Urbanisation is a global phenomenon with an important impact on the quality of human life. Europe has been widely affected by urbanisation. One of the main characteristics of urban growth is sprawl, a negative form of urban expansion, which affects large cities and most types of urban landscapes. Spatial indicators are applied to CORINE Urban Morphological Zones (UMZ) changes in order to measure urban sprawl between 1990–2000 and 2000–2006 in 24 European countries. The indicators calculate urban morphological properties such as shape, aggregation, compactness and dispersion. The results revealed that the urban areas (UMZ) increased by 146% during 1990–2006 and the urbanisation becomes more circle-shaped and less complex where mostly sprawl occurs. Moreover, urban form becomes less clumped or aggregated. Therefore, due to accelerating rates of urban sprawl, European urban planning should intensify appropriate initiatives to avoid negative impacts on human life.

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management.

Teaching remote sensing in higher education has been traditionally restricted in lecture and computer-aided laboratory activities. This paper presents and evaluates an engaging inquiry-based educational experiment. The experiment was incorporated in an introductory remote sensing undergraduate course to bridge the gap between theory and application of relevant technology. During this semester-long experiment, titled ‘ESF goes to space’, students designed, built, launched and successfully acquired imagery from the upper stratospheric parts (approximately 27 000 m). Replication guidelines are presented and a post-evaluation discusses benefits and limitations for students, instructors and university.

(1) Background: Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: This research paper explores the integration of machine learning (ML) approaches with soil, terrain and remotely sensed data to enhance SOC estimation. Various ML models, including Neural Networks (NNs), Random Forests (RFs), Support Vector Machines (SVMs) and Decision Trees (DTs), were trained and evaluated using a dataset comprising soil laboratory data, Sentinel-2 spectral indices, climate data and topographic features. Feature selection techniques were applied to indicate the most important predictors, improving model performance and interpretability. (3) Results: The results demonstrate the potential of ML-driven approaches to achieve high accuracy in SOC prediction. (4) Conclusions: This research highlights the advantages of leveraging big data and artificial intelligence in soil monitoring, providing a scalable and cost-effective framework for SOC assessment in agricultural and environmental applications.

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.