QUANTIFYING THE 2022 FLOOD EXTENT AND LAND USE CHANGE IN SOHBATPUR DISTRICT, PAKISTAN USING SENTINEL-1 SAR AND MODIS

Abstract

This study quantifies the 2022 flood extent and associated land-use changes in Sohbatpur District, Pakistan, by integrating Sentinel-1 SAR data, MODIS-based land-use/land-cover (LULC) classifications, and Global Flood Awareness System (GloFAS) hydrological data. The work addresses the problem of monitoring floods in monsoon-dominated, cloud-prone regions where optical imagery is limited, and it assesses how flood exposure interacts with agricultural land and rural populations. Sentinel-1 SAR’s cloud-penetrating capabilities are leveraged to map flood extent and severity, while MODIS-derived LULC provides a contemporaneous view of agricultural and other land-use classes. Results indicate substantial flood-affected agricultural areas and a clear spatiotemporal pattern of flood severity aligned with low-lying, near-channel zones. A 100-year flood hazard map from GloFAS supports hazard–exposure analysis and highlights persistent high-risk pockets corresponding to historic flood events. The study offers data-driven insights for disaster risk reduction, agricultural planning, and flood resilience in one of Balochistan’s most flood-vulnerable areas.