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Inter-comparison and calibration of SMAP and SMOS brightness temperature data in the Arctic

  • role： First author第一作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

  • Email：huangsw6@mail2.sysu.edu.cn
  • Introduction：黄森文，研究方向为极地遥感。E-mail： huangsw6@mail2.sysu.edu.cn
HUANG Senwen,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

  • Email：helian6@mail.sysu.edu.cn
  • Introduction：何连，研究方向为微波遥感。E-mail： helian6@mail.sysu.edu.cn
HE Lian*,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

HE Xiyi,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

HUI Fengming

résumé

Soil Moisture Active Passive (SMAP) Satellite and Soil Moisture and Ocean Salinity (SMOS) Satellite are passive microwave radiometers that operate at the L band. They can be used to estimate sea ice parameters, such as sea ice thickness, sea ice concentration, and snow depth on sea ice, so they play an important role in monitoring sea ice parameters and their changes in polar regions. A comprehensive comparison of SMAP and SMOS brightness temperatures (TBs) is necessary because it can help identify possible deficiencies in TB products and construct a highly consistent and reliable TB dataset for sea ice monitoring.In this study, the sea region north of 55°N in the Arctic was selected as the study area. Top Of Atmosphere (TOA) TB observations derived from SMAP L1B and SMOS L1C products were compared for the period of October 2015 to October 2020. Given that SMAP measures at a fixed incidence angle of 40°, SMOS observations at incidence angles between 37.5° and 42.5° were averaged and compared with SMAP TBs. The discrepancy between SMAP and SMOS TBs was evaluated by computing the Pearson correlation coefficient (r), bias (SMOS minus SMAP), and root mean squared deviation (RMSD). The dependence of the discrepancy parameters on polarization, sea ice type, and season was also investigated. Given the higher radiometric accuracy of SMAP compared with that of SMOS, the SMAP observations were used as the reference data, and the SMOS TBs were calibrated using a linear regression method, with the slope and intercept values provided for each month. The RMSD values between the calibrated SMOS and SMAP were evaluated at both polarizations for different sea ice types.Results indicated that the brightness temperatures of SMAP were generally lower than those of SMOS, with bias being 2.0—3.0 K and 3.0—4.5 K for H and V polarizations, respectively, and RMSD being 4.5—6.0 K for H and 5.0—6.0 K for V. During wintertime (October to April), multi-year ice (MYI) had the lowest bias and RMSD values, followed by first-year ice (FYI). Open water (OW) had the highest bias and RMSD. During summertime (May to September), the bias and RMSD values for sea ice were similar to those for OW. The slope and intercept values for calibrating the SMOS TBs showed strong seasonal variation. However, their inter-annual variabilities for each month were small, so averaged calibration coefficients could be achieved for each month. The obtained slope and intercept values were used to calibrate the SMOS TBs, and the results showed that the calibrated SMOS agreed well with SMAP, with the overall RMSD being 4.4 K and 3.8 K for H and V polarization, respectively. However, the different sea ice types had different RMSD values. Compared with MYI that had the lowest RMSD values, i.e., 2.4 K for H and 1.9 K for V, FYI had much higher RMSD values of 4.5 K and 3.9 K for H and V, respectively. Moreover, the calibration accuracy at the end of the sea ice growth stage (January to April) was higher than that at the beginning of the sea ice growth stage (October to December).This study helps understand the discrepancy between SMAP and SMOS TB observations. The obtained calibration coefficients can be used to calibrate SMOS and contribute to the construction of a long-time-series, L-band, consistent-brightness temperature dataset for Arctic sea ice monitoring.

mots-clés

remote sening;SMAP;SMOS;brightness temperature;consistency;inter-calibration

References

  1. 1.
    Al Bitar A, Mialon A, Kerr Y H, Cabot F, Richaume P, Jacquette E, Quesney A, Mahmoodi A, Tarot S, Parrens M, Al-Yaari A, Pellarin T, Rodriguez-Fernandez N and Wigneron J P. 2017. The global SMOS Level 3 daily soil moisture and brightness temperature maps. Earth System Science Data, 9(1): 293-315
  2. 2.
    Al-Yaari A, Wigneron J P, Kerr Y, Rodriguez-Fernandez N, O’Neill P E, Jackson T J, De Lannoy G J M, Al Bitar A, Mialon A, Richaume P, Walker J P, Mahmoodi A and Yueh S. 2017. Evaluating soil moisture retrievals from ESA’s SMOS and NASA’s SMAP brightness temperature datasets. Remote Sensing of Environment, 193: 257-273
  3. 3.
    Bindlish R, Jackson T J, Chan S, Colliander A and Kerr Y. 2017. Integration of SMAP and SMOS L-band observations//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth: IEEE: 2546-2549
  4. 4.
    Bindlish R, Jackson T J, Piepmeier J R, Yueh S and Kerr Y. 2016. Intercomparison of SMAP, SMOS and Aquarius L-band brightness temperature observations//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE: 2043-2046
  5. 5.
    Breivik L A, Eastwood S and Lavergne T. 2012. Use of C-Band scatterometer for sea ice edge identification. IEEE Transactions on Geoscience and Remote Sensing, 50(7): 2669-2677
  6. 6.
    Corbella I, Torres F, Duffo N, González-Gambau V, Pablos M, Duran I and Martín-Neira M. 2011. MIRAS calibration and performance: results from the SMOS In-Orbit commissioning phase. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3147-3155
  7. 7.
    De Lannoy G J M, Reichle R H, Peng J Z, Kerr Y, Castro R, Kim E J and Liu Q. 2015. Converting between SMOS and SMAP level-1 brightness temperature observations over nonfrozen land. IEEE Geoscience and Remote Sensing Letters, 12(9): 1908-1912
  8. 8.
    Gabarro C, Turiel A, Elosegui P, Pla-Resina J A and Portabella M. 2017. New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator. The Cryosphere, 11(4): 1987-2002
  9. 9.
    Huntemann M, Patilea C and Heygster G. 2016. Thickness of thin sea ice retrieved from SMOS and SMAP//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE: 5248-5251
  10. 10.
    Kaleschke L, Tian-Kunze X, Heygster G, Patilea C, Hendricks S, Ricker R, Tonboe R, Mäkynen M, Bertino L and Xie J P. 2017. ESA support to science element (STSE) SMOS + sea ice final report: Tech. Rep. University of Hamburg
  11. 11.
    Kaleschke L, Tian-Kunze X, Maaß N, Beitsch A, Wernecke A, Miernecki M, Müller G, Fock B H, Gierisch A M U, Schlünzen K H, Pohlmann T, Dobrynin M, Hendricks S, Asseng J, Gerdes R, Jochmann P, Reimer N, Holfort J, Melsheimer C, Heygster G, Spreen G, Gerland S, King J, Skou N, Søbjærg S S, Haas C, Richter F and Casal T. 2016. SMOS sea ice product: operational application and validation in the Barents Sea marginal ice zone. Remote Sensing of Environment, 180: 264-273
  12. 12.
    Ke C Q, Peng H T, Sun B and Xie H J. 2013. Spatio-temporal variability of Arctic sea ice from 2002 to 2011. Journal of Remote Sensing, 17(2): 459-466
  13. 13.
    Kerr Y H, Waldteufel P, Wigneron J P, Delwart S, Cabot F, Boutin J, Escorihuela M J, Font J, Reul N, Gruhier C, Juglea S E, Drinkwater M R, Hahne A, Martín-Neira M and Mecklenburg S. 2010. The SMOS mission: new tool for monitoring key elements ofthe global water cycle. Proceedings of the IEEE, 98(5): 666-687
  14. 14.
    Kwok R. 2018. Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958-2018). Environmental Research Letters, 13(10): 105005
  15. 15.
    Kwok R, Spreen G and Pang S. 2013. Arctic sea ice circulation and drift speed: decadal trends and ocean currents. Journal of Geophysical Research: Oceans, 118(5): 2408-2425
  16. 16.
    Maaß N, Kaleschke L, Tian-Kunze X and Drusch M. 2013. Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data. The Cryosphere, 7(6): 1971-1989
  17. 17.
    Markus T and Cavalieri D J. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1387-1398
  18. 18.
    Matzler C, Ramseier R and Svendsen E. 1984. Polarization effects in seaice signatures. IEEE Journal of Oceanic Engineering, 9(5): 333-338
  19. 19.
    McMullan K D, Brown M A, Martin-Neira M, Rits W, Ekholm S, Marti J and Lemanczyk J. 2008. SMOS: the payload. IEEE Transactions on Geoscience and Remote Sensing, 46(3): 594-605
  20. 20.
    Mills P and Heygster G. 2011. Retrieving ice concentration from SMOS. IEEE Geoscience and Remote Sensing Letters, 8(2): 283-287
  21. 21.
    Paţilea C, Heygster G, Huntemann M and Spreen G. 2019. Combined SMAP-SMOS thin sea ice thickness retrieval. The Cryosphere, 13(2): 675-691
  22. 22.
    Peng J Z, Misra S, Piepmeier J R, Dinnat E P, Yueh S H, Meissner T, Le Vine D M, Shelton K E, Freedman A P, Dunbar R S, Chan S K, Bindlish R, De Amici G, Mohammed P N, Hong L, Hudson D and Jackson T. 2019. Soil moisture Active/Passive (SMAP) L-band microwave radiometer post-launch calibration upgrade. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6): 1647-1657
  23. 23.
    Piepmeier J R, Focardi P, Horgan K A, Knuble J, Ehsan N, Lucey J, Brambora C, Brown P R, Hoffman P J, French R T, Mikhaylov R L, Kwack E Y, Slimko E M, Dawson D E, Hudson D, Peng J Z, Mohammed P N, De Amici G, Freedman A P, Medeiros J, Sacks F, Estep R, Spencer M W, Chen C W, Wheeler K B, Edelstein W N, O’Neill P E and Njoku E G. 2017. SMAP L-Band microwave radiometer: instrument design and first year on orbit. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 1954-1966
  24. 24.
    Piepmeier J R, Johnson J T, Mohammed P N, Bradley D, Ruf C, Aksoy M, Garcia R, Hudson D, Miles L and Wong M. 2014. Radio-Frequency interference mitigation for the soil moisture active passive microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 761-775
  25. 25.
    Schmitt A U and Kaleschke L. 2018. A consistent combination of brightness temperatures from SMOS and SMAP over Polar Oceans for sea ice applications. Remote Sensing, 10(4): 553
  26. 26.
    Wu K, Nie L and Shu H. 2020. A comparison of SMAP and SMOS L-band brightness temperature observations over the global landmass. International Journal of Remote Sensing, 41(2): 399-419
  27. 27.
    Yueh S H, Dinardo S J, Fore A G and Li F K. 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
  28. 28.
    Zhao J C, Zhou X, Sun X Y, Cheng J J, Hu B and Li C H. 2017. The inter comparison and assessment of satellite sea-ice concentration datasets from the arctic. Journal of Remote Sensing, 21(3): 351-364
  29. 29.
    Zhao J P, Shi J X, Wang Z M, Li Z J and Huang F. 2015. Arctic amplification produced by sea ice retreat and its global climate effects. Advances in Earth Science, 30(9): 985-995

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