NPG Nonlinear Processes in Geophysics NPG Nonlin. Processes Geophys. 1607-7946 Copernicus Publications Göttingen, Germany 10.5194/npg-15-115-2008 Estimation of soil types by non linear analysis of remote sensing data Hahn C. 1 Gloaguen R. 1 Remote Sensing Group, Geology Institute, TU Bergakademie Freiberg, 09599 Freiberg, Germany 15 02 2008 15 1 115 126 Copyright: © 2008 C. Hahn 2008 This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-sa/2.5/ This article is available from https://npg.copernicus.org/articles/15/115/2008/npg-15-115-2008.html The full text article is available as a PDF file from https://npg.copernicus.org/articles/15/115/2008/npg-15-115-2008.pdf

The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation.

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