干旱区研究

干旱区研究 >

2024 , Vol. 41 >Issue 7: 1120 - 1130

DOI: https://doi.org/10.13866/j.azr.2024.07.04

水土资源

流域绿洲土壤盐分多模型反演效果评估

  • 龙威夷 ,
  • 施建飞 ,
  • 李双媛 ,
  • 孙金金 ,
  • 王玉刚
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  • 1.中国科学院新疆生态与地理研究所，荒漠与绿洲生态国家重点实验室，新疆 乌鲁木齐 830011
    2.中国科学院大学，北京 100049
    3.中国科学院阜康荒漠生态系统国家站，新疆 阜康 831505
龙威夷（1998-），男，硕士研究生，研究方向为土壤盐渍化过程模拟. E-mail: longweiyi21@mails.ucas.ac.cn
王玉刚. E-mail: wangyg@ms.xjb.ac.cn

收稿日期: 2024-03-12

  修回日期: 2024-04-05

  网络出版日期: 2024-08-01

基金资助

“天山英才”培养计划(2023TSYCLJ0048);国家自然科学基金(42371126);国家自然科学基金(42330503)

收起

Evaluation of multimodel inversion effects on soil salinity in oasis basin

  • LONG Weiyi ,
  • SHI Jianfei ,
  • LI Shuangyuan ,
  • SUN Jinjin ,
  • WANG Yugang
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  • 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Fukang Station of Desert Ecology, Chinese Academy of Sciences, Fukang 831505, Xinjiang, China

Received date: 2024-03-12

  Revised date: 2024-04-05

  Online published: 2024-08-01

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摘要

为更好地实现区域土壤盐分的监测和治理，促进绿洲可持续发展，本文基于气候、地形、植被等相关数据，结合三工河流域平原绿洲土壤表层盐分调查，对比评估不同模型（随机森林，支持向量机，决策树，普通克里金）土壤盐分反演效果。结果表明：绿洲土壤样本盐分含量为0.29~30.18 g·kg-1，平均值为4.06 g·kg-1，变异系数为149.73%，属于强变异；随机森林模型相较于其他模型具有更高的反演精度，决定系数、均方根误差和绝对平均误差分别为0.73、1.89 g·kg-1和1.49 g·kg-1；随机森林模型反演显示，高值区主要分布在西北部和中部区域，并且在9种环境协变量中，高程和地下水矿化度对土壤盐分反演精度影响较大。总的来说，以随机森林模型为手段的机器学习方法，不仅能够避免数据的平滑效应和图斑边界两侧的突变，还能有助于识别绿洲局部空间盐分状况，研究结果可为干旱区绿洲土壤盐渍化的长期监测提供技术和方法参考。

关键词： 机器学习; 土壤盐分; 空间分布; 三工河流域; 干旱区

本文引用格式

龙威夷 , 施建飞 , 李双媛 , 孙金金 , 王玉刚 . 流域绿洲土壤盐分多模型反演效果评估[J]. 干旱区研究, 2024 , 41(7) : 1120 -1130 . DOI: 10.13866/j.azr.2024.07.04

Abstract

A case study was conducted on the plain oasis in the Sangong River Basin of Xinjiang, China, to monitor and control soil salinity to improve the sustainable development of oases. Based on the climate, topography, vegetation, groundwater, and salinity of the soil survey data, many model methods, such as the Random Forest model, Support Vector Machine, Decision Tree, and Ordinary Kriging, were applied to estimate the inversion accuracy and the spatial distribution of soil salinity in the topsoil. The results revealed that the range of soil salinity values was 0.29-30.18 g·kg-1 and an average of 4.06 g·kg-1 for the sample sites. The value of the coefficients of variation was 149.73%, indicating a robust spatial variability. Among the four models, the Random Forest model showed a higher simulation precision compared to the others, with a coefficient of determination value of 0.73, a root-mean-square error value of 1.89 g·kg-1, and an absolute mean error value of 1.49 g·kg-1. The results of the Random Forest model inversion revealed that areas of higher soil salinity were concentrated in the northwest and the midbasin. Among the nine environmental covariates, elevation and groundwater salinity had a significant impact on the accuracy of identifying spatial distribution characteristics of soil salinity. In general, the Random Forest model as a machine learning method can not only avoid the smoothing effect and abrupt changes on both sides of the map boundary but also identify the local spatial distribution of soil salinity in the basin. The results of this study can provide technical and methodological applications for the long-term monitoring of soil salinization in arid areas.

Key words： machine learning; soil salinity; spatial distribution; Sangong River Basin; arid area

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