Prediction of As in Soil with Reflectance Spectroscopy
ZHENG Guang-hui1,2, ZHOU Sheng-lu1*, WU Shao-hua1
1. School of Geographic and Oceanic Sciences, Nanjing University, Nanjing 210093, China 2. School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
Abstract：In the present study, visible-near infrared reflectance spectroscopy (VNIR) measured in laboratory was evaluated for prediction of the content of As in soils. Calibrations between As and reflectance were developed using cross-validation under partial least squares regression (PLSR). Prediction accuracy was tested via separate validation samples. The reflectance was pre-processed by several techniques like smoothing, multiplicative scatter correction (MSC), Log(1/R), first/second derivative (F/SD) and continuum removal (CR). The accuracy of prediction was evaluated with three statistics: coefficients of determination (R2), ratio of performance to deviation (RPD), and root mean square error of prediction (RMSEP). The results of calibration, cross-validation and prediction of different pre-processing techniques, spectral resolution and OM content were compared. MSC provided better prediction(Prediction R2=0.711, RPD=1.827, RMSEP=1.613) than other methods because it removed the effects of light scattering and sample thickness. All the results of different resolution are acceptable (Prediction 0.678＜R2＜0.711, 1.750＜RPD＜1.827, 1.613＜RMSEP＜1.685). The prediction accuracy of subsets with lower OM content(Prediction R2=0.694, RPD=1.697, RMSEP=1.644) was better than that with higher content. The study indicates that it is feasible to predict As element in soils using reflectance spectroscopy and the prediction accuracy can be improved by pre-processing. Thus this new rapid and cost-effective technique can be used in the monitoring of soil contamination.
[1] ZHAO Qi-guo(赵其国). Soils(土壤), 2007, 39(1): 19. [2] Ben-Dor E, Inbar Y, Chen Y. Remote Sensing of Environment, 1997, 61: 1. [3] Kemper T, Sommer S. Environmental Science & Technology, 2002, 36(12): 2742. [4] Deaton B C, Balsam W L . Journal of Sedimentary Research, 1991, 61(4): 628. [5] Ben-Dor E, Heller D, Chudnovsky A . Soil Science Society of America Journal, 2008, 72(4): 1113. [6] Institute of Soil Science, Chinese Academy of Sciences(中国科学院南京土壤研究所编). Physical and Chemical Analysis of Soil(土壤理化分析). Shanghai: Shanghai Scientific & Technical Press(上海: 上海科学技术出版社), 1978. 81. [7] ZHU Jian-guo, CHAI Zi-yuan, MAO Zhen-cai(祝建国, 柴自渊, 毛振才). Analysis and Testing Technology and Instruments(分析测试技术与仪器), 2002, 8(2): 103. [8] Shepherd K D, Walsh M G. Soil Science Society of America Journal, 2002, 66(3): 988. [9] WANG Hui-wen(王惠文). Partial Least Square Regression Method and Its Application(偏最小二乘回归方法及其应用). Beijing: National Defense Industry Press(北京: 国防工业出版社), 2006. 119. [10] Esbensen K H. Multivariate Data Analysis in Practice. Norway: CAMO Process, 2002. 158. [11] Yunzhao W, Jun C, Xinmin W, et al. Applied Geochemistry, 2005, 20(6): 1051. [12] Fearn T. NIR News, 2002, 13(6): 12. [13] Chang C W, Laird D A, Mausbach M J, et al. Soil Science Society of America Journal, 2001, 65(2): 480. [14] PU Rui-liang, GONG Peng(浦瑞良, 宫 鹏). Hyperspectral Remote Sensing and Its Application(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2000. 53. [15] Kooistra L, Wehrens R, Buydens L M C, et al . International Journal of Applied Earth Observation and Geoinformation, 2001, 3(4): 337. [16] LI Ju-bao, TIAN Qing-jiu, WU Yun-zhao(李巨宝, 田庆久, 吴昀昭). Information of Remote Sensing(遥感信息), 2005,(3): 10.
