Journal of Resources and Ecology ›› 2019, Vol. 10 ›› Issue (2): 112-126.DOI: 10.5814/j.issn.1674-764X.2019.02.002

• Forest Ecosystem • Previous Articles     Next Articles

Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

SUN Leigang1,2,3, WANG Shaoqiang1,2,4,*(), Robert A. MICKLER5, CHEN Jinghua1,2, YU Quanzhou6, QIAN Zhaohui1,2, ZHOU Guoyi7, MENG Ze7   

  1. 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Hebei Engineering Research Center for Geographic Information Application, Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, China
    4. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
    5. Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
    6. School of Environment and Planning, Liaocheng University, Liaocheng, Shandong 252059, China
    7. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
  • Received:2018-10-11 Accepted:2018-11-12 Online:2019-03-30 Published:2019-03-30
  • Contact: WANG Shaoqiang
  • Supported by:
    National Key Research and Development Program of China (2017YFC0503803);National Natural Science Foundation of China (41571192);Natural Science Foundation of Hebei, China (D2016302002);Science and Technology Planning Project of Hebei, China (17390313D).

Abstract:

The accurate measurement of the dynamics of photosynthesis in China’s subtropical evergreen forest ecosystems is an important contribution to carbon (C) sink estimates in global terrestrial ecosystems and their responses to climate change. Eddy covariance has historically been the only direct method to assess C flux of whole ecosystems with high temporal resolution, but it suffers from limited spatial resolution. During the last decade, continuous global monitoring of plant primary productivity from spectroradiometer sensors on flux towers and satellites has extended the temporal and spatial coverage of C flux observations. In this study, we evaluated the performance of two physiological remote sensing indices, fluorescence reflectance index (FRI) and photochemical reflectance index (PRI), to measure the seasonal variations of photosynthesis in a subtropical evergreen forest ecosystem using continuous canopy spectral and flux measurements in the Dinghushan Nature Reserve in southern China. The more commonly used NDVI has been shown to be saturated and mainly affected by illumination (R2=0.88, p < 0.001), but FRI and PRI could better track the seasonal dynamics of plant photosynthetic functioning by comparison and are less affected by illumination (R2=0.13 and R2=0.51, respectively) at the seasonal scale. FRI correlated better with daily gross primary production (GPP) in the morning hours than in the afternoon hours, in contrast to PRI which correlated better with light-use efficiency (LUE) in the afternoon hours. Both FRI and PRI could show greater correlations with GPP and LUE respectively in the senescence season than in the recovery-growth season. When incident PAR was taken into account, the relationship between GPP and FRI was improved and the correlation coefficient increased from 0.22 to 0.69 (p < 0.001). The strength of the correlation increased significantly in the senescence season (R 2=0.79, p < 0.001). Our results demonstrate the application of FRI and PRI as physiological indices for the accurate measurement of the seasonal dynamics of plant community photosynthesis in a subtropical evergreen forest, and suggest these indices may be applied to carbon cycle models to improve the estimation of regional carbon budgets.

Key words: fluorescence reflectance index (FRI), photochemical reflectance index (PRI), photosynthesis, gross primary productivity (GPP), light-use efficiency (LUE), subtropical evergreen forest