Zhao et al. (2026) Integrated linear and non-linear assessment of remote sensing drought indices for soil moisture monitoring across multiple temporal scales in China

Identification

• Journal: Ecological Indicators
• Year: 2026
• Date: 2026-03-11
• Authors: Anzhou Zhao, Shuai Shi, Lidong Zou, Wei Zhang, Muyi Li, Feng Yue, Xueyan Hu, Arturo Sanchez-Azofeifa
• DOI: 10.1016/j.ecolind.2026.114733

Research Groups

• School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan, China
• Institute of Applied Artificial Intelligence of The Guangdong-Hongkong-Macao Greater Bay, Shenzhen Polytechnic University, Shenzhen, China
• School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, China
• School of Civil Engineering and Architecture, Suqian University, Suqian, China
• School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou, China
• School of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Shaanxi, China
• Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Canada

Short Summary

This study systematically evaluated remote sensing drought indices (Vegetation Condition Index, Vegetation Water Index, Temperature Condition Index) against soil moisture across China using linear correlation and a Copula-based framework, revealing that optimal index performance varies significantly by ecosystem, temporal scale, and drought severity, especially under extreme conditions.

Objective

• To assess how the relationships between the Soil Moisture Condition Index (SMCI) and each remote sensing drought index (VCI, VWI, TCI) vary across monthly, seasonal, and annual scales, and how these relationships are shaped by vegetation type and climatic setting in China.
• To determine the extent to which these indices, through combined correlation and Copula-based analyses, can effectively characterize soil drought dynamics and reveal regional differences in their optimal applicability.

Study Configuration

• Spatial Scale: National scale across China, classified into four major land cover types (forest, grassland, cropland, non-vegetated areas). All datasets resampled to a common spatial resolution of 0.1° × 0.1°.
• Temporal Scale: Monthly, seasonal (spring, summer, autumn, winter), and annual scales, covering the period from January 2003 to February 2022.

Methodology and Data

• Models used:
  • Pearson correlation analysis (for linear association).
  • Copula-based conditional probability framework (for nonlinear and tail-dependent relationships).
• Data sources:
  • Vegetation Condition Index (VCI): Derived from monthly Normalized Difference Vegetation Index (NDVI) data from MODIS MOD13C2 product (0.05° spatial resolution).
  • Vegetation Water Index (VWI): Derived from monthly Normalized Difference Water Index (NDWI), calculated from bidirectional reflectance information from MODIS MCD43A4 product (0.05° spatial resolution).
  • Temperature Condition Index (TCI): Derived from monthly Land Surface Temperature (LST) data from the Global Daily 0.05° Spatio-Temporal Continuous Surface Temperature Dataset (reconstructed using Data Interpolating Empirical Orthogonal Functions and adjusted with Fifth-Generation European Reanalysis-Land data).
  • Soil Moisture Condition Index (SMCI): Derived from monthly surface soil moisture data (0–10 cm) from the Global Land Evaporation Amsterdam Model (GLEAM) (0.1° spatial resolution).
  • Land cover information: MODIS MCD12C1 product, classified according to the International Geosphere-Biosphere Programme (IGBP) scheme.
  • All datasets were reprojected to WGS_1984 and resampled to a common spatial resolution of 0.1° × 0.1°.

Main Results

• The Vegetation Condition Index (VCI) showed the strongest linear correlation with the Soil Moisture Condition Index (SMCI) at the annual scale and remained dominant during spring, summer, and autumn at shorter time scales.
• Ecosystem-dependent patterns emerged, particularly in summer: VWI performed better in forests, while TCI performed better in grasslands, reflecting differences in vegetation density and surface energy processes.
• Copula-based analysis revealed that remote sensing indices are less likely to reach extreme or severe drought thresholds than to indicate general drought under soil moisture deficits, suggesting weaker vegetation and temperature responses under extreme soil moisture deficits.
• Under SMCI drought conditions (SMCI ≤ 40), VCI showed the highest conditional drought probability in forested regions. VWI was more responsive in northern arid regions and grasslands, and TCI showed clearer responses during the growing season.
• Vegetation regulation and legacy effects can weaken synchronous responses during extreme droughts, and dominant drought signals vary among ecosystems.

Contributions

• Established a unified framework for systematically evaluating the multi-scale performance of remote sensing drought indices against soil moisture benchmarks across diverse ecological contexts in China.
• Integrated both linear (Pearson correlation) and non-linear (Copula-based conditional probability) analyses to provide a more robust and comprehensive understanding of the complex, often asynchronous, relationships between soil moisture and remote sensing drought indices, especially under extreme conditions.
• Identified the optimal applicability of VCI, VWI, and TCI across different temporal scales, ecosystems, and drought severities in China, offering crucial guidance for regional drought monitoring and early-warning systems.
• Highlighted that vegetation regulation and legacy effects can weaken synchronous responses during extreme droughts, emphasizing the limitations of linear approaches for severe drought assessment.

Funding

• Post-doctoral Later-stage Foundation Project of Shenzhen Polytechnic University (6023271029K)
• Shenzhen Polytechnic University Research Fund (6025310044K)
• National Natural Science Foundation of China (No. 42171212)
• Science Research Project of Hebei Education Department (No. JCZX2026037)
• Guangdong Basic and Applied Basic Research Foundation Youth Project (2022A1515110654)

Citation

@article{Zhao2026Integrated,
  author = {Zhao, Anzhou and Shi, Shuai and Zou, Lidong and Zhang, Wei and Li, Muyi and Yue, Feng and Hu, Xueyan and Sanchez-Azofeifa, Arturo},
  title = {Integrated linear and non-linear assessment of remote sensing drought indices for soil moisture monitoring across multiple temporal scales in China},
  journal = {Ecological Indicators},
  year = {2026},
  doi = {10.1016/j.ecolind.2026.114733},
  url = {https://doi.org/10.1016/j.ecolind.2026.114733}
}