干旱区生态环境知识资源中心(Arid): 基于野外VIS-NIR光谱的土壤盐分主要离子预测

Arid

	基于野外VIS-NIR光谱的土壤盐分主要离子预测
其他题名	Prediction of Major Ions in Soil Salinity Based on Field VIS-NIR Spectroscopy
	马利芳 1; 熊黑钢2 ; 张芳 1
来源期刊	土壤
ISSN	0253-9829
出版年	2020
卷号	52 期号:1 页码:188-194
中文摘要	为明确干旱区土壤盐分主要离子的特征光谱,建立精度高和稳定性好的盐渍土预测模型,以新疆阜康市为研究区域,采用网格法采集55个土壤样本,利用实测VIS-NIR光谱,选择多元线性回归(MLR)、支持向量机(SVM)、随机森林(RF)法构建土壤盐分主要离子含量反演模型,而后对反演精度进行检验。结果显示:①在0.01显著水平下,土壤盐分与Na~+、Cl~-、Ca~(2+)含量均呈显著相关,相关系数分别为0.978、0.814、0.645;②综合光谱响应和相关性分析确定土壤盐分主要离子的特征波段为459、537、1381、1386nm,显著特征波段为459、537nm;③3种模型拟合效果从高到低依次为RF>MLR>SVM,采用RF所建模型盐分主要离子(Na~+、Cl~-、Ca~(2+))R~2最高,RMSE最小,RPD最大,分别为2.11、2.03、1.80,为最优预测模型。通过选取土壤主要离子显著特征波段,进而采用RF法构建其估测模型,可以有效提取干旱区土壤盐分的主要离子信息。
英文摘要	In order to clarify the characteristic spectrum of main salt ions in arid areas,a prediction model for high-precision and stable saline soils was established.Taking Fukang City of Xinjiang as the study area,collected 55 soil samples and field measured spectral data based on VIS-NIR,using multiple linear regression(MLR),support vector machine(SVM) and random forest(RF) method three inversion model of soil salinity and main ion content were established,and the model was tested.The results showed that:1) At 0.01 significant level,soil salinity had a significant correlation with Na~+,Cl~- and Ca~(2+),and the correlation coefficients were 0.978,0.814 and 0.645,respectively;2) Comprehensive spectrum response and correlation analysis determined the dominant ion bands of soil salt at 459,537,1 381,and 1 386 nm,and the significant characteristic bands at 459 and 537 nm;3) The three model fitting effects from high to low were RF>MLR>SVM in order,and using the model established by RF,the salt main ions (Na~+,Cl~-,Ca~(2+)) had the highest R~2,the smallest RMSE,and the largest RPD,which were 2.11,2.03,and 1.80,respectively,and were the optimal prediction models.By selecting the dominant characteristic bands of major ions in the soil,RF method was used to construct the estimation model in this area,which can effectively extract the main ion information of soil salinity in the arid area.
中文关键词	土壤 ; 盐分 ; 高光谱 ; 反演 ; 支持向量机 ; 随机森林
英文关键词	Soil Salt Hyperspectral Inversion Support vector machine Random forest
语种	中文
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6673788
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318437
作者单位	1.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, 乌鲁木齐, 新疆 830046, 中国; 2.北京联合大学应用文理学院, 北京 100083, 中国
推荐引用方式 GB/T 7714	马利芳,熊黑钢,张芳. 基于野外VIS-NIR光谱的土壤盐分主要离子预测[J]. 新疆大学,2020,52(1):188-194.
APA	马利芳,熊黑钢,&张芳.(2020).基于野外VIS-NIR光谱的土壤盐分主要离子预测.土壤,52(1),188-194.
MLA	马利芳,et al."基于野外VIS-NIR光谱的土壤盐分主要离子预测".土壤 52.1(2020):188-194.