Algorithms and analysis tools for carbon content modeling in soil based on satellite data

摘要

Estimate of the organic carbon content in soil is critical for global change modeling activities. Therefore, the predictive model for estimating soil carbon would provide an important tool for the scientific community. We used remotely sensed TM imaginery data together with the soil profiles and moss layer carbon data for the Northern Study Area (NSA) of the BOREAS project. Different classification and functional models of the carbon dependency on remotely sensed data were developed. The complexityof the models was scrutinized. Based on these techniques, we have developed a set of analysis tools. These tools and an Internet based access to some of these tools will be presented.