基于多模型对比的土壤盐分制图及不确定性研究

王瑾杰; 丁建丽; 张子鹏; 张喆

doi:10.13568/j.cnki.651094.651316.2022.07.13.0002

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基于多模型对比的土壤盐分制图及不确定性研究

研究精粹 | 更新时间：2026-01-21

- 基于多模型对比的土壤盐分制图及不确定性研究
- 基于多模型对比的土壤盐分制图及不确定性研究
- 新疆大学学报（自然科学版中英文） 2022年39卷第5期页码：513-521
- 作者机构：
  
  1. 新疆大学地理与遥感科学学院
  2. 新疆绿洲生态重点实验室
  3. 智慧城市与环境建模普通高校重点实验室
- 作者简介：
- 基金信息：
  
  国家自然科学基金联合重点项目（U2003202）;国家自然科学基金（41961059）;新疆维吾尔自治区自然科学基金重点项目（2021D01D06）;新疆维吾尔自治区科技厅重点实验室开放课题（2020D04038）;新疆维吾尔自治区教育厅高校科研计划（XJEDU2021Y009）
- DOI：10.13568/j.cnki.651094.651316.2022.07.13.0002
  中图分类号： S156.4
- 纸质出版：2022
- 稿件说明：
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[1]王瑾杰,丁建丽,张子鹏,等.基于多模型对比的土壤盐分制图及不确定性研究[J].新疆大学学报(自然科学版)(中英文),2022,39(05):513-521+529.
[1]王瑾杰,丁建丽,张子鹏,等.基于多模型对比的土壤盐分制图及不确定性研究[J].新疆大学学报(自然科学版)(中英文),2022,39(05):513-521+529. DOI： 10.13568/j.cnki.651094.651316.2022.07.13.0002.

DOI：10.13568/j.cnki.651094.651316.2022.07.13.0002.

摘要

基于野外采集的110个表层土壤盐分样本，采用四种机器学习算法(随机森林、极限学习机、多元自适应样条回归和人工神经网络)与46个环境协变量来绘制新疆艾比湖地区土壤盐分的空间分布图并预测了其不确定性．通过10折交叉验证对各模型的精度进行比较，研究结果表明：(1)使用随机森林(Random Forest

RF)模型能够更稳定更准确地预测土壤EC值，其R2均值达到了0.662，该性能优于人工神经网络(0.622)、极限学习机(0.637)和多元自适应样条回归模型(0.549).(2)Sentinel-2光谱数据是土壤EC预测最重要的变量，其次是盐分指数、气候、地形数据以及植被指数，相对重要性分别为44%、31%、20%和5%.(3)RF模型的结果揭示了区域土壤盐分空间分布的变化信息，模拟结果精度优于其余三个模型，确定RF模型是干旱区尾闾湖流域土壤盐分监测的有效方法．

Abstract

Based on 110 topsoil salinity samples collected in the field

this study used four machine learning algorithms(Random Forest

Extreme Learning Machine

Multivariate Adaptive Regression Splines and Artificial Neural Network) and 46 environmental covariates to map the spatial pattern of soil salinity(EC) and predicted its uncertainty in the Ebinur Lake area

Xinjiang

China. The precision of each model was compared through 10-fold cross validation. The results indicated that:(1) The Random Forest model could predict the soil EC value more stably and accurately

and its average R~2reached 0.662; The performance was better than that of Artificial Neural Network(0.622)

Extreme Learning Machine(0.637) and Multivariate Adaptive Regression Splines model(0.549).(2) The results revealed that Sentinel-2 spectral data was the main variable for soil EC prediction

followed by salinity index

climate

terrain data and vegetation index

with relative importance of 44%

31%

20% and 5%respectively.(3) In addition

the map based on the RF model reveals the most reasonable change information in the spatial distribution of EC

while the other three models have produced some errors on the salinization degree of the region. Therefore

this study determined that the RF model is an efective method for monitoring soil salinity in the terminal lake of the arid region.

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