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
S. M. Bharthisha *
Department of Agronomy, College of Agriculture, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
D. N. Sharan
Department of Agronomy, College of Agriculture, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
Bhojaraj Biradar
Department of Agronomy, College of Agriculture, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
M. P. Jeevan
Department of Agronomy, College of Agriculture, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
R. L. Chavan
Department of Environmental Science, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
Channaveer Mali Patil
Department of Seed Science and Technology, College of Agriculture, University of Agricultural Sciences, Dharwad – 580 005, Karnataka, India.
S. M. Kishore
Department of Agricultural Entomology, KSNUAHS, Shivamogga – 577 204, India.
*Author to whom correspondence should be addressed.
Abstract
The global agricultural sector is facing unprecedented challenges driven by climate change, poor irrigation management and natural hydrogeological factors, leading to widespread soil salinization and waterlogging. These interconnected environmental hazards threaten global food security by severely degrading arable land, reducing crop yields and altering ecosystem dynamics. Traditional field-based monitoring methods are highly resource-intensive, localized and often fail to capture the dynamic, large-scale spatial distribution of salinity and waterlogging over time. Consequently, the integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has emerged as an indispensable paradigm for the real-time, cost-effective and synoptic assessment of these land degradation processes. This comprehensive narrative review synthesizes the contemporary advancements, methodologies and challenges in deploying RS and GIS technologies for mapping and modeling soil salinity and waterlogging. We delve into the theoretical mechanisms underlying primary and secondary salinization and the hydrological imbalances causing waterlogging. Furthermore, we critically evaluate the efficacy of diverse satellite platforms, ranging from optical and multispectral sensors to advanced microwave and radar systems that overcome cloud cover limitations and provide precise soil moisture estimates. The review also explores the integration of geospatial modeling and Topographic Wetness Indices (TWI) alongside cutting-edge Machine Learning (ML) algorithms. These algorithms, such as Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN), have revolutionized predictive accuracy by effectively managing multicollinearity and complex environmental datasets. By analyzing recent global case studies from arid and semi-arid regions, including the Nile Delta, the Shiyang River Basin and agricultural zones in India, this paper identifies critical environmental drivers like topography, climate change and inappropriate land-cover management. Finally, we outline future directions emphasizing multi-sensor data fusion, cloud-based geocomputation and the integration of Internet of Things (IoT) sensors to facilitate sustainable land management and mitigate the escalating threats of salinization and waterlogging.
Keywords: Soil Salinity, waterlogging, remote sensing, geographic information systems (GIS), machine learning, land degradation, satellite imagery, spatial modeling