Temporal variations in soil moisture content and its influence on biomass estimates, observed by UAVSAR, ALOS PALSAR, and in-situ field data
Abstract
Temporal changes of repeat-pass SAR backscatter over bare ground or forests results mostly from changes in the target's dielectric properties or moisture content; especially when the timescale is on the order of a few days or weeks. It is important to properly correct for moisture content when using SAR based estimates of tree height or biomass. The objective of this work is to quantify the error in biomass estimates associated with variations in moisture content in temperate and boreal forested areas. In addition, the accuracy of three polarimetric soil moisture surface inversion models (Dubois et al., 1995, Oh et al., 1992; Oh, 2004) are tested on UAVSAR and PALSAR data of bare soils in temperate and boreal forested areas. In addition to PALSAR data from 2007 to 2009, a JPL/UAVSAR campaign over parts of New England and Quebec was completed in August, 2009; L-band SAR images were acquired on August 5th, August 7th, and August 14th. In-situ soil moisture probes at three locations gathered hourly soil moisture content data. LVIS LIDAR is used for quantifying and classifying biomass ranges. Slope corrected backscatter values resampled to 1 hectare at HH, HV, and VV polarizations, and ratios thereof, are compared with soil moisture, precipitation, biomass, and incidence angle. It is seen that the backscatter for high biomass areas varies significantly due to moisture variations. An increase in 1% soil moisture content at the Laurentides field site leads to a change in HV backscatter of 1dB. Regions with high biomass do not vary uniformly with varying moisture content: this can be explained by saturation of the L-band at higher biomass levels. The three inversion algorithms produce varying results with the ‘Dubois et al’ inversion producing the best correlation at the Bartlett Forest site while the ‘Oh 2004’ inversion produces better results at the Laurentides site. Although the accuracy is often poor, the temporal variation of the moisture content for all three inversion algorithms is generally captured.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2010
- Bibcode:
- 2010AGUFM.H23F1279C
- Keywords:
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- 0480 BIOGEOSCIENCES / Remote sensing;
- 1855 HYDROLOGY / Remote sensing;
- 1866 HYDROLOGY / Soil moisture