A hybrid model for refining gross primary productivity estimation by integrating multiple environmental factors

Zhilong Li; Ziti Jiao; Zheyou Tan; Chenxia Wang; Jing Guo; Sizhe Chen; Ge Gao; Fangwen Yang; Xin Dong

doi:10.1016/j.mex.2024.103091

A hybrid model for refining gross primary productivity estimation by integrating multiple environmental factors

MethodsX. 2024 Dec 9:14:103091. doi: 10.1016/j.mex.2024.103091. eCollection 2025 Jun.

Authors

Zhilong Li^{1

2}, Ziti Jiao^{1

2

3}, Zheyou Tan^{1

2}, Chenxia Wang^{1

2}, Jing Guo^{1

2}, Sizhe Chen^{1

2}, Ge Gao^{1

2}, Fangwen Yang^{1

2}, Xin Dong^{1

2}

Affiliations

¹ State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China.
² Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
³ Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China.

Abstract

Environmental factors lead mainly to the uncertainty of gross primary productivity estimation in most light use efficiency (LUE, ε) models since the simple physical formulas are inadequate to fully express the overall constraint of diverse environmental factors on the maximum ε (ε_max). In contrast, machine learning has the natural potential to detect intricate patterns and relationships among various environmental variables. Here, we presented a hybrid model (TL-CRF) that utilizes the random forest (RF) technique to incorporate various ecological stress factors into the two-leaf LUE (TL-LUE) model, meanwhile, seasonal differences in the clumping index (CI) on a global scale are considered to adjust seasonal patterns of canopy structure. The comprehensive integration of complex environmental variables based on this RF submodule is conducive to scaling theoretical ε_max to actual ε as much as possible. The proposed TL-CRF model considerably improves global GPP estimation by complementing innate advantages between the process-based and data-driven models.•The seasonal CI averages in different stages of the leaf life cycle are estimated for different vegetation types on a global scale.•Various environmental stress factors are integrated via the RF technique.•The RF submodule is embedded into the TL-LUE model to establish a hybrid model.

Keywords: Carbon cycle; Large-scale GPP estimation via combining RF technique with TL-LUE model; Random forest; Stress variables; Two-leaf light use efficiency model.