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、1386 nm,显著特征波段为459、537 nm;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+)R2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。
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 459,537,1381,and 1386 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 R2,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.
作者
马利芳
熊黑钢
张芳
MA Lifang;XIONG Heigang;ZHANG Fang(Key Laboratory of Oasis Ecological Education,College of Resources and Environment Science,Xinjiang University,Urumqi 830046,China;College of Applied Arts and Sciences,Beijing Union University,Beijing 100083,China)
出处
《土壤》
CAS
CSCD
北大核心
2020年第1期188-194,共7页
Soils
基金
国家自然科学基金项目(41671198,41761041)资助。
关键词
土壤
盐分
高光谱
反演
支持向量机
随机森林
Soil
Salt
Hyperspectral
Inversion
Support vector machine
Random forest
作者简介
马利芳(1993-),女,安徽亳州人,硕士研究生,主要研究方向为干旱区资源与环境遥感。E-mail:1491983080@qq.com;通讯作者:熊黑钢,heigang@buu.edu.cn。
