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Remote Sens. 2017, 9(2), 149; doi:10.3390/rs9020149

The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China

1
School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
2
Transmissivity B.V./VanderSat B.V., Space Technology Business Park, Huygenstraat 34, Noordwijk DK 2201, The Netherlands
3
ARC Centre of Excellence for Climate Systems Science & Climate Change Research Centre, University of New South Wales, Sydney 2052, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Prashant K. Srivastava, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 12 December 2016 / Revised: 31 January 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
View Full-Text   |   Download PDF [5449 KB, uploaded 13 February 2017]   |  

Abstract

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. View Full-Text
Keywords: soil moisture; passive microwaves; evaluation soil moisture; passive microwaves; evaluation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Parinussa, R.M.; Wang, G.; Liu, Y.Y.; Hagan, D.F.T.; Lin, F.; van der Schalie, R.; de Jeu, R.A.M. The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China. Remote Sens. 2017, 9, 149.

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