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A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products. [ABSTRACT FROM AUTHOR]

Abstract: Due to the high temporal sampling rate of ASAR Global Monitoring (GM) mode, it has a high application potential for analyzing the land surface freeze/thaw process in high latitudes. This study aims to develop effective methods of extracting freeze/thaw transition dates of permafrost areas from ASAR GM data sets. In order to use ASAR GM time-series for analyzing freeze/thaw states, a least square fitting of piecewise step function is introduced. The thawing date can be determined by minimizing the sum of squared residuals between measured backscattering time-series and a pre-defined step function. An experimental result for a Siberian permafrost region illustrates that it can be a promising approach in monitoring permafrost ecosystems. [Copyright &y& Elsevier]