[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7780/kjrs.2019.35.2.5

Simulation of Sentinel-2 Product Using Airborne Hyperspectral Image and Analysis of TOA and BOA Reflectance for Evaluation of Sen2cor Atmosphere Correction: Focused on Agricultural Land

Cho, Kangjoon (Department of Civil and Environmental Engineering, Seoul National University)
Kim, Yongil (Department of Civil and Environmental Engineering, Seoul National University)

Publication Information

Korean Journal of Remote Sensing / v.35, no.2, 2019 , pp. 251-263 More about this Journal

Abstract

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.

Sentinel-2 위성은 기존 Landsat 시리즈보다 높은 공간해상도, 시간해상도 및 13개의 가시광, Red-edge, 적외, 단파적외 영역을 포함하는 다중분광 영상을 제공하고 있다. 이는 Landsat 시리즈와의 비슷한 파장대역 구성으로 향후 Landsat 시리즈와 융합분석이 가능하다는 이점이 있다. 그 동안 Landsat 위성영상은 국내의 다양한 연구에 적용되고 있으나 Sentinel-2 광학 위성영상은 많은 활용 사례가 보고되지 않았다. Sentinel-2 광학 위성은 기존 Landsat 위성이 제공하는 Top-of-Atmosphere(TOA) 반사율 영상을 Level-1C(L1C)에서 제공하고 있으며, Level-2A(L2A)영상을 통해 Landsat 시리즈보다 한 단계 더 대기보정이 수행된 Bottom-of-Atmosphere(BOA) 반사율 영상을 제공할 예정에 있다. BOA 반사율 영상은 에어로졸 광학 두께(AOT: Aerosol optical thickness)와 대기 중 수증기(WV: Water Vapor) 자료를 Sentinel-2 영상으로부터 얻고 이를 보정하여 TOA 반사율 영상을 BOA 반사율 영상으로 변환을 가능하게 한다. 현재, 유럽 전역지역에서 L2A 자료를 무료로 다운로드 가능하며 이외 지역의 경우 L2A 자료의 실시간 제공이 예정되어 있다. 따라서, Sentinel-2 L2A 자료의 활용가능성이 국내에서 점점 커질 것으로 기대되는 바이며 농업지역에서 Sentinel-2 L2A 영상이 제공하는 BOA 반사율 자료의 활용가능성을 확인하기 위해 경상남도 합천군에서 촬영된 항공 초분광영상을 활용하여 Sentinel-2 L2A 자료를 모의해 보고 정량적인 분석을 통해 모의영상과 실제 촬영된 영상을 비교해보았다. 본 연구에서는 Sentinel-2 L2A 자료와 항공기 기반의 초분광 영상을 통해 모의된 Sentinel-2 영상에 대한 정량적인 비교를 수행하였으며, 가시광 영역대의 밴드와 식생지수에 대하여 참조 영상인 모의영상과 대기보정이 수행된 L2A 자료의 RMSE의 감소 및 상관관계의 증가 경향이 뚜렷하게 나타나는 것으로 확인되었다.

Keywords

Sentinel-2; Bottom-Of-Atmosphere (BOA) reflectance; Sen2Cor; Airborne hyperspectral imagery; Spectral response function;

Citations & Related Records

Times Cited By KSCI : 2 (Citation Analysis)

Reference
Cited By KSCI

1	Barry, P., J. Mendenhall, P. Jarecke, M. Folkman, J. Pearlman, and B. Markham, 2002. EO-1 Hyperion hyperspectral aggregation and comparison with EO-1 Advanced Land Imager and Landsat 7 ETM+, Proc. of IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Jun. 24-28, vol. 6, pp. 3504-3506.
2	Belgiu, M. and O. Csillik, 2018. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sensing of Environment, 204: 509-523. DOI
3	Choi, J. W., S. T. Noh, and S. K. Choi, 2014. Unsupervised classification of Landsat-8 OLI satellite imagery based on iterative spectral mixture model, Journal of Korean Society for Geospatial Information System, 22(4): 53-61 (in Korean with English abstract). DOI
4	Clevers, J., L. Kooistra, and M. Van Den Brande, 2017. Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop, Remote Sensing, 9(5): 405. DOI
5	Gascon, F., C. Bouzinac, O. Thepaut, M. Jung, B. Francesconi, J. Louis, V. Lonjou, B. Lafrance, S. Massera, and A. Gaudel-Vacaresse, 2017. Copernicus Sentinel-2A Calibration and Products Validation Status, Remote Sensing, 9(6): 584. DOI
6	Gray, J. and C. Song, 2012. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors, Remote Sensing of Environment, 119: 173-183. DOI
7	Kim, D. S. and M. W. Pyeon, 2012. Aggregation of Hyperion Hyperspectral Bands to ALI and ETM+ Bands Using Spectral Response Information and the Weighted Sum Method, International Journal of Digital Content Technology and its Applications, 6(5): 189-199. DOI
8	Kim, G. H., Y. G. Lee, J. H. Kim, H. W. Choi, and B. J. Kim, 2018. Analysis of the Cooling Effects in Urban Green Areas using the Landsat 8 Satellite Data, Korean Journal of Remote Sensing, 34(2): 167-178 (in Korean with English abstract). DOI
9	Kim, H. J., D. K. Seo, Y. D. Eo, M. C. Jeon, and W. Y. Park, 2019. Multi-temporal Nonlinear Regression Method for Landsat Image Simulation, KSCE Journal of Civil Engineering, 23(2): 777-787. DOI
10	Lee, S. J., K. J. Kim, Y. H. Kim, J. W. Kim, and Y. W. Lee, 2017. Development of FBI(Fire Burn Index) for Sentinel-2 images and an experiment for detection of burned areas in Korea, Journal of Photo Geography, 27(4): 187-202 (in Korean with English abstract).
11	Li, Y., J. Chen, Q. Ma, H. K. Zhang, and J. Liu, 2018. Evaluation of Sentinel-2A surface reflectance derived using Sen2Cor in North America, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6): 1997-2021. DOI
12	Van Der Werff, H. and F. Van Der Meer, 2016. Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing, Remote Sensing, 8(11): 883. DOI
13	D'Odorico, P., A. Gonsamo, A. Damm, and M. E. Schaepman, 2013. Experimental evaluation of Sentinel-2 spectral response functions for NDVI time-series continuity, IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1336-1348. DOI
14	Li, J., X. Yang, C. Maffei, S. Tooth, and G. Yao, 2018. Applying Independent Component Analysis on Sentinel-2 Imagery to Characterize Geomorphological Responses to an Extreme Flood Event near the Non-Vegetated Rio Colorado Terminus, Salar de Uyuni, Bolivia, Remote Sensing, 10(5): 725. DOI
15	Louis, J., V. Debaecker, B. Pflug, M. Main-Knorn, J. Bieniarz, U. Mueller-Wilm, E. Cadau, and F. Gascon, 2016. Sentinel-2 Sen2Cor: L2A processor for users, Proc. of the Living Planet Symposium, Prague, Czech Republic, May 9-13, pp. 9-13.
16	Shin, J. S., K. Y. Kim, J. E. Min, and J. H. Ryu, 2018. Red Tide Detection through Image Fusion of GOCI and Landsat OLI, Korean Journal of Remote Sensing, 34(2): 377-391 (in Korean with English abstract). DOI
17	Thanh Noi, P. and M. Kappas, 2018. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery, Sensors, 18(1): 18. DOI
18	Yim, J. S. and G. H. Lee, 2017. Estimating Urban Temperature by Combining Remote Sensing Data and Terrain Based Spatial Interpolation Method, Journal of the Korean Cartographic Association, 17(2): 75-88 (in Korean with English abstract). DOI