Journal of Resources and Ecology ›› 2017, Vol. 8 ›› Issue (1): 57-66.doi: 10.5814/j.issn.1674-764x.2017.01.008

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Satellite-based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau: A Multi-model Comparison

NIU Ben1,2, ZHANG Xianzhou1,2, HE Yongtao1,*, SHI Peili1, FU Gang1, DU Mingyuan3, ZHANG Yangjian1, ZONG Ning1, ZHANG Jing4, WU Jianshuang1   

  1. 1. Lhasa Plateau Ecosystem Research Station, 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. Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan;
    4. College of Global Change and Earth System Sciences, Beijing Normal University, Beijing 100875, China
  • Received:2016-10-31 Online:2017-01-20 Published:2017-01-24
  • Contact: HE Yongtao, E-mail: heyt@igsnrr.ac.cn.
  • Supported by:
    National Natural Science Foundation of China (41571042, 40603024)

Abstract: Alpine swamp meadows on the Tibetan Plateau, with the highest soil organic carbon content across the globe, are extremely vulnerable to climate change. To accurately and continually quantify the gross primary production (GPP) is critical for understanding the dynamics of carbon cycles from site-scale to global scale. Eddy covariance technique (EC) provides the best approach to measure the site-specific carbon flux, while satellite-based models can estimate GPP from local, small scale sites to regional and global scales. However, the suitability of most satellite-based models for alpine swamp meadow is unknown. Here we tested the performance of four widely-used models, the MOD17 algorithm (MOD), the vegetation photosynthesis model (VPM), the photosynthetic capacity model (PCM), and the alpine vegetation model (AVM), in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived GPP. Our results indicated that all these models provided good descriptions of the intra-annual GPP patterns (R2>0.89, P<0.0001), but hardly agreed with the inter-annual GPP trends. VPM strongly underestimated the GPP of alpine swamp meadow, only accounting for 54.0% of GPP_EC. However, the other three satellite-based GPP models could serve as alternative tools for tower-based GPP observation. GPP estimated from AVM captured 94.5% of daily GPP_EC with the lowest average RMSE of 1.47 g C m-2. PCM slightly overestimated GPP by 12.0% while MODR slightly underestimated by 8.1% GPP compared to the daily GPP_EC. Our results suggested that GPP estimations for this alpine swamp meadow using AVM were superior to GPP estimations using the other relatively complex models.

Key words: alpine swamp meadow, eddy covariance technique (EC), gross primary production (GPP), model performance, Tibetan Plateau, satellite remote sensing