Canadian Forest Service Publications
Relationship between forest microwave transmissivity and structural parameters for the Canadian boreal forest. 2014. Roy, A.; Royer, A.; Hall, R.J. IEEE Geoscience and Remote Sensing Letters 11(10):1802-1806.
Available from: Northern Forestry Centre
Catalog ID: 35512
CFS Availability: PDF (request by e-mail)
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This letter proposes relationships between boreal forest microwave transmissivity and four forest structural parameters: summer and winter Leaf Area Index (LAI) from MODIS, biomass (t ha−1), and total volume (m3 ha−1) for northern Québec, Canada. These relationships were derived for summer AMSR-E data sets that took into account the effects of canopy emission and scattering. Root mean square error results between brightness temperature simulations and ASMR-E observations are approximately 5 K. Results reported in this study can be used as forest correction equations for key surface parameter retrievals under the boreal forest canopy, such as soil moisture or snow depth/water equivalent.
Plain Language Summary
The boreal forest covers a large ecological and climatic region around the Northern Hemisphere, and has significant effects on climate and water cycle. But boreal regions are mainly remote, with few weather stations that collect climate information. Finding new ways to collect data from this region would therefore be useful for forest and climate science. Microwaves transmitted from satellites and reflected from the soil can provide some useful data. This study used the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), a sensor that acquires data over a large area (1400 km swath) up to twice a day in northern regions. It looked at how microwave measurements were related to three variables of high interest: timber volume, biomass above the ground, and a measure of the surface area of leaves (which indicates carbon and energy exchange) in the boreal region of northern Québec. The study showed that AMSR-E data can be used to create forest maps over large regions. These maps may be valuable in corroborating maps generated from multiple other satellites with more limited data.
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