Retrieving agricultural variables by microwave radiometry using a neural network algorithm trained by a physical model

Author

Del Frate, Fabio ; Ferrazzoli, P. ; Schiavon, G. ; Wigneron, J.-P. ; Chanzy, A.

Volume

4

fYear

1999

fDate

1999

Firstpage

2134

Abstract

A neural network algorithm trained by a physical vegetation model is used to retrieve soil moisture of a wheat crop during the whole crop cycle. The retrieval algorithm uses multifrequency and multiangular microwave radiometric data as inputs. The procedure is tested by using extensive measurements carried out in 1993 at the INRA Avignon test site

Keywords

agriculture; geophysical signal processing; geophysical techniques; geophysics computing; hydrological techniques; learning (artificial intelligence); neural nets; radiometry; remote sensing; soil; terrain mapping; vegetation mapping; AD 1993; France; INRA Avignon test site; agricultural variables; agriculture; crops; geophysical measurement technique; hydrology; microwave radiometry; multiangle method; multifrequency method; neural net; neural network algorithm; physical model; remote sensing; retrieval algorithm; soil moisture; terrain mapping; trained; training; vegetation mapping; wheat crop; Crops; Electromagnetic scattering; Electronic mail; Frequency; Microwave radiometry; Neural networks; Soil measurements; Soil moisture; Testing; Vegetation;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International

Conference_Location

Hamburg

Print_ISBN

0-7803-5207-6

Type

conf

DOI

10.1109/IGARSS.1999.775054

Filename

775054