Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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A preliminary assessment of high-spatial-resolution satellite rainfall estimation from SAR Sentinel-1 over the central region of South Korea

한반도 중부지역에서의 SAR Sentinel-1 위성강우량 추정에 관한 예비평가

  • Received : 2022.02.16
  • Accepted : 2022.04.01
  • Published : 2022.06.30
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Abstract

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

위성에서 보다 미세한 공간 분해능으로 신뢰할 수 있는 지상 강우 관측은 도시 수문학적 및 미시적 농업 수요에 필수적이다. 전통적으로 "톱다운" 접근 방식 기반 위성 강우 산출물이 널리 사용되고 있지만 공간 분해능에 한계가 있다. 본 연구는 C-밴드 SAR Sentinel-1 위성 데이터(SM2RAIN-S1)에 적용되는 매개 변수화된 SM2RAIN 모델인 강우 추정을 위한 새로운 "상향식" 접근 방식의 가능성을 평가하여 중부지방에 대한 높은 공간 분해능 지상 강우 추정치(0.01° 그리드/6일)를 생성하는 것을 목표로 한다. 그것의 성능은 중부지방 두 개의 다른 하위 지역, 즉 혼합 산림 중심, 중간 하위 지역, 그리고 경작 중심, 서해안 하위 지역의 1년 기간 동안 기존의 재분석 프로덕트와 우량계 네트워크의 각각의 강우 데이터를 사용하여 공간 및 시간적 가변성에 대해 평가되었다. 평가결과에 따르면 SM2RAIN-S1 프로덕트는 중부지방의 일반적인 강우 패턴을 포착할 수 있고, 서로 다른 토지 피복으로 지역 규모에서 공간 분해능 강우량 측정 가능성을 보유할 수 있으며, 강우량 관측치에 대한 편중된 강우량 추정치가 제공되었다. 또한 SM2RAIN-S1 강우량은 피어슨의 상관 계수(R = 0.69)를 고려할 때 혼합림에서 더 우수했으며, 이는 혼합림에서 토양 수분과 강우의 시간 역학을 포착하는 데 6일 SM2RAIN-S1 데이터의 적합성을 암시했다. 그러나, RMSE와 바이어스 측면에서, 혼합림보다는 경작지의 SM2RAIN-S1 강우 생성물에서 더 나은 성능을 얻었으며, 이는 높은 증발증산 손실(특히 혼합림)에 의해 유도된 더 큰 오류를 SM2RAIN의 추가 개선에 포함해야 한다는 것을 나타낸다.

Keywords

Acknowledgement

We acknowledge the European Space Agency for the SAR Sentinel-1 dataset, the European Centre for Medium-range Weather Forecasts and the Copernicus Climate Change Service for the ERA5-Land dataset, the Korea Meteorological Administration for the AWS rain gauge dataset, and the Department of Ecology and Evolutionary Biology, Yale University, USA for the GCLC product.

References

  1. Balenzano, A., Mattia, F., Satalino, G., Lovergine, F.P., Palmisano, D., Peng, J., Marzahn, P., Wegmuller, U., Cartus, O., Dabrowska-Zielinska, K., Musial, J.P., Davidson, M.W.J., Pauwels, V.R.N., Cosh, M.H., McNairn, H., Johnson, J.T., Walker, J.P., Yueh, S.H., Entekhabi, D., Kerr, Y.H., and Jackson, T.J. (2021). "Sentinel-1 soil moisture at 1 km resolution: A validation study." Remote Sensing of Environment, Vol. 263, 112554. https://doi.org/10.1016/j.rse.2021.112554
  2. Bauer-Marschallinger, B., Cao, S., Navacchi, C., Freeman, V., Reuss, F., Geudtner, D., Rommen, B., Vega, F.C., Snoeij, P., Attema, E., Reimer, C., and Wagner, W. (2021). "The normalised Sentinel-1 global backscatter model, mapping earth's land surface with C-band microwaves." Scientific Data, Vol. 8, No. 1, pp. 1-18. https://doi.org/10.1038/s41597-020-00786-7
  3. Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Ciabatta, L., Brocca, L., and Wagner, W. (2018). "Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles." IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 1, pp. 520-539. https://doi.org/10.1109/tgrs.2018.2858004
  4. Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V. (2014). "Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data." Journal of Geophysical Research: Atmospheres, Vol. 119, No. 9, pp. 5128-5141. https://doi.org/10.1002/2014jd021489
  5. Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schuller, L., Bojkov, B., and Wagner, W. (2019). "SM2RAIN-ASCAT (2007-2018): Global daily satellite rainfall data from ASCAT soil moisture observations." Earth System Science Data, Vol. 11, No. 4, pp. 1583-1601. https://doi.org/10.5194/essd-11-1583-2019
  6. Brocca, L., Massari, C., Ciabatta, L., Wagner, W., and Stoffelen, A. (2016). "Remote sensing of terrestrial rainfall from Ku-band scatterometers." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 1, pp. 533-539. https://doi.org/10.1109/JSTARS.2015.2508065
  7. Brocca, L., Moramarco, T., Melone, F., and Wagner, W. (2013). "A new method for rainfall estimation through soil moisture observations." Geophysical Research Letters, Vol. 40, No. 5, pp. 853-858. https://doi.org/10.1002/grl.50173
  8. Capecchi, V., and Brocca, L. (2014). "A simple assimilation method to ingest satellite soil moisture into a limited-area NWP model." Meteorologische. Zeitschrift, Vol. 23, No. 2, pp. 105-121. https://doi.org/10.1127/0941-2948/2014/0585
  9. Chen, F., Gao, Y., Wang, Y., Qin, F., and Li, X. (2018). "Downscaling satellite-derived daily precipitation products with an integrated framework." International Journal of Climatology, Vol. 39, No. 3, pp. 1287-1304. https://doi.org/10.1002/joc.5879
  10. Ciabatta, L., Brocca, L., Massari, C., Moramarco, T., Puca, S., Rinollo, A., Gabellani, S., and Wagner, W. (2015). "Integration of satellite soil moisture and rainfall observations over the Italian territory." Journal of Hydrometeorology, Vol. 16, No. 3, pp. 1341-1355. https://doi.org/10.1175/JHM-D-14-0108.1
  11. Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W. (2018). "SM2RAIN-CCI: A new global long-term rainfall data set derived from ESA CCI soil moisture." Earth System Science Data, Vol. 10, No. 1, pp. 267-280. https://doi.org/10.5194/essd-10-267-2018
  12. Fang, X., Zhao, W., Wang, L., Feng, Q., Ding, J., Liu, Y., and Zhang, X. (2016). "Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China." Hydrology and Earth System Sciences, Vol. 20, No. 8, pp. 3309-3323. https://doi.org/10.5194/hess-20-3309-2016
  13. Filippucci, P., Brocca, L., Massari, C., Saltalippi, C., Wagner, W., and Tarpanelli, A. (2021). "Toward a self-calibrated and independent SM2RAIN rainfall product." Journal of Hydrology, Vol. 603, 126837. https://doi.org/10.1016/j.jhydrol.2021.126837
  14. Fletcher, T.D., Andrieu, H., and Hamel, P. (2013). "Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art." Advances in Water Resources, Vol. 51, pp. 261-279. https://doi.org/10.1016/j.advwatres.2012.09.001
  15. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.N. (2020). "The ERA5 global reanalysis." Quarterly Journal of the Royal Meteorological Society, Vol. 146, No. 730, pp. 1999-2049. https://doi.org/10.1002/qj.3803
  16. Hong, M., Lee, S.H., Lee, S.J., and Choi, J.Y. (2021). "Application of high-resolution meteorological data from NCAM-WRF to characterize agricultural drought in small-scale farmlands based on soil moisture deficit." Agricultural Water Management, Vol. 243, 106494. https://doi.org/10.1016/j.agwat.2020.106494
  17. Kim, J., and Han, H. (2021). "Evaluation of the CMORPH highresolution precipitation product for hydrological applications over South Korea." Atmospheric Research, Vol. 258, 105650. https://doi.org/10.1016/j.atmosres.2021.105650
  18. Koster, R.D., Brocca, L., Crow, W.T., Burgin, M.S., and De Lannoy, G.J. (2016). "Precipitation estimation using L-band and C-band soil moisture retrievals." Water Resources Research, Vol. 52, No. 9, pp. 7213-7225. https://doi.org/10.1002/2016WR019024
  19. Nguyen, H.H., Cho, S., and Choi, M. (2022). "Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images." Agricultural and Forest Meteorology, Vol. 312, 108719. https://doi.org/10.1016/j.agrformet.2021.108719
  20. Nguyen, H.H., Cho, S., Jeong, J., and Choi, M. (2021). "A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains." Remote Sensing of Environment, Vol. 255, 112283. https://doi.org/10.1016/j.rse.2021.112283
  21. Nguyen, H.H., Kim, H., and Choi, M. (2017). "Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region." Advances in Water Resources, Vol. 108, pp. 125-138. https://doi.org/10.1016/j.advwatres.2017.07.020
  22. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and Carvalhais, N. (2019). "Deep learning and process understanding for data-driven Earth system science." Nature, Vol. 566, No. 7743, pp. 195-204. https://doi.org/10.1038/s41586-019-0912-1
  23. Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.L. (2018). "A review of global precipitation data sets: Data sources, estimation, and intercomparisons." Reviews of Geophysics, Vol. 56, No. 1, pp. 79-107. https://doi.org/10.1002/2017rg000574
  24. Tang, G., Clark, M.P., Papalexiou, S.M., Ma, Z., and Hong, Y. (2020). "Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets." Remote Sensing of Environment, Vol. 240, 111697. https://doi.org/10.1016/j.rse.2020.111697
  25. Tarpanelli, A., Massari, C., Ciabatta, L., Filippucci, P., Amarnath, G., and Brocca, L. (2017). "Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation." Advances in Water Resources, Vol. 108, pp. 249-255. https://doi.org/10.1016/j.advwatres.2017.08.010
  26. Trenberth, K.E., and Asrar, G.R. (2014). "Challenges and opportunities in water cycle research: WCRP contributions." The Earth's Hydrological Cycle. Springer, Dordrecht, Netherland, pp. 515-532.
  27. Tuanmu, M.N., and Jetz, W. (2014). "A global 1-km consensus land-cover product for biodiversity and ecosystem modelling." Global Ecology and Biogeography, Vol. 23, No. 9, pp. 1031-1045. https://doi.org/10.1111/geb.12182
  28. Wagner, W., Lemoine, G., and Rott, H. (1999). "A method for estimating soil moisture from ERS scatterometer and soil data." Remote Sensing of Environment, Vol. 70, No. 2, pp. 191-207. https://doi.org/10.1016/S0034-4257(99)00036-X
  29. Zhang, L., Li, X., Cao, Y., Nan, Z., Wang, W., Ge, Y., Wang, P., and Yu, W. (2020). "Evaluation and integration of the top-down and bottom-up satellite precipitation products over mainland China." Journal of Hydrology, Vol. 581, 124456. https://doi.org/10.1016/j.jhydrol.2019.124456