In this paper, sea surface salinity data of the equatorial Pacific Ocean in 2011~2012, acquired by the Soil Moisture and Ocean Salinity (SMOS) satellite, was processed for quality control and then analyzed for the first time in terms of dynamic process factors that may impact salinity retrieval. The neural network method was introduced to improve the quality-controlled salinity data of the same time period. It was found that precipitation and surface wave induced by precipitation can increase salinity error substantially in a negative trend. Sea surface roughness caused by wind also increased salinity errors. There was a weak positive correlation between wind speed and salinity error. Changes in sea surface temperature had little effect on salinity retrieval. Considering rainfall, wind speed and other major marine dynamic processes, the neural network method was used to revise sea surface salinity data of the equatorial Pacific in December 2011. The results showed that the RMS (root mean square) of salinity reduced from 0.383 7 to 0.244 1. It was found that not only was the salinity error caused by precipitation and other dynamic processes eliminated, but also the high salinity tongue in the equatorial Pacific was revealed, which the Level-3 data failed to.
ZENG Zhi
,
CHEN Xue-en
,
TANG Sheng-quan
,
WANG Wei-dong
,
GAO Rong-lu
,
YUAN Nan
. Evaluation of SMOS sea surface salinity data in the equatorial Pacific and its correction using neural network[J]. Journal of Tropical Oceanography, 2015
, 34(6)
: 35
-41
.
DOI: 10.11978/2014140
1 陈建, 张韧, 安玉柱, 等. 2013. SMOS卫星遥感海表盐度资料处理应用研究进展[J]. 海洋科学进展, 31(2): 295-304.
2 陈上及, 杜兵. 1990. 西赤道太平洋上层水水团的软划分[J]. 海洋学报(中文版), 12(4): 405-415.
3 陈廷娣, 王连仲, 窦贤康. 2008. TRMM卫星与机载雷达在降雨反演中的数据对比个例研究[J]. 应用气象学报, 19(4): 454-462.
4 侯瑞. 2011. 人工神经网络BP算法简介及应用[J]. 科技信息, 18(3): I0075
5 李一楠, 李浩, 吕容川, 等. 2012. SMOS在轨定标概述[J]. 空间电子技术, 18(2): 20-25, 36.
6 卢红丽, 王振占, 殷晓斌. 2014. 利用SMOS卫星数据反演海洋盐度方法研究[J]. 遥感技术与应用, 29(3): 401-409.
7 马继瑞, 林春发, 李斌. 1985. 太平洋西部赤道区域海流、温度、盐度的分布和变化[J]. 海洋学报(中文版), 7(2): 131-142.
8 邱吉尔, 王莹. 2013. 激光衍射法测量液体表面波的波长并确定其属性[J]. 大学物理实验, 20(3): 4-5, 11.
9 张卫国, 姜景山, 刘和光, 等. 2006. 应用SMOS卫星探测土壤湿度和海面盐度[C]//姜景山. 第二届微波遥感技术研讨会摘要全集. 深圳: 中国空间科学学会遥感分会: 109.
10 BOUTIN J, WALDTEUFEL P, MARTIN N, et al. 2004. Surface salinity retrieved from SMOS measurements over global ocean: Imprecisions due to surface roughness and temperature uncertainties[J]. Journal of Atmospheric and Oceanic Technology, 21(9): 1432-1447.
11 BOUTIN J, MARTIN N, YIN X, et al. 2012. First assessment of SMOS measurements over open ocean: Part Ⅱ Sea surface salinity[J]. IEEE Transaction on Geoscience and Remote Sensing, 50(5): 1648-1661.
12 FONT J, CAMPS A, BORGES A, et al 2010. SMOS: The challenging sea surface salinity measurement from space[J]. Proceedings of the IEEE, 98(5): 649-665.
13 MERLIN O, ESCORIHUELA M J, MAYORAL M A, et al. 2013. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain[J]. Remote Sensing of Environment, 130: 25-28.
14 RUF C S, SWIFT C T, TANNER A B, et al. 1988. Interferometric synthetic aperture microwave radiometry for the remote sensing of the Earth[J]. IEEE Transactions on Geoscience and Remote Sensing, 26(3): 597-611.
15 SKOU N, MISRA S, BALLING J E, et al. 2010. L-Band RFI as experienced during airborne campaigns in preparation for SMOS[J]. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1398-1407.
16 ZINE S, BOUTIN J, FONT J, et al. 2008. Overview of the SMOS sea surface salinity prototype processor[J]. IEEE Transactions on Geoscience and Remote Sensing, 46(3): 621-645.
