Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models

Sensors, Год журнала: 2025, Номер 25(7), С. 2184 - 2184

Опубликована: Март 30, 2025

Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential time-series S-2 still remains unclear. To fill this gap, study introduced an innovative approach mining data. Using 200 top samples as example, we revealed temporal variation patterns in correlation between SOC and subsequently identified optimal monitoring time window SOC. The integration environmental covariates with multiple ensemble models enabled precise arid region southern Xinjiang, China (6109 km2). Our results indicated following: (a) exhibited both interannual monthly variations, while July August is SOC; (b) adding properties texture information could greatly improve accuracy prediction models. Soil contribute 8.85% 61.78% best model, respectively; (c) among different models, stacking model outperformed weight averaging sample terms performance. Therefore, our proved that spectral from window, integrated has a high accurate mapping.

Язык: Английский

---

Comparing sentinel-2 and Landsat 8 spectral reflectance indices for predicting soil organic carbon

Environmental Earth Sciences, Год журнала: 2025, Номер 84(8)

Опубликована: Апрель 1, 2025

Язык: Английский

---