DocumentCode
2224077
Title
The retrieval and monitoring of vegetation parameters from COSMO-SkyMed images
Author
Santi, E. ; Fontanelli, G. ; Montomoli, F. ; Brogioni, M. ; Macelloni, G. ; Paloscia, S. ; Pettinato, S. ; Pampaloni, P.
Author_Institution
Res. Area of Florence, IFAC, Florence, Italy
fYear
2012
fDate
22-27 July 2012
Firstpage
7031
Lastpage
7034
Abstract
The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in this paper. SAR data from COSMO-SkyMed were collected on two agricultural areas in Italy in 2010 at different dates during the vegetation cycle. The performances of X-band data have been compared with accurate ground truth measurements of soil and vegetation carried out simultaneously to satellite passes. Experimental data have been compared with model simulations obtained with a discrete element radiative transfer model. Moreover, an inversion algorithm, based on an Artificial Neural Network and trained by using AIEM and the radiative transfer model, has been applied to retrieve the plant water content of wheat and sunflower crops and to generate the corresponding plant water content maps.
Keywords
crops; geophysical signal processing; inverse problems; neural nets; radiative transfer; remote sensing by radar; vegetation mapping; AD 2010; AIEM training; COSMO SkyMed images; Italy; SAR data; X-band data performance; artificial neural network; discrete element radiative transfer model; ground truth measurements; inversion algorithm; plant water content; sunflower crop; vegetation biomass estimation; vegetation cycle; vegetation parameter monitoring; vegetation parameter retrieval; wheat crop; Agriculture; Backscatter; Biological system modeling; Biomass; Data models; Market research; Vegetation mapping; ANN inversion algorithm; Cosmo-SkyMed; X-band; discrete element radiative transfer model; sensitivity to biomass; vegetation parameters;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location
Munich
ISSN
2153-6996
Print_ISBN
978-1-4673-1160-1
Electronic_ISBN
2153-6996
Type
conf
DOI
10.1109/IGARSS.2012.6351952
Filename
6351952
Link To Document