Title :
Multitemporal behavior of L- and C-band SAR observations of boreal forests
Author :
Pulliainen, Jouni T. ; Kurvonen, Lauri ; Hallikainen, Martti T.
Author_Institution :
Lab. of Space Technol., Helsinki Univ. of Technol., Espoo, Finland
fDate :
3/1/1999 12:00:00 AM
Abstract :
An analysis of L- and C-band boreal forest backscattering properties with respect to various temporally changing parameters is presented. The seasonal and weather dependent parameters considered include the depth of soil frost, topsoil moisture, snow water equivalent, air temperature and precipitation. The effect of these parameters on σ° are studied for various stem volume (biomass) classes by comparing the results against a cloud model-based semi-empirical modeling approach. Semi-empirical modeling is also used for a forest biomass retrieval experiment. The SAR data set includes 4 JERS-1 (L-band, HH-polarization) and 19 ERS-1 (C-band, VV-polarization) images for a test area in southern Finland. Additionally, a set of 2 JERS-1 and 3 ERS-1 images for another test area in northern Finland is employed. The results show that radar response to forest biomass is more sensitive to changes in temporally varying parameters at C-band than at L-band. The semi-empirical modeling approach describes well the behavior of σ° at both frequency bands when large forest areas are considered. Moreover, the modeling approach appears to be applicable for different conifer-dominated boreal forest types. Since the modeling approach explains satisfactorily the average backscattering behavior, the results in biomass retrieval show high accuracies (25-30% relative RMSE) when areas under investigation are large enough, i.e. about 20 ha
Keywords :
backscatter; forestry; geophysical techniques; radar cross-sections; remote sensing by radar; spaceborne radar; synthetic aperture radar; vegetation mapping; C-band; Finland; L-band; SAR; SHF; UHF; backscatter; backscattering; biomass; boreal forest; conifer; forestry; geophysical measurement technique; multitemporal behavior; radar remote sensing; radar scattering; season; stem volume; synthetic aperture radar; vegetation mapping; weather dependent parameters; Backscatter; Biomass; Clouds; L-band; Moisture; Radar imaging; Snow; Soil; Temperature dependence; Testing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on