Soil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment essential for sustainable agriculture. Visible and near-infrared (Vis–NIR) spectroscopy offers a non-destructive and cost-effective alternative to conventional laboratory analyses, allowing for the simultaneous estimation of multiple soil properties from a single spectrum. This study aimed to predict SOC content using machine learning techniques applied to Vis–NIR spectra of 860 soil samples collected in the Sierra Nevada de Santa Marta, Colombia. The spectra (400–2500 nm) were acquired using a NIR spectrophotometer, and the soil organic carbon (SOC) content was quantified using a wet oxidation method that employs dichromate in an acidic medium. A hybrid modeling framework combining Random Forest (RF) with support vector regression (SVR) and XGBoost was implemented. Spectral pretreatments (Savitzky–Golay first derivative, MSC, and SNV) were compared, and spectral bands were selected every 10 nm. The 30 most relevant wavelengths were identified using RF importance analysis. Data were divided into training (80%) and test (20%) subsets using stratified random sampling, and five-fold cross-validation was applied for parameter optimization and overfitting control. The RF–XGBoost (R² = 0.86) and RF–SVR (R² = 0.85) models outperformed the individual RF and SVR models (R² < 0.7). The proposed hybrid approach, optimized through features, and advanced spectral preprocessing demonstrate a robust and scalable framework for rapid prediction of SOC and sustainable soil monitoring.

The generation of reliable and up-to-date national land cover information is essential for environmental management, climate action, and territorial planning. In Colombia, the CORINE Land Cover Colombia (CLCC) framework has been the official reference for land cover monitoring since 2000, traditionally updated through expert-based Computer-Assisted PhotoInterpretation (CAPI) at a 1:100,000 scale. However, increasing demands for higher spatial resolution and more frequent temporal updates have made process optimization necessary, driving the incorporation of cloud-based processing and artificial intelligence (AI), including machine learning and deep learning algorithms. This study presents a semi-automated methodology for generating a new generation of harmonized CLCC-compatible raster land cover maps at a 1:50,000 scale—offering four times greater spatial detail than the official vector product—with the capacity for semi-automated annual updates. The approach combines legend harmonization from 55 to 23 classes, historical CORINE Land Cover (CLC) polygon-guided sample generation, spectral stability analysis, and regionalized classification across 190 homogeneous subregions, supported by a reproducible cloud-based architecture. National land cover maps were produced for 2020, 2022, and 2024 with thematic accuracies above 80% and Kappa coefficients up to 0.87, alongside change maps for the 2022–2024 period capturing key dynamics in agricultural frontier expansion, wetland variability, and urban expansion. The resulting products also provide structured inputs for expert-based CAPI workflows, supporting the continuous updating of the official 1:100,000 CLCC map. The results demonstrate the operational capacity of integrating AI, cloud computing, and expert knowledge to strengthen Colombia’s national land cover monitoring system.