DOI: 10.1016/j.jag.2016.06.002
Scopus记录号: 2-s2.0-84997784995
论文题名: Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants
作者: Shi T ; , Wang J ; , Chen Y ; , Wu G
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 95
结束页码: 103
语种: 英语
英文关键词: Genetic algorithm
; Leaf chlorophyll
; Paddy soil
; Partial least squares regression
; Soil organic matter
; Visible and near-infrared reflectance spectroscopy
Scopus关键词: agricultural soil
; arsenic
; chlorophyll
; genetic algorithm
; leaf
; least squares method
; soil organic matter
; spectral reflectance
英文摘要: Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv 2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg−1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv 2 = 0.42, RMSEv = 13.35 mg kg−1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv 2 = 0.63, RMSEv = 11.94 mg kg−1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80045
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China; School of Resource and Environmental Science, Wuhan University, Wuhan, China
Recommended Citation:
Shi T,, Wang J,, Chen Y,et al. Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52