Evaluation of post-retrieval de-noising of active and passive microwave satellite soil moisture

REMOTE SENSING OF ENVIRONMENT

卷:163

Article

Su, Chun-Hsu¹  Narsey, Sugata Y.²  Gruber, Alexander³  Xaver, Angelika³  Chung, Daniel³  Ryu, Dongryeol¹  Wagner, Wolfgang³ 

[1] Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic 3052, Australia

[2] Monash Univ, ARC Ctr Excellence Climate Syst Sci, Clayton, Vic 3800, Australia

[3] Vienna Univ Technol, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria

关键词: Satellite soil moisture;    De-noising;    Signal processing;    Spectral analysis;    Evaluation;    Gap-filling;   

DOI  :  10.1016/j.rse.2015.03.010

来源: Elsevier

【 摘 要 】

Active and passive microwave satellite remote sensing are enabling sub-daily global observations of surface soil moisture (SM) for hydrological, meteorological and climatological studies. Because the retrieved SM data can be quite noisy, post-retrieval processing such as de-noising can play an important role to aid interpretation of the observed dynamics or enhance their utility for data assimilation. To date, the merits of such techniques have not yet been fully evaluated. Here we consider the applications of Fourier-based de-noising filters of Su et al. (2013a) for improving SM retrieved by AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) and ASCAT (Advanced Scatterometer of MetOp-A) sensors. The filters are calibrated in the frequency domain based on a water-balance model, without the need for ancillary data. The evaluation of the de-noising methods was conducted globally against in situ data distributed via the International Soil Moisture Network (ISMN) at 277 AMSR-E and 385 ASCAT pixels. Systematic improvements were found for all considered metrics, namely root-mean-square deviation, linear correlation and signal-to-noise ratio, for both SM products, with improvements more striking for AMSR-E. However, the originally proposed implementation of the filters can induce undesirable over-smoothing and distortion of SM timeseries. To overcome this, based on a simple heuristic argument, we propose the use of ancillary precipitation data in the filtering process, although at some expense of overall agreements with the in situ data. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

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