LIN Pengda,TONG Zhijun,ZHANG Jiquan,et al.Inversion of Black Soil Organic Matter Content with Field Hyperspectral Reflectance Based on Continuous Wavelet Transformation[J].Research of Soil and Water Conservation,2018,25(02):46-52,57.
基于CWT的黑土有机质含量野外高光谱反演模型
- Title:
- Inversion of Black Soil Organic Matter Content with Field Hyperspectral Reflectance Based on Continuous Wavelet Transformation
- Keywords:
- black soil organic matter content; field hyperspectral; continuous wavelet transformation; multiple stepwise regression; partial least square regression
- 分类号:
- TP79;S127;S153
- 摘要:
- 为解决黑土有机质含量野外高光谱反演存在的困难，提高反演模型精准性，对吉林省黑土进行了采样、野外光谱测量和室内化验分析，剔除噪声波段，将测得的光谱数据（R）进行倒数的对数[lg（1/R）]及一阶微分（R’）变换，经连续小波变换（CWT）得到了R，lg（1/R）、R’不同分解尺度下对应的小波系数；通过分析R，lg（1/R）、R’及对应小波系数与土壤有机质含量的相关性，得到了显著性强相关的敏感波段；采用多元逐步回归（MSR）及偏最小二乘回归（PLSR）方法建立了黑土土壤有机质含量多种高光谱反演模型。结果表明：常用光谱分析方法中，R’与土壤有机质含量的R2高于R，lg（1/R）；经CWT变换后得到的小波系数与土壤有机质含量的R2则提高显著，提高了0.3左右；R-CWT，lg（1/R）-CWT，R’-CWT的两种模型建模集的效果均较显著（R2 ≥ 0.75，RMSE ≤ 0.25），且均优于对应的常规变换下的效果；建模集中PLSR比MSR模型更佳；验证集中结果相似，但精度稍低；总体上，经过CWT处理后的模型均具有较高的估测精度，以R’-CWT的PLSR模型最佳，能够较为全面稳定地反演土壤有机质含量。
- Abstract:
- In order to solve the difficulty of inversion of organic matter content using the field hyperspectral data and improve the accuracy of inversion model of soil organic matter content, sample black soils and the detected field hyperspectral data in Jilin Province, and the soil organic matter contents of each samples were analyzed. Prior to performing the statistical analyses, the bands with large noise were eliminated, the raw soil spectral reflectance (R) was transformed to logarithm of the reciprocal of reflectance[lg(1/R)] and the first derivative of reflectance (R’), which were commonly used in soil spectroscopy to reduce possible spectra nonlinearities and enhance the spectral features. In addition, continuous wavelet transformation was applied to R, lg(1/R) and R’ to generate a wavelet power scalogram at different scales. The correlation of R, lg(1/R), R’, the wavelet coefficients of R-CWT, lg(1/R) -CWT, R’-CWT to the soil organic matter contents were analyzed respectively, the significant correlation band was obtained as the sensitive band. Then, after extracting reflectance from R, lg(1/R), R’ and wavelet coefficients from R-CWT, lg(1/R) CWT, R’-CWT, a variety of inversion models of black soil organic matter content were established by the multiple stepwise regression (MSR) and partial least squares regression methods(PLSR), respectively. The results showed that the R2 of R’ and soil organic matter content was higher than the R2 of R, lg(1/R) and soil organic matter content. The R2 of CWT wavelet coefficients and soil organic matter content increased by about 0.3; the results of the MSR models and the PLSR models were established by using R-CWT,lg(1/R) -CWT,R’-CWT were all significant in the calibration sets (R2 ≥ 0.75, RMSE ≤ 0.25), and were better than using R, lg(1/R) and R’. The PLSR models were better than the MSR models in the calibration sets and the validation sets, but the R2 of the validation sets was slightly lower. In general, the inversion models using CWT were more accurate, R’-CWT-PLSR model was the best, which could estimate black soil organic matter content comprehensively and stably.
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备注/Memo
收稿日期:2017-5-16;改回日期:2017-6-1。
基金项目:国家自然科学基金（41671347）；水利部公益性行业科研专项经费项目（201401015）；国家自然科学基金（41571491）
作者简介:林鹏达(1992-),女,四川成都人,硕士研究生,研究方向为黑土地生态安全评价。E-mail:linpd316@nenu.edu.cn
通讯作者:佟志军(1977-),男,辽宁葫芦岛人,博士,副教授,主要从事遥感及生态安全评价与管理研究。E-mail:gis@nenu.edu.cn