The Brazilian Soil Spectral Service (BraSpecS): A User‐Friendly System for Global Soil Spectra Communication

José A.M. Demattê*, Ariane Francine da Silveira Paiva, Raul Roberto Poppiel, Nícolas Augusto Rosin, Luis Fernando Chimelo Ruiz, Fellipe Alcantara de Oliveira Mello, Budiman Minasny, Sabine Grunwald, Yufeng Ge, Eyal Ben Dor, Asa Gholizadeh, Cecile Gomez, Sabine Chabrillat, Nicolas Francos, Shamsollah Ayoubi, Dian Fiantis, James Kobina Mensah Biney, Changkun Wang, Abdelaziz Belal, Salman NaimiNajmeh Asgari Hafshejani, Henrique Bellinaso, Jean Michel Moura‐bueno, Nélida E.Q. Silvero

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 custo-dians 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 (, 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 R2 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.

Original languageEnglish
Article number740
JournalRemote Sensing
Issue number3
StatePublished - 1 Feb 2022


  • Community practice
  • Precision agriculture
  • Proximal soil sensing
  • Soil analysis
  • Soil health monitoring
  • Soil quality
  • Soil spectral library
  • Spectroscopy


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