A catchment-scale dataset of soil properties and their mid-infrared spectra
The dataset presents information on soil properties and their associated mid-infrared spectra for a drainage basin of 4.2 km<sup>2</sup>, referred to as Upper Badong catchment (31°1'24''N, 110°20'35''E) in the Hubei province, China. Data were collected for topsoil in a highly diverse terrace catchment composed of woodland, cropland and small farm building. Soil properties included in this dataset are pH, texture (i.e. clay, silt and sand content), total carbon, organic carbon and CaCO<sub>3</sub>. In addition, the soil samples were scanned in the mid-infrared range. The data collection processed involved three field campaigns during 2013 and 2014 where topsoil samples were collected in a standardized way across all sites, and soil analyses in the laboratory of soil science following standard procedures. The dataset offers insights into the spatial variation of soil properties in a highly diverse catchment of central China. Researchers interested in soil research can use this dataset for various purposes, including building digital soil mapping models or soil spectroscopic models, benchmarking of models with other datasets, and research in soil erosion modelling.
Authors
- Felix Stumpf
- Thomas Scholten (ORCID: https://orcid.org/0000-0002-4875-2602)
- Alexandre M.J.‐C. Wadoux (ORCID: https://orcid.org/0000-0001-7325-9716)
Institutions
- Université de Montpellier (FR)
- AgroParisTech (FR)
- Institut Agro Montpellier (FR)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Swiss Federal University for Vocational Education and Training SFUVET (CH)
- Institut de Recherche pour le Développement (FR)
- Laboratoire d'Etude des Interactions entre Sol-Agrosystème-Hydrosystème (FR)
- University of Tübingen (DE)
Publication Details
- Journal
- Data in Brief
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1016/j.dib.2025.111510
- Citations
- 1
- Primary Topic
- Soil Geostatistics and Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Bundesministerium für Bildung und Forschung
- China University of Geosciences