Dematte J. A. M. auteur aut IRD Paiva A. F. D. auteur aut IRD Poppiel R. R. auteur aut IRD Rosin N. A. auteur aut IRD Ruiz L. F. C. auteur aut IRD Mello F. A. D. auteur aut IRD Minasny B. auteur aut IRD Grunwald S. auteur aut IRD Ge Y. F. auteur aut IRD Ben Dor E. auteur aut IRD Gholizadeh A. auteur aut IRD Gomez Cécile auteur aut IRD Chabrillat S. auteur aut IRD Francos N. auteur aut IRD Ayoubi S. auteur aut IRD Fiantis D. auteur aut IRD Biney J. K. M. auteur aut IRD Wang C. K. auteur aut IRD Belal A. auteur aut IRD Naimi S. auteur aut IRD Hafshejani N. A. auteur aut IRD Bellinaso H. auteur aut IRD Moura-Bueno J. M. auteur aut IRD Silvero N. E. Q. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible-Near-Short-wave infrared (vis-NIR-SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R-2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis-NIR-SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique. specialized proximal soil sensing soil spectral library spectroscopy soil analysis soil quality precision agriculture community practice soil health monitoring BRESIL MONDE 068 126 020 122 14 3 740 [27 + Correction in Remote Sensing.2022, 14, 6,1459, 2 p.] 2022 https://www.documentation.ird.fr/hor/fdi:010084296 10.3390/rs14030740 [F B010084296] https://www.documentation.ird.fr/hor/fdi:010084296 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-04/010084296.pdf IRD - Base Horizon / Pleins textes 2022-04-06 2025-02-24 fdi:010084296 fre