Improving L-band radiation model and representation of small-scale variability to simulate brightness temperature of sea ice

Zhou, Lu; Xu, Shiming; Liu, Jiping; Lu, Hui; Wang, Bin

doi:10.1080/01431161.2017.1371862

Cited by 13 publications

(29 citation statements)

References 33 publications

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“…The convergence of the modeled TB with respect to the layer count is witnessed, which is consistent with the study in Maaß et al (2013b). In Zhou et al (2017), it is demonstrated that the multi-layer treatment and the salinity profile MYI yield good fit between the simulated TB and SMOS TB. Appendix A covers details of the model and the verification with OIB and SMOS data.…”

Section: L-band Radiation Modelsupporting

confidence: 73%

“…The multi-layer treatment of the sea ice is also explored in Maaß et al (2013b). According to the study in Zhou et al (2017), with treatment of the salinity profile in MYI (i.e., salinity drainage in the top layers), the modeled TB is more consistent with the SMOS TB.…”

Section: A3 Radiative Propertiesmentioning

confidence: 99%

“…Second, there is small-scale variability of the sea ice cover such as leads, which were not considered for the analysis and verification in this study. As shown in Zhou et al (2017), the presence of sea ice leads has a profound effect in lowering the overall TB on the scale of SMOS observations. Leads can be treated as small-scale heterogeneity of the sea ice cover, and the incorporation of lead maps such as Willmes and Heinemann (2015b) effectively reduces the overestimation of TB, as studied by Zhou et al (2017).…”

Section: Outlook Of Satellite-based Retrievalmentioning

confidence: 99%

“…As shown in Zhou et al (2017), the presence of sea ice leads has a profound effect in lowering the overall TB on the scale of SMOS observations. Leads can be treated as small-scale heterogeneity of the sea ice cover, and the incorporation of lead maps such as Willmes and Heinemann (2015b) effectively reduces the overestimation of TB, as studied by Zhou et al (2017). Specifically, the lead map can be adopted by the retrieval through the integration with the forward radiation model.…”

Section: Outlook Of Satellite-based Retrievalmentioning

confidence: 99%

“…The model was originally developed for soil moisture applications in Burke et al (1979) and further adopted for sea ice in Maaß et al (2013b). As introduced in Zhou et al (2017), improvements to the model are made to better characterize the vertical structure of the sea ice that is specific to each sea ice type. Details are provided below.…”

Section: Appendix A: L-band Radiation Modelmentioning

confidence: 99%

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On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Zhou

Liu

et al. 2018

The Cryosphere

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Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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Section: L-band Radiation Modelsupporting

confidence: 73%

Section: A3 Radiative Propertiesmentioning

confidence: 99%

Section: Outlook Of Satellite-based Retrievalmentioning

confidence: 99%

Section: Outlook Of Satellite-based Retrievalmentioning

confidence: 99%

Section: Appendix A: L-band Radiation Modelmentioning

confidence: 99%

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On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Zhou

Liu

et al. 2018

The Cryosphere

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A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

et al. 2022

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Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.

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Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

Chen

et al. 2023

Remote Sensing

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Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters.

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Improving L-band radiation model and representation of small-scale variability to simulate brightness temperature of sea ice

Cited by 13 publications

References 33 publications

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

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