Ms. Lu Wang Profile

Lu Wang

at East China Normal Univ

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Proceedings Article | 30 October 2009 Paper

Possibilities of reflectance spectra data for the assessment of soil potassium concentration

Lu Wang, Yunxuan Zhou, Qizhong Lin, Yongming Xu

Proceedings Volume 7498, 749858 (2009) https://doi.org/10.1117/12.832738

KEYWORDS: Potassium, Reflectivity, Soil science, Absorption, Chemical analysis, Chemical elements, Remote sensing, Statistical analysis, Data modeling, Calibration

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This paper presents the possibilities of extracting total potassium concentration in topsoil from Visible-near-infrared (VNIR) spectra and reflectance of image data. Stepwise multiple linear regression (SMLR) and partial least-square regression (PLSR) were used to select wavelengths which were highly correlated with the concentration of potassium. For spectral measurements (from 400nm to 2480nm, at 2 nm increments) and chemical analyses, 70 topsoil (0~20 cm) samples were collected in Tianjin City, North of China. Three methodologies of the reflectance spectra of topsoil samples were employed: derivative reflectance spectra (FDR), inverse-log spectra (log (1/R)) and band depth (Depth). According to the root mean square error of prediction (RMSEP), the best model was picked up. The optimal experiential model (R=0.73, RMSEP=1.33) was achieved by PLSR method with parameter- log (1/R). Based on these credible results, space distribution map of soil potassium concentration of Tianjin was drawn by ETM+ image. The coefficient showed that the first and second bands of ETM were important for soil potassium concentration prediction. The potassium concentration of seaboard is higher than that of inland area. Good prediction performance indicates that VNIR spectra are potentially useful for rapid estimation of potassium concentration in topsoil, and inverse-log spectra (log (1/R)) are the best parameter for prediction. Even the image data can be used for soil potassium concentration extraction and the influences of the atmosphere and proper pre-processing are very important to prediction precision.

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