Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO₂, SO₂, and O₃ in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO₂, SO₂, and O₃ were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO₂, SO₂, and O₃ for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO₂, SO₂, and O₃, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO₂, SO₂, and O₃, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.

This study examines the effects of land use and slope aspect on soil erodibility (K-factor) and the fractal dimension (D) of soil particle size distribution (PSD) in calcareous soils at the watershed scale in western Iran. The study analyzed 113 soil samples collected from four land uses (slope-farmland, farmland, pasture, and woodland) at a depth of 0–20 cm, measuring common soil properties such as soil texture, soil organic matter (SOM), calcium carbonate (CaCO₃), pH, and cation exchange capacity (CEC). The PSD of soil samples was measured using the international system of soil size fractions, and the D for PSD was calculated. The K-factor was calculated using the RUSLE model. The results showed that the K-factor was highest in slope farmlands with SOM at 1.6% and lowest in woodlands at 0.02 Mg h MJ⁻¹ mm⁻¹ with SOM at 3.5%. The study also found that there were significant correlations between D and clay content (r = 0.52), sand content (r = −0.29), and CEC (r = 0.36). Woodland soils had the highest SOM content, with a mean D value of 2.895, significantly higher than the mean D value of slope farmland soils, which had the lowest SOM at 1.6%. The study concludes that woodland soils retain finer particles, particularly clay, resulting in lower soil loss and land degradation compared to other land uses. Finally, the study suggests that shady slope aspects (south aspect) contain more organic matter due to less solar radiation and higher soil water content, resulting in lower soil erodibility (0.02 Mg h MJ⁻¹ mm⁻¹) and higher D values compared to other slope aspects.

The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.

For Sentinel-5P products, the European Space Agency (ESA) validates the data collected by the TROPOMI instrument onboard the Sentinel-5P satellite using a network of ground stations and various techniques such as ZSL-DOAS, Pandora, and MAXDOAS

Landslides are among the most destructive natural hazards in mountainous and environmentally sensitive regions, causing significant damage to infrastructure, ecosystems, and human life. This study proposes a framework as a prototype Geo Digital Twin for landslide susceptibility assessment and scenario analysis in Eastern Kentucky, USA. A Random Forest model was developed using topographic, hydrological, geological, and environmental conditioning factors, including aspect, elevation, lithology, distance to streams, and NDVI. The model achieved strong predictive performance, with an accuracy of 0.87, an F1 score of 0.85, and a ROC–AUC of 0.94. The trained model was integrated into a service-oriented prototype Geo Digital Twin platform implemented with Django and Mapbox, enabling interactive spatial visualization and scenario simulation. In particular, vegetation change scenarios were evaluated by modifying NDVI values to examine their influence on landslide susceptibility. To improve the interpretation of these scenario outcomes, a fuzzy inference system was introduced by combining changes in predicted susceptibility (ΔP) with NDVI values into a Fuzzy Impact Index (FII). The fuzzy analysis indicates that vegetation reduction can substantially alter slope responses depending on baseline environmental conditions. The results demonstrate that the proposed framework not only provides accurate susceptibility mapping but also facilitates explainable scenario analysis to support geospatial decision-making regarding landslides.