Article
Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra
International Journal of Remote Sensing
(2011)
Abstract
This study evaluated the effectiveness of using Hyperion hyperspectral data in improving existing remote-sensing methodologies for estimating soil organic carbon (SOC) content on farmland. The study area is Big Creek Watershed in Southern Illinois, USA. Several data-mining techniques were tested to calibrate and validate models that could be used for predicting SOC content using Hyperion bands as predictors. A combined model of stepwise regression followed by a five hidden nodes artificial neural network was selected as the best model, with a calibration coefficient of determination (R 2) of 78.9% and a root mean square error (RMSE) of 3.3 tonnes per hectare (t ha−1). The validation RMSE, however, was found to be 11.3 t ha−1. Map algebra was implemented to extrapolate this model and produce a SOC map for the watershed. Hyperspectral data improved marginally the predictability of SOC compared to multispectral data under natural field conditions.
Disciplines
Publication Date
2011
DOI
https://doi.org/10.1080/01431161.2010.494637
Citation Information
Christopher L. Lant. "Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra" International Journal of Remote Sensing Vol. 32 Iss. 18 (2011) p. 5077 - 5103 Available at: http://works.bepress.com/christopher_lant/182/