Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression

In this paper, we propose to estimate soil moisture in bare soils directly from hyperspectral imagery using support vector regression (nu-SVR). nu-SVR is a supervised non-parametric learning technique, e.g. making no assumption on the underlying data distribution, which shows good generalization properties even when only a limited number of training samples is available (which is often the case in soil moisture estimation). Estimation in six tilled bare soil fields shows the potential of using non-linear nu-SVR for the prediction of gravimetric soil moisture. Dependence to the origin of training samples, as well as their number, is thoroughly considered.

Presented at:
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, Florida, USA, June 25-28, 2013

 Record created 2013-03-26, last modified 2018-03-17

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