Quantitative Prediction of Soil Salinity Content with Visible-Near Infrared Hyper-Spectra in Northeast China
ZHANG Xiao-guang1,2, HUANG Biao1*, JI Jun-feng3, HU Wen-you1, SUN Wei-xia1, ZHAO Yong-cun1
1. Nanjing Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210093, China
Abstract:Studying the spectral property of salinized soil is an important work, for it is the base of monitoring soil salinization by remote sense. To investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Northeast China and then reflectance spectra were measured. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil salinity content and the reflectance of hyperspectra. The feasibility of soil salinity prediction by hyperspectra was decided by analyzed calibration model and independent validation. Models accuracy was also analyzed, which was established in the conditions of different treatment methods and different re-sampling intervals. The results showed that it was feasible to predict soil salinity content based on measured reflectance spectrum .The results also revealed that it was necessary to smooth measured hyperspectra for spectral prediction accuracy to be improved significantly after smoothing. The best model was established based on smoothed and log(1/x) transformed hyperspectra with high determination coefficients (R2 ) of 0.667 7 and RPD=1.61,which showed that this math transformation could eliminate noise effectively and so as to improve the prediction accuracy. The largest re-sampling interval is 8 nm that could meet the accuracy of the soil salinity prediction. Therefore, it provided scientific reference of monitoring soil salinization by remote sensing from satellite platform.
Key words:Visible-near infrared spectroscopy;Hyper-spectral data;Northeast district of China;Soda saline soils;Soil salinity content
[1] ZHANG Shu-wen, YANG Jiu-chun, LI Ying, et al(张树文, 杨久春, 李 颖, 等). Journal of Natural Resources(自然资源学报), 2010, 25(3): 435. [2] LIU Huan-jun, ZHANG Xin-le, ZHENG Shu-feng, et al(刘焕军, 张新乐, 郑树峰, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(12): 3355. [3] Chang C, David Laird. Soil Science, 2002, 167: 110. [4] Liu W, Baret F, Gu X, et al. International Journal of Remote Sensing, 2003, 24(10): 2069. [5] Wu Y Z, Chen J, Wu X M, et al. Applied Geochemistry, 2005, 20(6): 1051. [6] Weng Y L, Gong P, Zhu Z L. International Journal of Remote Sensing, 2008, 29(19): 5511. [7] Soil Fertilizer Station of Jilin(吉林省土壤肥料总站). Soil of Jilin(吉林省土壤). Beijing: China Agricultural Press(北京: 中国农业出版社), 1998. 19. [8] 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 and Technical Press(上海: 上海科学技术出版社), 1978. 81. [9] Chang C W, Laird D A, Mausbach M, et al. Soil Science Society of America Journal, 2001, 65(2): 480. [10] LI Bin, WANG Zhi-chun, CHI Chun-ming(李 彬, 王志春, 迟春明). Agricultural Research in the Arid Areas(干旱地区农业研究), 2006, 24(4): 168. [11] WANG Zun-qin(王遵亲). Salinized Soil of China(中国盐渍土). Beijing: Science Press(北京: 科学出版社), 1993. 10. [12] WANG Ji-hua, ZHAO Chun-jiang, HUANG Wen-jiang(王纪华,赵春江,黄文江). Quantitative Remote Sensing Technique and Application(农业定量遥感基础与应用). Beijing: Science Press(北京: 科学出版社), 2008. 32. [13] 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. [14] Shepherd K D, Walsh M G. Soil Science Society of America Journal, 2002, 66(3): 988. [15] Kemper T, Sommer S. Environment Science and Technology, 2002, 36(12): 2742. [16] Ben-Dor E, Inbar Y, Chen Y. Remote Sensing of Environment, 1997, 61: 1.