Aquarius Sea Surface Salinity Gridding Method Based on Dual Quality–Distance Weighting
Yanyan Li 
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of the Chinese Academy of Sciences, Beijing 100049, China
3
Hainan Key Laboratory of Earth Observation, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1131; https://doi.org/10.3390/rs11091131
Received: 9 April 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 11 May 2019
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data conditions for measurements. In the weighting progress, 14 data conditions were considered, and their geospatial distributions and influences on the SSS were also visualized and evaluated. Three interpolation methods were employed, and weekly gridded SSS maps were produced for the period from September 2011 to May 2015. These maps were evaluated via comparisons with concurrent Argo buoy measurements. The results show that the proposed method improved the accuracy of the SSS fields by approximately 36% compared to the officially released weekly level 3 products and yielded root mean squared difference (RMSD), correlation and bias values of 0.19 psu, 0.98 and 0.01 psu, respectively. These findings indicate a significant improvement in the accuracy of the SSS fields and provide a better understanding of the influences of different conditions on salinity.
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Keywords:
Aquarius; sea surface salinity (SSS); dual weighting; quality
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MDPI and ACS Style
Li, Y.; Dong, Q.; Ren, Y. Aquarius Sea Surface Salinity Gridding Method Based on Dual Quality–Distance Weighting. Remote Sens. 2019, 11, 1131.
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