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[1]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.

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Inversion of Black Soil Organic Matter Content with Field Hyperspectral Reflectance Based on Continuous Wavelet Transformation

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 25 Number of periods: 2018 02 Page number: 46-52,57 Column: Public date: 2018-04-03

Title:: Inversion of Black Soil Organic Matter Content with Field Hyperspectral Reflectance Based on Continuous Wavelet Transformation

Author(s):: LIN Pengda^1,2, TONG Zhijun^1,2, ZHANG Jiquan^1,2, ZHAO Yunsheng³, LI Xiangqian^1,2, ZHU Xiaomeng^1,2; 1. School of Environment, Northeast Normal University, Changchun 130117, China;
2. Institute of Natural Disaster Research, Northeast Normal University, Changchun 130117, China;
3. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

Keywords:: black soil organic matter content; field hyperspectral; continuous wavelet transformation; multiple stepwise regression; partial least square regression

CLC:: TP79;S127;S153

DOI:: -

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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