Soil moisture estimation using 1.4GHz passive microwave radiometer data from local to regional
scales.
C. Duke1, Protz, R.1, Rubenstein, I.2, Parkin, G.1, von Bertoldi, P.1, Soffer, R.2, VandenBygaart,
A. J.1, Hersom, C.2, and D. Redman3.
Cduke@lrs.uoguelph.ca
1 Image Analysis and Remote Sensing Laboratory, Department of Land Resource Science, University of Guelph, Guelph, Ontario, N1G 2W1.
2 CRESTech, 4850 Keele St., North York, Ontario, M3J 3K1.
3 Department of Earth Sciences, University of Waterloo, Waterloo, Ontario, N2L 3G1.
Soil moisture estimation is important for agroecosystem monitoring and as an input to climatic
models. Use and interpretation of 1.4GHz passive microwave radiometer data is confounded by land
cover, soil surface conditions, and moisture and soil texture variability in the soil profile. Additionally,
the low spatial resolution of passive microwave sensors makes analysis difficult over an already
variable surface.
Three multi-year experiments were preformed on agricultural soils for the combined goal of soil
moisture estimation at multiple scales [pedon to regional] in southwestern Ontario. Three phenomena
were examined: 1] evolution of emitted radiation as soil moisture profile evolves, 2] effect of soil
texture and roughness on soil temperature brightness [TB, K], and 3] fusion of multi-source data
for synergetic effect on soil moisture estimation. Spatial analysis of influencing parameters on soil
moisture estimation was analysed to determine method of scaling.
For the first experiment, soil moisture was measured 0-1m over two soil types [silt loam and sand]
and four adjacent pedons with variable surface conditions [grass, residue, smooth, tilled] using TDR.
Passive microwave measurements were acquired frequently as moisture decreased from near
saturated to field dry conditions. Measurements were acquired at nadir with a 1m2 spatial resolution.
For the second experiment, a boom-mounted radiometer measured over three soil types [clay, sand,
silty loam] where residue and surface roughness was variable. Field conditions were very wet so most
variability in TB was explained and quantified by surface conditions. For the third experiment, data
fusion using airborne CASI optical imagery, Radarsat fine beam C-band radar, and SLFMR passive
microwave data were acquired at 3 altitudes over four soil types. The three data sets were
geometrically corrected and spatially registered. Soil moisture [0-5cm and 0-20cm] were correlated
to remotely sensed data. The ability to define land cover variability with optical imagery permitted
a refined interpretation of microwave data at multiple scales.
Analysis and utilisation of passive microwave data at local to regional scales requires information
about heterogeneous land characteristics contributing to TB. It has been shown that TB is not
independent of soil surface conditions. The ability to obtain certain of these characteristics from
alternative data sources permits a synergism to obtain regional soil moisture information and soil
moisture in the soil column at multiple scales.