| DC Field | Value | Language |
|---|
| dc.contributor.author | Poppiel, Raúl R. | - |
| dc.contributor.author | Lacerda, Marilusa Pinto Coelho | - |
| dc.contributor.author | Demattê, José A. M. | - |
| dc.contributor.author | Oliveira Jr., Manuel P. | - |
| dc.contributor.author | Gallo, Bruna C. | - |
| dc.contributor.author | Safanelli, José L. | - |
| dc.date.accessioned | 2020-01-21T13:09:01Z | - |
| dc.date.available | 2020-01-21T13:09:01Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | POPPIEL, Raúl R. et al. Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method. Data in Brief, v. 25, 104070, 2019. DOI: https://doi.org/10.1016/j.dib.2019.104070. Disponível em: https://www.sciencedirect.com/science/article/pii/S235234091930424X. Acesso em: 21 jan. 2020. | pt_BR |
| dc.identifier.uri | https://repositorio.unb.br/handle/10482/36174 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Elsevier Inc. | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Mapeamento digital do solo | pt_BR |
| dc.subject.keyword | Solos - manejo | pt_BR |
| dc.subject.keyword | Planejamento agrícola | pt_BR |
| dc.rights.license | © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.1016/j.dib.2019.104070 | pt_BR |
| dc.description.abstract1 | Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis,
sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture
and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on
pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil
Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color. | pt_BR |
| dc.description.unidade | Faculdade de Agronomia e Medicina Veterinária (FAV) | - |
| Appears in Collections: | Artigos publicados em periódicos e afins
|
