Integrating geospatial and multi‐depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil |
| |
| Authors: | G M Vasques J A M Demattê R A Viscarra Rossel L Ramírez López F S Terra R Rizzo C R De Souza Filho |
| |
| Institution: | 1. Embrapa Soils, Rio de Janeiro, Brazil;2. Soil Science Department, ‘Luiz de Queiroz’ College of Agriculture, University S?o Paulo, Piracicaba, Brazil;3. Bruce E. Butler Laboratory, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia;4. Institute of Terrestrial Ecosystems, Swiss Federal Institute of Technology, 8092 Zürich, Switzerland;5. Institute of Agrarian Sciences, Federal University of the Valleys of Jequitinhonha and Mucuri, Unaí, Brazil;6. Environmental Analysis and Geoprocessing Laboratory, Center for Nuclear Energy in Agriculture, Piracicaba, Brazil;7. Geology and Natural Resources Department, Institute of Geosciences, State University of Campinas, Campinas, Brazil |
| |
| Abstract: | Soil mapping across large areas can be enhanced by integrating different methods and data sources. This study merges laboratory, field and remote sensing data to create digital maps of soil suborders based on the Brazilian Soil Classification System, with and without additional textural classification, in an area of 13 000 ha in the state of São Paulo, southeastern Brazil. Data from 289 visited soil profiles were used in multinomial logistic regression to predict soil suborders from geospatial data (geology, topography, emissivity and vegetation index) and visible–near infrared (400–2500 nm) reflectance of soil samples collected at three depths (0–20, 40–60 and 80–100 cm). The derived maps were validated with 47 external observations, and compared with two conventional soil maps at scales of 1:100 000 and 1:20 000. Soil suborders with and without textural classification were predicted correctly for 44 and 52% of the soil profiles, respectively. The derived suborder maps agreed with the 1:100 000 and 1:20 000 conventional maps in 20 and 23% (with textural classification) and 41 and 46% (without textural classification) of the area, respectively. Soils that were well defined along relief gradients (Latosols and Argisols) were predicted with up to 91% agreement, whereas soils in complex areas (Cambisols and Neosols) were poorly predicted. Adding textural classification to suborders considerably degraded classification accuracy; thus modelling at the suborder level alone is recommended. Stream density and laboratory soil reflectance improved all classification models, showing their potential to aid digital soil mapping in complex tropical environments. |
| |
| Keywords: | |
|
|
