A Land Information Sensor Web (LISW) Study in Support of Land Surface Studies

Land Surface Model (LSM) predictions are regular in time and space, but these predictions are influenced by errors in model structure, input variables, parameters and inadequate treatment of sub-grid scale spatial variability. Consequently, LSM predictions are significantly improved through observation constraints made in a data assimilation framework. Several multi-sensor satellites are currently operating which provide multiple global observations of the land surface, and its related near-atmospheric properties. However, these observations are not optimal for addressing current and future land surface environmental problems. To meet future earth system science challenges, NASA will likely develop constellations of smart satellites in sensor web configurations that provide timely on-demand data and analysis to users, and that can be reconfigured based on the changing needs of science and available technology. The prototype Land Information Sensor Web (LISW) project is aimed at integrating the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface studies using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. These synthetic experiments provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various design sensor web design trade-offs and the eventual value of sensor webs for particular prediction or decision support. In this paper, the progress of the LISW study will be presented, especially in scenario experiment design, sensor web framework and uncertainties in current land surface modeling.