Earth

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments.

Agricultural heritage is a cultural pillar of the Mediterranean region, where durum wheat plays a central role in traditional landscapes and food systems. Projected climate change is expected to alter crop productivity and place additional pressure on water resources. This study assesses future variability in durum wheat productivity and related implications for water resource management in Sardinia, Italy, where durum wheat is a major rainfed C3 crop. The AquaCrop-OpenSource model was calibrated to local conditions and applied to simulate historical (1950–2023) and near-future (2024–2050) scenarios using projections from seven climate models. Results indicate a modest increase in average yields under future conditions, accompanied by a higher frequency of crop failures. Elevated atmospheric CO₂ concentrations emerge as the primary driver of yield increases, while changes in precipitation represent the main limiting factor. The role of aid irrigation as an adaptation strategy to stabilize yields and enhance productivity was evaluated. Scenario analysis shows that aid irrigation aimed at preventing crop failure remains sustainable in the near future, requiring approximately 14–17% of current agricultural water use in Sardinia. In contrast, irrigation used to maximize productivity would increase water demand by more than 40%, intensifying competition for water resources.

This paper provides insight into the development of Earth Observation (EO) research within geographic and environmental sciences from 1978 to 2024, using a spatially explicit bibliometric approach. The research is based on 28,871 publications indexed in the Web of Science database, which includes four EO-related subject categories: remote sensing, environmental science, geography physical, and geography. Two main phases of the de velopment of EO research are identified. The first period (1978–2011) is marked by fundamental research on early satellite imagery, while the second period (2012–2024) represents a strong growth spurred by open data policies, the Sentinel missions and the development of cloud computing platforms. The results indicate marked geographical asymmetries. Research activities are concentrated in the United States, China, Canada and Western Europe, while many countries of the Global South remain underrepresented and rely more heavily on international collaboration. These spatial disparities reflect the uneven global distribution of scientific and technological capacity. Thematic and network analyses show a shift in focus from sensor- and data-driven research towards the application of machine learning, time-series analysis, land use and land cover change studies and Sentinel-based applications. The results provide a contextual framework for understanding how the development of environmental observation research capacity and technological change are shaping contemporary environmental research and its ability to respond to global environmental change.