Journal
JOURNAL OF APPLIED REMOTE SENSING
Volume 9, Issue -, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JRS.9.096077
Keywords
synthetic aperture radar; polarimetric decomposition; canopy backscatter model; biomass; back propagation artificial neural network
Funding
- Natural Science Foundation of China [41401483]
- Key National Natural Science Foundation of China [61132006]
- Director Foundation of CEODE, CAS [Y1ZZ05101B]
- Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS201205]
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Poyang Lake is the largest freshwater lake in China and one of the most important wetlands in the world. Vegetation, an important component of wetland ecosystems, is one of the main sources of the carbon in the atmosphere. Biomass can quantify the contribution of wetland vegetation to carbon sinks and carbon sources. Synthetic aperture radar (SAR), which can operate in all day and weather conditions and penetrate vegetation to some extent, can be used to retrieve information about vegetation structure and the aboveground biomass. In this study, RADARSAT-2 polarimetric SAR data were used to retrieve aboveground vegetation biomass in the Poyang Lake wetland. Based on the canopy backscatter model, the vegetation backscatter characteristics in the C-band were studied, and a good relation between simulated backscatter and backscatter in the RADARSAT-2 imagery was achieved. Using the backscatter model, pairs of training data were built and used to train the back propagation artificial neural network. The biomass was retrieved using this ANN and compared with the field survey results. The root-mean-square error in the biomass estimation was 45.57 g/m(2). This shows that the combination of the model and polarimetric decomposition components can efficiently improve the inversion precision. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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