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生态学杂志 ›› 2024, Vol. 43 ›› Issue (6): 1859-1869.doi: 10.13292/j.1000-4890.202406.006

• 恢复生态 • 上一篇    下一篇

基于多种遥感产品的青藏高原长时间序列总初级生产力对比分析

廖玮杰1,2,3,焦悦1,2,3,李世禧1,2,胡中民1,2,3,白磊1,2*
  

  1. 1海南大学生态与环境学院, 海口 570208; 2海南省农林环境过程与生态调控重点实验室, 海口 570208; 3海南热带雨林国家生态质量综合监测站, 海南保亭 572316)

  • 出版日期:2024-06-10 发布日期:2024-06-20

Comparative analysis of gross primary productivity in the Qinghai-Tibet Plateau based on longtime series of remote sensing products.

LIAO Weijie1,2, JIAO Yue1,2, LI Shixi1,2, HU Zhongmin1,2,3, BAI Lei1,2*   

  1. 1College of Ecology and Environment, Hainan University, Haikou 570208, China; 2 Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province (Hainan University), Haikou 570208, China; 3Hainan Tropical Rainforest National Ecological Quality Monitoring Station, Baoting 572316, Hainan, China).

  • Online:2024-06-10 Published:2024-06-20

摘要: 青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production, GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以及预测其未来的变化趋势。本研究使用6种常见的遥感GPP产品(GLASS、MODIS MOD17A2、FLUXCOM、VODCA2、改进的EC-LUE数据及VPM数据),结合涡度协方差通量观测数据(海北灌丛、海北湿地和当雄)进行验证后,对青藏高原2001—2015年生态系统GPP空间分布格局及时间变化趋势进行分析。结果表明:不同生态遥感产品得到的青藏高原年平均GPP、区域年均GPP时空分布格局与变化趋势存在较大差异,6套产品得到的2001—2015年变化趋势分别-0.77 g C·m-2·a-1(GLASS)、3.63 g C·m-2·a-1(MOD17A2)、-1.21 g C·m-2·a-1(FLUXCOM)、1.53 g C·m-2·a-1(VODCA2)、4.73 g C·m-2·a-1(VPM)和-0.81 g C·m-2·a-1(改进的EC-LUE);在空间分布上多年平均GPP总体呈现“东南高、西北低”的特点,区域差异较大;在青藏高原生态系统中,GLASS产品区域平均年GPP最高(827.78 Tg C·a-1),MOD17A2产品最低(484.04 Tg C·a-1),2001—2015年青藏高原生态系统GPP变化程度分布区域基本相同,东南部最剧烈,而西部最为稳定;经过站点数据验证,MOD17A2在8天尺度上结果相对更好,而FLUXCOM数据集在月尺度上结果相对更好,结合在区域尺度上的表现,MOD17A2数据集更加适用于青藏高原地区。


关键词: 光能利用率模型, VPM, VODCA2, FLUXCOM, MODIS

Abstract: The Qinghai-Tibet Plateau is a hotspot in the research of terrestrial carbon cycling. In the context of global climate change, gross primary production (GPP) plays a crucial role in regional carbon cycle. Model simulation combined with remote sensing data aids in understanding the changes of ecosystem productivity at the regional scale across the Qinghai-Tibet Plateau and predicts its future trends. With six common remote sensing GPP products (GLASS, MODIS MOD17A2, FLUXCOM, VODCA2, improved EC-LUE data, and VPM data) being validated by eddy covariance flux observation data from three sites (Haibei shrub, Haibei wetland, and Damxung), we used them to analyze the spatial distribution pattern and temporal change trend of GPP in the Qinghai-Tibet Plateau ecosystems from 2001 to 2015. We found significant differences in annual mean GPP, annual total GPP spatial distribution pattern, and the trends across different remote sensing GPP datasets. The trends calculated based on the six products are -0.77 g C·m-2·a-1 (GLASS), 3.63 g C·m-2·a-1 (MOD17A2), -1.21 g C·m-2·a-1 (FLUXCOM), 1.53 g C·m-2·a-1 (VODCA2), 4.73 g C·m-2·a-1 (VPM), and -0.81 g C·m-2·a-1 (improved EC-LUE). The overall spatial distribution of multi-year mean GPP generally displays a pattern of higher values in the southeast and lower values in the northwest, with significant regional variations. The GLASS product shows the highest multi-year average annual GPP (827.78 Tg C·a-1), while the MOD17A2 product shows the lowest (484.04 Tg C·a-1). The other products (VODCA2, improved EC-LUE, VPM, and FLUXCOM) report 827.20 Tg C·a-1, 714.55 Tg C·a-1, 634.00 Tg C·a-1, and 587.86 Tg C·a-1, respectively. Over the 15 years from 2001 to 2015, the regions with the most significant changes in GPP are consistent across the plateau, with the most intense changes in the southeast and the most stable in the west. Site data validation indicates that MOD17A2 performs relatively better on an 8-day scale, whereas the FLUXCOM dataset shows superior results on a monthly scale. Combined with its performance at the regional scale, the MOD17A2 dataset is more suitable for the Qinghai-Tibet Plateau region.


Key words: eddy covariance-light use efficiency (EC-LUE) model, VPM, VODCA2, FLUXCOM, MODIS