The objective of this study was to evaluate the separability potential of Sentinel-2A (MultiSpectral Instrument, MSI) and Landsat (Operational Land Imager, OLI and Thermal Infrared Sensor, TIRS) derived indices for detecting small-extent (70%. In fact, the dNBR-Sentinel-2/Landsat-8 overall accuracy and Kappa coefficient for classifying fire severity degree were less than 70% across the studied sites, except for Sentinel-dNBR in Dibbeen (72.5%). In conclusion, Sentinel-dNBR and Landsat promise to delimitate forest fire perimeters of small-scale (<25 ha) areas, but further remotely-sensed techniques are require (e.g., Landsat-Sentinel data fusion) to improve the fire severity-separability potential.

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices.