Title
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
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.
Year
DOI
Venue
2015
10.3390/rs71215841
REMOTE SENSING
Keywords
Field
DocType
remote sensing,soil moisture,biomass,retrieval algorithms,machine learning,artificial neural networks,SVM,regression,biophysical parameters
Data mining,Vegetation biomass,Remote sensing,Artificial intelligence,Water content,Artificial neural network,Retrieval algorithm,Biomass,Satellite,Support vector machine,Curse of dimensionality,Geology,Machine learning
Journal
Volume
Issue
Citations 
7
12
15
PageRank 
References 
Authors
0.73
40
5
Name
Order
Citations
PageRank
Iftikhar Ali1283.01
f greifeneder2245.69
jelena stamenkovic3182.21
Maxim Neumann416915.34
Claudia Notarnicola516640.15