A roadmap of geospatial soil quality analysis systems
This provides a roadmap for researchers and practitioners in sustainable agriculture and land management, though it appears incremental as it consolidates existing advancements into a cohesive framework.
The paper tackles the problem of inefficient soil quality assessment by proposing a unified, modular pipeline that integrates multi-source soil data, GIS, remote sensing, and machine learning to support transparent and scalable evaluation, including practical applications.
Soil quality (SQ) plays a crucial role in sustainable agriculture, environmental conservation, and land-use planning. Traditional SQ assessment techniques rely on costly, labor-intensive sampling and laboratory analysis, limiting their spatial and temporal coverage. Advances in Geographic Information Systems (GIS), remote sensing, and machine learning (ML) enabled efficient SQ evaluation. This paper presents a comprehensive roadmap distinguishing it from previous reviews by proposing a unified and modular pipeline that integrates multi-source soil data, GIS and remote sensing tools, and machine learning techniques to support transparent and scalable soil quality assessment. It also includes practical applications. Contrary to existing studies that predominantly target isolated soil parameters or specific modeling methodologies, this approach consolidates recent advancements in Geographic Information Systems (GIS), remote sensing technologies, and machine learning algorithms within the entire soil quality assessment pipeline. It also addresses existing challenges and limitations while exploring future developments and emerging trends in the field that can deliver the next generation of soil quality systems making them more transparent, adaptive, and aligned with sustainable land management.