基于三种空间预测方法的安庆市耕地土壤速效钾空间分布预测
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作者单位:

(1.安徽农业大学资源与环境学院,安徽 合肥 230036;2.安庆市种植业管理局,安徽 安庆 246000)

作者简介:

朱福斌(1995-),男,浙江永嘉人,硕士研究生,主要研究方向为土地信息技术。E-mail:2433720246@qq.com。

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基金项目:

基金项目:2019年度高校协同创新“整合一批”协同创新项目“国产高分辨率对地观测系统安徽区域综合应用示范”(GXXT-2019-047)。


Prediction of spatial distribution of available potassium in cultivated soil of Anqing city based on three spatial prediction methods
Author:
Affiliation:

(1. School of Resources and Environment,Anhui Agricultural University,Hefei Anhui 230036;2. Anqing City Plantation Authority,Anqing Anhui 246000)

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    摘要:

    以安徽省安庆市为研究区,选取环境变量因子(空间位置变量因子、地形变量因子、土壤变量因子、气候变量因子)作为变量因素,通过构建随机森林(Random Forest,RF)模型对研究区耕地土壤速效钾含量进行预测,并与普通克里金(Ordinary Kriging,OK)和反距离权重(Inverse Distance Weighting,IDW)这两种传统空间预测方法作对比。结果表明:研究区内速效钾空间分布的3种方法的预测精度高低顺序为RF>OK>IDW,其中RF 模型的平均绝对误差(MAE)、均方根误差(RMSE)和决定系数(R2)分别为30.93 mg·kg-1、41.31 mg·kg-1和0.58,相较于OK和IDW分别高出了3.36%、5.95%,6.71%、11.86%和18.37%、23.40%;3种空间分布预测方法整体趋势一致,呈东南高西北低分布。综合而言,RF模型能较好地预测安庆市耕地土壤速效钾含量,且纬度、年平均温度、成土母质、高程、经度、年平均降水量是影响RF模型精度的主要因素。

    Abstract:

    Taking Anqing City of Anhui Province as the research area,a Random Forest model(RF)is constructed by selecting environmental variable factors(space position variable factor,topographic variable factor,soil variable factor,climate variable factor)as the variable factor.The content of available potassium is predicted and compared with the results by traditional spatial prediction methods of Ordinary Kriging(OK)and Inverse Distance Weight(IDW).The results show that the prediction accuracy of the three methods for the spatial distribution of available potassium in the study area is RF>OK>IDW,where the mean absolute error(MAE)and root mean square error(RMSE)of the RF model and the coefficient of determination(R2)are 30.93 mg·kg-1,41.31 mg·kg-1 and 0.58,which are 3.36%,5.95%,6.71%,11.86% and 18.37%,23.40% higher than those of OK and IDW.The overall trends of the three spatial distribution prediction methods are consistent,showing a high distribution in the southeast and a low distribution in the northwest.In summary,the RF model can better predict the available potassium content of cultivated soil in Anqing.Latitude,annual average temperature,soil parent material,elevation,longitude,and annual average precipitation are the main factors that affect the accuracy of the RF model.

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朱福斌,丁世伟,甘晓玉,黄海,吴锦松,马友华.基于三种空间预测方法的安庆市耕地土壤速效钾空间分布预测[J].中国土壤与肥料,2021,(1):1-8.

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  • 收稿日期:2019-11-15
  • 最后修改日期:
  • 录用日期:2020-04-28
  • 在线发布日期: 2021-03-18