Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Circumpolar Scaling Layer for downscaling of Scatterometer derived soil moisture with links to geotiff images and NetCDF files (2005-01 to 2011-12)

This dataset provides scaling information applicable to satellite derived coarse resolution surface soil moisture datasets following the approach by Wagner et al. (2008). It is based on ENVISAT ASAR data and can be utilized to apply the Metop ASCAT dataset (25 km) for local studies as well as to assess the representativeness of in-situ measurement sites and thus their potential for upscaling. The approach based on temporal stability (Wagner et al. 2008) consists of the assessment of the validity of the coarse resolution datasets at medium resolution (1 km, product is the so called 'scaling layer')

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20. m spatial resolution. The high temporal sampling rate a

Similarities between spaceborne active and airborne passive microwave observations at 1 km resolution

For the first time, airborne passive microwave data were collected at 1 km resolution over parts of Central Australia coinciding with spaceborne active data, allowing a comparison of such data sets acquired at medium (1 km) spatial resolution. L-band airborne passive microwave scenes were compared with C-band scenes and temporal parameters from the Advanced Synthetic Aperture Radar. It was found that the radar-returned signal, as well as the 'sensitivity' and 'correlation' parameters derived from the long time-series of the ASAR GM data, is similar to spatial patterns in the passive microwave data, suggesting that similar physical interactions are underlying both data sets, especially across heterogeneous landscapes. Comparable patterns found over the dry Lake Eyre salt bed $(r=0.37) suggest that very high-resolution C-band radar data may be used to describe subpixel heterogeneity within coarse resolution radiometer data, such as the future Soil Moisture Active Passive mission

How do Spatial Scale, Noise, and Reference Data affect Empirical Estimates of Error in ASAR Derived 1 km Resolution Soil Moisture?

The performance of the advanced synthetic aperture radar (ASAR) global mode (GM) surface soil moisture (SSM) data was studied over Australia by means of two widely used bivariate measures, the root-mean-square error (RMSE) and the Pearson correlation coefficient (R). By computing RMSE and R at multiple spatial scales and for different data combinations, we assessed how, and at which scales, the spatial sampling error, noise, and the choice of the reference data impact on RMSE and R. The results reveal large changes in RMSE and R with continental average values of 8% and 18% for the RMSE of relative soil moisture saturation and between 0.4 and 0.7 for R depending on the spatial scale of aggregation and the choice of reference data. The combined effect of noise and spatial sampling error accounted for a 79% RMSE increase at 1 km and predominated over the error due to the choise of the reference data also at 5 km scale. The effect of noise on RMSE strongly diminished at spatial scales ≥ km. By contrast, the impact of uncertainties in the reference data was larger on than on RMSE. This highlights the better potential of to estimate the benefit of observations prior to data assimilation. Based on our results, it is further suggested that a potential way for an improved ASAR GM SSM error assessment is to: 1) aggregate the data to ≥ km resolution to minimize the noise; 2) subtract the spatial sampling error within the coarse resolution footprint; and 3) remove the reference uncertainty using advanced techniques such as triple collocation

Enabling the use of earth observation data for integrated water resource management in Africa with the Water Observation and Information System

The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under the TIGER-NET project, which is a major component of the TIGER initiative of the European Space Agency (ESA) and whose main goal is to support the African Earth Observation Capacity for Water Resource Monitoring. TIGER-NET aims to support the satellite-based assessment and monitoring of water resources from watershed to cross-border basin levels through the provision of a free and powerful software package, with associated capacity building, to African authorities. More than 28 EO data processing solutions for water resource management tasks have been developed, in correspondence with the requirements of the participating key African water authorities, and demonstrated with dedicated case studies utilizing the software in operational scenarios. They cover a wide range of themes and information products, including basin-wide characterization of land and water resources, lake water quality monitoring, hydrological modeling and flood forecasting and mapping. For each monitoring task, step-by-step workflows were developed, which can either be adjusted by the user or largely automatized to feed into existing data streams and reporting schemes. The WOIS enables African water authorities to fully exploit the increasing EO capacity offered by current and upcoming generations of satellites, including the Sentinel missions.ESA’s TIGE