Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes.

The integration of rooftop greenhouses (RTGs) in urban buildings is a practice that is becoming increasingly important in the world for their contribution to food security and sustainable development. However, the supply of tools and procedures to facilitate their implementation at the city scale is limited and laborious. This work aims to develop a specific and automated methodology for identifying the feasibility of implementation of rooftop greenhouses in non-residential urban areas, using airborne sensors. The use of Light Detection and Ranging (LIDAR) and Long Wave Infrared (LWIR) data and the Leica ALS50-II and TASI-600 sensors allow for the identification of some building roof parameters (area, slope, materials, and solar radiation) to determine the potential for constructing a RTG. This development represents an improvement in time and accuracy with respect to previous methodology, where all the relevant information must be acquired manually. The methodology has been applied and validated in a case study corresponding to a non-residential urban area in the industrial municipality of Rubí, Barcelona (Spain). Based on this practical application, an area of 36,312m2 out of a total area of 1,243,540m2 of roofs with ideal characteristics for the construction of RTGs was identified. This area can produce approximately 600tons of tomatoes per year, which represents the average yearly consumption for about 50% of Rubí total population. The use of this methodology also facilitates the decision making process in urban agriculture, allowing a quick identification of optimal surfaces for the future implementation of urban agriculture in housing. It also opens new avenues for the use of airborne technology in environmental topics in cities.

In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly.

The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth’s species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data.

The 3Cat-3/MOTS (3: Cube, Cat: Catalunya, 3: 3rd CubeSat mission/Missió Observació Terra Satèl·lit) mission is a joint initiative between the Institut Cartogràfic i Geològic de Catalunya (ICGC) and the Universitat Politècnica de Catalunya-BarcelonaTech (UPC) to foster innovative Earth Observation (EO) techniques based on data fusion of Global Navigation Satellite Systems Reflectometry (GNSS-R) and optical payloads. It is based on a 6U CubeSat platform, roughly a 10 cm × 20 cm × 30 cm parallelepiped. Since 2012, there has been a fast growing trend to use small satellites, especially nanosatellites, and in particular those following the CubeSat form factor. Small satellites possess intrinsic advantages over larger platforms in terms of cost, flexibility, and scalability, and may also enable constellations, trains, federations, or fractionated satellites or payloads based on a large number of individual satellites at an affordable cost. This work summarizes the mission analysis of 3Cat-3/MOTS, including its payload results, power budget (PB), thermal budget (TB), and data budget (DB). This mission analysis is addressed to transform EO data into territorial climate variables (soil moisture and land cover change) at the best possible achievable spatio-temporal resolution.

Europe’s big cities and metropolitan areas constitute an ecosystem where climate trends are having, and will continue to have, a big impact on the population in the short, medium, and long term. Therefore, the task of generating a mapping approach over urban ecosystems to provide indicators assessing their sustainability and facilitating the design and implementation of policies and derived actions has become a priority for the Institut Cartogràfic i Geològic de Catalunya (ICGC) as a mapping agency.The ICGC has been using its own assets and competences in airborne Earth Observation for data capture and processing as well as inclusion of additional data such as cadastral databases and digital surface models to generate a set of urban sustainability indicators. ICGC has designed flight campaigns at a very high spatial and spectral resolution in visible, near infrared and long wave infrared regions, processed geospatial data and finally transformed geoinformation into demanded ICGC products over urban ecosystems such as urban green, solar potential, light pollution, energy leaks or urban heat island effect. This approach has been applied in coordination with urban planning stakeholders in different cities and is planned to be extended to the majority of urban ecosystems in Catalonia.This paper presents a critical summary of the main advantages, disadvantages, constrains and lessons learned in the search for feasible, available and desirable responses from the earth-observation airborne-sensor point of view to the main urban ecosystem challenges.