Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method.

The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m² for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m². While the use of low-density point clouds (0.5 point/m²) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m²) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m²) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m². Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m², and that higher point densities do not improve the results with the same intensity as they increase computation time.

Albedo and Land Surface Temperature (LST) are thermophysical parameters that define the behavior of cities in terms of Urban Heat Islands (UHIs). Both parameters are correlated in such a way that materials with low values of albedo (associated with low reflection rates of solar radiation) result in higher heat absorption, and consequently, in higher LST values. This tendency reinforces the effect of UHI. Thus, the use of materials with high values of albedo in building envelopes can be a solution to reduce heat accumulation within cities and to subsequently improve the temperature reduction at nighttime.

Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of Baldinelli and Silva with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively.

Close-range remote sensing techniques employing multispectral sensors on unoccupied aerial vehicles (UAVs) offer both advantages and drawbacks in comparison to traditional remote sensing using satellite-mounted sensors. Close-range remote sensing techniques have been increasingly used in the field of precision agriculture. Planning the flight, including optimal flight altitudes, can enhance both geometric and temporal resolution, facilitating on-demand flights and the selection of the most suitable time of day for various applications. However, the main drawbacks stem from the lower quality of the sensors being used compared to satellites. Close-range sensors can capture spectral responses of plants from multiple viewpoints, mitigating satellite remote sensing challenges, such as atmospheric interference, while intensifying issues such as bidirectional reflectance distribution function (BRDF) effects due to diverse observation angles and morphological variances associated with flight altitude. This paper introduces a methodology for achieving high-quality vegetation indices under varied observation conditions, enhancing reflectance by selectively utilizing well-geometry vegetation pixels, while considering factors such as hotspot, occultation, and BRDF effects. A non-parametric ANOVA analysis demonstrates significant statistical differences between the proposed methodology and the commercial photogrammetric software AgiSoft Metashape, in a case study of a vineyard in Fuente-Alamo (Albacete, Spain). The BRDF model is expected to substantially improve vegetation index calculations in comparison to the methodologies used in satellite remote sensing and those used in close-range remote sensing.

The PATCULT 3D project was developed during the 2024–2025 academic year as part of the Degree in Geoinformation and Geomatics at the University of Salamanca (Spain) as a service-learning initiative designed to integrate technical training with social commitment. The main objective was to provide students with practical experience in photogrammetry, 3D laser scanning, and additive manufacturing applied to the documentation and reproduction of cultural heritage. A particular feature of the project was its collaboration with the Spanish National Organization of the Blind (ONCE), placing accessibility for people with visual impairments as a central methodological tenet. Students developed tactile replicas of heritage assets from the province of Ávila, applying universal design principles and implementing Braille-based information systems to ensure fully inclusive modes of cultural engagement. In addition to the digital preservation of heritage, the activity reinforced students’ technical skills (covering data acquisition, 3D modeling, mesh refinement, and digital fabrication workflows) while fostering transversal competences such as teamwork, communication, critical reflection, and social awareness. The evaluation instruments demonstrated high levels of motivation and satisfaction, as well as a growing sensitivity to the social responsibilities of geomatics. The project is explicitly aligned with Sustainable Development Goals 4, 9, 10, and 11, thereby contributing to quality education, technological innovation, the reduction in structural inequalities, and the fostering of inclusive and sustainable communities. Overall, the experience illustrates how the integration of digital technologies with service-learning can strengthen academic training and, at the same time, generate measurable social value by making cultural heritage more accessible.