Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.5.2.5

Generation of Daily High-resolution Sea Surface Temperature for the Seas around the Korean Peninsula Using Multi-satellite Data and Artificial Intelligence  

Jung, Sihun (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Choo, Minki (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Cho, Dongjin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_2, 2022 , pp. 707-723 More about this Journal
Abstract
Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R2 of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.
Keywords
Sea surface temperature; Reconstruction; Artificial intelligence; DINCAE; LGBM; Korea peninsula;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
1 Bulgin, C.E., C.J. Merchant, and D. Ferreira, 2020. Tendencies, variability and persistence of sea surface temperature anomalies, Scientific Reports, 10(1): 1-13. https://doi.org/10.1038/s41598-020-64785-9   DOI
2 Chin, T.M., J. Vazquez-Cuervo, and E.M. Armstrong, 2017. A multi-scale high-resolution analysis of global sea surface temperature, Remote Sensing of Environment, 200: 154-169. https://doi.org/10.1016/j.rse.2017.07.029   DOI
3 Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, 2017. Lightgbm: A highly efficient gradient boosting decision tree, Advances in neural information processing systems, Proc. of 31st Conference on Neural Information Processing Systems, Long Beach, CA, vol. 30, pp. 3146-3154.
4 Kim, T., S. Chung, C.Y. Chung, and S. Baek, 2017. An estimation of the composite sea surface temperature using COMS and polar orbit Satellites data in Northwest Pacific Ocean, Korean Journal of Remote Sensing, 33(3): 275-285 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.3.3   DOI
5 Kurihara, Y., H. Murakami, and M. Kachi, 2016. Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite, Geophysical Research Letters, 43(3): 1234-1240. https://doi.org/10.1002/2015GL067159   DOI
6 Levy, R.C., S. Mattoo, V. Sawyer, Y. Shi, P.R. Colarco, A.I. Lyapustin, Y. Wang, and L.A. Remer, 2018. Exploring systematic offsets between aerosol products from the two MODIS sensors, Atmospheric Measurement Techniques, 11(7): 4073-4092. https://doi.org/10.5194/amt-11-4073-2018   DOI
7 Liu, X. and M. Wang, 2018. Gap filling of missing data for VIIRS global ocean color products using the DINEOF method, IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4464-4476. https://doi.org/10.1109/TGRS.2018.2820423   DOI
8 Jung, S., C. Yoo, and J. Im, 2022. High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension, Remote Sensing, 14(3): 575. https://doi.org/10.3390/rs14030575   DOI
9 Kang, Y., M. Kim, E. Kang, D. Cho, and J. Im, 2022. Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia, ISPRS Journal of Photogrammetry and Remote Sensing, 183: 253-268. https://doi.org/10.1016/j.isprsjprs.2021.11.016   DOI
10 Woo, H.J. and K. Park, 2020. Inter-comparisons of daily sea surface temperatures and in-situ temperatures in the coastal regions, Remote Sensing, 12(10):1592. https://doi.org/10.3390/rs12101592   DOI
11 Xiao, C., N. Chen, C. Hu, K. Wang, Z. Xu, Y. Cai, L. Xu, Z. Chen, and J. Gong, 2019. A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data, Environmental Modelling & Software, 120: 104502. https://doi.org/10.1016/j.envsoft.2019.104502   DOI
12 Xu, F. and A. Ignatov, 2014. In situ SST quality monitor (iQuam), Journal of Atmospheric and Oceanic Technology, 31(1): 164-180. https://doi.org/10.1175/JTECH-D-13-00121.1   DOI
13 Xu, F. and A. Ignatov, 2016. Error characterization in iQuam SSTs using triple collocations with satellite measurements, Geophysical Research Letters, 43(20): 10826-10834. https://doi.org/10.1002/2016GL070287   DOI
14 Choi, H., Y. Kang, and J. Im, 2021. Estimation of TRO POMI-derived Ground-level SO 2 Concentrations Using Machine Learning Over East Asia, Korean Journal of Remote Sensing, 37(2): 275-290 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.2.8   DOI
15 Donlon, C., P. Minnett, C. Gentemann, T. Nightingale, I. Barton, B. Ward, and M. Murray, 2002. Toward improved validation of satellite sea surface skin temperature measurements for climate research, Journal of Climate, 15(4): 353-369. https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2   DOI
16 Donlon, C.J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sensing of Environment, 116: 140-158. https://doi.org/10.1016/j.rse.2010.10.017   DOI
17 Good, S., E. Fiedler, C. Mao, M. J. Martin, A. Maycock, R. Reid, J. Roberts-Jones, T. Searle, J. Waters, and J. While, 2020. The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses, Remote Sensing, 12(4):720. https://doi.org/10.3390/rs12040720   DOI
18 Han, Z., Y. He, G. Liu, and W. Perrie, 2020. Application of DINCAE to Reconstruct the Gaps in Chlorophylla Satellite Observations in the South China Sea and West Philippine Sea, Remote Sensing, 12(3):480. https://doi.org/10.3390/rs12030480   DOI
19 Jang, E., Y.J. Kim, J. Im, Y.-G. Park, and T. Sung, 2022. Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning, Remote Sensing of Environment, 273: 112980. https://doi.org/10.1016/j.rse.2022.112980   DOI
20 Ji, C., Y. Zhang, Q. Cheng, and J. Y. Tsou, 2021. Investigating ocean surface responses to typhoons using reconstructed satellite data, International Journal of Applied Earth Observation and Geoinformation, 103: 102474. https://doi.org/10.1016/j.jag.2021.102474   DOI
21 Park, K., F. Sakaida, and H. Kawamura, 2008. Oceanic skin-bulk temperature difference through the comparison of satellite-observed sea surface temperature and in-situ measurements, Korean Journal of Remote Sensing, 24(4): 273-287 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2008.24.4.273   DOI
22 Park, S., M. Kim, and J. Im, 2021. Estimation of Ground-level PM 10 and PM 2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data, Korean Journal of Remote Sensing, 37(2): 321-335 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.2.11   DOI
23 Stark, J.D., C.J. Donlon, M.J. Martin, and M.E. McCulloch, 2007. OSTIA: An operational, high resolution, real time, global sea surface temperature analysis system, Proc. of Oceans 2007-Europe, Aberdeen, UK, Jun. 18-21, pp. 1-4. https://doi.org/10.1109/OCEANSE.2007.4302251   DOI
24 Sunder, S., R. Ramsankaran, and B. Ramakrishnan, 2020. Machine learning techniques for regional scale estimation of high-resolution cloud-free daily sea surface temperatures from MODIS data, ISPRS Journal of Photogrammetry and Remote Sensing, 166: 228-240. https://doi.org/10.1016/j.isprsjprs.2020.06.008   DOI
25 Wentz, F.J., C. Gentemann, D. Smith, and D. Chelton, 2000. Satellite measurements of sea surface temperature through clouds, Science, 288(5467): 847-850. https://doi.org/10.1126/science.288.5467.84   DOI
26 Alvera-Azcarate, A., A. Barth, G. Parard, and J.-M. Beckers, 2016. Analysis of SMOS sea surface salinity data using DINEOF, Remote Sensing of Environment, 180: 137-145. https://doi.org/10.1016/j.rse.2016.02.044   DOI
27 Alvera-Azcarate, A., A. Barth, J. M. Beckers, and R. H. Weisberg, 2007. Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields, Journal of Geophysical Research: Oceans, 112(C3). https://doi.org/10.1029/2006JC003660   DOI
28 Barth, A., A. Alvera-Azcarate, M. Licer, and J.-M. Beckers, 2020. DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations, Geoscientific Model Development, 13(3): 1609-1622. https://doi.org/10.5194/gmd-13-1609-2020   DOI
29 Barth, A., A. Alvera-Azcarate, C. Troupin, and J.-M. Beckers, 2022. DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations, Geoscientific Model Development, 15(5): 2183-2196. https://doi.org/10.5194/gmd-15-2183-2022   DOI
30 Beckers, J.-M. and M. Rixen, 2003. EOF calculations and data filling from incomplete oceanographic datasets, Journal of Atmospheric and Oceanic Technology, 20(12): 1839-1856. https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2   DOI
31 Jonasson, O., I. Gladkova, A. Ignatov, and Y. Kihai, 2021. Algorithmic improvements and consistency checks of the NOAA global gridded supercollated SSTs from low Earth orbiting satellites (L3S-LEO), Proc. of SPIE 11752 Ocean Sensing and Monitoring XIII, May 25, vol. 11752, pp. 5-18. https://doi.org/10.1117/12.2585819   DOI
32 Jung, S., Y.J. Kim, S. Park, and J. Im, 2020. Prediction of sea surface temperature and detection of ocean heat wave in the South Sea of Korea using time-series deep-learning approaches, Korean Journal of Remote Sensing, 36(5-3): 1077-1093 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.3.7   DOI
33 Luo, X., J. Song, J. Guo, Y. Fu, L. Wang, and Y. Cai, 2022. Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method, International Journal of Remote Sensing, 43(9): 3336-3358. https://doi.org/10.1080/01431161.2022.2090872   DOI
34 Merchant, C., A. Harris, M. Murray, and A. Zavody, 1999. Toward the elimination of bias in satellite retrievals of sea surface temperature: 1. Theory, modeling and interalgorithm comparison, Journal of Geophysical Research: Oceans, 104(C10): 23565-23578. https://doi.org/10.1029/1999JC900105   DOI
35 Nechad, B., A. Alvera-Azcarate, K. Ruddick, and N. Greenwood, 2011. Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data, Ocean Dynamics, 61(8): 1205-1214. https://doi.org/10.1007/s10236-011-0425-4   DOI
36 NOAA/STAR, 2021. GHRSST NOAA/STAR ACSPO v2.800.02 degree L3S Dataset from Afternoon LEO Satellites (GDS v2), Physical Oceanography Distributed Active Archive Center (PODAAC), CA, USA. https://doi.org/10.5067/GHLPM-3SS28   DOI
37 O'Carroll, A.G., E.M. Armstrong, H.M. Beggs, M. Bouali, K.S. Casey, G.K. Corlett, P. Dash, C.J. Donlon, C.L. Gentemann, and J.L. Hoyer, 2019. Observational needs of sea surface temperature, Frontiers in Marine Science, 6: 420. https://doi.org/10.3389/fmars.2019.00420   DOI
38 O'Carroll, A.G., J.R. Eyre, and R.W. Saunders, 2008. Three-way error analysis between AATSR, AMSR-E, and in situ sea surface temperature observations, Journal of Atmospheric and Oceanic Technology, 25(7): 1197-1207. https://doi.org/10.1175/2007JTECHO542.1   DOI
39 Ouala, S., R. Fablet, C. Herzet, B. Chapron, A. Pascual, F. Collard, and L. Gaultier, 2018. Neural network based Kalman filters for the Spatio-temporal interpolation of satellite-derived sea surface temperature, Remote Sensing, 10(12): 1864. https://doi.org/10.3390/rs10121864   DOI
40 Park, J., D.-W. Kim, Y.-H. Jo, and D. Kim, 2018. Accuracy evaluation of daily-gridded ASCAT satellite data around the Korean Peninsula, Korean Journal of Remote Sensing, 34(2-1): 213-225 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.2.1.5   DOI
41 Park, K. and Y.-H. Kim, 2009. A Methodology for 3-D Optimally-Interpolated Satellite Sea Surface Temperature Field and Limitation, Journal of the Korean Earth Science Society, 30(2): 223-233 (in Korean with English abstract). https://doi.org/10.5467/JKESS.2009.30.2.223   DOI
42 Park, K., 2019. GK-2A AMI Algorithm Theoretical Basis Document (ATBD) Sea Surface Temperature, National Meteorological Satellite Center (NMSC), Jincheon, Korea.