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

您当前的位置：

首页 >

文章列表页 >

基于多模型对比的土壤盐分制图及不确定性研究

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
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 39, Issue 5, Pages: 513-521(2022)
- 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：
- DOI：10.13568/j.cnki.651094.651316.2022.07.13.0002
  CLC： S156.4
- Published：2022
- 稿件说明：
移动端阅览
[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.

关键词

Keywords

references

李建国,濮励杰,朱明,等.土壤盐渍化研究现状及未来研究热点[J].地理学报,2012,67(9):1233-1245.

张振华,丁建丽,王敬哲,等.集成土壤-环境关系与机器学习的干旱区土壤属性数字制图[J].中国农业科学,2020,53(3):563-573.

ZHANG Z P,DING J L,ZHU C M,et al.Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy:optimal band combination algorithm and spectral degradation[J].Geoderma,2021,382:114729.

MCBRATNEY A B,SANTOS M L M,MINASNY B.On digital soil mapping[J].Geoderma,2003,117(1/2):3-52.

JENNY H.Factors of soil formation:a system of quantitative pedology[M].New York:Dover Publications Inc,1994.

VAUDOUR E,GOMEZ C,FOUAD Y,et al.Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems[J].Remote Sensing of Environment,2019,223:21-33.

PENG J,BISWAS A,JIANG Q S,et al.Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province,China[J].Geoderma,2019,337:1309-1319.

YU H,LIU M Y,DU B J,et al.Mapping soil salinity/sodicity by using Landsat OLI imagery and PLSR algorithm over semiarid West Jilin Province,China[J].Sensors,2018,18(4):1048.

徐欢欢,曾从盛,王维奇,等.艾比湖湿地土壤有机碳垂直分布特征及其影响因子分析[J].福建师范大学学报(自然科学版),2010,26(5):86-91.

ZHANG Z P,DING J L,WANG J Z,et al.Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices[J].Catena,2020,185:104257.

ZHANG Z P,DING J L,ZHU C M,et al.Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2020,240(15):118553.

BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.

WANG S,JIN X X,ADHIKARI K,et al.Mapping total soil nitrogen from a site in northeastern China[J].Catena,2018,166:134-146.

PETERS J,DE BAETS B,VERHOEST N E C,et al.Random forests as a tool for ecohydrological distribution modelling[J].Ecological Modelling,2007,207(2/3/4):304-318.

朱大奇.人工神经网络研究现状及其展望[J].江南大学学报(自然科学版),2004,3(1):103-110.

RICHTER K,HANK T B,VUOLO F,et al.Optimal exploitation of the Sentinel-2 spectral capabilities for crop leaf area index mapping[J].Remote Sensing,2012,4(3):561-582.

HUANG G B,ZHOU H M,DING X J,et al.Extreme learning machine for regression and multiclass classification[J].IEEETransactions on Systems,Man and Cybernetics,Part B(Cybernetics),2011,42(2):513-529.

FRIEDMAN J H.Multivariate adaptive regression splines[J].The Annals of Statistics,1991,19(1):1-67.

NAWAR S,BUDDENBAUM H,HILL J,et al.Modeling and mapping of soil salinity with reflectance spectroscopy and landsat data using two quantitative methods(PLSR and MARS)[J].Remote Sensing,2014,6(11):10813-10834.

KHOSRAVI V,ARDEJANI F D,YOUSEFI S,et al.Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods[J].Geoderma,2018,318:29-41.

DENG X F,CHEN X J,MA W Z,et al.Baseline map of organic carbon stock in farmland topsoil in East China[J].Agriculture,Ecosystems&Environment,2018,254:213-223.

CHEN S C,LIANG Z Z,WEBSTER R,et al.A high-resolution map of soil p H in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution[J].Science of the Total Environment,2019,655:273-283.

WANG S,ZHUANG Q L,WANG Q B,et al.Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China[J].Geoderma,2017,305:250-263.

ZHANG Z P,DING J L,ZHU C M,et al.Bivariate empirical mode decomposition of the spatial variation in the soil organic matter content:a case study from NW China[J].Catena,2021,206:105572.

TAGHADOSI M M,HASANLOU M,EFTEKHARI K.Retrieval of soil salinity from Sentinel-2 multispectral imagery[J].European Journal of Remote Sensing,2019,52(1):138-154.

丁建丽,王飞.干旱区大尺度土壤盐度信息环境建模:以新疆天山南北中低海拔冲积平原为例[J].地理学报,2017,72(1):64-78.

丁建丽,姚远,王飞.基于三维光谱特征空间的干旱区土壤盐渍化遥感定量研究[J].土壤学报,2013,50(5):853-861.

贺青山,刘志辉,魏召才.基于水热平衡的融雪过程研究[J].新疆大学学报(自然科学版),2012,29(2):132-136.

吕昕,朱瑞君.新疆水资源与新疆经济的可持续发展[J].新疆大学学报(自然科学版),2000,17(1):87-90.

Views

396

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于定量遥感的绿洲土壤盐渍化研究(英文)

Related Author

丁建丽

Related Institution

新疆大学资源与环境科学学院绿洲生态教育部重点实验室

⁰