Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote …

Open AccessThis article is freely available re-usable Remote Sens. 2015, 7(12), 16398-16421; doi:10.3390/rs71215841 (registering DOI) Review 1 Department of Geography, University College Cork, Cork, Ireland 2 Spatial Analysis Unit, Teagasc, Dublin, Ireland 3 Institute for Applied Remote Sensing, EURAC Research, Bolzano, Italy 4 Signal Processing Laboratory, EPFL, Lausanne, Switzerland 5 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA * Author to whom correspondence should be addressed. Received: 17 September 2015 / Accepted: 25 November 2015 / Published: 4 December 2015 No The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high…


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