Data/hora: 30/05/2024 17:20:34

Biblioteca(s): 	Embrapa Meio Ambiente.	
Data corrente: 	19/11/2019	
Data da última atualização: 	19/11/2019	
Autoria: 	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.	
Afiliação: 	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.	
Título: 	Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	Remote Sensing, v. 10, n. 10, p. 1-21, 2018. Article 1555.	
DOI: 	https://doi.org/10.3390/rs10101555	
Idioma: 	Inglês	
Conteúdo: 	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 &#8722; 1 ) and sand (R 2 = 0.86; RMSE = 79.9 g kg &#8722; 1 ). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.	
Palavras-Chave: 	Imagem de satélite; Mapeamento do solo.	
Thesagro: 	Satélite; Sensoriamento Remoto; Solo Arenoso; Solo Argiloso.	
Thesaurus Nal: 	Clay soils; Multispectral imagery; Precision agriculture; Reflectance spectroscopy; Remote sensing; Sandy soils; Satellites; Soil degradation; Soil map.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/204955/1/Vicente-Clay-Sand-AP-2019.pdf	
Registro original: 	Embrapa Meio Ambiente (CNPMA)	
Exemplares: 	CNPMA - (16498-1) - UPC - AP - DD -  -  -  - DIS