Natural ecosystems, especially those regulated by floods, are sensitive to prolonged temperature fluctuations that affect hydrology and the lateral connection between the river and its floodplain. Here, we analyzed a series of zooplankton data collected monthly from 2007 to 2016 during the ice-free period in Kopački Rit Nature Park in the Middle Danube, an area important as a food source and nursery area for fish stocks in the Danube. The aim was to find out how the long-term change in temperature and fluctuating environmental parameters affect the succession of zooplankton in the warmer (from April to September) and colder parts of the year (from October to March). Throughout the decade, total nitrogen concentrations showed significant differences between years, with an increase since 2012. Despite the increase in nitrogen levels and the expected increase in primary production, the higher nitrogen levels were accompanied by lower zooplankton biomass. A significant difference was found between the values of the zooplankton geometric mean index, with 73% of the variance explained by the difference between groups. In general, a trend toward a significant decrease in zooplankton biomass, with a simultaneous increase in the number of species and high turnover rates, was observed throughout the decade.

This paper provides insight into the development of Earth Observation (EO) research within geographic and environmental sciences from 1978 to 2024, using a spatially explicit bibliometric approach. The research is based on 28,871 publications indexed in the Web of Science database, which includes four EO-related subject categories: remote sensing, environmental science, geography physical, and geography. Two main phases of the de velopment of EO research are identified. The first period (1978–2011) is marked by fundamental research on early satellite imagery, while the second period (2012–2024) represents a strong growth spurred by open data policies, the Sentinel missions and the development of cloud computing platforms. The results indicate marked geographical asymmetries. Research activities are concentrated in the United States, China, Canada and Western Europe, while many countries of the Global South remain underrepresented and rely more heavily on international collaboration. These spatial disparities reflect the uneven global distribution of scientific and technological capacity. Thematic and network analyses show a shift in focus from sensor- and data-driven research towards the application of machine learning, time-series analysis, land use and land cover change studies and Sentinel-based applications. The results provide a contextual framework for understanding how the development of environmental observation research capacity and technological change are shaping contemporary environmental research and its ability to respond to global environmental change.

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 173 AmeriFlux sites across North and South America for the period 2017-2024 and compared with MODIS MCD18 and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.84Wm−2, while the all-sky cloud adjustment reduced the mean bias to −1.11Wm−2 with an RMSE of 23.54Wm−2 and correlation of r=0.87. The largest improvements occured 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 17.60Wm−2 and 15.56Wm−2, respectively, but at substantially coarser spatial resolution. These Sentinel-2 estimates are especially usefull when PAR needs to be analyzed together with Sentinel-2 bands, vegetation indices, and other Sentinel-2 based products within a consistent observational framework. Rather than replacing dedicated radiative transfer based products, the proposed method provides a practical, high-resolution, and operationally useful PAR input for land surface analysis and future productivity modeling applications, while reducing the dependence on ancillary PAR products from other satellite missions.

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.