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Remote Sens. 2015, 7(12), 16398-16421; doi:10.3390/rs71215841

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

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.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 17 September 2015 / Accepted: 25 November 2015 / Published: 4 December 2015
View Full-Text   |   Download PDF [1248 KB, uploaded 7 December 2015]   |  

Abstract

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 dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods. View Full-Text
Keywords: remote sensing; soil moisture; biomass; retrieval algorithms; machine learning; artificial neural networks; SVM; regression; biophysical parameters remote sensing; soil moisture; biomass; retrieval algorithms; machine learning; artificial neural networks; SVM; regression; biophysical parameters
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ali, I.; Greifeneder, F.; Stamenkovic, J.; Neumann, M.; Notarnicola, C. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sens. 2015, 7, 16398-16421.

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