Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

Remote Sensing

Yaolin Liu²  Qinghu Jiang²  Teng Fei²  Junjie Wang²  Tiezhu Shi²  Kai Guo²  Xiran Li¹ 

[1] College of Urban and Environmental Sciences, Peking University, No.5 Yiheyuan Road, Beijing 100871, China; E-Mail:;School of Resource and Environmental Science, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China; E-Mails:

关键词: VNIR spectroscopy;    soil organic matter;    model transferability;    riparian landscape;   

DOI  :  10.3390/rs6054305

来源: mdpi

【 摘 要 】

The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r²_Pre = 0.66, RMSE = 2.78 g·kg⁻¹, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland