Terrestrial Observation and Prediction System (TOPS): Developing ecological nowcasts and forecasts by integrating surface, satellite and climate data with simulation models

Shared by Petr Votava on Jun 05, 2010

Summary

Author(s) :
Ramakrishna Nemani, Petr Votava, Andrew Michaelis, Michael White, Forrest Melton, Cristina Milesi, Lars Pierce , Keith Golden, Hirofumi Hashimoto, Kazuhito Ichii, Lee Johnson, Matt Jolly, Ranga Myneni, Christina Tague, Joseph Coughlan, Steve Running
Abstract

Ecological Forecasting, predicting the effects of changes in the physical, chemical, and biological environments on ecosystem state and activity, is an emerging field with significant socio-economic implications. Though the concepts behind and expectations for ecological forecasting are clear, progress towards producing consistent, reliable, and objective forecasts has been slow. Lack of infrastructure for integrating a variety of modeling tools, information technologies, and ground and satellite data sets that could serve the diverse needs of eco-hydrological community has been one key impediment. Here, we describe our efforts at such an integrated system called the Terrestrial Observation and Prediction System (TOPS). TOPS is a data and modeling software system designed to seamlessly integrate data from satellite, aircraft, and ground sensors with weather/climate and application models to expeditiously produce operational nowcasts and forecasts of ecological conditions. TOPS has been operating at a variety of spatial scales, ranging from individual vineyard blocks in California, and predicting weekly irrigation requirements, to global scale producing regular monthly assessments of global vegetation net primary production.

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Publication Name
Research and Economic Applications of Remote Sensing Data Products, American Geophysical Union
Publication Location
N/A
Year Published
2005

Files

TOPS_AGU.pdf
Terrestrial Observation and Prediction System (TOPS): Developing ecological nowcasts and forecasts by integrating surface, satellite and climate data with simulation models
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