Lv et al. (2025) Dry-wet seasonality effects on the satellite-based land cover types identification in the Nile River Basin

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

Identification

• Journal: Big Earth Data
• Year: 2025
• Date: 2025-12-28
• Authors: Yulong Lv, Dan Peng, Zihang Lou, Hongyan Wang, L S Yu, Yaqiong Zhang, Qiaoyun Xie, Jinkang Hu, Shijun Zheng, Enhui Cheng, Hongchi Zhang, Yizhou Zhang, Hao Peng
• DOI: 10.1080/20964471.2025.2603812

## Research Groups -

Short Summary

This study assessed the impact of seasonal dry-wet variations on land cover type (LCT) mapping in the Nile River Basin, demonstrating that integrating spectral characteristics from both dry and wet seasons significantly improves classification accuracy.

Objective

• To assess the impacts of seasonal variations on land cover type (LCT) mapping in the Nile River Basin.

Study Configuration

• Spatial Scale: Regional (Nile River Basin)
• Temporal Scale: Seasonal and monthly (analysis of dry-wet transitions, monthly SPEI computation, and use of dry-season, wet-season, and combined spectral characteristics)

Methodology and Data

• Models used: Random Forest (RF) models
• Data sources: Spectral characteristics (likely satellite imagery) from dry and wet seasons, Standardized Precipitation Evapotranspiration Index (SPEI) computed monthly.

Main Results

• The Nile River Basin exhibits clear spatiotemporal alternation between dry and wet seasons.
• Land cover identification using dry-season spectral characteristics achieved a higher classification accuracy (Overall Accuracy (OA) = 83.68%, Kappa = 0.8134) than wet-season data (OA = 80.39%, Kappa = 0.7758).
• 35.6% of the study areas showed seasonal discrepancies in land cover identification.
• Combining dry and wet season spectral datasets further improved accuracy significantly (OA = 88.14%, Kappa = 0.8644).
• Discrepancies in identification primarily stem from variations in spectral responses between dry and wet seasons.

Contributions

• Demonstrates that integrating dry and wet season spectral characteristics substantially improves land cover classification accuracy, particularly in regions with pronounced wet-dry seasonal alternation.
• Offers a reliable approach for high-precision land cover dynamic monitoring in such environments.

## Funding -

Citation

@article{Lv2025Drywet,
  author = {Lv, Yulong and Peng, Dan and Lou, Zihang and Wang, Hongyan and Yu, L S and Zhang, Yaqiong and Xie, Qiaoyun and Hu, Jinkang and Zheng, Shijun and Cheng, Enhui and Zhang, Hongchi and Zhang, Yizhou and Peng, Hao},
  title = {Dry-wet seasonality effects on the satellite-based land cover types identification in the Nile River Basin},
  journal = {Big Earth Data},
  year = {2025},
  doi = {10.1080/20964471.2025.2603812},
  url = {https://doi.org/10.1080/20964471.2025.2603812}
}