Qu et al. (2026) Vegetation Productivity Loss and Recovery Associated with the July 2023 Hot–Dry Event on the Huang–Huai–Hai Plain

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Land
• Year: 2026
• Date: 2026-09-14
• Authors: Fuqiang Qu, Xi Liu, Xing Li
• DOI: 10.3390/land15091701

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study quantified the spatially uneven vegetation productivity loss and subsequent recovery on China's Huang–Huai–Hai Plain in response to the July 2023 extreme heat and drying, finding widespread initial declines and substantial recovery by October, with root zone soil moisture being a key environmental driver.

Objective

• To quantify July 2023 vegetation productivity anomalies on the Huang–Huai–Hai Plain relative to the same months during 2018–2022.
• To identify affected pixels using a standardized solar-induced chlorophyll fluorescence (SIF) anomaly (zi ≤ −1.5).
• To track within-season recovery of vegetation productivity from August to October 2023.
• To evaluate the environmental associations with SIF anomalies and recovery.

Study Configuration

• Spatial Scale: Huang–Huai–Hai (HHH) Plain, China, covering a significant agricultural belt and vegetated areas, including different vegetation types and elevation ranges (0–500 m and above 1000 m).
• Temporal Scale: July to October 2023 for anomaly and recovery tracking, with a reference period of July 2018–2022 for baseline comparisons.

Methodology and Data

• Models used: Extreme gradient boosting (XGBoost) with spatial block validation, SHapley Additive exPlanations (SHAP).
• Data sources: Satellite-derived solar-induced chlorophyll fluorescence (SIF), MODIS gross primary productivity (GPP), environmental variables (air temperature, vapor pressure deficit, downward shortwave radiation, root zone soil moisture), vegetation type stratification (deciduous broadleaf forest, grasslands, croplands), and elevation stratification.

Main Results

• In July 2023, 59.6% of the vegetated area on the HHH Plain experienced temperatures exceeding its local July 90th-percentile.
• Area-weighted anomalies for July 2023 were: SIF = −0.0129 W m⁻² μm⁻¹ sr⁻¹, GPP = −0.4853 g C m⁻² d⁻¹, and root zone soil moisture (SMrz) = −0.0147 m³ m⁻³.
• Negative productivity anomalies were widespread, affecting 57.3% of vegetated pixels for SIF and 73.4% for GPP.
• Among vegetation types, grasslands (GRA) exhibited the largest mean SIF loss, while croplands (CRO) showed the smallest despite widespread local declines.
• Productivity losses at elevations above 1000 m were greater than those at 0–500 m.
• By October 2023, 85.6% of the 2833 affected pixels had returned to non-negative SIF anomalies, though recovery above 1000 m was lower at 75.6%.
• Root zone soil moisture (SMrz) had the largest individual predictive contribution to July productivity anomalies, with the combined contribution of air temperature, vapor pressure deficit, and downward shortwave radiation being comparable.
• In the recovery model, recovery stage, initial July loss, radiation, and atmospheric demand contained substantial predictive information.

Contributions

• Provides the first comprehensive characterization of the spatially uneven vegetation productivity loss and subsequent within-season recovery on China's Huang–Huai–Hai Plain in response to the extreme climatic events of July 2023.
• Quantifies the magnitude and spatial extent of productivity anomalies for both SIF and GPP, offering detailed insights into the impact of heat and drought.
• Identifies key environmental drivers, particularly root zone soil moisture, influencing both the initial productivity decline and the subsequent recovery, utilizing advanced machine learning techniques (XGBoost, SHAP).
• Highlights differential responses and recovery rates across various vegetation types and elevation gradients, contributing to a better understanding of ecosystem resilience to extreme events.

Funding

Not explicitly stated in the provided text.

Citation

@article{Qu2026Vegetation,
  author = {Qu, Fuqiang and Liu, Xi and Li, Xing},
  title = {Vegetation Productivity Loss and Recovery Associated with the July 2023 Hot–Dry Event on the Huang–Huai–Hai Plain},
  journal = {Land},
  year = {2026},
  doi = {10.3390/land15091701},
  url = {https://doi.org/10.3390/land15091701}
}