Pedosphere 29(6): 810--818, 2019
ISSN 1002-0160/CN 32-1315/P
©2019 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Spectroscopy-Based Soil Organic Matter Estimation in Brown Forest Soil Areas of the Shandong Peninsula, China |
GAO Lulu1, ZHU Xicun1,2 , HAN Zhaoying1, WANG Ling1, ZHAO Gengxing1, JIANG Yuanmao3 |
1College of Resource and Environment, Shandong Agricultural University, Tai'an 271018(China)
2National Engineering Laboratory for Efficient Utilization of Soil Resources, Tai'an 271018(China)
3College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an 271018(China) |
| ABSTRACT |
| Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer (Analytical Spectral Devices Inc., Boulder, USA). Raw spectral reflectance data were transformed using four methods:nine points weighted moving average (NWMA), NWMA with first derivative (NWMA + FD), NWMA with standard normal variate (NWMA + SNV), and NWMA with min-max standardization (NWMA + MS). These data were analyzed and correlated with SOM content. The evaluation model was established using support vector machine regression (SVM) with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991, 1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant (R2=0.875, root mean square error (RMSE)=0.107 g kg-1 for calibration set; R2=0.853, RMSE=0.097 g kg-1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients. |
| Key Words: brown forest soil,hyperspectral remote sensing,nine points weighted moving average,standard normal variate,sensitive wavelength,spectral reflectance,support vector machine regression |
| Citation: Gao L L, Zhu X C, Han Z Y, Wang L, Zhao G X, Jiang Y M. 2019. Spectroscopy-based soil organic matter estimation in brown forest soil areas of the Shandong Peninsula, China. Pedosphere. 29(6):810-818. |
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