Major projects

Implementation of SEBS on Google Earth Engine

Project location Global
Starting date 03 September 2010
Completion date 31 March 2011
Client Google Ireland Limited
Project leader Bob Su
Project officer Bert Boer
Project type Contract research
Budget Euro 73,815

Mapping the global evapotranspiration, based on observational data, can be accomplished only by using remote sensing data. Several algorithms have been developed to estimate the evapotranspiration from remote sensing data. However, most of these algorithms either need to be calibrated locally, or are too complex and require too many input parameters that it becomes infeasible to be applied globally. The Surface Energy Balance System (SEBS) algorithm addresses some of these issues by parameterizing the underlying physical processes. Therefore, SEBS provides the best current compromise between the model complexity and the input requirements. Despite the low requirements of the input parameters in SEBS, the remote sensing data needed for one year of daily evapotranspiration estimations with 1km2 resolution over the entire earth reaches about 160 Terabyte. In addition, the computation of the daily evapotranspiration over the entire globe, i.e. 550.000.000 km2, results in a tremendous computational demand.

Google Earth Engine, a sponsored project, is an advance in earth observation/informatics computing platforms for public benefits and to support the emerging green economy. Google Earth Engine will store and organize petabytes of raw satellite imagery data and make it available (for the first time) for global-scale data mining. Imagery data will include historical earth imagery going back for more than 30 years, and future imagery as it is collected. Earth Engine will also provide an application framework for developing and running domain-specific image-processing algorithms over this and other related earth data.

The Earth Engine platform is a new, inherently parallel system that aims to offer users a vastly reduced amount of code for image processing, massive parallelization, immediate visualization, automatic data management, and access to vast data stores, among others. is exploring the implementation of water and terrain related algorithms, specifically algorithms that use the energy balance to assess the water consumed (evapotranspirated) in the landscape, specifically, SEBS (Surface Energy Balance System).

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