This study analyzes soil sealing and its repercussions in the loss of fertile soils, which are more appropriate for agriculture use. Also, soil sealing increases flood risk. The main objective is to estimate soil loss by sealing in the Guadarrama River Basin (Madrid, Spain) between 1961 and 2011. The combination of digital processing (Normalized Difference Vegetation Index (NDVI), principal components and convolution filters) of satellite imagery with the digital terrain model helps to detect risk areas and allows quick updating of sealed soil mapping. The supervised classifications of the images were used to estimate the actual soil loss by sealing (9% in 2011) in the Guadarrama River Basin and the types and agrologic classes that have been lost. Soil loss occurs to a greater extent in highly permeable soils (sands) and in the most fertile soils. The main sealed soil associations are luvisols (alfisols), regosols (entisols) and cambisols (inceptisols).

Following a copious and famous snowfall in Madrid city (Spain) and its surrounding area, the tree masses were analysed to assess the impact of this snowfall. In this way, this paper proposes an approach towards urban forest impact inventory mapping as a consequence of the snowfall of January 2021, and the subsequent atmospheric stability, which kept surface temperatures in Madrid close to −10 °C. The study has been carried out using snowfall data from 1920 to 2020 and images from the Sentinel, Landsat and MODIS satellites. The results obtained by means of image changes in the NDVI reveal a clear impact on the trees, with 11% of the winter vegetation cover of the municipality being affected. Especially significant has been the damage detected both in the forest areas in the city and in the parks, gardens or urban roads.

Landsat images supply significant information on the different aspects of the physical environment necessary to determine the distribution of salty soils. This work centres on the La Mancha area (Central Spain) which, due to its flat topography, exhibits ideal conditions to show surface and groundwater movement, as well as sediment distribution. Combination of these factors is essential for development. The main goal of this study is to derive information on the distribution of salt‐affected soils, as well as the area subject to flood risk. Multitemporal Landsat TM images selected from the 1990s have been used for this purpose. TM images are useful for delineation of mesozoic sediments rich in salts and/or areas with high humidity content. These images have been geo‐referenced to the UTM coordinate system and have been digitally enhanced. Principal components, NDVI and supervised classification were computed. Therefore, it is quite possible to predict with a high degree of probability the development of saline soils, but their precise classification is not always possible, owing to the fact that they are usually associated with other soil groups: calcisols, regosols, luvisols and kastanozems. Salt‐affected soils have been found to be associated with clayish mesozoic sediments, hollows, flood plain area and subsurface and groundwater movements. Complementary fieldwork is necessary in order to confirm digital values.

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix.