Premium
Potentials of polarimetric SAR interferometry for agriculture monitoring
Author(s) -
LopezSanchez Juan M.,
BallesterBerman J. David
Publication year - 2009
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2008rs004078
Subject(s) - decorrelation , polarimetry , remote sensing , computer science , synthetic aperture radar , interferometry , baseline (sea) , mode (computer interface) , algorithm , geology , oceanography , physics , astronomy , scattering , optics , operating system
This paper is aimed to define the main specifications and system requirements of a future spaceborne synthetic aperture radar (SAR) mission with polarimetric and interferometric capabilities, to be applied in agriculture monitoring. Firstly, a previous discussion concerning the applications of remote sensing to agriculture and the requirements demanded by end users is introduced. Then, a review of polarimetric SAR and interferometric SAR techniques employed in agriculture is performed in order to explore and justify the potential contributions to crop parameter retrieval of polarimetric SAR interferometry (PolInSAR). The current status of the research about PolInSAR when applied to the retrieval of biophysical parameters of agricultural crops is also addressed, covering recent advances in theoretical modeling aspects (both direct and inverse models), the validation carried out so far with indoor data, and complementary information provided by other different but related experiments. From this experience, we describe some system specifications that will be important for the success of this technique. Among them it is emphasized the need of baselines larger than usual, medium‐high frequency band, and a mandatory single‐pass mode for overcoming temporal decorrelation. Finally, a set of future experiments is also proposed for additional testing and confirmation of observations made so far regarding minimum baseline requirements, temporal evolution of observables and modeling issues, among others.