牧草中N,P,K素含量与冠层高光谱相关性较强的波段大多分布在红光区域(680~760 nm);3)基于光谱位置变量构建的估测模型能更好地反演高寒草甸N,P,K素含量.其中,以光谱位置变量R'708.88为自变量的对数模型对氮素含量估测效果较好,R2为0.67,估测精度达到83.56%;以光谱位置变量R'704.85为自变量的对数模型对磷素含量估测效果较好,R2为0.55,估测精度达到92.15%;以光谱位置变量R'697.36为自变量的对数模型对钾素含量估测效果较好,R2为0.86,估测精度达到82.44%.

Abstract: Using typical alpine meadows in Maqin and Guinan Counties in Qinghai province as the research area, hyperspectral data from 20 plots were collected by spectrometer, while the forage nutrient concentrations were measured in the laboratory for each plot. Using regression analysis, the correlations between the nitrogen, phosphorus and potassium contents of the alpine meadow forage and the original reflectance data and the first-order differential of reflectance were analyzed. Inversion models were established for estimating the nitrogen, phosphorus and potassium content of alpine meadow forage based on spectrum location, spectrum area and vegetation index, and the accuracy of the models was also evaluated. It was found that first-order differential reflectance curve better predicts nitrogen, phosphorus and potassium content in the forage, than does the original data. In the red band (680-760 nm), the nitrogen, phosphorus and potassium levels in forage show a strong relationship with canopy hyperspectral reflectance curve parameters. The model which included spectrum location worked well for estimating concentration of nitrogen, phosphorus, and potassium in alpine meadow forage. A logarithmic model for spectrum location (R'708.88) can estimated forage nitrogen content with an R2 of 0.67, and an accuracy of 83.56%, while a logarithmic model for spectrum location (R'704.85) can estimated forage phosphorus content, with an R2 of 0.55, and an accuracy of 92.15%, and a logarithmic model for spectrum location (R'697.36) estimated potassium content, with an R2 of 0.86, and an accuracy of 82.44%.