Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

DOI QR Code

Sun-induced Fluorescence Data: Case of the Rice Paddy Field in Naju

논벼에서 관측된 태양 유도 엽록소 형광 자료: 나주에서 2020년 6월 10일부터 10월 5일까지

  • Ryu, Jae-Hyun (Department of Applied Plant Science, Chonnam National University) ;
  • Jang, Seon Woong (IREMTECH, Co., Ltd) ;
  • Kim, Hyunki (Department of Applied Plant Science, Chonnam National University) ;
  • Moon, Hyun-Dong (Department of Applied Plant Science, Chonnam National University) ;
  • Sin, Seo-Ho (Food Crop Research Center, Agricultural Research & Extension Services) ;
  • Lee, Yang-Won (Department of Spatial Information Engineering, Pukyong National University) ;
  • Cho, Jaeil (Department of Applied Plant Science, Chonnam National University)
  • 류재현 (전남대학교 농업생명과학대학 응용식물학과) ;
  • 장선웅 ((주)아이렘기술개발) ;
  • 김현기 (전남대학교 농업생명과학대학 응용식물학과) ;
  • 문현동 (전남대학교 농업생명과학대학 응용식물학과) ;
  • 신서호 (전남농업기술원 식량작물연구소) ;
  • 이양원 (부경대학교 환경해양대학 공간정보시스템공학과) ;
  • 조재일 (전남대학교 농업생명과학대학 응용식물학과)
  • Received : 2020.12.03
  • Accepted : 2021.02.08
  • Published : 2021.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Sun-induced fluorescence (SIF) retrieval using remote sensing technique has been used in an effort to understand the photosynthetic efficiency and stress condition of vegetation. Although optical devices and SIF retrieval methodologies were established in order to retrieve SIF, the SIF measurements are domestically sparse. SIF data of paddy rice w as measured in Naju, South Korea from June 10, 2020 to October 5, 2020. The SIFs based red (O2A) and far-red (O2B) w ere retrieved using a spectral fitting method and an improved Fraunhofer line depth, and photosynthetically active radiation was also produced. In addition, the SIF data was filtered considering solar zenith angle, saturation conditions, the rapid and sudden change of solar irradiance, and sun glint. The provided SIF data can help to understand a SIF product and the filtering method of SIF data can contribute to producing high-quality SIF data.

원격탐사 기법을 이용하여 산출된 태양 유도 엽록소 형광(Sun-Induced Fluorescence, SIF)을 통해 식생의 광합성 효율 및 스트레스 상태를 이해하기 위한 노력이 세계적으로 진행되고 있다. 최근 SIF 관측을 위한 광학 기기 개발과 SIF 산출 방법론이 정립되고 있으나 아직 국내에서는 SIF 관측이 드물며 관측 자료의 수도 적다. 본고에서는 나주에서 2020년 6월 10일부터 2020년 10월 5일까지 벼 생육기간 동안 광학 기기를 이용하여 관측된 SIF 자료를 공개하였다. SFM 방법과 iFLD 방법으로 O2A과 O2B 흡수 대역의 SIF를 산출하였으며, 광합성 유효 복사량에 대한 정보도 제공하였다. 또한, 태양천정각, 센서의 광 포화상태, 관측 순간의 태양광의 급격한 변화, 태양광 정반사 영향 등을 고려하여 품질이 낮은 SIF 자료를 필터링하였다. 본 자료를 통해 연구자들이 SIF에 대한 이해를 높이고, 제안된 SIF 자료 필터링 방법으로 SIF 산출물 품질관리에 도움이 될 것으로 기대한다.

Keywords

References

  1. Alonso, L., L. Gomez-Chova, J. Vila-Frances, J. Amoros-Lopez, L. Guanter, J. Calpe, and J. Moreno, 2008: Improved fraunhofer line discrimination method for vegetation fluorescence quantification. IEEE Geoscience and Remote Sensing Letters 5 (4), 620-624. https://doi.org/10.1109/LGRS.2008.2001180
  2. Cendrero-Mateo, M. P., S. Wieneke, A. Damm, L. Alonso, F. Pinto, J. Moreno, L. Guanter, M. Celesti, M. Rossini, N. Sabater, S. Cogliati, T. Julitta, U. Rascher, Y. Goulas, H. Aasen, J. Pacheco-Labrador, and A. M. Arthur, 2019: Sun-induced chlorophyll fluorescence III: Benchmarking retrieval methods and sensor characteristics for proximal sensing. Remote Sensing 11(8), 962pp. https://doi.org/10.3390/rs11080962
  3. Cogliati, S., W. Verhoef, S. Kraft, N. Sabater, L. Alonso, J. Vicent, J. Moreno, M. Drusch, and R. Colombo, 2015: Retrieval of sun-induced fluorescence using advanced spectral fitting methods. Remote Sensing of Environment 169, 344-357. https://doi.org/10.1016/j.rse.2015.08.022
  4. Guanter, L., Y. Zhang, M. Jung, J. Joiner, M. Voigt, J. A. Berry, C. Frankenberg, A. R. Huete, P. Zarco-Tejada, J.-E. Lee, M. S. Moran, G. Ponce-Campos, C. Beer, G. Camps-Valls, N. Buchmann, D. Gianelle, K. Klumpp, A. Cescatti, J. M. Baker, and M. S. Moran, 2014: Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences 111(14), E1327-E1333. https://doi.org/10.1073/pnas.1320008111
  5. Goulas, Y., A. Fournier, F. Daumard, S. Champagne, A. Ounis, O. Marloie, I. Moya, 2017: Gross primary production of a wheat canopy relates stronger to far red than to red solar-induced chlorophyll fluorescence. Remote Sensing 9(1), 97. https://doi.org/10.3390/rs9010097
  6. Hong, J. Y., C. S. Shim, M. J. Lee, G. H. Baek, W. K. Song, S. W. Jeon, and Y. H. Park, 2011: Net primary production changes over Korea and climate factors. Korean Journal of Remote Sensing 27(4), 467-480. https://doi.org/10.7780/kjrs.2011.27.4.467
  7. Julitta, T., L. Corp, M. Rossini, A. Burkart, S. Cogliati, N. Davies, M. Hom, A. Mac Arthur, E. M. Middleton, U. Rascher, A. Schickling, and R. Colombo, 2016: Comparison of sun-induced chlorophyll fluorescence estimates obtained from four portable field spectroradiometers. Remote Sensing 8(2), 122. https://doi.org/10.3390/rs8020122
  8. Lee, B., E. Kim, J. H. Lim, M. Kang, and J. Kim, 2019: Application of machine learning algorithm and remote-sensed data to estimate forest gross primary production at multi-sites level. Korean Journal of Remote Sensing 35(6_2), 1117-1132.
  9. Sun, M., C. H. Cho, Y. Kim, J. Lee, K. O. Boo, and Y. H. Byun, 2017: Response of the terrestrial carbon exchange to the climate variability. Atmosphere 27(2), 163-175. https://doi.org/10.14191/Atmos.2017.27.2.163