Unraveling the Dominant Influences on the Evolution of Land-Surface Variables using Data Mining

Introduction: The objective of our research project is to develop data mining and knowledge discovery in databases (KDD) techniques, using the “Data to Knowledge” (D2K) platform developed by National Center for Supercomputing Application (NCSA), to facilitate analysis, visualization and modeling of land-surface variables obtained from the TERRA and AQUA platforms in support of climate and weather applications. The project targets to address the science question: “How is the global Earth system changing?” In particular it focuses on the theme: What factors influence/modulate the changes in global ecosystem? The specific science questions that this project is focused on are: 1) How are evolving surface variables such as vegetation indices, temperature, and emissivity, as obtained from the TERRA and AQUA platforms, dynamically linked? 2) How do they evolve in response to climate variability such as ENSO (El Niño Southern Oscillation)? and 3) How are they dependent on temporally invariant factors such as topography (and derived variables such as slope, aspect, nearness to streams), soil characteristics, land cover classification, etc? Answers to these questions, at the continental to global scales will enable us to develop better parameterization of the relevant processes in forecast models for weather, and inter-seasonal to inter-annual climate prediction. However, answering these questions at the continental to global scale requires the ability to perform analysis of a multitude of variables using very large datasets. Our data mining system is building this capability for Earth science datasets being collected by NASA.