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With the advances in remote sensing, various machine learning techniques could be applied to study variable relationships. Although prediction models obtained using machine learning techniques has proven to be suitable for predictions, they do not explicitly provide means for determining input-output variable relevance. We investigated the issue of(More)
A continental scale dataset was assembled to examine the drivers of greenness indices. Easily parallelized algorithms for ordinary least squares linear regression and a binary regression tree were implemented and used for the analysis. The most important drivers were found to be long and shortwave radiation, precipitation, elevation, and soil pH. This(More)
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