Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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On Using Near-surface Remote Sensing Observation for Evaluation Gross Primary Productivity and Net Ecosystem CO2 Partitioning

근거리 원격탐사 기법을 이용한 총일차생산량 추정 및 순생태계 CO2 교환량 배분의 정확도 평가에 관하여

  • Park, Juhan (National Center for AgroMeteorology) ;
  • Kang, Minseok (National Center for AgroMeteorology) ;
  • Cho, Sungsik (National Center for AgroMeteorology) ;
  • Sohn, Seungwon (National Center for AgroMeteorology) ;
  • Kim, Jongho (National Center for AgroMeteorology) ;
  • Kim, Su-Jin (Forest Ecology Division, National Institute of Forest Science) ;
  • Lim, Jong-Hwan (Forest Ecology Division, National Institute of Forest Science) ;
  • Kang, Mingu (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Shim, Kyo-Moon (Climate Change Assessment Division, National Institute of Agricultural Sciences)
  • 박주한 (국가농림기상센터) ;
  • 강민석 (국가농림기상센터) ;
  • 조성식 (국가농림기상센터) ;
  • 손승원 (국가농림기상센터) ;
  • 김종호 (국가농림기상센터) ;
  • 김수진 (국립산림과학원 산림생태연구과) ;
  • 임종환 (국립산림과학원 산림생태연구과) ;
  • 강민구 (국립농업과학원 기후변화평가과) ;
  • 심교문 (국립농업과학원 기후변화평가과)
  • Received : 2021.11.30
  • Accepted : 2021.12.28
  • Published : 2021.12.30
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Abstract

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPPOri) and (2) machine-learning based method (GPPANN). GPPANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

원격 탐사 기반의 식생지수들은 광합성을 조절하는 식물생리적 특성과 경험적 상관관계를 보이며, 여러공간 규모에서의 총일차생산량(GPP) 추정에 활용되고 있다. 하지만 시간 해상도가 높아질수록 식생지수를 이용한 GPP 추정의 불확실성이 커지는 한계가 존재한다. 또한 식생지수 관련 분석에 주로 사용되는 에디공분산법을 이용하여 추정한 GPP 역시 실제 측정한 순생태계교환량(NEE)을 GPP와 생태계 호흡(RE)으로 배분하는 데 사용하는 방법에 따라 추정값이 달라지는 불확실성이 존재한다. 본 연구에서는 플럭스 타워가 설치된 네 곳의 농림생태계를 대상으로 근지표에서 관측한 식생의 분광 특성을 이용한 다양한 식생지수를 계산하였고, 이를 다양한 시간 해상도에서 GPP 추정에 적용가능한 지를 분석하였다. 동시에 이를 이용하여 NEE 배분 방법의 불확실성을 평가하였다. 비교에 사용한 정규식생지수, 개량식생지수, 적외반사식생지수(NIRv)에 비해 적외반사식생지수와 광합성유효광(PAR)을 결합한 NIRvP이 식생 및 지형 조건에 의한 공간 이질성으로 인해 관측지에 따라 약간의 차이가 나타났지만, 농경지와 산림에서 모두 30분과 일 단위 시간 해상도에서 GPP와 높은 상관성(r2 = 0.63, 0.68)을 보였다. 또한 기존 KoFlux 표준 NEE 배분방법에 비해 기계학습 기반의 NEE 배분 방법을 적용할 경우, 산림에서 30분 단위의 GPP와 NIRvP 사이의 상관성이 향상되었지만, 일 단위에는 그 차이가 크지 않았다. 하지만 광조건 이외에 다른 요인에 의해 광합성이 제한되는 경우 NIRvP와 GPP 간의 상관성이 떨어져 NIRvP를 이용해 실제 배분 결과를 직접 평가하긴 어려웠으며, 주로 광 조건에 의해 광합성이 제한되는 흐린 날의 경우 NEE 배분 정확도를 평가할 수 있는 가능성이 존재하였다. 그러나 높은 시간해상도의 Vis 기반의 GPP 추정이 의미를 가지려면, VIs와 GPP간의 경험적 관계를 넘어서는 시스템 사고 및 자기-조직화와 관련된 복잡계 기반의 분석 방법이 요구된다.

Keywords

Acknowledgement

본 연구는 산림청(한국임업진흥원) 산림과학기술연구개발사업'(2020180A00-2122-BB01)'과 농촌진흥청 국립농업과학원 농업과학기술 연구개발사업(PJ014892022021)의 지원에 의해 이루어진 것입니다. 논문의 품위를 높여 주신 두 분의 심사위원분들께 감사드립니다.

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