Niu et al. (2025) Long-Term Trends and Seasonally Resolved Drivers of Surface Albedo Across China Using GTWR

Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-11-12
• Authors: Jiqiang Niu, Ziming Wang, Hao Lin, Zijian Liu, Mengyang Li, Xiaodong Deng, Bohan Wang, Tong Wu, Junkuan Zhu
• DOI: 10.3390/atmos16111287

Research Groups

• School of Geographic Sciences, Xinyang Normal University, Xinyang, China
• Henan Engineering Technology Research Center for Intelligent Perception and Analysis of Land Surface Ecosystem Health in Huaihe River Basin, Xinyang Normal University, Xinyang, China

Short Summary

This study analyzed the spatiotemporal variations and long-term trends of surface albedo across China from 2001 to 2020, revealing an overall decline primarily driven by the Normalized Difference Vegetation Index (NDVI) (approximately 48%), with air temperature and precipitation also playing significant, seasonally varying roles.

Objective

• To systematically analyze the spatiotemporal variations and long-term trends of surface albedo across China from 2001 to 2020.
• To quantify the seasonally resolved contributions of NDVI, air temperature, and precipitation as driving factors, considering spatiotemporal non-stationarity and distinguishing between snow-covered and snow-free conditions.

Study Configuration

• Spatial Scale: National scale, covering the entirety of China (approximately 9.6 million square kilometers), with analyses performed at a 1-kilometer gridded resolution.
• Temporal Scale: 20 years (2001–2020), with annual and seasonal analyses.

Methodology and Data

• Models used:
  • Theil–Sen slope estimator for robust trend quantification.
  • Mann–Kendall (M-K) non-parametric statistical test for trend significance.
  • Geographically and Temporally Weighted Regression (GTWR) model for assessing spatiotemporal non-stationary relationships and contributions of driving factors.
• Data sources:
  • Albedo data: MODIS shortwave albedo (MCD43A3, V061) at 500 meters spatial resolution, aggregated to 1 kilometer.
  • Vegetation data: MODIS Normalized Difference Vegetation Index (MOD13A3) at 1 kilometer spatial resolution.
  • Climate data: 1-kilometer gridded monthly precipitation and monthly mean air temperature datasets from the National Earth System Science Data Center, China.

Main Results

• China's mean annual shortwave albedo from 2001–2020 was 0.186, showing a significant overall decline with a cumulative decrease of approximately 0.018, representing a relative reduction of 9–10%.
• Spatially, surface albedo exhibited a pattern of "decreasing in the north and increasing in the south." High albedo values were concentrated in arid/semi-arid Northwest China and high-altitude, cold regions (average 0.483), while low values were in humid monsoon regions of Eastern China (average 0.126).
• Seasonal mean albedo ranked as: winter (0.283) > autumn (0.214) > spring (0.161) > summer (0.143). Declines were most pronounced in autumn and winter.
• NDVI was identified as the dominant driver of surface albedo changes, with seasonal contribution rates ranging from 43.94% to 52.02% (average approximately 48%).
• Air temperature contributed approximately 26.81% to 28.07% seasonally (average approximately 27%), and precipitation contributed approximately 21.17% to 28.57% seasonally (average approximately 25%).
• Relationships between albedo and drivers showed strong spatiotemporal heterogeneity:
  • In high-latitude and high-elevation snow-dominated regions, albedo tended to decrease with warmer conditions and increase with greater precipitation.
  • In much of eastern China, albedo was generally positively associated with temperature and negatively with precipitation.

Contributions

• Established a national, 1-kilometer resolution, 2001–2020 albedo trend baseline using robust non-parametric estimators.
• Provided a seasonally resolved GTWR attribution that captures the spatiotemporal non-stationarity of NDVI, temperature, and precipitation effects under both snow-covered and snow-free conditions.
• Offered a set of region- and season-specific action implications for land-surface radiative management, such as cryosphere monitoring in alpine zones and cropping/urban surface management in monsoon regions.

Funding

• Natural Science Foundation of Henan Province (Key Project), grant number 252300421290.
• National Natural Science Foundation of China (NSFC), grant number 41771438.

Citation

@article{Niu2025LongTerm,
  author = {Niu, Jiqiang and Wang, Ziming and Lin, Hao and Li, Hongrui and Liu, Zijian and Li, Mengyang and Deng, Xiaodong and Wang, Bohan and Wu, Tong and Zhu, Junkuan},
  title = {Long-Term Trends and Seasonally Resolved Drivers of Surface Albedo Across China Using GTWR},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16111287},
  url = {https://doi.org/10.3390/atmos16111287}
}