Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation.

High-resolution and temporally consistent satellite observations are essential for effectively monitoring, modeling, and mitigating environmental challenges. However, optically based remote sensing faces cross-sensor interoperability issues and is inherently affected by cloud contamination and atmospheric interference, resulting in temporal discontinuities that limit the availability of timely and uninterrupted observations. Existing approaches have primarily focused on retrospective gap-filling of missing data. In contrast, forecasting surface dynamics introduces additional challenges, particularly the need for high-fidelity and temporally continuous information to support near-real-time monitoring and predictive applications as the time since the last observation increases.To address this challenge, we developed a physics-guided transformer framework trained on Harmonized Landsat, Sentinel-2, and PlanetScope (HLSP) data to forecast uninterrupted daily 30-m surface reflectance during periods with missing optical observations. HLSP is a radiometrically and geometrically harmonized multi-sensor optical dataset integrating Landsat 8–9, Sentinel-2, and PlanetScope imagery to provide sensor-agnostic, temporally consistent surface reflectance products. The model was trained using a multi-year (2017–2025) archive of HLSP surface reflectance imagery across eight agricultural regions in the United States, Brazil, France, Spain, Egypt, South Africa, Thailand, and China. Spectral features from daily HLSP data (30 m resolution) were combined with daily land surface temperature (LST) and soil water content (SWC) at 100-m resolution derived from passive microwave observations. Additional temporal covariates, including day-of-year encoded using sine and cosine transformations, were incorporated to explicitly represent seasonal and phenological timing and enable the network to capture key biophysical, hydroclimatic, and seasonal controls on surface reflectance dynamics.The physics-guided framework constrains predictions using land–surface energy balance relationships linking surface reflectance, land surface temperature, and soil moisture. These constraints promote physically consistent interactions among surface variables while learning temporally coherent surface reflectance dynamics associated with vegetation growth, moisture persistence, and land–surface energy exchanges.Model skill was evaluated using RMSE and MAE under a forward-looking temporal validation strategy, in which the model was trained on eight years of historical HLSP data and used to forecast surface reflectance over multiple lead times (2, 5, 10, 15, and 20 days) following the last available optical observation in the final year. Forecasts were validated against independently observed HLSP data for the corresponding periods, allowing assessment of skill degradation as forecast horizons increased. Results demonstrate that incorporating LST, SWC, NDVI, and time-related covariates substantially improves forecast stability and fidelity, particularly under variable climatic and land-cover conditions. The proposed approach provides a scalable and generalizable machine-learning framework for short-term forecasting of EO surface reflectance time series, with applications in climate-impact assessment, drought monitoring, evapotranspiration modeling, and carbon–water flux analysis.