干旱区生态环境知识资源中心

This study aimed at testing the hypothesis that augmenting Landsat TM-based models for predicting soil organic carbon (SOC) with auxiliary data about variables that might affect the spatial distribution of SOC might improve the predictability of these models in the Zarqa Basin in Jordan (a typical semi-arid watershed) and enable them to be used for implementing carbon crediting programs in semi-arid environments. Six modeling procedures, namely stepwise regression, partial least squares, recursive partitioning analysis, screening regression analysis, artificial neural networks, and combined models, were calibrated and validated for the basin and for the land cover types that exist in the basin. Although none of the developed models was powerful for predicting SOC, artificial neural networks models were more applicable specifically in agricultural lands. However, the margins of error associated with the best models were high, and hence hindered the applicability of these models in carbon crediting programs in semi-arid environments.