DocumentCode
576227
Title
Wetland vegetation biomass inversion using polarimetric RADARSAT-2 data
Author
Shen, Guozhuang ; Liao, Jingjuan ; Guo, Huadong ; Liu, Ju ; Zhang, Lu ; Chen, Jie
Author_Institution
Key Lab. of Digital Earth, Center for Earth Obs. & Digital Earth, Beijing, China
fYear
2012
fDate
22-27 July 2012
Firstpage
750
Lastpage
753
Abstract
Biomass, as an indicator of vegetation productivity, can evaluate the contribution of wetland vegetation to carbon sink and carbon source. Long time and quantitative biomass study can help to acknowledge and understand the global carbon balance and carbon cycle. RADAR, which can work all day/weather and can penetrate vegetation in some extent, can be used to retrieve vegetation structure information, even the biomass. Here, the RADARSAT-2 data was used to retrieve vegetation biomass in Poyang Lake wetland. Based on the canopy scattering model, which is based on radioactive transfer model, the vegetation backscatter characteristics at C band were studied and good relationship between simulation results and backscatter in RADATSAT-2 image were achieved. Using the backscatter model, pairs of training data (backscatter coefficients in HH, VV, HV polarization mode and polarization decomposed components) were built and were used to train the Back Propagation (BP) artificial neural network (ANN). The biomass was inversed using this ANN, and compared to the field survey. It shows that the combination of the canopy scatter model and polarimetric decomposition components can improve the inversion precision efficiently.
Keywords
lakes; neural nets; remote sensing by radar; vegetation; Poyang Lake wetland; back propagation artificial neural network; canopy scattering model; carbon sink; carbon source; global carbon balance; global carbon cycle; polarimetric RADARSAT-2 data; polarimetric decomposition components; radioactive transfer model; vegetation productivity indicator; vegetation structure information; wetland vegetation biomass inversion; Artificial neural networks; Backscatter; Biological system modeling; Biomass; Lakes; Remote sensing; Vegetation mapping; BP ANN; biomass; canopy scatter model; polarimetric SAR; polarimetric decomposition;
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.2012.6351456
Filename
6351456
Link To Document