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黄土高原春小麦地上鲜生物量高光谱遥感估算模型

张凯1,2;王润元1;王小平1;赵鸿1;韩海涛3   

  1. 1中国气象局兰州干旱气象研究所, 甘肃省干旱气候变化与减灾重点实验室, 中国气象局干旱气候变化与减灾重点开放实验室, 兰州 730020;2中国科学院寒区旱区环境与工程研究所, 兰州 730000;3甘肃省气象信息中心, 兰州 730020
  • 收稿日期:2008-10-21 修回日期:1900-01-01 出版日期:2009-06-10 发布日期:2009-06-10

Hyperspectral remote sensing estimation models for aboveground fresh biomass of spring wheat on Loess Plateau.

ZHANG Kai1,2;WANG Run-yuan1;WANG Xiao-ping1;ZHAO Hong1;HAN Hai-tao3   

  1. 1Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China;2Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;3Gansu Meteorological Information Center, Lanzhou 730020, China
  • Received:2008-10-21 Revised:1900-01-01 Online:2009-06-10 Published:2009-06-10

摘要: 通过田间小区试验,测定了4个春小麦品种(定西24号、陇春8139、高原602和定西38号)在不同生育期和不同种植密度下冠层光谱反射率及其对应的地上鲜生物量,分析了春小麦地上鲜生物量随生育期的变化以及地上鲜生物量与冠层反射光谱和一阶微分光谱之间的相关关系,采用相关系数较大的特征波段及其组合构建光谱特征参数以其作为变量,建立了春小麦地上生物量的高光谱估算模型,并对模型进行检验。结果表明:以参数F780D719为变量的对数形式y=3.9498ln F780+7.0596和乘幂形式y=512.99D7191.0174估算水平最高,前者均方根误差(RMSE)为0.2173,相对误差(RE)为10.45%,预测值与实测值相关系数为0.854;后者RMSE为0.2188,RE为9.96%,预测值与实测值相关系数为0.853。因此,上述两个模型可作为陇中黄土高原地区春小麦地上鲜生物量的最佳估算模型。

关键词: NO, 排放系数, 农田, 降水

Abstract: A field plot experiment was conducted to measure the canopy spectral r eflectance and aboveground fresh biomass of four spring wheat varieties (Dingxi 24, Longchun 8139, Gaoyuan 602 and Dingxi 38) at their different growth stages a nd under different planting densities. The variations of the aboveground fresh b iomass with growth stages as well as the correlations of the aboveground fresh b iomass with canopy reflective spectrum and first derivative spectrum were analyz ed, and based on these, hyperspectral remote sensing estimation models for sprin g wheat aboveground fresh biomass were established, with the characteristic band s and their combinations strongly correlated with the aboveground fresh biomass as the variables. The tests with experimental data showed that models y=3 9498 ln F780+70596 and y=51299 D71910174 had the h ighest estimation level, with the root mean square error, relative error, and co rrelation coefficient between estimated and measured values being 02173, 104 5% and 0854, and 02188, 996%, and 0853, respectively. These two models c ould be used as the best models for the estimation of spring wheat aboveground f resh biomass on Longzhong Loess Plateau.

Key words: N2O, Emission factor, Agricultural field, Precipitation