Title :
Soil moisture retrieval with S-band SAR data
Author :
Raffaella Guida;Vasillis Fotias
Author_Institution :
Surrey Space Centre - University of Surrey Guildford, UK
fDate :
7/1/2015 12:00:00 AM
Abstract :
In June 2014 an airborne Synthetic Aperture Radar (SAR) flight campaign was run in the south UK in order to acquire S-band datasets on several scenarios and for different applications in preparation for the launch of the first UK SAR mission NovaSAR-S. On request, X-band data could be acquired concurrently. This paper shows some of the results of the research project AS14-12 aimed at assessing S-band performance in retrieving soil moisture in bare or poorly vegetated areas. Comparison with X-band dataset was also among the objectives and, at this purpose, the Integral Equation Model (IEM) Inversion through an Artificial Neural Network (ANN) was considered a suitable technique at both frequencies and applied. Retrieval results at both frequencies are here presented against ground truth collected on the site concurrently with the airborne datasets. They show that, for the application considered, the retrieval from S-band data is more accurate than that from X-band with an average error smaller than 4%.
Keywords :
"Soil moisture","Synthetic aperture radar","Soil measurements","Artificial neural networks","Moisture measurement","Satellites"
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
Electronic_ISBN :
2153-7003
DOI :
10.1109/IGARSS.2015.7326014