Soil Classification and Survey
Dr. S. Balaselvakumar
Department of Geography, Government Arts College Tiruchirappalli - 620 022, Tamil Nadu, India.
S. B. Hemavarthinii
School of Agricultural Sciences, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore 641114, Tamil Nadu, India.
*Author to whom correspondence should be addressed.
Abstract
Soil classification and survey provide a systematic basis for describing, comparing, and interpreting soil variation across landscapes. This chapter examines the conceptual foundations of soil taxonomy, with emphasis on diagnostic horizons, soil orders, soil moisture and temperature regimes, soil series, and benchmark soils. It compares major classification frameworks, including USDA Soil Taxonomy, the World Reference Base for Soil Resources, the Chinese Soil Taxonomy, and the Australian Soil Classification, while outlining their structural logic and practical relevance. The chapter also reviews soil survey methods, ranging from conventional field-based approaches to digital soil mapping supported by geographic information systems, remote sensing, environmental covariates, and machine learning. Particular attention is given to the role of diagnostic surface and subsurface horizons, the taxonomic significance of soil climate regimes, and the operational value of soil series and benchmark soils in land evaluation. The discussion further considers the implications of climate change, permafrost thaw, salinisation, carbon mapping, and legacy soil data rescue for contemporary soil survey practice. Case studies from the Indo-Gangetic Plain, Siberian Yedoma regions, and irrigated landscapes in Rajasthan illustrate the applied value of integrating classification, survey, and spatial analysis. Overall, the chapter presents soil classification and survey as interconnected tools for land management, environmental monitoring, and evidence-based decision-making.
Keywords: Soil taxonomy, diagnostic horizons, soil moisture regime, soil temperature regime, soil series, benchmark soils, digital soil mapping, remote sensing, machine learning