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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.)

Pedology and soil class mapping from proximal and remote sensed data

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Author(s):
Poppiel, Raul R. [1] ; Lacerda, Marilusa P. C. [1] ; Dematte, Jose A. M. [2] ; Oliveira Jr, Manuel P. ; Gallo, Bruna C. [2] ; Safanelli, Jose L. [2]
Total Authors: 6
Affiliation:
[1] Univ Brasilia, Fac Agron & Vet Med, ICC Sul, Darcy Ribero Univ Campus, Postal Box 4508, BR-70910960 Brasilia, DF - Brazil
[2] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Soil Sci, Padua Dias Av 11, Postal Box 09, BR-13416900 Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Geoderma; v. 348, p. 189-206, AUG 15 2019.
Web of Science Citations: 0
Abstract

Pedological assessment and mapping by hyperspectral proximal and multispectral remote sensing comes up as an important alternative for large extent areas with high soil variability. Strategies indicating how to integrate these multi-source data for digital soil mapping are emerging. In this paper, we used proximal and remote sensed data to perform pedological assessments and to support a detailed soil class mapping by MESMA. The study area is located in Southeast of Federal District, Brazil. Six toposequences were defined according to pedomorpho-geological assessments and 34 sites were collected for laboratory analysis (texture and chemical) and VIS-NIR-SWIR (350-2500 mu) spectroscopy reflectance analysis. We assessed soil mineralogy based on derivative analysis of topsoil reflectance and we grouped observations to recognize topsoil spectral patterns by clustering method. Topsoil patterns (mean spectral curve for each cluster) were convolved using a Gaussian function of Landsat 5-TM spectral bands to obtain endmembers. Then, we used a Landsat 5 TM time series to produce a bare soil composite denominated Synthetic Soil Image (SYSI). Endmembers and SYSI were used as input data for Multiple Endmember Spectral Mixture Analysis (MESMA) to map the soil classes. Topsoil spectra clustered soil samples that were similar in texture, mineralogy and color, and identified 13 topsoil patterns (endmembers). SYSI retrieved 74% of bare soil area coverage and presented very similar spectral patterns to endmembers. The RGB 543 composite highlighted the mineralogy (i.g. sesquioxides, kaolinite and quartz), texture and color of the soils. MESMA modeled almost 100% of SYSI from endmembers, with low global RMSE of 0.86% and high global fraction of 62%. Soil classes were mapped using topsoil reflectance patterns and satellite images by MESMA method. We validated the digital soil map by using independent field-visited sites, which reached a Kappa coefficient of 0.73. We efficiently assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. We successfully mapped soil classes using topsoil reflectance patterns and a bare soil composite image by MESMA method. (AU)

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 type: Scholarships in Brazil - Doctorate (Direct)