Assessing Drought Impacts on Gross Primary Productivity of Rubber Plantations Using Flux Observations and Remote Sensing in China and Thailand

被引:0
|
作者
Li, Weiguang [1 ,2 ]
Hou, Meiting [3 ,4 ]
Liu, Shaojun [1 ,5 ]
Zhang, Jinghong [1 ,2 ]
Zou, Haiping [1 ,2 ]
Chen, Xiaomin [1 ,2 ]
Bai, Rui [1 ,2 ]
Lv, Run [1 ,2 ]
Hou, Wei [1 ,2 ]
机构
[1] Climate Ctr Hainan Prov, Haikou 570203, Peoples R China
[2] Key Lab Meteorol Disaster Prevent & Reduct South C, Haikou 570203, Peoples R China
[3] China Meteorol Adm Training Ctr, Beijing 100081, Peoples R China
[4] Univ Helsinki, Dept Forest Sci, Helsinki 00014, Finland
[5] Hainan Prov Meteorol Sci Res Inst, Haikou 570311, Peoples R China
来源
FORESTS | 2024年 / 15卷 / 10期
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
rubber plantations; gross primary production; drought; remote sensing indices; PHOTOSYNTHESIS; ECOSYSTEMS; EXPANSION; PHENOLOGY; GROWTH;
D O I
10.3390/f15101732
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model's predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought's impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context.
引用
收藏
页数:21
相关论文
共 46 条
  • [31] Analysis of light use efficiency and gross primary productivity based on remote sensing data over a phragmites-dominated wetland in Zhangye, China
    Jiang Guoqing
    Sun Rui
    Zhang Lei
    Liu Shaomin
    Xu Ziwei
    Qiao Chen
    LAND SURFACE REMOTE SENSING II, 2014, 9260
  • [32] Assessing the Spatial-Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index
    Wang, Yihao
    Wu, Yongfeng
    Ji, Lin
    Zhang, Jinshui
    Meng, Linghua
    REMOTE SENSING, 2023, 15 (17)
  • [33] Monitoring terrestrial net primary productivity of China using BIOME-BGC model based on remote sensing
    Meng, JH
    Wu, BF
    Zhou, YM
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 3105 - 3108
  • [34] Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model
    Wang, Jian
    Wu, Chaoyang
    Zhang, Chunhua
    Ju, Weimin
    Wang, Xiaoyue
    Chen, Zhi
    Fang, Bin
    ECOLOGICAL INDICATORS, 2018, 88 : 332 - 340
  • [35] USING A LAND SURFACE MODEL TO SIMULATE NET PRIMARY PRODUCTIVITY IN CHINA COMPARING WITH THE PROCESS MODEL DERIVED BY REMOTE SENSING
    Zhang, Liang
    Li, Yaohui
    Zhang, Huqiang
    Wang, Jinsong
    2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 3291 - 3294
  • [36] Comparison of empirical remote-sensing based models for the estimation of gross primary productivity using eddy covariance and satellite data over agroecosystem
    Pokhariyal, Shweta
    Patel, N. R.
    TROPICAL ECOLOGY, 2021, 62 (04) : 600 - 611
  • [37] Comparison of empirical remote-sensing based models for the estimation of gross primary productivity using eddy covariance and satellite data over agroecosystem
    Shweta Pokhariyal
    N. R. Patel
    Tropical Ecology, 2021, 62 : 600 - 611
  • [38] Evaluating Ecohydrological Impacts of Vegetation Activities on Climatological Perspectives Using MODIS Gross Primary Productivity and Evapotranspiration Products at Korean Regional Flux Network Site
    Sur, Chanyang
    Choi, Minha
    REMOTE SENSING, 2013, 5 (05) : 2534 - 2553
  • [39] Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
    Yan, Yuchao
    Wu, Changjiang
    Wen, Youyue
    Wu, Changjiang (15705210791@163.com), 1600, Elsevier B.V. (127):
  • [40] Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
    Yan, Yuchao
    Wu, Changjiang
    Wen, Youyue
    ECOLOGICAL INDICATORS, 2021, 127