Flood events in agricultural floodplains reflect not only rainfall intensity but the vulnerability of the affected area at the moment of the event. This study examines the January–February 2026 flood in Larache Province, Morocco, through an integrated remote sensing workflow combining Sentinel-1 SAR, CHIRPS precipitation, and Dynamic World land cover in Google Earth Engine, processed via the rgee R package. Flood extent was mapped using SAR backscatter change detection and cross-checked against rainfall dynamics, yielding about 6660 ha of inundation (2.4% of the province), concentrated along the main Loukkos river corridor and adjacent floodplain around Ksar El Kebir, with smaller scattered patches to the south. Land cover was assessed across four temporal windows (spring 2025, pre-event, post-event, and spring 2026) to evaluate how the landscape entered the event and how it recovered. A significant share of normally cultivated land was in a bare-soil state before the flood, a condition that the literature associates with increased runoff. Post-flood analysis across 25 sample areas grouped into three geomorphic zones shows spatially uneven recovery, with cropland still below seasonal norms in spring 2026. These patterns are consistent with structural land-use conditions (wetland loss, intensive seasonal agriculture, drought-degraded soils) that may have amplified an already severe event. The findings support cover cropping, updated flood-hazard zoning, and targeted wetland restoration to reduce vulnerability in the Loukkos floodplain and comparable Mediterranean alluvial floodplains.

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning.

Flood events in agricultural floodplains reflect not only the intensity of rainfall but the vulnerability of the land surface at the moment of the event. This study examines the January–February 2026 flood in Larache Province, Morocco, through an integrated remote sensing workflow combining Sentinel-1 SAR imagery, CHIRPS precipitation data, and Dynamic World land cover within Google Earth Engine, processed via the rgee R package. Flood extent was mapped using SAR backscatter change detection and validated against rainfall dynamics. Land cover conditions were assessed across four temporal windows — spring 2025, pre-event, post-event, and spring 2026 — to evaluate how the landscape entered the event and how it recovered. Results show that a significant share of normally cultivated land was in a bare soil state before the flood, increasing runoff generation and susceptibility to inundation. Post-flood analysis across 25 sample areas documents spatially uneven recovery, with cropland coverage still below seasonal norms in spring 2026. These patterns reflect structural land use conditions — wetland loss, intensive seasonal agriculture, drought-degraded soils — that amplified the impact of an already severe event. The findings have direct implications for land use management: cover cropping practices, updated flood hazard zoning, and targeted wetland restoration could meaningfully reduce vulnerability in the Loukkos floodplain and in comparable alluvial agricultural systems across the region.