12 March 2015 Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data
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Abstract
We aimed at analyzing the potential of two ensemble tree machine learning methods—boosted regression trees and random forests—for (early) prediction of winter wheat yield from short time series of remotely sensed vegetation indices at low spatial resolution and of
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Stien Heremans, Stien Heremans, Qinghan Dong, Qinghan Dong, Beier Zhang, Beier Zhang, Lieven Bydekerke, Lieven Bydekerke, Jos Van Orshoven, Jos Van Orshoven, } "Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data," Journal of Applied Remote Sensing 9(1), 097095 (12 March 2015). https://doi.org/10.1117/1.JRS.9.097095 . Submission:
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