Soil moisture is important to agriculture, weather, and climate. Current soil moisture networks measure at single points, while large spatial averages are needed for some crop, weather, and climate models. Large spatial average soil moisture can be measured by microwave satellites. Two missions, the European Space Agency\u27s Soil Moisture Ocean Salinity mission (SMOS) and NASA\u27s Soil Moisture Active Passive mission (SMAP), can or will measure L-band microwave radiation, which can see through denser vegetation and deeper in to the soil than previous missions that used X-band or C-band measurements.
Both SMOS and SMAP require knowledge of vegetation optical thickness (τ) to retrieve soil moisture. SMOS is able to measure τ directly through multi-angular measurements. SMAP, which will measure at a single incidence angle, requires an outside source of τ data. The current SMAP baseline algorithm will use a climatology of optical vegetation measurements, the normalized difference vegetation index (NDVI), to estimate τ. SMAP will convert the NDVI climatology to vegetation water content (VWC), then convert VWC to τ through the b parameter.
This dissertation aimed to validate SMOS τ using county crop yield estimates in Iowa. SMOS τ was found to be noisy while still having a clear response to vegetation. Counties with higher yields had higher increases in $tau; over growing seasons, so it appears that SMOS τ is valid during the growing season. However, SMOS τ had odd behavior outside of growing seasons which can be attributed to soil tillage and residue management.
Next, this dissertation attempted to estimate values of the b parameter at the satellite scale using SMOS τ data, county crop yields, and allometric relationships, such as harvest index. A new allometric relationship was defined, theta_gv_max, which is the ratio of maximum VWC to maximum dry biomass. While uncertainty in the estimated values of b was large, the values were close in magnitude to those found in literature for field-based studies.
Finally, this dissertation compared SMOS τ to τ from SMAP\u27s NDVI-based algorithm. At the peak of the growing season, SMAP τ was similar in timing to SMOS τ, while SMAP τ was larger in magnitude than SMOS τ. The larger SMAP τ could be attributed to SMAP\u27s handling of vegetation scattering in its soil moisture retrieval algorithm. For one example case, the difference between SMAP τ and SMOS τ at the peak of the growing season did not appear to cause a large difference in retrieved soil moisture
