Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of spatial distribution of snow depth using DInSAR of Sentinel-1 SAR satellite images

Sentinel-1 SAR 위성영상의 위상차분간섭기법(DInSAR)을 이용한 적설심의 공간분포 추정

  • Park, Heeseong ( Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Chung, Gunhui (Department of Civil Engineering, Hoseo University)
  • 박희성 (한국건설기술연구원 수자원하천연구본부) ;
  • 정건희 (호서대학교 건축토목공학부)
  • Received : 2022.10.11
  • Accepted : 2022.12.01
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

적설에 의한 피해는 자주 발생하지 않지만 발생하면 광범위한 지역에 피해를 준다. 적설에 의한 피해를 예방하기 위해서는 지역별로 피해를 유발하는 적설심을 미리 파악해 둘 필요가 있다. 하지만 관측하고 있는 적설심은 특정 관측지점으로 한정되어 피해를 유발하는 지역별 피해유발적설심을 파악하는데 어려움이 있다. 이를 극복하기 위한 일반적인 방법은 관측지점의 적설을 보간하여 공간적으로 확대하는 것이다. 하지만 이것은 매우 적은 자료를 가지고 고도 등 지형적인 특성이 다른 넓은 영역을 통계적으로 추론해야 하는 한계로 인해 지역에 대한 피해유발 피해유발적설심의 구명에 더 혼란을 주기도 한다. 이를 보완하기 위해서는 넓은 영역을 관측하는 위성영상을 활용할 수 있으며, 그 중에서도 합성개구레이더(Synthetic Aperture Radar; SAR)를 이용한 위상차분 간섭기법(DInSAR)을 활용할 수 있다. 위상간섭영상은 두 개의 다른 시기에 측정된 합성개구레이더 영상의 위상간섭을 이용한 것으로 일반적으로 미세한 지형의 변화를 추적할 때 사용되기도 한다. 본 연구에서는 유럽우주국(ESA)에서 운영하는 Sentinel-1B 위성의 dual polarimetric IW 모드 C-band SAR 데이터를 사용하여 DInSAR 분석을 수행하여 적설심의 공간분포를 추정하였다. 또한 정지궤도복합위성 천리안 2호(GK-2A)의 L2 적설심 추정 자료를 이용하여 비교하였다. 적용 결과, 적설예측의 정확도는 격자별로 계산할 경우, DInSAR 는 약 0.92%, GK-2A 는 약 0.71% 를 나타내 DInSAR의 적용성이 높게 나타났다. 즉, DInSAR 방법을 이용하여 계산된 적설심과 기상관측소에서 관측된 적설심을 공간보간하여 비교한 결과, 적설의 분석 결과 적설심을 과대추정하는 경우가 발생하기는 했으나, 적설심의 공간분포를 추정하는데 충분한 정보를 제공했으며, 이러한 방법으로 파악된 적설심의 공간분포는 실제 피해발생지역의 적설심을 보다 정확하게 추정하는데 기여할 수 있으며, 이것은 지역별 피해유발적설심을 파악하는데 도움이 될 것이다.

Keywords

Acknowledgement

이 논문은 행정안전부 '기후변화대응 AI기반 풍수해 위험도 예측기술개발' 사업의 지원을 받아 수행된 연구임(2022-MOIS61-003).

References

  1. Chen, J., Gunther, F., Grosse, G., Liu, L., and Lin, H. (2018). "Sentinel-1 InSAR measurements of elevation changes over Yedoma uplands on Sobo-Sise island, Lena Delta." Remote Sensing, Vol. 10, No. 7, 1152. doi: 10.3390/rs10071152.
  2. Chini, M., Atzori, S., Trasatti, E., Bignami, C., Kyriakopoulos, C., Tolomei, C., and Stramondo, S. (2010). "The May 12, 2008, (Mw 7.9) Sichuan earthquake (China): Multiframe ALOS-PALSAR DInSAR analysis of coseismic deformation." IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 2, pp. 266-270. https://doi.org/10.1109/LGRS.2009.2032564
  3. Dozier, J., and Shi, J. (2000). "Estimation of snow water equivalence using SIR-C/X-SAR. II. Inferring snow depth and particle size." IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 6, pp. 2475-2488. doi: 10.1109/36.885196.
  4. European Space Agency (ESA) (2021). Copernicus open access hub, accessed 21 November 2022, .
  5. Evans, J.R., Kruse, F.A., Bickel, D.L., and Dunkel, R. (2014). "Determining snow depth using Ku-band Interferometric synthetic Aperture Radar (Insar)." In SPIE Proceedings: SPIE, Edited by Ranney, K.I. and Doerry, A., 90770V, Baltimore, MD, U.S.
  6. Guneriussen, T., Hogda, K.A., Johnsen, H., and Lauknes, I. (2001). "InSAR for estimation of changes in snow water equivalent of dry snow." IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 10, pp. 2101-2108. doi: 10.1109/36.957273.
  7. Jiang, L., Lin, H., Ma, J., Kong, B., and Wang, Y. (2011). "Potential of small-baseline SAR interferometry for monitoring land subsidence related to underground coal fires: Wuda (Northern China) case study." Remote Sensing of Environment, Vol. 115, No. 2, pp. 257-268. https://doi.org/10.1016/j.rse.2010.08.008
  8. Jung, H.S. (2019). GK-2A AMI algorithm theoretical basis document: SD (Snow Depth). National Meteorological Satellite Center.
  9. Kim, S.B., Shin, H.-J., Lee, J.-W., Yu, Y.-S., and Kim, S.J. (2011). "Mapping technique for heavy snowfall distribution using Terra MODIS images and ground measured snowfall data." Journal of the Korean Association of Geographic Information Studies, Vol. 14, No. 4, pp. 33-43. doi: 10.11108/KAGIS.2011.14.4.033.
  10. Lee, H. (2017). "Application of KOMPSAT-5 SAR interferometry by using SNAP software." Korean Journal of Remote Sensing, Vol. 33, No. 6_3, pp. 1215-1221. doi: 10.7780/kjrs.2017.33.6.3.5.
  11. Leinss, S., Parrella, G., and Hajnsek, I. (2014). "Snow height determination by polarimetric phase differences in X-Band SAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 9, pp. 3794-3810. doi: 10.1109/JSTARS.2014.2323199.
  12. Leinss, S., Wiesmann, A., Lemmetyinen, J., and Hajnsek, I. (2015). "Snow water equivalent of dry snow measured by differential interferometry." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 8, pp. 3773-3790. doi: 10.1109/JSTARS.2015.2432031.
  13. Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K., and Rabaute, T. (1993). "The displacement field of the Landers earthquake mapped by radar interferometry." Nature, Vol. 364, No. 8, pp. 138-142. https://doi.org/10.1038/364138a0
  14. Matzler, C. (1996). "Microwave permittivity of dry snow." IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 2, pp. 573-581. doi: 10.1109/36.485133.
  15. Ministry of the Interior and Safety (MOIS) (2021). Annual natural disaster report.
  16. Nagler, T., Rott, H., and Kamelger, A. (2002). "Analysis of landslides in Alpine Areas by means of SAR Interferometry." IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada, pp. 198-200.
  17. Pipia, L., Fabregas, X., Aguasca, A., Lopez Martinez, C., Duque, S., Mallorqui, J.J., and Marturia, J. (2009). "Polarimetric differential SAR interferometry: First results with ground-based measurements." IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 1, pp. 167-171. https://doi.org/10.1109/LGRS.2008.2009007
  18. Rott, H., Nagler, T., and Scheiber, R. (2004). "Snow mass retrieval by means of SAR interferometry." FRINGE 2003 Workshop, Edited by Lacoste, H., Vol. 550, SA Special Publication, ESA/ESRIN, Frascati, Italy.
  19. ami, C., Salvi, S., and Atzori, S. (2011). "X-, C-, and L-band DInSAR investigation of the April 6, 2009, Abruzzi earthquake." IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 1, pp. 49-53. https://doi.org/10.1109/LGRS.2010.2051015
  20. Varade, D., Maurya, A.K., Dikshit, O., Singh, G., and Manickam, S. (2020). "Snow depth in Dhundi: An estimate based on weighted bias corrected differential phase observations of dual polarimetric bi-temporal Sentinel-1 data." International Journal of Remote Sensing, Vol. 41, No. 8, pp. 3031-3053. doi: 10.1080/01431161.2019.1698076.
  21. Wang, Y., Zhou, G., and You, H. (2019). "An energy based SAR image segmentation method with weighted feature." Remote Sensing, Vol. 11, No. 10, 1169.
  22. Yen, J., Chen, K., Chang, C., and Boerner, W. (2008). "Evaluation of earthquake potential and surface deformation by differential interferometry." Remote Sensing of Environment, Vol. 112, No. 3, pp. 782-795. https://doi.org/10.1016/j.rse.2007.06.012
  23. Yu, F., Sun, W., Li, J., Zhao, Y., Zhang, Y., and Chen, G. (2017). "An improved Otsu method for oil spill detection from SAR images." Oceanologia, Vol. 59, No. 3, pp. 311-317. https://doi.org/10.1016/j.oceano.2017.03.005