Over the last 25 years, research on geoparks has moved from basic research to comprehensive multidisciplinary studies related to the creation and development of geoparks, integrating the principle of sustainability. This research focuses on exploring geoparks as the core subject. The aim of this study is to synthesize the heterogeneous body of knowledge about geoparks in an exhaustive way by leveraging a multi-database bibliometric approach. The methodology applied is based on quantitative bibliometric analysis using R, including its application for non-coders and ensuring reliability with the PRISMA Statement framework. Ten databases were taken as the sources of research papers: Web of Science, Scopus, PubMed, Nature Journals, SpringerLink, Taylor & Francis, Wiley Journals, IEEE Xplore, and CABI. The method we used has limitations, providing a restricted number of trends aligned and scaled to the database boundary conditions used in analysis. The main goals of quantitative bibliometric analysis are as follows: (1) The impact of data integration—Evaluating how merging the data from the ten databases improves research coverage. (2) Global research trends—Identifying the evolution of geopark-related studies over time. (3) Three-year forecast—Predicting the upcoming research directions using a polynomial regression model. (4) Academic performance—Assessing geographical distribution, citation impact, and productivity using bibliometric laws. (5) Conceptual contribution—Identifying the key research themes that drive future studies and potential areas for exploration. Among these, we highlighted the key elements. The integration of the ten databases provides 63% greater insight into scientific research compared to that of the Web of Science (WoS) database. Geographically, the scientific output spans 102 countries, with China leading in production over the last two decades. The most impactful paper has accumulated 768 citations, while Ruben D.A. and Wu Fandong emerge as the most prolific authors. According to the bibliometric law, the core source of scientific output is Geoheritage. The future research directions are expected to address global challenges, particularly natural disasters in alignment with the Sustainable Development Goals (SDGs). Additionally, GIS-based subtopics leveraging advanced technologies for analyzing, mapping, and promoting geological resources represent a promising area for further exploration. The projections indicate that by the end of 2026, scientific production in this field could reach 5226 published papers, underscoring the growing significance of geopark research and interdisciplinary advancements.

Incomplete environmental datasets pose significant challenges in developing accurate predictive models, particularly in hydrological research. This study addresses data missingness by investigating gap imputation methodologies for datasets with 5–20% data absence, focusing on the Mureș River in Romania. Utilizing a novel approach, we applied various imputation techniques, including the ratio method, Kalman filtering, and machine learning algorithms (XGBoost, Gradient Boosting, Random Forest and CatBoost), while developing an innovative self-assessment metric for evaluating imputation performance without relying on external reference data. Through systematic analysis of hydrological station data from four monitoring points, we artificially introduced data gaps to rigorously test method applicability. The research demonstrates the feasibility of constructing a robust self-evaluation framework for selecting optimal imputation techniques, potentially enhancing data reliability and analytical precision in environmental and geospatial research. Our findings contribute a structured methodology for addressing data incompleteness, offering researchers a quantitative approach to improving dataset integrity and predictive modeling in complex environmental systems.

Many studies have developed methods for assessing attractiveness, but the question is which one should be used to evaluate geopark designated areas? Therefore, the aim of this study is to find suitable criteria for assessing the attractiveness of a natural area such as a geopark in order to find the best quantifiable method as a methodological guide to evaluate attractiveness. The principle of the methodology takes into account three facts: the use of available attractiveness methods elaborated over time, the UNESCO Global Geoparks (UGGp) geopark requirements, and a certified geopark (Haţeg UNESCO Global Geopark was chosen as etalon—benchmark), then matching these together. To this end, the following specific objectives have been set: (i) an inventory of factors used to estimate attractiveness; (ii) through analysis, identifying the appropriate evaluation criteria for the field (a set for criteria’s SMART parameter, which can be clearly expressed, quantitatively measurable, and achievable); and (iii) developing a methodological guideline for geopark attractiveness assessment. The methodology is based on an analytical thinking approach, builds on the experience of existing methods, and stands out by the attributes matched to the UGGp’s evaluation criteria using the certified geopark as a benchmark. The result itself presents a method for assessing the attractiveness of geoparks. The outcome offers the attractiveness suitability for new geopark-designated areas as well as for existing geoparks aspiring to UNESCO certification.

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate–carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22–35% on average and narrows the 95% confidence interval by 25–40% compared to the Kalman filter or LSTM alone. The three-dimensional spatio-temporal analysis demonstrates that the KF–LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series.

Satellite imagery has become a widespread resource for modeling variability in lake surfaces. However, the extended monitoring of a lake’s perimeter faces significant challenges due to atmospheric obstacles that cannot be rectified. Due to the atmosphere’s everchanging opacity, only half of the acquired satellite images have reliable qualitative accuracy making it possible to identify a lake’s contour. Consequently, approximately 50% of the monthly lake outline values can be determined using remote sensing methods, leaving the remaining 50% unknown. This situation is applicable to three lakes in Morocco (Abakhan, Ouiouan, and Tiglmanine), the subjects of the current research for the period between 1984 and 2022. What can we do if, during a period of time in which we monitored the evolution of the surface of a lake by satellite means, we obtain only about 50% of the possible images? Shall we just settle for this and stop the analysis? Although it may be challenging to believe, the present study introduces two statistical methods for interpolating and validating the monthly values of the lake outline: the iterative ratio method based on the autocorrelation of the monthly water balance and the Kalman filter. We estimated the reconstruction errors of the missing values and validated the methodology using an inverse philosophy, reconstructing the initial data from the table of the simulation results. Given that the difference between the initial values and the reconstructed initial values resembles white noise or an AR (1) process with a low coefficient, we deemed the methodological approach acceptable. Several comparison criteria between the two interpolation methods were employed, yet determining the more appropriate one remains challenging. Based on our surface reconstruction method, Lake Abakhan, with an average area of 22 hectares, experienced significant fluctuations, ranging from a maximum of 34 hectares in 2010 to a minimum of 0.8 hectares in 2022. Lake Ouiouan, with an average area of 14 hectares, displayed much lower variation, with a maximum of 17 hectares in 2020 and a minimum of 6.5 hectares in 1988. Lake Tiglmanine showed a pattern similar to that of Lake Abakhan but with less pronounced fluctuations. With an average area of 6.1 hectares, its maximum was 9.2 hectares in 2011 and its minimum was 4.1 hectares in 1984.