Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

Citation
S. Frolking et al., Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons, J GEO RES-A, 104(D22), 1999, pp. 27895-27907
Citations number
37
Categorie Soggetti
Earth Sciences
Volume
104
Issue
D22
Year of publication
1999
Pages
27895 - 27907
Database
ISI
SICI code
Abstract
We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any wate r near the surface should make space-borne radar observations a powerful to ol for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA sca tterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial re solution and approximately twice-daily temporal coverage at the BOREAS lati tude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around -8 dB in winter and -12 dB in early summer and fall. Th e NSCAT signal for the southern sites had less seasonality. At all three si tes there was a strong decrease in backscatter during spring thaw (4-6 dB). At the southern deciduous site, NSCAT backscatter rose from -11 to -9.2 dB during spring leaf-out. All sites showed 1-2 dB backscatter shifts corresp onding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (T-max < -25 degrees C). Freeze/thaw dete ction algorithms developed for other radar instruments gave reasonable resu lts for the northern site but were not successful at the two southern sites . We developed a change detection algorithm based on first differences of 5 -day smoothed NSCAT backscatter measurements. This algorithm had some succe ss in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state gener ally coincided with the arrival and departure of the seasonal snow cover an d with simulated shifts in the directions of net carbon exchange at each of the study sites.