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The baseline radiometer brightness temperature ( Tb ) downscaling algorithm for NASA's Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, is tested using an airborne simulation of the SMAP data stream. The algorithm synergistically uses 3 km Synthetic Aperture Radar (SAR) backscatter ( σ ) to downscale a 36 km radiometer Tb to 9 km. While the algorithm has already been tested using experimental datasets from field campaigns in the USA, it is imperative that it is tested for a comprehensive range of land surface conditions (i.e. in different hydro-climatic regions) before global application. Consequently, this study evaluates the algorithm using data collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, that closely simulate the SMAP data stream for a single SMAP radiometer pixel over a 3-week interval, with repeat coverage every 2–3 days. The results suggest that the average root-mean-square error (RMSE) in downscaled Tb is 3.1 K and 2.6 K for h - and v -polarizations respectively, when downscaled to 9 km resolution. This increases to 8.2 K and 6.6 K when applied at 1 km resolution. Downscaling over the relatively homogeneous grassland areas resulted in 2 K lower RMSE than for the heterogeneous cropping area. Overall, the downscaling error was around 2.4 K when applied at 9 km resolution for five of the nine days, which meets the 2.4 K error target of the SMAP mission. [ABSTRACT FROM AUTHOR]

Abstract: The National Airborne Field Experiment 2006 (NAFE’06) was conducted during a three week period of November 2006 in the Murrumbidgee River catchment, located in southeastern Australia. One objective of NAFE’06 was to explore the suitability of the area for SMOS (Soil Moisture and Ocean Salinity) calibration/validation and develop downscaling and assimilation techniques for when SMOS does come on line. Airborne L-band brightness temperature was mapped at 1km resolution 11 times (every 1–3 days) over a 40 by 55km area in the Yanco region and 3 times over a 40 by 50km area that includes Kyeamba Creek catchment. Moreover, multi-resolution, multi-angle and multi-spectral airborne data including surface temperature, surface reflectance (green, read and near infrared), lidar data and aerial photos were acquired over selected areas to develop downscaling algorithms and test multi-angle and multi-spectral retrieval approaches. The near-surface soil moisture was measured extensively on the ground in eight sampling areas concurrently with aircraft flights, and the soil moisture profile was continuously monitored at 41 sites. Preliminary analyses indicate that (i) the uncertainty of a single ground measurement was typically less than 5% vol. (ii) the spatial variability of ground measurements at 1km resolution was up to 10% vol. and (iii) the validation of 1km resolution L-band data is facilitated by selecting pixels with a spatial soil moisture variability lower than the point-scale uncertainty. The sensitivity of passive microwave and thermal data is also compared at 1km resolution to illustrate the multi-spectral synergy for soil moisture monitoring at improved accuracy and resolution. The data described in this paper are available at www.nafe.unimelb.edu.au. [Copyright &y& Elsevier]

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-rn resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-rn "coarse-scale" passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): 1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and 2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture. [ABSTRACT FROM AUTHOR]