Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1063163
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dc.contributor.authorCEDDIA, M. B.pt_BR
dc.contributor.authorGOMES, A. S.pt_BR
dc.contributor.authorVASQUES, G. M.pt_BR
dc.contributor.authorPINHEIRO, E. F. M.pt_BR
dc.date.accessioned2017-02-08T11:11:11Zpt_BR
dc.date.available2017-02-08T11:11:11Zpt_BR
dc.date.created2017-02-08pt_BR
dc.date.issued2017pt_BR
dc.identifier.citationRemote Sensing, v. 9, n. 2, Feb. 2017.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1063163pt_BR
dc.descriptionSoils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributeseng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMapeamento digital do solopt_BR
dc.subjectGeoestatísticapt_BR
dc.subjectKrigagempt_BR
dc.titleSoil carbon stock and particle size fractions in the Central Amazon predicted from remotely sensed relief, multispectral and radar data.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2018-03-06T11:11:11Zpt_BR
riaa.ainfo.id1063163pt_BR
riaa.ainfo.lastupdate2020-09-08 -03:00:00pt_BR
dc.identifier.doihttps://doi.org/10.3390/rs9020124pt_BR
dc.contributor.institutionMARCOS BACIS CEDDIA, UFRRJpt_BR
dc.contributor.institutionANDRÉA S. GOMES, UFRRJeng
dc.contributor.institutionGUSTAVO DE MATTOS VASQUES, CNPSeng
dc.contributor.institutionÉRIKA F. M. PINHEIRO, UFRRJ.eng
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