Title:	Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images.
Authors:	FONGARO, C. T. DEMATTÊ, J. A. M. RIZZO, R. SAFANELLI, J. L. MENDES, W. de S. DOTTO, A. C. VICENTE, L. E. FRANCESCHINI, M. H. D. USTIN, S. L.
Affiliation:	CAIO TROULA FONGARO, ESALQ-USP; JOSE ALEXANDRE MELO DEMATTE, ESALQ-USP; RODNEI RIZZO, CENA-USP; JOSE LUCAS SAFANELLI, ESALQ-USP; WANDERSON DE SOUSA MENDES, ESALQ-USP; ANDRE CARNIELETTO DOTTO, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; MARSTON HERACLES DOMINGUES FRANCESCHINI, Wageningen University; SUSAN L USTIN, University of California-Davis.
Date Issued:	2018
Citation:	Remote Sensing, v. 10, n. 10, p. 1-21, 2018. Article 1555.
Description:	Abstract: Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0?20 cm depth, 919 points) from an area of 14,614 km 2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R 2 = 0.83; RMSE = 65.0 g kg − 1 ) and sand (R 2 = 0.86; RMSE = 79.9 g kg − 1 ). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.
Thesagro:	Sensoriamento Remoto Satélite Solo Arenoso Solo Argiloso
NAL Thesaurus:	Soil map Remote sensing Multispectral imagery Satellites Clay soils Sandy soils Reflectance spectroscopy Precision agriculture Soil degradation
Keywords:	Mapeamento do solo Imagem de satélite
DOI:	https://doi.org/10.3390/rs10101555
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPMA)