| Full text | |
| Author(s): |
Novais, Jean Jesus Macedo
;
Melo, Borges Marfrann Dias
;
Neves Junior, Afranio Ferreira
;
Lima, Raimundo Humberto Cavalcante
;
de Souza, Renato Epifanio
;
Melo, Valdinar Ferreira
;
do Amaral, Eufran Ferreira
;
Tziolas, Nikolaos
;
Dematte, JoseA. M.
Total Authors: 9
|
| Document type: | Journal article |
| Source: | Journal of Environmental Management; v. 375, p. 11-pg., 2025-01-22. |
| Abstract | |
Analyzing soil in large and remote areas such as the Amazon River Basin (ARB) is unviable when it is entirely performed by wet labs using traditional methods due to the scarcity of labs and the significant workforce requirements, increasing costs, time, and waste. Remote sensing, combined with cloud computing, enhances soil analysis by modeling soil from spectral data and overcoming the limitations of traditional methods. We verified the potential of soil spectroscopy in conjunction with cloud-based computing to predict soil organic carbon (SOC) and particle size (sand, silt, and clay) content from the Amazon region. To this end, we request physicochemical attribute values determined by wet laboratory analyses of 211 soil samples from the ARB. These samples were submitted to spectroscopy Vis-NIR-SWIR in the laboratory. Two approaches modeled the soil attributes: M-I) cloud-computing-based using the Brazilian Soil Spectral Service (BraSpecS) platform, and M-II) computing-based in an offline environment using R programming language. Both methods used the Cubist machine learning algorithm for modeling. The coefficient of determination (R2), mean absolute error (MAE) and root mean squared error (RMSE) served as criteria for performance assessment. The soil attributes prediction was highly consistent, considering the measured and predicted by both approaches M-I and M-II. The M-II outperformed the M-I in predicting both particle size and SOC. For clay content, the offline model achieved an R2 of 0.85, with an MAE of 86.16 g kg-1 and RMSE of 111.73 g kg-1, while the online model had an R2 of 0.70, MAE of 111.73 g kg-1, and RMSE of 144.19 g kg-1. For SOC, the offline model also showed better performance, with an R2 of 0.81, MAE of 3.42 g kg-1, and RMSE of 4.57 g kg-1, compared to an R2 of 0.72, MAE of 3.66 g kg-1, and RMSE of 5.53 g kg-1 for the M-I. Both modeling methods demonstrated the power of reflectance spectroscopy and cloud computing to survey soils in remote and large areas such as ARB. The synergetic use of these techniques can support policies and sustainable development. (AU) | |
| FAPESP's process: | 21/05129-8 - The Brazilian soil quality determined by geotechnologies: mapping, interpretation and agricultural/environmental applications: a legacy for society |
| Grantee: | José Alexandre Melo Demattê |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 22/14935-0 - Data surveying and detailed evaluation of soil quality in pilot areas: bases for validating the soil quality map in Brazil |
| Grantee: | Jean de Jesus Macedo Novais |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |