Land Management Explains the Contrasting Greening Pattern Across China‐Russia Border Based on Paired Land Use Experiment Approach

Abstract

The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data‐driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border‐intensified agricultural development on China side (CNSP) versus largely little‐disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10−3 yr−1 and 11.3 × 10−3 yr−1, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8‐day periods from 26 June to 28 July. During this period, the LAI trend of CNSP is much higher than that of RUSP, at 92.7 × 10−3 yr−1 (p < 0.01) and 43.8 × 10−3 yr−1 (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.

Variation of gross primary productivity dominated by leaf area index in significantly greening area

“…One of the most typical is the inclusion of critical processes. Using statistical methods based on remote sensing, the extensive impacts of land management on vegetation from global to regional scales have been further identified and emphasized (Chen et al, 2019a;Chen et al, 2022a;2022b). However, due to the complexity of the management process and the lack of management data, although some DGVMs have made efforts, the characterization of the management process by the model remains unfortunate, which may require more theoretical and mechanistic research.…”

Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach

“…A large number of research results showed that the currently widely used machine learning GPP data set (FLUXCOM_GPP) cannot reproduce the long‐term trend and interannual variation of GPP (Jung et al., 2020; Wang et al., 2021; Zhang et al., 2017; Zheng et al., 2020), and which was contrary to the currently recognized significant greening from regional to global scales (Chen et al., 2022; Piao et al., 2020; Yuan et al., 2014). As illustrated above, GPP interannual trend of ECGC_GPP (0.21 Pg C yr −2 ) was comparable to other LUE models and was much larger than ML‐based FLUXCOM_GPP.…”