4.7 Article

Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests

Journal

REMOTE SENSING
Volume 8, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/rs8060522

Keywords

radar; biomass; saturation; ALOS PALSAR; GLAS; NISAR

Funding

  1. National Aeronautic and Space Administration as part of the NASA grant [209249.04.01.01.19]

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Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements, and is influenced by the structure of the forest and environmental conditions. Here, we examine the sensitivity of SAR at the L-band frequency (similar to 25 cm wavelength) to AGB in order to examine the performance of future joint National Aeronautics and Space Administration, Indian Space Research Organisation NASA-ISRO SAR mission in mapping the AGB of global forests. For SAR data, we use the Phased Array L-Band SAR (PALSAR) backscatter from the Advanced Land Observing Satellite (ALOS) aggregated at a 100-m spatial resolution; and for AGB data, we use more than three million AGB values derived from the Geoscience Laser Altimeter System (GLAS) LiDAR height metrics at about 0.16-0.25 ha footprints across eleven different forest types globally. The results from statistical analysis show that, over all eleven forest types, saturation level of L-band radar at HV polarization on average remains >= 100 Mgha(-1). Fresh water swamp forests have the lowest saturation with AGB at similar to 80 Mgha(-1), while needleleaf forests have the highest saturation at similar to 250 Mgha(-1). Swamp forests show a strong backscatter from the vegetation-surface specular reflection due to inundation that requires to be treated separately from those on terra firme. Our results demonstrate that L-Band backscatter relations to AGB can be significantly different depending on forest types and environmental effects, requiring multiple algorithms to map AGB from time series of satellite radar observations globally.

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