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基于不同插值方法的三江平原白浆土磷素空间分布预测及其适用性分析

张迪 姜柏志 刘国辉 张慧 聂凡 孙琦 纪明元

张迪, 姜柏志, 刘国辉, 张慧, 聂凡, 孙琦, 纪明元. 基于不同插值方法的三江平原白浆土磷素空间分布预测及其适用性分析[J]. 中国生态农业学报(中英文), 2021, 29(8): 1405-1416. doi: 10.13930/j.cnki.cjea.200955
引用本文: 张迪, 姜柏志, 刘国辉, 张慧, 聂凡, 孙琦, 纪明元. 基于不同插值方法的三江平原白浆土磷素空间分布预测及其适用性分析[J]. 中国生态农业学报(中英文), 2021, 29(8): 1405-1416. doi: 10.13930/j.cnki.cjea.200955
ZHANG Di, JIANG Baizhi, LIU Guohui, ZHANG Hui, NIE Fan, SUN Qi, JI Mingyuan. Applicability of spatial interpolation methods to predict total phosphorus in the typical irrigated areas of the Sanjiang Plain[J]. Chinese Journal of Eco-Agriculture, 2021, 29(8): 1405-1416. doi: 10.13930/j.cnki.cjea.200955
Citation: ZHANG Di, JIANG Baizhi, LIU Guohui, ZHANG Hui, NIE Fan, SUN Qi, JI Mingyuan. Applicability of spatial interpolation methods to predict total phosphorus in the typical irrigated areas of the Sanjiang Plain[J]. Chinese Journal of Eco-Agriculture, 2021, 29(8): 1405-1416. doi: 10.13930/j.cnki.cjea.200955

基于不同插值方法的三江平原白浆土磷素空间分布预测及其适用性分析

doi: 10.13930/j.cnki.cjea.200955
基金项目: 

国家重点研发计划项目 2017YFD0300500

详细信息
    通讯作者:

    张迪, 主要从事土壤地球化学调查与评价研究。E-mail: zhangdi6283@neau.edu.cn

  • 中图分类号: S159.2

Applicability of spatial interpolation methods to predict total phosphorus in the typical irrigated areas of the Sanjiang Plain

Funds: 

the National Key Research and Development Project of China 2017YFD0300500

More Information
  • 摘要: 土壤磷素含量是反映农业生态系统土壤肥力的重要指标。准确预测磷素空间异质性是评价土壤生产力和质量的关键。本研究采用反距离加权法(IDW)、径向基函数法(RBF)、普通克里金法(OK)、全局多项式法(GPI)、局部多项式法(LPI)、地理加权回归(GWR)和地理加权回归克里金法(GWRK)等插值方法,分别预测了三江平原白浆土典型灌区八五三、七里沁以及大兴灌区土壤磷素分布,并运用交叉验证法,通过平均误差(ME)、均方根误差(RMSE)和改进效果(RI)对各种方法精度进行比较,以期确定同一土壤类型不同采样密度土壤中磷素空间异质性最佳插值方法。对比7种插值方法,在空间插值平滑性方面,LPI、GPI、GWR、GWRK表现较好,在插值速度方面,IDW、RBF、LPI、GPI、OK较快,GWR和GWRK方法运算复杂、速度较慢。IDW、RBF等6种方法与OK相比,根据RI判定,GWRK方法提高了磷素空间分布模拟精度,IDW、GPI和LPI方法降低了磷素空间分布模拟精度,RBF方法在提高磷素空间分布模拟精度上表现不一致。采样密度会影响预测结果,对于本文而言,不论采样密度如何,GWRK方法均方根误差(RMSE)均最低,为最佳插值方法,而RBF方法是在采样密度较低时一种可选方法。GWRK法在本文是最佳的插值方法,但其结果会受到辅助变量多少和各变量之间是否存在共线性的影响。
  • 图  1  三江平原典型灌区的白浆土样点分布

    Figure  1.  General situation and distribution of sample points in Albic soil area in the typical irrigation area of the Sanjiang Plain

    图  2  三江平原典型灌区白浆土磷素正态性分布检验

    Figure  2.  Inspection of normal distribution of phosphorus in Albic soil in the typical irrigation areas of the Sanjiang Plain

    图  3  八五三灌区不同插值方法磷素(TP)空间分布预测

    IDW: 反距离加权; RBF: 径向基函数; OK: 普通克里金; GPI: 全局多项式法; LPI: 局部多项式法; GWR: 地理加权回归; GWRK: 地理加权回归克里金法。

    Figure  3.  Prediction of spatial distribution of phosphorus (TP) by different interpolation methods in Bawusan Irrigation Area

    IDW: inverse distance weighting method; RBF: radial basis function; OK: ordinary Kriging; GPI: global polynomial method; LPI: local polynomial method; GWR: geographic weighted regression; GWRK: geographic weighting regression to Kriging.

    图  4  大兴灌区不同插值方法磷素(TP)空间分布预测

    IDW: 反距离加权; RBF: 径向基函数; OK: 普通克里金; GPI: 全局多项式法; LPI: 局部多项式法; GWR: 地理加权回归; GWRK: 地理加权回归克里金法。

    Figure  4.  Prediction of spatial distribution of phosphorus (TP) by different interpolation methods in Daxing Irrigation Area

    IDW: inverse distance weighting method; RBF: radial basis function; OK: ordinary Kriging; GPI: global polynomial method; LPI: local polynomial method; GWR: geographic weighted regression; GWRK: geographic weighting regression to Kriging.

    图  5  七里沁灌区不同插值方法磷素空间分布预测

    IDW: 反距离加权; RBF: 径向基函数; OK: 普通克里金; GPI: 全局多项式法; LPI: 局部多项式法; GWR: 地理加权回归; GWRK: 地理加权回归克里金法。

    Figure  5.  Prediction of spatial distribution of phosphorus by different interpolation methods in Qiliqin Irrigation Area

    IDW: inverse distance weighting method; RBF: radial basis function; OK: ordinary Kriging; GPI: global polynomial method; LPI: local polynomial method; GWR: geographic weighted regression; GWRK: geographic weighting regression to Kriging.

    表  1  研究区土壤概况

    Table  1.   Soil profile in the typical irrigation area of the Sanjiang Plain

    研究区
    Research area
    面积
    Area
    (km2)
    土类
    Soil
    亚类
    Subcategory
    成土母质
    Parent material
    ≥10 ℃积温
    ≥10 ℃ accumulated temperature (℃)
    年降水量
    Annual precipitation
    (mm)
    土壤侵蚀类
    Soil erosion
    障碍层类型
    Type of barrier
    八五三灌区
    Bawusan
    Irrigation Area
    829.0639 白浆土
    Albic soil
    典型白浆土
    Typical Albic soil
    黄土状黏质土
    Loess-like clay soil
    2487 565.0 混合侵蚀
    Mixed
    erosion
    白浆层
    Albic layer
    大兴灌区
    Daxing
    Irrigation Area
    387.9494 白浆土
    Albic soil
    潜育白浆土、
    草甸白浆土
    Gleed Albic soil,
    meadow Albic soil
    残积物
    Residue
    2761 561.0 无侵蚀
    No erosion

    Null
    七里沁灌区
    Qiliqin
    Irrigation Area
    121.4848 白浆土
    Albic soil
    潜育白浆土
    Gleed Albic soil
    冲积物
    Alluvial deposit
    2330 586.3 混合侵蚀
    Mixed
    erosion

    Null
    下载: 导出CSV

    表  2  典型灌区白浆土采样点磷素含量分析

    Table  2.   Phosphorus contents in sampling points of Albic soil in the typical irrigation areas

    灌区
    Irrigation area
    采样点数
    Sampling points
    最小值
    Min
    (g∙kg–1)
    最大值
    Max
    (g∙kg–1)
    平均值
    Average
    (g∙kg–1)
    方差
    Variance
    标准偏差
    Standard deviation (g∙kg–1)
    变异系数
    Coefficient of variation
    峰度
    Kurtosis
    偏度
    Skewness
    采样密度
    Sampling density
    (points∙km–2)
    七里沁
    Qiliqin
    7 0.83 1.21 1.00 0.0219 0.1480 0.1482 –1.8930 0.4090 0.06
    八五三
    Bawusan
    27 0.97 1.58 1.19 0.0194 0.1392 0.1169 0.8357 0.6007 0.03
    大兴
    Daxing
    39 0.65 1.22 0.92 0.0194 0.1394 0.1514 –0.8081 –0.0064 0.10
    下载: 导出CSV

    表  3  典型灌区白浆土辅助变量与磷素相关显著性分析

    Table  3.   Significance analysis of the correlation between auxiliary variables and phosphorus of Albic soil in the typicalirrigation areas %

    辅助变量
    Auxiliary
    variable
    八五三灌区
    Bawusan
    Irrigation Area
    七里沁灌区
    Qiliqin Irrigation Area
    大兴灌区
    Daxing Irrigation Area
    辅助变量
    Auxiliary
    variable
    八五三灌区
    Bawusan Irrigation Area
    七里沁灌区
    Qiliqin Irrigation Area
    大兴灌区
    Daxing Irrigation Area
    土壤pH
    Soil pH
    50.67* 土壤有效铜
    Soil available cuprum
    2.26 3.15 82.61*
    土壤交换性钠
    Soil exchangeable sodium
    44.95* 1.18 13.58* 土壤速效钾
    Soil available potassium
    1.88 0.79 0.14
    土壤全氮
    Soil total nitrogen
    41.15* 3.15 1.08 土壤锌
    Soil zinc
    0.31 0.39 0.48
    土壤有效磷
    Soil available phosphorus
    31.66* 0.39 0.18 水稻产量
    Rice yield
    0.25
    海拔
    Altitude
    29.63* 耕层容重
    Cultivated layer bulk
    density
    0.14 2.04
    土壤有效硅
    Soil available silicon
    24.23 0.39 72.41* 土壤有效硼
    Soil available boron
    0.14 1.18 0.05
    土壤镉
    Soil cadmium
    22.51 土壤铜
    Soil cuprum
    0.14 2.76
    土壤CEC
    Soil CEC
    20.20 2.36 土壤有效锌
    Soil available zinc
    0.07 10.24* 100.00*
    土壤铬
    Soil chromium
    7.96 26.38* 0.07 土壤缓效钾
    Soil slow potassium
    0.02 4.37
    土壤有效锰
    Soil available manganese
    3.44 12.20* 土壤全钾
    Soil total potassium
    0.01 1.57 55.24
    土壤镍
    Soil nickel
    2.75 1.18 2.45 土壤有效铁
    Soil available iron
    0.01 1.07
    土壤铅
    Soil plumbum
    2.30 7.87 28.74 土壤有机质
    Soil organic matter
    3.15
    表中数据大小表示该变量作为预测因子的强度, 数值越大表明其作为预测因子趋势越大, *代表该辅助变量与磷元素具有显著相关性。Data in the table indicates the strength of the variable used as a predictor parameter, and the larger the data, the greater the trend as a predictor. “*” means that the auxiliary variable is significantly correlated with soil phosphorus content.
    下载: 导出CSV

    表  4  典型灌区白浆土辅助变量与磷素多元线性逐步回归分析

    Table  4.   Multiple linear stepwise regression analysis between auxiliary variables and phosphorus of Albic soil in the typical irrigation areas

    灌区
    Irrigation area
    变量
    Variable
    系数
    Coefficient
    标准差
    Standard deviation
    T统计量
    T statistics
    概率
    Probability
    VIF值
    VIF value
    八五三
    Bawusan
    土壤交换性钠Soil exchangeable sodium –1.8121 0.3927 –4.6149 0.0001 1.4022
    耕层容重Cultivated layer bulk density –0.6753 0.2895 –2.3329 0.0297 2.0655
    土壤CEC Soil CEC 0.0141 0.0050 2.8038 0.0106 2.1943
    土壤有效磷Soil available phosphorus 0.0033 0.0016 2.0901 0.0490 1.4460
    土壤有效锰Soil available manganese 0.0054 0.0015 3.5205 0.0020 2.3576
    大兴
    Daxing
    土壤交换性钠Soil exchangeable sodium –0.3065 0.1185 –2.5870 0.0141 1.1004
    土壤有效锌Soil available zinc –0.0961 0.0346 –2.7776 0.0089 1.1790
    土壤有效铜Soil available cuprum 0.1311 0.0476 2.7533 0.0094 1.300
    土壤镉Soil cadmium 0.9035 0.8165 1.1065 0.2763 1.0242
    七里沁
    Qiliqin
    土壤有机质Soil organic matter 0.0141 0.0038 3.7262 0.0223 1.7061
    土壤有效锌Soil available zinc –0.6213 0.1789 –3.4733 0.0331 1.6344
    土壤有效硼Soil available boron 1.7556 0.5317 3.3019 0.0411 2.3092
    下载: 导出CSV

    表  5  三江平原白浆土磷素空间分布的7种插值方法评价

    Table  5.   Evaluation of seven interpolation methods for spatial distribution of phosphorus of Albic soil in the typical irrigation areas of the Sanjiang Plain

    灌区
    Irrigation area
    指标
    Index
    OK RBF IDW LPI GPI GWR GWRK
    七里沁
    Qiliqin
    ME 0.0008 0.0017 0.0049 0.0215 0.0105 0.0004
    RMSE 0.1494 0.1530 0.1579 0.2201 0.1733 0.0084
    RI (%) –2.41 –5.79 –47.32 –16.00 94.38
    八五三
    Bawusan
    ME 0.0088 0.0024 0.0073 0.0075 0.0048 0.0003
    RMSE 0.1366 0.1130 0.1374 0.1348 0.1393 0.1426
    RI (%) 17.28 –0.59 1.32 –1.98 –4.39
    大兴
    Daxing
    ME 0.0088 0.0014 0.0053 0.0137 0.0004 0.0022
    RMSE 0.1198 0.1231 0.1235 0.1292 0.1335 0.0511
    RI (%) –2.75 –3.09 –7.85 –11.44 57.35
    ME: 平均误差; RMSE: 均方根误差; RI: 相对改进程度。IDW: 用反距离加权; RBF: 径向基函数; OK: 普通克里金; GPI: 全局多项式法; LPI: 局部多项式法; GWR: 地理加权回归; GWRK: 地理加权回归克里金法。ME: mean error; RMSE: root-mean-square error; RI: relative improvement. IDW: inverse distance weighting method; RBF: radial basis function; OK: ordinary Kriging; GPI: global polynomial method; LPI: local polynomial method; GWR: geographic weighted regression; GWRK: geographic weighting regression to Kriging.
    下载: 导出CSV
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  • 收稿日期:  2020-12-02
  • 录用日期:  2021-03-05
  • 刊出日期:  2021-08-01

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