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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Is it possible to map subsurface soil attributes by satellite spectral transfer models?

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Author(s):
Mendes, Wanderson de S. [1] ; Medeiros Neto, Luiz G. [1] ; Dematte, Jose A. M. [1] ; Gallo, Bruna C. [2] ; Rizzo, Rodnei [3] ; Safanelli, Jose L. [1] ; Fongaro, Caio T. [1]
Total Authors: 7
Affiliation:
[1] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Soil Sci, Piracicaba, SP - Brazil
[2] Univ Estadual Campinas, Bioenergy Program, Campinas, SP - Brazil
[3] Univ Sao Paulo, Ctr Nucl Energy Agr, Piracicaba, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Geoderma; v. 343, p. 269-279, JUN 1 2019.
Web of Science Citations: 3
Abstract

It is impossible to make pedological maps without understanding subsurface attributes. Several strategies can be used for soil mapping, from a tacit knowledge to mathematical modeling. However, there are still gaps in knowledge regarding how to optimize subsurface mapping. This work aimed to quantify subsurface soil attributes using satellite spectral reflectance and geographically weighted regression (GWR) techniques. The study was carried out in Sao Paulo, Brazil, in an area spanning 47,882 ha. Multitemporal satellite images (Landsat-5) were initially processed in order to retrieve spectral reflectance from the bare soil surface. Based on a toposequence method, 328 points were then distributed across the area (at depths between 0 and 20 cm and 80 and 100 cm) and analyzed for their soil chemical and physical attributes (including the reflectance spectra (400 to 2500 run)) in the laboratory. We achieved 67.72% of bare soil for the whole study area, with the remaining 32.28% of the unmapped surface being filled by kriging interpolation. All 328 samples were modeled using surface (Landsat-5 TM spectral reflectance) and subsurface (acquired in the laboratory) data, reaching up to 0.72 RLi. The correlation between the spectra of both depths was significant and the soil attributes prediction reached an R!dj of validation above 0.6 for clay, hue, value, and chroma at 0-20 and 80-100 cm depths. The satellite soil surface reflectance allowed the estimation of soil subsurface attributes. These results demonstrate that diagnostic soil attributes can be quantified based on spectral pedotransfer (SPEDO) functions to assist digital soil mapping and soil monitoring. Despite our efforts to determine soil subsurface properties using digital soil mapping approach, this task still need considerable refinement. Thus, research must continue to aggregate outcomes from other techniques. (AU)

FAPESP's process: 16/26124-6 - Precision pedology: soil characterisation and mapping in real time using geotechnologies
Grantee:Wanderson de Sousa Mendes
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 14/22262-0 - Geotechnologies on a detailed digital soil mapping and the Brazilian soil spectral library: development and applications
Grantee:José Alexandre Melo Demattê
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/01597-9 - Pedotransfer functions by geotecnologies associated with photopedology for pedological mapping in agricultural areas of São Paulo State
Grantee:José Lucas Safanelli
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)