Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model

Zhang, Zhenyu; Li, Xiaoyü; Ju, Weimin; Zhou, Yanlian; Cheng, Xiao

doi:10.1016/j.scitotenv.2022.156172

Cited by 7 publications

(3 citation statements)

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“…This complexity can introduce uncertainties, particularly when parameters are set based on broad vegetation types, potentially impacting the accuracy of regional and global GPP estimates [35]. Despite the advancements in data-driven models, remote sensing models based on LUE remain predominant in multi-scale GPP estimation due to their greater data accessibility and fewer parameters [36]. Accurately determining the actual LUE is both a challenge and a prerequisite for reducing uncertainties in GPP estimation [37].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the P model by Stocker et al, which calculates absorbed photosynthetically active radiation (APAR) using a big-leaf algorithm, fails to distinguish between sunlit and shaded leaves, potentially leading to inaccuracies in capturing the actual APAR [40]. Consequently, this paper utilizes Zhang et al's improved P model, which integrates five water stress factors, thereby significantly enhancing the accuracy of GPP estimation [36].…”

Section: Introductionmentioning

confidence: 99%

“…The P model, a LUE-like model, captures deeper mechanisms of photosynthesis and simplifies parameterization [39]. The enhanced P model, integrating the water stress factors, effectively considers the impact of environmental factors such as drought on photosynthesis efficiency [36]. This approach provides new perspectives and methodologies for global ecological studies and the future improvement of GPP models.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

Chen,

Zhang,

Guo

et al. 2024

Forests

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Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change and preserving global biodiversity. Thus, investigating changes in vegetation productivity within this region holds substantial practical importance for estimating global vegetation productivity. In this study, we employed an enhanced P model to estimate vegetation GPP in the tropical region from 2001 to 2020, based on which we quantified the spatiotemporal changes and associated mechanisms. The results reveal that the annual mean GPP in the tropical region ranged from 2603.9 to 2757.1 g·cm−2 a−1, demonstrating an overall apparent increasing trend. Inland areas were mainly influenced by precipitation, while coastal areas were primarily influenced by temperature. Land cover changes, especially conversion to cropland, significantly influence GPP, with deciduous—evergreen forest transitions causing notable decreases. Climate change emerges as the dominant factor affecting GPP, as indicated by the contribution rate analysis. This research interprets the spatiotemporal pattern and mechanisms of GPP in the tropics, offering valuable insights for sustainable ecosystem management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%