期刊
GEODERMA
卷 237, 期 -, 页码 308-317出版社
ELSEVIER
DOI: 10.1016/j.geoderma.2014.09.014
关键词
Diffuse reflectance spectroscopy; Soil organic carbon; Partial least square regression; Anthrosols; Amazonian Dark Earths; Pre-Columbian land use
类别
资金
- Bank of Sweden Tercentenary Foundation [P10-0323:1]
- Sao Paulo Research Foundation (FAPESP) [Pn 09/541448]
- National Council for Scientific and Technological Development (CNPq, Brazil) [Pn 140261/2009-5]
- Swedish Foundation for Humanities and Social Sciences [P10-0323:1] Funding Source: Swedish Foundation for Humanities and Social Sciences
In the Brazilian Amazon patches of anthropogenic soils known as Amazonian Dark Earths (ADEs) occur. These soils are rich in carbon (C) and plant nutrients compared to the naturally occurring strongly weathered soils. In this paper we explore the potential of visible to near infrared (vis-NIR) and mid infrared (MIR) spectroscopy as an alternative to traditional soil analysis of ADE properties for predicting and assessing spatial distributions. We also test whether partial least square regression (PLSR) models generated from soil data at one ADE site can serve as a basis for predictive assessments of soil characteristics at another. The study was carried out at two locations on the Belterra Plateau, Para state, Brazil, each including soils that displayed typical ADE characteristics. Laboratory analyses confirmed the occurrence of general properties typical of ADE: elevated pH, phosphorus (P), exchangeable calcium (Ca), cation exchange capacity (CEC), and soil organic carbon (SOC) in the study areas. For C and CEC, MIR models were more efficient than those based on vis-NIR (R-2 = 0.90 and 0.82 vs. 0.72 and 0.63). The soil maps produced from the PLSR models adequately described the spatial pattern of SOC, CEC, and Ca. However, useful models of soil P could not be produced. We conclude that spectroscopy can be useful for assessing the spatial distribution of some of the most important ADE properties. MIR spectroscopy models in particular have the potential to be an alternative to traditional soil analysis. (C) 2014 Elsevier B.V. All rights reserved.
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