The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts.

The study of unregulated and regulated (legal and illegal) landfills on the basis satellite and field data allows complex monitoring and analysis of waste sites. This approach combines high-resolution satellite imagery to identify and map landfills with detailed field observations to verify data and assess their condition. This provides up-to-date information on the location, volume and potential impact of landfills on the environment, which is critical for effective waste management and nature conservation. The study covers examples of different NUTS 2 planning areas (under the Regional Development and Improvement Act) such as South East (BG 41) and South Central (BG 42). The data generated is for a period of at least five years. Regulated landfills are of national importance and selected events from the territory of Bulgaria have been investigated and monitored through a complex approach based on satellite data, Unmanned Aerial Systems (UAS) and ground-based spectrometric equipment, a thermal camera and an Automatic recording weather station (AWG).The optical monitoring indices used are Normalized Difference Vegetation Index (NDVI),Tasseled cap transformation (TCT) and Normalized Differential Greenness Index (NDGI). The satellite data used are Sentinel 2MSI, Landsat 9 (OLI/TIRS), Sentinel 3 SLTRS and Sentinel 1SAR. The study of landfills based on complex methods of remote sensing and validation of the results through ground data brings significant benefits to the administration, society and NGOs. It facilitates the identification and monitoring of illegal landfills and dumps, supports the planning of cleanup measures and pollution prevention. This improves waste management, protects the environment and ensures a healthier life for people, while reducing costs for society and administration in the long term.

Surface and ground air temperatures are one of the variables that best distinguish and characterize the specific climate in urbanized areas. Over the years, studies have shown that urbanized areas have experienced consistently higher temperatures, which is defined as the Urban Heat Island (UHI) effect. The same effect can be observed in non-urbanized areas, such as places with industry, landfills for domestic waste and illegal dumps. This effect was also studied over the waste dump in Bratovo - West, Dolno Ezerovo residential district, Burgas region, South East planning region. The study covers example from the planning region defined in the Law on Regional Development of the Republic of Bulgaria under Art. 11, which will support the Integrated Territorial Development Strategy of NUTS 2 planning area as BG34 Sout-Еast region. These are areas that have extremely high economic and ecological importance for monitoring the normal course of natural processes, disasters and consequences of sudden changes in the area of the entire Black Sea coast, which is heavily populated in summer. Various indicators and indices from the optical and microwave range, such as Tasseled cap transformation (TCT), Normalized Difference Vegetation Index (NDVI), Normalized Differential Greenness Index (NDGI), Land Surface Temperature (LST), etc., have been used for different areas of interest. Spectral reflectance characteristics of natural and anthropogenic objects have been used to classify temperature. Different methods and models for processing LST data from the Landsat and Copernicus programs were used for the study.

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025.