The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000–2020+ Landsat data, 2017–2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the article are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use/land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal.

A 4-year monitoring of bat fauna at Rudine wind farm in Croatia aimed to produce mitigation measures to minimize both the number of bat fatalities and power loss in energy production. During the first 2 years, a high number of carcasses was found from mid-July to the end of October, indicating the need for some mitigation strategy. Based on the results of carcass searches, meteorological data and bat activity monitored at a weather mast and four wind turbine nacelles, mitigation measures were proposed. During the next 2 years, wind turbine curtailment was implemented in the high collision risk period based on critical wind speed thresholds varying from 5.0 to 6.5 ms⁻¹. Estimation of a total number of bat fatalities was conducted with the GenEst software for each monitoring year. A 78% reduction in estimated number of fatalities was recorded indicating the effectiveness of implemented measures.

By performing a high-resolution spatial-genetic analysis of a partially clonal Salvia brachyodon population, we elucidated its clonal architecture and seedling recruitment strategy. The sampling of the entire population was based on a 1 × 1 m grid and each sampled individual was genotyped. Population-genetic statistics were combined with geospatial analyses. On the population level, the presence of both sexual and clonal reproduction and repeated seedling recruitment as the prevailing strategy of new genets establishment were confirmed. On the patch level, a phalanx clonal architecture was detected. A significant negative correlation between patches’ sizes and genotypic richness was observed as young plants were not identified within existing patches of large genets but almost exclusively in surrounding areas. The erosion of the genetic variability of older patches is likely caused by the inter-genet competition and resulting selection or by a random die-off of individual genets accompanied by the absence of new seedlings establishment. This study contributes to our understanding of how clonal architecture and seedling recruitment strategies can shape the spatial-genetic structure of a partially clonal population and lays the foundation for the future research of the influence of the population’s clonal organization on its sexual reproduction.

Privatne šume prema Šumskogospodarskoj osnovi RH 2006-2015 zauzimaju 22 % ukupne površine šuma i šumskog zemljišta u RH tj. oko 600.000 ha. Neke od značajki privatnih šuma su: da se radi o sitnim česticama i posjedima, na istoj površini nalazi se veći broj posjednika te iznimna nesređenost podataka u katastru. U ovom radu obrađeni su podaci o česticama i posjednicima iz 319 katastarskih općina (KO) raspoređenih u 5 regija Republike Hrvatske tj. u 53 gospodarske jedinice šuma šumoposjednika. Površinski uzorak predstavlja 8,74 % RH tj. prema ŠGO RH 15,69 % privatnih šuma RH. Osim prikaza sumarnih podataka po regijama, provedene su i dvije klaster analize radi dodatne karakterizacije regija. Prva klaster analiza se odnosi na varijable posjedništva: srednja površina čestica unutar KO obuhvaćenih programima gospodarenja šumama šumoposjednika (u daljem tekstu Programima), srednja površina posjeda unutar KO obuhvaćenim Programima te srednja količina posjednika na posjedovnim listovima unutar KO obuhvaćenim Programom. Druga klaster analiza se odnosi na varijable površina: ukupna površina KO, omjer zašumljenih površina šumama šumoposjednika pojedinačnih KO te omjer „nešumskog“ zemljišta prema katastarskom načinu uporabe obuhvaćenih Programima. Ovo istraživanje dalo nam je uvid u najizraženiju značajku određene regije, kao i u jedinstvene odnose unutar promatranih značajki svake regije. Analizom je utvrđeno da se regije Istra i Kvarner, Slavonija te središnja Hrvatska više razlikuju od ostalih po vlasničkim varijablama, dok su regije Dalmacija te Gorski kotar i Lika bolje okarakterizirane prostornim varijablama. Također, kao rezultat klaster analiza potvrđeno je više zakonitosti vezanih za veličinu šumske čestice i količinu posjednika tj. za udio privatnih šuma u KO i udio „nešumskog“ zemljišta obuhvaćenim Programima.

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m⁻², while the all-sky cloud adjustment reduced the mean bias to −1.44 W m⁻² with an RMSE of 23.53 W m⁻² and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m⁻² and 15.56 W m⁻², respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework.