Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1187931
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dc.contributor.authorCARDOSO, M. C.
dc.contributor.authorDEMATTÊ, J. A. M.
dc.contributor.authorBARTSCH, B. dos A.
dc.contributor.authorTZIOLAS, N.
dc.contributor.authorKRITHAROULA, A.
dc.contributor.authorGALLIOS, G.
dc.contributor.authorROSAS, J. T. F.
dc.contributor.authorROSIN, N. A.
dc.contributor.authorNOVAIS, J. de J. M.
dc.contributor.authorSOUSA, G. P. B. de
dc.contributor.authorFALCIONI, R.
dc.contributor.authorVOGEL, L. G.
dc.date.accessioned2026-06-30T21:34:51Z-
dc.date.available2026-06-30T21:34:51Z-
dc.date.created2026-06-30
dc.date.issued2026
dc.identifier.citationGeoderma Regional, v. 45, e01100, Jun. 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187931-
dc.descriptionSoil spectral libraries (SSLs) are commonly built with expensive proximal sensors, while satellite spectral data are free and widely available. This study investigates how service providers can construct a satellite-based SSL to deliver soil information to stakeholders. We propose the Soil Satellite Spectral Service (Sat4-Service) strategy to estimate clay content and soil organic carbon (SOC) in agricultural bare topsoil (the first 10 cm) from farm to national scale in Brazil. Bare-soil reflectance from Landsat-8 and Sentinel-2 was compared with laboratory spectra from 7711 topsoil samples, showing strong correspondence between lab spectra resampled to the satellite bandpasses and the SySI composites, with a mean Pearson correlation across all multispectral bands and both sensors of r ≈ 0.72. We then simulated a provider progressively building a satellite-derived SSL as new clients and soil data were added. With each farm, the dataset expanded and predictive models were retrained. Model accuracy varied initially but stabilized after ∼50 clients, reaching R2 = 0.63 for SOC and 0.79 for clay using both satellites. In contrast, a national model based on >60,000 samples performed worse on the same farms (R2 = 0.43 for SOC, 0.65 for clay). Findings show that multispectral imagery can reliably estimate soil clay and carbon, and that models built on regional or farm-level data often outperform broad continental-scale libraries. While global models provide useful first approximations—especially for newcomers or data-scarce areas—regionally focused spectral libraries ensure higher accuracy. The Sat4-Service framework highlights the potential of satellite data to deliver cost-effective, scalable soil information, with applications in precision agriculture, carbon crediting, and digital soil mapping.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectPedometric
dc.subjectSentinel-2
dc.subjectCarbon credits
dc.subjectEspectroscopia
dc.subjectCrédito de carbono
dc.subjectPedometria
dc.titleA soil satellite spectral service (Sat4): a strategy for stakeholders.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusSpectroscopy
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusLandsat
riaa.ainfo.id1187931
riaa.ainfo.lastupdate2026-06-30
dc.identifier.doihttps://doi.org/10.1016/j.geodrs.2026.e01100
dc.contributor.institutionMATHEUS CARRACO CARDOSO, UNIVERSIDADE DE SÃO PAULO; JOSÉ ALEXANDRE MELO DEMATTÊ, UNIVERSIDADE DE SÃO PAULO; BRUNO DOS ANJOS BARTSCH, UNIVERSIDADE DE SÃO PAULO; NIKOLAOS TZIOLAS, UNIVERSITY OF FLORIDA; ANASTASIA KRITHAROULA, UNIVERSITY OF FLORIDA; GIANNIS GALLIOS, UNIVERSITY OF FLORIDA; JORGE TADEU FIM ROSAS, UNIVERSIDADE DO OESTE PAULISTA; NICOLAS AUGUSTO ROSIN, CNPS; JEAN DE JESUS MACEDO NOVAIS, CNPS; GABRIEL PIMENTA BARBOSA DE SOUSA, UNIVERSIDADE DE SÃO PAULO; RENAN FALCIONI, UNIVERSIDADE ESTADUAL DE MARINGÁ; LETÍCIA GUADAGNIN VOGEL, UNIVERSIDADE DE SÃO PAULO.
Appears in Collections:Artigo em periódico indexado (CNPS)

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