The use of weather satellite recordings has been growing rapidly over the last three decades. Determining the patterns between meteorological and topographical features is an important scientific job. Cloud cover analysis and properties can be of the utmost significance for potential cloud seeding. Here, the analysis of the cloud properties was conducted by means of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite recordings. The resolution of used data was 1 km² within the period of 30 years (1989–2019). This research showed moderate changing of cloudiness in the territory of Serbia with a high cloudiness in February, followed by cloudiness in January and November. For the past three decades, May has been the month with the highest cloudiness. The regions in the east and south-west, and particularly in the west, have a high absolute cloudiness, which is connected with the high elevation of the country. By means of long term monitoring, the whole territory of Serbia was analyzed for the first time, in terms of cloudiness. Apart from the statistical and numerical results obtained, this research showed a connection between relief and clouds, especially in the winter season. Linear regression MK (Mann-Kendall test) has proven this theory right, connecting high elevation sides with high absolute cloudiness through the year.

Assessing flash flood susceptibility is crucial for disaster management, yet Montenegro lacks research using geoinformation technologies. In northeastern Montenegro, the Ibar River Basin, mainly in Rožaje, has a well-developed hydrological network with torrential streams prone to flash flooding. This study compares two multi-criteria GIS decision analysis (GIS–MCDA) methodologies, the Analytic Hierarchy Process (AHP) and the Best-Worst Method (BWM), for assessing flood susceptibility. The analysis uses the Flash Flood Susceptibility Index (FFSI), integrating geoenvironmental and climatic factors. The geoenvironmental criteria considered include terrain slope, distance from the drainage network, geology, land cover, drainage density, bare soil index, and the BIO16 variable, which represents the mean monthly precipitation of the wettest quarter to enhance precipitation pattern assessment. The AHP model classifies 2.78% of the area as high to very high susceptibility, while the BWM model identifies 3.21% in these categories. Both models perform excellently based on AUC values, with minor, non-significant differences. Sensitivity analysis shows AHP provides a more stable weight distribution, whereas BWM is more sensitive to weight changes, emphasizing dominant criteria more strongly. This study introduces BWM for the first time in flash flood modeling, demonstrating its suitability for susceptibility assessment. The key novelty lies in its comparative analysis with AHP, highlighting differences in weight distribution and model stability.

Soil erosion assessment enables the identification of manifested soil erosion categories, their spatial distribution and the relationship between soil erosion and land use patterns. The integrated approach of the RUSLE method and GIS tools was used in estimating soil erosion rates in Bačka (Serbia). Previous research of soil erosion intensity in the study area was last performed 50 years ago. The spatial distribution of the calculated erosive factors shows the diversity of erosive conditions. The results indicate that the average annual soil loss is from 0 to 28.6 t ha–¹ yr–¹. The soil erosion is particularly pronounced in the area of the Titel loess plateau (up to 28.6 t ha–¹ yr–¹). The results indicate the diversity of the erosion rates depending on the land use. Multiple correlation indicates that the LS factor has the largest effect on erosion (correlation coefficient 1). The results in this paper can be used in planning future conservation measures, especially if the recent field measurements of soil erosion are added. This research is also important since the RUSLE method represents a new approach in determining the soil erosion intensity.