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Wentao Ma, Guihong Liu, Yang Yu, Yanlei Du. Roughness correction method for salinity remote sensing using combined active/passive observations[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1744-z
Citation: Wentao Ma, Guihong Liu, Yang Yu, Yanlei Du. Roughness correction method for salinity remote sensing using combined active/passive observations[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1744-z

Roughness correction method for salinity remote sensing using combined active/passive observations

doi: 10.1007/s13131-021-1744-z
Funds:  The National Key Research and Development Program of China under contract Nos 2018YFA0605403 and 2016YFB0500204; the Hainan Provincial Natural Science Foundation of China under contract No. 418QN301; the National Natural Science Foundation of China under contract No. 41801238
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  • Corresponding author: E-mail: liugh@aircas.ac.cn
  • Received Date: 2020-12-14
  • Accepted Date: 2020-12-25
  • Available Online: 2021-04-22
  • Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing. In previous studies, the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means, which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements. The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application. The combined active/passive observations of normalized radar cross-sections (NRCSs) and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission, and the auxiliary wind directions collocated from the National Centers for Environmental Prediction (NCEP) dataset are used for model development. The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction. Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs, which can be better than 0.3 K. However, for crosswind directions and larger NRCSs, the model accuracy is relatively low. A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones. For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data, the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources. Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.
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