Quantitative assessment of spatiotemporal variations and drivers of gross primary productivity in tropical ecosystems at higher resolution

• An improved BEPS model achieved high spatiotemporal resolution simulation of GPP. • 98.5 % of vegetated areas had increase in GPP, with an annual rise of 437.02 g C/m 2 . • Considering compound and lagged effects of various factors boost GPP explanation. • Relative humidity had stronger effect on GPP than precipitation and soil moisture. • More GPP were affected by the lag effects of radiation and surface temperature. Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate monthly GPP at a 30 m resolution across Hainan Island from 2000 to 2020. The finer GPP can capture more spatial details and show higher accuracy at site scales (R = 0.79 and NRMSE = 14.79 %). Trend analysis and Hurst exponent were used to reveal spatiotemporal dynamics and sustainability of GPP. Meanwhile, nonlinear Granger causality tests quantified both concurrent and lagged correlations between various environmental factors and GPP. The results indicated significant GPP increases across 98.5 % of vegetated areas, with an annual rise of 437.02 g C/m 2 , and a marked improvement in trends around 2011. Future projections suggest sustained high GPP sustainability (Hurst = 0.53), and reducing “positive-inconsistent” areas in the northeast and southwest is crucial for enhancing local carbon sinks. Furthermore, water availability, temperature, and radiation were primary drivers of GPP changes, affecting 53.55 %, 27.77 %, and 14.43 % of vegetated areas, respectively, with their compounded effects enhancing explanatory power by 35.84 %. Relative humidity dominated water availability impacts on GPP (10.02 % to 79.98 % variation), surpassing precipitation and soil moisture impacts. Lag effects were observed in 68.83 % of vegetated areas, with 1 to 4-month delays in responses to net solar radiation and surface temperature, especially in forest and shrubland ecosystems. This study provides deeper insights into fine-scale GPP simulations and analysis of climate interactions, which are crucial for effective carbon cycle management in tropical ecosystems.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2024-11-06
DOI
https://doi.org/10.1016/j.jag.2024.104248
Citations
8
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.67
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article

Quantitative assessment of spatiotemporal variations and drivers of gross primary productivity in tropical ecosystems at higher resolution

Hidayat Ullah, Ali Salem Al‐Sakkaf, Jiahua Zhang, Bo Yu et al.
8 citations
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing in Agriculture
3.67
article

Quantitative assessment of spatiotemporal variations and drivers of gross primary productivity in tropical ecosystems at higher resolution

Hidayat Ullah, Ali Salem Al‐Sakkaf, Jiahua Zhang, Bo Yu, Shawkat Ali, Ruize Xu
article en
8 citations

Abstract

• An improved BEPS model achieved high spatiotemporal resolution simulation of GPP. • 98.5 % of vegetated areas had increase in GPP, with an annual rise of 437.02 g C/m 2 . • Considering compound and lagged effects of various factors boost GPP explanation. • Relative humidity had stronger effect on GPP than precipitation and soil moisture. • More GPP were affected by the lag effects of radiation and surface temperature. Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate monthly GPP at a 30 m resolution across Hainan Island from 2000 to 2020. The finer GPP can capture more spatial details and show higher accuracy at site scales (R = 0.79 and NRMSE = 14.79 %). Trend analysis and Hurst exponent were used to reveal spatiotemporal dynamics and sustainability of GPP. Meanwhile, nonlinear Granger causality tests quantified both concurrent and lagged correlations between various environmental factors and GPP. The results indicated significant GPP increases across 98.5 % of vegetated areas, with an annual rise of 437.02 g C/m 2 , and a marked improvement in trends around 2011. Future projections suggest sustained high GPP sustainability (Hurst = 0.53), and reducing “positive-inconsistent” areas in the northeast and southwest is crucial for enhancing local carbon sinks. Furthermore, water availability, temperature, and radiation were primary drivers of GPP changes, affecting 53.55 %, 27.77 %, and 14.43 % of vegetated areas, respectively, with their compounded effects enhancing explanatory power by 35.84 %. Relative humidity dominated water availability impacts on GPP (10.02 % to 79.98 % variation), surpassing precipitation and soil moisture impacts. Lag effects were observed in 68.83 % of vegetated areas, with 1 to 4-month delays in responses to net solar radiation and surface temperature, especially in forest and shrubland ecosystems. This study provides deeper insights into fine-scale GPP simulations and analysis of climate interactions, which are crucial for effective carbon cycle management in tropical ecosystems.

International Journal of Applied Earth Observation and GeoinformationVol. 135
Qingdao University of Science and Technology (CN), Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
Zero hunger
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
3.67
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