Accurate observation of precipitation data is crucial for hydrometeorological applications, requiring temporal and spatial precision. Satellite precipitation products offer a promising solution for obtaining precipitation estimates, facilitating long-term observations from global to local scales. However, assessing their accuracy compared to rain gauge observations is essential. This study aims to assess the accuracy and applicability of precipitation data from CMORPH, IMERG, and PERSIANN CCS in the Rio Grande–San Juan Basin in northeast Mexico. The evaluation of estimated precipitation was assessed using the Pearson and Spearman correlations, RMSE, MAE, and BIAS for both monthly and yearly averages. CMORPH showed minimal errors and low underestimation, while IMERG exhibited high correlations with consistent underestimation. PERSIANN CCS had lower correlations, significant overestimation, and higher errors. The Mann–Kendall (MK) test was used to determinate the precipitation trends of observed and estimated data. The observed data showed a significant positive trend in monthly averages, which is not reflected in the annual trend. Furthermore, negative annual trends were found in at least 10 stations across the basin. The application of satellite precipitation data yielded mixed outcomes, with CMORPH showing the highest level of agreement with the trend analysis results from rain gauge data. This demonstrates its reliability for weather and climate studies and suggests the potential for CMORPH to be used as an input in hydrological modeling.

The applied and technical contributions of the Geoinformation Conference 2025, a joint international event that brought together research, education, and operational communities with a shared focus on the effective use of geospatial information for disaster management, territorial planning, and societal resilience. The conference was held in Mérida, Yucatán, Mexico, from 24 to 28 November 2025, and integrated the 26th National Congress of SELPER Mexico, the 16th International Conference on Geoinformation for Disaster Management (Gi4DM), and the SELPER International XI Workshops on Geospatial Education for Central America and the Caribbean. Since its creation, SELPER has played a central role in strengthening the geospatial community in Latin America through scientific exchange, education, and capacity-building, while Gi4DM has provided a dedicated international forum for advancing geoinformation methods supporting disaster preparedness, emergency response, damage assessment, and recovery planning. Reflecting this dual heritage, the Archives volume places strong emphasis on operationally relevant and regionally grounded research, highlighting geospatial workflows, tools, and case studies that enable timely, reliable, and actionable information for decision-makers, practitioners, and institutions working in riskprone environments.

Geoinformation is a fundamental pillar of data science that has become relevant for its effectiveness in understanding the territory, understanding, monitoring and managing complex environmental, urban and social systems. Earth observation, photogrammetry, remote sensing, spatial data infrastructures and geospatial analysis are becoming more and more widely integrated and accepted in decision-making every day, as they support these decisions with evidence that helps disaster risk reduction, climate change adaptation, urban resilience and sustainable development. Through geospatial frameworks, interoperable data architectures or innovative analytical methods, governments are becoming increasingly accountable, as are other international organizations and research institutions, all of which require better information to face global challenges, as well as the interconnectedness of local phenomena. In the last 40 years, geoinformation has evolved in each of its facets from data capture and acquisition, processing, modeling and analysis, as well as the visualization and dissemination of information. This progress has been significant and can be obtained from multiple sources and scales, as well as dimensions (3D or 4D), through artificial intelligence or GeoAI. In addition, this data can be historical or captured in real-time monitoring systems, whose adoption is increasingly easy to find, accessible, interoperable and reusable. Despite all these advances, the reality is that there are still great challenges for geoinformation such as the efficient integration of heterogeneous data, semantic harmonization, analytical scalability, the ethical development of artificial intelligence, equitable access and quality of spatial data, etc. These challenges are particularly important and greater for regions of rapid urbanization, and with the most vulnerable populations. The Geoinformation Conference 2025 was conceived to address these challenges by convening an international and interdisciplinary community of researchers, practitioners, policymakers, and students.

This study assessed the potential of the ASCAT SSM 6.25 km product for agricultural drought monitoring across Mexico. The drought monitoring in Mexico currently relies on precipitation-based indices, without considering the use of in situ soil moisture observations and satellite-derived soil moisture products. To evaluate the performance, cross-validation was performed between ASCAT, ERA5-Land, and ESA CCI soil moisture products for assessing correlations under varying climatic and land cover conditions. Furthermore, drought indicators, including SMAPI and SPEI, were calculated and compared against the SPI from the Mexican Drought Monitor to evaluate the assessment during drought events. Results showed strong potential of ASCAT SSM in central and northeastern Mexico, while arid regions exhibited low correlation due to subsurface scattering effects. Otherwise, soil moisture anomalies derived from ASCAT aligned well with precipitation anomalies outside arid zones. In conclusion, ASCAT SSM 6.25 km demonstrates significant potential for integration into Mexico’s drought monitoring systems as drought indicators based on ASCAT effectively captured precipitation deficits and drought conditions, mirroring SPI patterns reported by national monitoring agencies.

Urban development is often accompanied by anthropogenic activities, changes in land morphology and serious damage to natural areas. Consequently, the urban climate is also affected, as temperatures are higher in urban centers and because of the presence of the urban heat island phenomenon, which poses a health threat to local citizens. The Monterrey Metropolitan Area (MMA) is the second-largest urban area in Mexico and is characterized for rapid urbanization and industrialization processes, steep climate conditions and the presence of urban heat islands. This combination makes living conditions rough for its inhabitants, especially for vulnerable groups. In order to quantify and compare heat vulnerability in urban areas, metrics such as the Heat Index measure the heat exposure and its effects on the human body. This study interpolated both relative humidity and temperature information from 15 local climate monitoring stations to determine the Heat Index for the six hottest weeks of the 2023 summer in the Monterrey Metropolitan Area. The interpolation methodologies used (IDW, Kriging and Spline) were later compared in order to cross-validate the results and define the most accurate performance based on both MAE and RMSE statistical analysis.