Roughness correction method for salinity remote sensing using combined active/passive observations
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Abstract: 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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Key words:
- salinity remote sensing /
- roughness correction /
- Aquarius satellite /
- active/passive
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Table 1. Statistics of calculated brightness temperature (TB) compared with measured TB
Beamand polarization VV HH NCEP Bias/K RMSE/K Bias/K RMSE/K Bias/K RMSE/K Beam-1 V –6.81×10–3 3.31×10–1 1.77×10–3 2.77×10–1 –4.25×10–2 3.34×10–1 Beam–2 V –1.34×10–2 3.99×10–1 3.96×10–3 2.81×10–1 –2.95×10–2 3.42×10–1 Beam–3 V –9.60×10–3 3.73×10–1 6.59×10–3 2.59×10–1 –3.96×10–2 3.16×10–1 Beam–1 H –1.72×10–2 4.13×10–1 –4.83×10–3 3.28×10–1 –4.67×10–2 3.44×10–1 Beam–2 H –9.90×10–3 5.61×10–1 1.53×10–2 3.57×10–1 –2.61×10–3 3.75×10–1 Beam–3 H –1.56×10–2 6.17×10–1 1.47×10–2 3.84×10–1 1.89×10–3 3.95×10–1 Table 2. Statistics of calculated brightness temperature (TB) compared with measured TB of different methods
Beam and polarization HH+NCEP (MLE1) HH+NCEP+RAD (MLE2) V5 model-HHwind V5 model-HHHwind Bias/K RMSE/K Bias/K RMSE/K Bias/K RMSE/K Bias/K RMSE/K Beam-1 V 1.31×10–2 2.34×10–1 2.34×10–2 2.08×10–1 –3.41×10–2 2.43×10–1 –7.64×10–3 2.13×10–1 Beam-2 V 1.23×10–2 2.36×10–1 1.36×10–2 2.14×10–1 –3.27×10–2 2.58×10–1 –2.39×10–2 2.20×10–1 Beam-3 V 1.65×10–2 2.32×10–1 2.01×10–2 2.13×10–1 –4.15×10–2 2.42×10–1 –3.04×10–2 2.23×10–1 Beam-1 H –2.28×10–2 2.37×10–1 –2.85×10–2 2.11×10–1 –4.31×10–2 2.63×10–1 –3.45×10–2 2.02×10–1 Beam-2 H –1.65×10–2 2.41×10–1 –2.51×10–2 2.19×10–1 –1.60×10–2 2.78×10–1 –5.25×10–2 2.04×10–1 Beam-3 H 2.57×10–3 2.76×10–1 –1.78×10–2 2.47×10–1 –1.16×10–2 2.79×10–1 –4.41×10–2 2.10×10–1 -
[1] Camps A, Font J, Vall-Llossera M, et al. 2004. The WISE 2000 and 2001 field experiments in support of the SMOS mission: sea surface L-band brightness temperature observations and their application to sea surface salinity retrieval. IEEE Transactions on Geoscience and Remote Sensing, 42(4): 804–823. doi: 10.1109/TGRS.2003.819444 [2] Dinnat E P, Boutin J, Caudal G, et al. 2003. On the use of EUROSTARRS and WISE data for validating L-band emissivity models. In: Proceedings of the First Results Workshop on Eurostarrs, Wise, Losac Campaigns. Noordwijk, Netherlands: ESA, 117–124 [3] Du Yanlei, Yang Xiaofeng, Chen Kunshan, et al. 2017. An improved spectrum model for sea surface radar backscattering at L-Band. Remote Sensing, 9(8): 776. doi: 10.3390/rs9080776 [4] Etcheto J, Dinnat E P, Boutin J, et al. 2004. Wind speed effect on L-band brightness temperature inferred from EuroSTARRS and WISE 2001 field experiments. IEEE Transactions on Geoscience and Remote Sensing, 42(10): 2206–2213. doi: 10.1109/TGRS.2004.834644 [5] Fore A G, Yueh S H, Tang Wenqiang, et al. 2014. Aquarius wind speed products: algorithms and validation. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2920–2927. doi: 10.1109/TGRS.2013.2267616 [6] Guimbard S, Gourrion J, Portabella M, et al. 2012. SMOS semi-empirical ocean forward model adjustment. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1676–1687. doi: 10.1109/TGRS.2012.2188410 [7] Hollinger J P. 1971. Passive microwave measurements of sea surface roughness. IEEE Transactions on Geoscience Electronics, 9(3): 165–169. doi: 10.1109/TGE.1971.271489 [8] Isoguchi O, Shimada M. 2009. An L-band ocean geophysical model function derived from PALSAR. IEEE Transactions on Geoscience and Remote Sensing, 47(7): 1925–1936. doi: 10.1109/TGRS.2008.2010864 [9] Lagerloef G, Colomb F, Le Vine D, et al. 2008. The Aquarius/SAC-D mission: designed to meet the salinity remote-sensing challenge. Oceanography, 21(1): 68–81. doi: 10.5670/oceanog.2008.68 [10] Ma Wentao, Yang Xiaofeng, Liu Guihong, et al. 2014. An improved model for L-band brightness temperature estimation over foam-covered seas under low and moderate winds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3784–3793. doi: 10.1109/JSTARS.2014.2318432 [11] Meissner T, Wentz F J. 2004. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Transactions on Geoscience and Remote Sensing, 42(9): 1836–1849. doi: 10.1109/TGRS.2004.831888 [12] Meissner T, Wentz F J. 2012. The emissivity of the ocean surface between 6 and 90 GHz over a large range of wind speeds and earth incidence angles. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3004–3026. doi: 10.1109/TGRS.2011.2179662 [13] Meissner T, Wentz F, Hilburn K, et al. 2012. The aquarius salinity retrieval algorithm. In: Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE, 386–388 [14] Meissner T, Wentz F J, Le Vine D M. 2018. The salinity retrieval algorithms for the NASA aquarius version 5 and SMAP version 3 releases. Remote Sensing, 10(7): 1121. doi: 10.3390/rs10071121 [15] Meissner T, Wentz F J, Ricciardulli L. 2014. The emission and scattering of L-band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor. Journal of Geophysical Research: Oceans, 119(9): 6499–6522. doi: 10.1002/2014JC009837 [16] Reul N, Grodsky S A, Arias M, et al. 2020. Sea surface salinity estimates from spaceborne L-band radiometers: an overview of the first decade of observation (2010–2019). Remote Sensing of Environment, 242: 111769. doi: 10.1016/j.rse.2020.111769 [17] Yin Xiaobin, Boutin J, Dinnat E, et al. 2016. Roughness and foam signature on SMOS-MIRAS brightness temperatures: a semi-theoretical approach. Remote Sensing of Environment, 180: 221–233. doi: 10.1016/j.rse.2016.02.005 [18] Yin Xiaobin, Boutin J, Martin N, et al. 2012. Optimization of L-band sea surface emissivity models deduced from SMOS data. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1414–1426. doi: 10.1109/TGRS.2012.2184547 [19] Yueh S H, Dinardo S J, Fore A G, et al. 2010. Passive and active L-band microwave observations and modeling of ocean surface winds. IEEE Transactions on Geoscience and Remote Sensing, 48(8): 3087–3100. doi: 10.1109/TGRS.2010.2045002 [20] Yueh S H, Tang Wenqing, Fore A G, et al. 2013. L-band passive and active microwave geophysical model functions of ocean surface winds and applications to Aquarius retrieval. IEEE Transactions on Geoscience and Remote Sensing, 51(9): 4619–4632. doi: 10.1109/TGRS.2013.2266915 [21] Yueh S H, West R, Wilson W J, et al. 2001. Error sources and feasibility for microwave remote sensing of ocean surface salinity. IEEE Transactions on Geoscience and Remote Sensing, 39(5): 1049–1060. doi: 10.1109/36.921423