TY - JOUR
T1 - The Brazilian Soil Spectral Service (BraSpecS)
T2 - A User‐Friendly System for Global Soil Spectra Communication
AU - Demattê, José A.M.
AU - Paiva, Ariane Francine da Silveira
AU - Poppiel, Raul Roberto
AU - Rosin, Nícolas Augusto
AU - Ruiz, Luis Fernando Chimelo
AU - Mello, Fellipe Alcantara de Oliveira
AU - Minasny, Budiman
AU - Grunwald, Sabine
AU - Ge, Yufeng
AU - Dor, Eyal Ben
AU - Gholizadeh, Asa
AU - Gomez, Cecile
AU - Chabrillat, Sabine
AU - Francos, Nicolas
AU - Ayoubi, Shamsollah
AU - Fiantis, Dian
AU - Biney, James Kobina Mensah
AU - Wang, Changkun
AU - Belal, Abdelaziz
AU - Naimi, Salman
AU - Hafshejani, Najmeh Asgari
AU - Bellinaso, Henrique
AU - Moura‐bueno, Jean Michel
AU - Silvero, Nélida E.Q.
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzer-land.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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 (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 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.
AB - 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 (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 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.
KW - Community practice
KW - Precision agriculture
KW - Proximal soil sensing
KW - Soil analysis
KW - Soil health monitoring
KW - Soil quality
KW - Soil spectral library
KW - Spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85124190083&partnerID=8YFLogxK
U2 - 10.3390/rs14030740
DO - 10.3390/rs14030740
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AN - SCOPUS:85124190083
SN - 2072-4292
VL - 14
JO - Remote Sensing
JF - Remote Sensing
IS - 3
M1 - 740
ER -
