Gao et al. (2026) Nonlinear characteristics and driving factors of vegetation-soil moisture feedback at fine scale in Northeast China

Identification

• Journal: Environmental Monitoring and Assessment
• Year: 2026
• Date: 2026-02-09
• Authors: Yang Gao, Fang Huang, Ping Wang, Jiameng Gao, Pei Wu, Yue Zhang
• DOI: 10.1007/s10661-026-15053-2

Research Groups

Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, China

Short Summary

This study investigated the nonlinear, bidirectional feedback mechanisms between vegetation gross primary productivity (GPP) and soil moisture (SM) at fine spatial scales (1 km) and different depths (0–100 cm) in Northeast China from 2000 to 2022. It revealed predominant synergistic growth and bidirectional causality, with SM's influence on GPP generally stronger, a 2–3 month lagged response, and regulation by climatic and geographical factors.

Objective

• To analyze the spatiotemporal variations of vegetation GPP and SM from 2000 to 2022 in Northeast China.
• To investigate the bidirectional feedback relationship between GPP and SM at different soil depths (0–10 cm, 10–40 cm, 40–100 cm).
• To reveal the influence of climatic and geo-ecological factors on the GPP-SM interactions.

Study Configuration

• Spatial Scale: Northeast China (38°N–56°N, 115°E–135°E), approximately 1.24 million square kilometers, with data processed at 1 km × 1 km resolution.
• Temporal Scale: 2000–2022 (23 years), using monthly time series.

Methodology and Data

• Models used:
  • Theil–Sen trend analysis and Mann–Kendall test for trend detection.
  • Random Forest-based nonlinear Granger causality analysis for bidirectional feedback.
  • Partial correlation analysis for quantifying relationships and lag effects.
  • Random Forest regression for analyzing driving factors and their importance.
• Data sources:
  • GPP data: MODIS GPP product (MOD17A2HGF) at 500 m spatial resolution (8-day temporal resolution), accumulated to monthly and resampled to 1 km.
  • SM data: National Tibetan Plateau Data Center dataset at 1 km spatial resolution (daily temporal resolution), providing SM at 10 cm intervals from 0 to 100 cm depth, aggregated to monthly.
  • Climate data:
    • Precipitation (pre), relative humidity (rh), vapor pressure deficit (vpd), actual vapor pressure (avp), potential evapotranspiration (pet) from National Tibetan Plateau Data Center (1 km, monthly).
    • Temperature (tem) and surface solar radiation downwards (ssrd) from ERA5-Land reanalysis (0.1° spatial resolution, monthly temporal resolution), downscaled to 1 km.
  • Geographic data: Digital elevation model (DEM), land use, vegetation type, and soil type data from the Resource and Environment Science and Data Center, Chinese Academy of Sciences.

Main Results

• Both GPP and SM showed significant increasing trends from 2000 to 2022 in Northeast China. GPP increased from west to east, while SM exhibited a "lower in the west and south, higher in the east and north" pattern, with the rate of SM increase intensifying with depth.
• A dominant pattern of synergistic growth (GPP + SM +) accounted for more than 94% of the study area, with this proportion slightly decreasing with increasing soil depth.
• Bidirectional causal relationships between GPP and SM were observed in 41.24% to 71.87% of the study area, with the proportion declining as soil depth increased. The influence of SM on GPP was generally stronger than the feedback from GPP to SM.
• The mutual feedback exhibited a lagged response of approximately 2–3 months, showing a nonlinear pattern that first decreased and then increased with soil depth. Longer lags (up to 6 months) were observed in specific regions and for negative influences.
• The GPP-SM feedback relationship was jointly regulated by solar radiation, precipitation, and temperature, and varied significantly across different vegetation types (e.g., strong positive correlations in shrublands, broadleaf forests, cultivated vegetation) and soil types (e.g., strong in cinnamon, chestnut, fluvo-aquic soils; weakened in saline soil).
• Solar radiation was identified as the principal climatic driver, with its influence weakening with depth. Precipitation showed a pulse-like response, and temperature's regulatory effect diminished with increasing soil depth.

Contributions

• This study provides a more refined characterization of vegetation-soil moisture feedback by employing high-resolution (1 km) satellite-based datasets and a Random Forest-based nonlinear Granger causality analysis, addressing limitations of coarse spatial resolution and linear methods in previous studies.
• It systematically investigates bidirectional feedback relationships and temporal lag effects between GPP and SM across different soil depths (0–100 cm), which were previously limited, especially at finer spatial scales.
• The research quantifies the influence of climatic and geo-ecological factors on these complex, nonlinear interactions, offering a comprehensive understanding of the dynamic coupling between vegetation and SM in a climate-sensitive and drought-prone region.

Funding

• Science and Technology Development Plan Project of Jilin Province, China (grant number 20220101155JC and 20250801018FG)
• National Natural Science Foundation of China (grant number 42471289)

Citation

@article{Gao2026Nonlinear,
  author = {Gao, Yang and Huang, Fang and Wang, Ping and Gao, Jiameng and Wu, Pei and Zhang, Yue},
  title = {Nonlinear characteristics and driving factors of vegetation-soil moisture feedback at fine scale in Northeast China},
  journal = {Environmental Monitoring and Assessment},
  year = {2026},
  doi = {10.1007/s10661-026-15053-2},
  url = {https://doi.org/10.1007/s10661-026-15053-2}
}