Bharthisha et al. (2026) Integrated Remote Sensing and GIS Approaches for Mapping Soil Salinity and Waterlogging in Arid and Semi-Arid Environments: A Systematic Review of Statistical and Hydrological Models

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Identification

• Journal: Journal of biology and nature
• Year: 2026
• Date: 2026-05-28
• Authors: S. M. Bharthisha, D. N. Sharan, Bhojaraj Biradar, M. P. Jeevan, R L Chavan, Channaveer Mali Patil, S. M. Kishore
• DOI: 10.56557/joban/2026/v18i210647

Research Groups

Not specified

Short Summary

This narrative review synthesizes the use of Remote Sensing (RS) and GIS technologies, integrated with machine learning, to provide a cost-effective and large-scale alternative to traditional field monitoring for soil salinity and waterlogging.

Objective

• To evaluate contemporary advancements, methodologies, and challenges in deploying RS and GIS for the mapping and modeling of soil salinity and waterlogging.

Study Configuration

• Spatial Scale: Global (with specific case studies in the Nile Delta, Shiyang River Basin, and agricultural zones in India).
• Temporal Scale: Contemporary (Review of current technological advancements).

Methodology and Data

• Models used: Topographic Wetness Indices (TWI) and Machine Learning (ML) algorithms, specifically Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN).
• Data sources: Satellite platforms (optical, multispectral, microwave, and radar sensors).

Main Results

• RS and GIS integration enables real-time, synoptic assessment of land degradation, overcoming the resource-intensive nature of field-based methods.
• Microwave and radar systems are highlighted for their ability to penetrate cloud cover and provide precise soil moisture estimates.
• ML algorithms significantly improve predictive accuracy by effectively managing multicollinearity and complex environmental datasets.
• Topography, climate change, and inappropriate land-cover management are identified as the primary drivers of salinization and waterlogging.

Contributions

• Provides a comprehensive synthesis of the theoretical mechanisms of soil degradation and the efficacy of diverse satellite sensors and ML algorithms.
• Establishes a framework for future sustainable land management by proposing the integration of multi-sensor data fusion, cloud-based geocomputation, and Internet of Things (IoT) sensors.

Funding

Not specified

Citation

@article{Bharthisha2026Integrated,
  author = {Bharthisha, S. M. and Sharan, D. N. and Biradar, Bhojaraj and Jeevan, M. P. and Chavan, R L and Patil, Channaveer Mali and Kishore, S. M.},
  title = {Integrated Remote Sensing and GIS Approaches for Mapping Soil Salinity and Waterlogging in Arid and Semi-Arid Environments: A Systematic Review of Statistical and Hydrological Models},
  journal = {Journal of biology and nature},
  year = {2026},
  doi = {10.56557/joban/2026/v18i210647},
  url = {https://doi.org/10.56557/joban/2026/v18i210647}
}