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In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the Advanced Microwave Scanning Radiometer 2 (AMSR2) developed by the Japan Aerospace Exploration Agency (JAXA), and the Microwave Radiation Imager (MWRI) from China's National Satellite Meteorological Centre (NSMC). In this study, the quality of surface soil moisture anomalies derived from these passive microwave instruments was sequentially assessed over the mainland of the People's Republic of China. First, the impact of a recent update in the Land Parameter Retrieval Model (LPRM) was assessed for MWRI observations. Then, the soil moisture measurements retrieved from the X-band observations of MWRI were compared with those of AMSR2, followed by an internal comparison of the multiple frequencies of AMSR2. Finally, SMOS retrievals from two different algorithms were also included in the comparison. For each sequential step, processing and verification chains were specifically designed to isolate the impact of algorithm (version), observation frequency or instrument characteristics. Two verification techniques are used: the statistical Triple Collocation technique is used as the primary verification tool, while the precipitation-based Rvalue technique is used to confirm key results. Our results indicate a consistently better performance throughout the entire study area after the implementation of an update of the LPRM.We also find that passive microwave observations in the AMSR2 C-band frequency (6.9 GHz) have an advantage over the AMSR2 X-band frequency (10.7 GHz) over moderate to densely vegetated regions. This finding is in line with theoretical expectations as emitted soil radiation will become masked under a dense canopy with stricter thresholds for higher passive microwave frequencies. Both AMSR2 and MWRI make X-band observations; a direct comparison between them reveals a consistently higher quality obtained by AMSR2, specifically over semi-arid climate regimes. Unfortunately, Radio Frequency Interference hampers the usefulness of soil moisture products for the SMOS L-band mission, leading to a significantly reduced revisit time over the densely populated eastern part of the country. Nevertheless, our analysis demonstrates that soil moisture products from a number of multi-frequency microwave sensors are credible alternatives for this dedicated L-band mission over the mainland of the People's Republic of China. [ABSTRACT FROM AUTHOR]

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer--Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures (Tb) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E Tb's to soil temperature (Tsoil ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E Tb's to retrieve Tsoil is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and Tsoil information was chosen to obtain SM in the 2003-2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe's surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003-2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. [ABSTRACT FROM AUTHOR]