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Research on Improving the Accuracy of SIF Data in Estimating Gross Primary Productivity in Arid Regions

Author

Listed:
  • Wei Liu

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Yu Wang

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Ali Mamtimin

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Yongqiang Liu

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China)

  • Jiacheng Gao

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Meiqi Song

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Ailiyaer Aihaiti

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Cong Wen

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Fan Yang

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Wen Huo

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Chenglong Zhou

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Jian Peng

    (Xinjiang Meteorological Technology Equipment Center, Urumqi 830001, China)

  • Hajigul Sayit

    (Xinjiang Meteorological Society, Urumqi 830002, China)

Abstract

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m 2 /month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R 2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R 2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R 2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m 2 /nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Suggested Citation

  • Wei Liu & Yu Wang & Ali Mamtimin & Yongqiang Liu & Jiacheng Gao & Meiqi Song & Ailiyaer Aihaiti & Cong Wen & Fan Yang & Wen Huo & Chenglong Zhou & Jian Peng & Hajigul Sayit, 2024. "Research on Improving the Accuracy of SIF Data in Estimating Gross Primary Productivity in Arid Regions," Land, MDPI, vol. 13(8), pages 1-25, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1222-:d:1451545
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