Li Zhilong, Jiao Ziti, Gao Ge, Guo Jing, Wang Chenxia, Chen Sizhe, Tan Zheyou, Zhao Wenyu
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2024 Dec 1;954:176673. doi: 10.1016/j.scitotenv.2024.176673. Epub 2024 Oct 2.
Distinguishing gross primary productivity (GPP) into sunlit (GPP) and shaded (GPP) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPP and GPP. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPP, and GPP from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R = 0.87, RMSE = 0.94 g C m d, MAE = 0.61 g C m d) in the TL-CRF model is greater than that (R = 0.76, RMSE = 2.18 g C m d, MAE = 1.50 g C m d) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.
将总初级生产力（GPP）区分为受光照（GPP）和遮荫（GPP）部分，对于理解气候变化下大气与陆地生态系统之间的碳交换至关重要。最近，双叶光利用效率（TL-LUE）模型已被证明在模拟全球GPP和GPP方面是有效的。然而，尚无已知的物理方法专注于整合复杂环境因素对光合能力的整体约束，以及叶丛聚集指数（CI）的季节差异，而这很可能会影响LUE模型中的GPP估算。在此，我们提出了TL-CRF模型，该模型使用随机森林技术将各种环境变量，特别是陆地水储量（TWS），整合到TL-LUE模型中。此外，我们在全球尺度上考虑了CI的季节差异。基于267个全球涡度协方差通量站点，我们探究了植被光合作用对关键环境因素的功能响应，并对TL-CRF模型进行了训练和评估。然后使用TL-CRF模型模拟了2002年至2020年全球八天的GPP、GPP和GPP。结果表明，当通过随机森林（RF）模型整合这些环境胁迫因素时，它们对最大LUE的相对预测误差降低了约52%。因此，TL-CRF模型中全球GPP估算的准确性（R = 0.87，RMSE = 0.94 g C m² d⁻¹，MAE = 0.61 g C m² d⁻¹）高于TL-LUE模型中的准确性（R = 0.76，RMSE = 2.18 g C m² d⁻¹，MAE = －1.50 g C m² d⁻¹），尽管这一准确性有待在已发布的GPP产品中作进一步研究。TWS对生态系统光合作用强度的控制作用最大，使其成为一个合适的水分指标。此外，结果证实了光合作用存在一个最佳最低气温。总体而言，这些发现表明了一种有望生成新的全球GPP数据集的方法，增进了我们对光合作用与环境因素之间动态关系和相互作用的理解。
