21 Remote Sensing Data, Modeling Frameworks and Applications

Thursday, 10 June 2010
8:00 AM - 12:00 PM
Centennial

DEVELOPING ESSENTIAL CLIMATE VARIABLES FOR TERRESTRIAL MODELING AND MONITORING

Paper number: 21-8
Program: Special Sessions
Time: 10:35 AM – 10:55 AM

Authors

Abstract

The growing record of satellite observations and in situ data collected from ground-based networks, coupled with advances in computational resources and data assimilation models, afford the opportunity to provide more meaningful and usable datasets at regional and continental scales. Numerous agencies from the U.S. and the international community, in collaboration with university researchers, have defined a strategy for developing tiers of higher level data products derived from satellite remotely sensed data that address the needs for terrestrial monitoring and predictive modeling scenarios. Fundamental climate data records are geophysical parameters (e.g. surface reflectance, temperature) that can be derived from well-calibrated remotely sensed data and further processed to generate essential climate variables (e.g. surface albedo, surface water extent, snow and ice, land cover, fire disturbance). The USGS Geography Discipline is currently developing a science strategy and implementation plan by which to develop a suite of essential climate variables that address the requirements of the Department of the Interior land managers as well as the Nation’s international interests.

Remotely sensed data collected by satellite and aircraft platforms are used in a broad range of research investigations and land management applications to monitor changes to the state and condition of the landscape. Remotely sensed data complement in situ measurements collected from numerous existing and planned ground networks by providing a framework within which to integrate measurements and observations collected at varying spatial and temporal scales. The increasing amount and types of data being collected drive requirements to process and reduce these data into geophysical and biophysical parameters that can be assimilated into numerical models, data visualizations, and decision support systems that are being used by scientists and land managers perform quantitative assessments of the response of terrestrial systems to climate variability and human activity.