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

Remote Sensing

Iftikhar Ali⁴  Felix Greifeneder²  Jelena Stamenkovic¹  Maxim Neumann⁵  Claudia Notarnicola²  Nicolas Baghdadi³ 

[1] Signal Processing Laboratory, EPFL, Lausanne, Switzerland;Institute for Applied Remote Sensing, EURAC Research, Bolzano, Italy;;id="af1-remotesensing-07-15841">1Department of Geography, University College Cork, Cork, Irela;Department of Geography, University College Cork, Cork, IrelandJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA;

关键词: remote sensing;    soil moisture;    biomass;    retrieval algorithms;    machine learning;    artificial neural networks;    SVM;    regression;    biophysical parameters;   

DOI  :  10.3390/rs71215841

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.