Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.7717/peerj.4703 |
Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China | |
Wang, Jingzhe; Ding, Jianli1; Abulimiti, Aerzuna; Cai, Lianghong | |
通讯作者 | Ding, Jianli |
来源期刊 | PEERJ |
ISSN | 2167-8359 |
出版年 | 2018 |
卷号 | 6 |
英文摘要 | Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS-NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0-2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R-2 (0.93), RMSE (4.57 dS m(-1)), and RPD (2.78 >= 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity. |
英文关键词 | Ebinur Lake RF VIS-NIR PLSR Soil salinity Machine learning Wetland |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000431380300004 |
WOS关键词 | RANDOM FOREST CLASSIFIER ; REFLECTANCE SPECTROSCOPY ; ORGANIC-MATTER ; METHODS PLSR ; PREDICTION ; SPECTRA ; INDICATORS ; REGRESSION ; NITROGEN ; CARBON |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
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/2XILL650/212026 |
作者单位 | 1.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi, Xinjiang, Peoples R China; 2.Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi, Xinjiang, Peoples R China; 3.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi, Xinjiang, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Jingzhe,Ding, Jianli,Abulimiti, Aerzuna,et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China[J]. 新疆大学,2018,6. |
APA | Wang, Jingzhe,Ding, Jianli,Abulimiti, Aerzuna,&Cai, Lianghong.(2018).Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China.PEERJ,6. |
MLA | Wang, Jingzhe,et al."Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China".PEERJ 6(2018). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。