Liu et al. (2026) GF-5 Hyperspectral Soil Moisture Content Inversion Based on Fractional-Order Differentiation and Dual-Band Spectral Index Selection

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Identification

• Journal: Agronomy
• Year: 2026
• Date: 2026-07-24
• Authors: L S Liu, Deng Yang, Shengqi Tian, Yukun Ren, Shaoyu Wang, Zhitao Zhang, Jiang Bian, Junying Chen
• DOI: 10.3390/agronomy16151407

Research Groups

Not specified in the provided text.

Short Summary

This study utilizes GF-5 satellite hyperspectral data and Fractional-Order Differentiation (FOD) to enhance the inversion accuracy of soil moisture content (SMC) in the arid Xinjiang region. The BSS-PLSR model was identified as the optimal scheme for mapping the spatial distribution of SMC.

Objective

• To overcome the challenges of complex soil background noise and weak moisture absorption features to improve the accuracy and reliability of remote sensing-based soil moisture content (SMC) inversion in arid regions.

Study Configuration

• Spatial Scale: Xinjiang region, China (arid farmlands).
• Temporal Scale: Not specified.

Methodology and Data

• Models used: Fractional-Order Differentiation (FOD), Dual-Band Spectral Indices (DBIs), and nine inversion schemes combining three variable screening methods with three machine learning models (optimal: BSS-PLSR).
• Data sources: GF-5 satellite hyperspectral data and ground-measured SMC data.

Main Results

• Fractional-Order Differentiation (FOD) with orders between 0.8 and 1.2 effectively enhanced spectral responses within soil moisture absorption bands.
• The construction of 60 dual-band spectral indices (DBIs) successfully concentrated high-correlation band combinations in moisture-sensitive regions.
• The BSS-PLSR (Backward Stepwise Selection - Partial Least Squares Regression) scheme demonstrated the highest predictive performance and stability among the tested models.

Contributions

• Provides a refined methodology for SMC inversion in arid environments by combining spectral differentiation (FOD) and index construction (DBI) to mitigate soil background noise.
• Offers a validated model combination (BSS-PLSR) for high-precision soil moisture mapping to support precision irrigation and sustainable agricultural management.

Funding

Not specified in the provided text.

Citation

@article{Liu2026GF5,
  author = {Liu, L S and Yang, Deng and Tian, Shengqi and Ren, Yukun and Wang, Shaoyu and Zhang, Zhitao and Bian, Jiang and Chen, Junying},
  title = {GF-5 Hyperspectral Soil Moisture Content Inversion Based on Fractional-Order Differentiation and Dual-Band Spectral Index Selection},
  journal = {Agronomy},
  year = {2026},
  doi = {10.3390/agronomy16151407},
  url = {https://doi.org/10.3390/agronomy16151407}
}