Premium
Active polarimetric microwave remote sensing of corn and multiparametric retrieval of corn parameters with an artificial neural network
Author(s) -
Chen Zhengxlao,
Tsang Leung,
Kuga Yasuo,
Chan Chi
Publication year - 1993
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.4650061016
Subject(s) - radiative transfer , artificial neural network , remote sensing , polarimetry , microwave , canopy , polarization (electrochemistry) , moisture , environmental science , water content , mathematics , physics , meteorology , scattering , optics , chemistry , computer science , geography , engineering , machine learning , botany , biology , telecommunications , geotechnical engineering
Abstract Polarimetric signatures of a corn canopy are studied with the vector radiative transfer theory. Comparisons are made with experimental data at L and C bands. Multiparametric inversions of corn canopy parameters are performed with an artificial neural network (ANN) trained with vector radiative transfer theory. We have performed simultaneous retrieval of three parameters: corn height, volumetric moisture of corn stalks, and volumetric soil moisture from a total of eight averaged Mueller matrix elements at L and C bands and at one incidence angle. It is shown that the performance of the neural network is good, with errors of less than 10%. It is also shown that the performance of the ANN is better if measurements at both frequencies are used and also if polarimetric measurements including information about the polarization phase difference are used. © 1993 John Wiley & Sons, Inc.