This study focuses on evaluating the potential and limitations of earth observation data in the context of likely climate change and beekeeping support. The study area for the first part of the analysis is the European continent, where the pattern of likely climate change is investigated and visualized using multidimensional raster data of temperature and precipitation obtained from Earth observations. Trend analysis was applied to both annual average temperature and yearly total precipitation from 1950 to 2021 in Europe. The spatial pattern of the changing trend of these two variables is visualized, which not only gives an overview of where and how the climate has changed in Europe but also offers bee experts and beekeepers valuable reference information in the decision-making process that can benefit beekeeping and bee conservation. A before-after comparison for the annual average temperature over the 72 years has also been visualized based on the results from the multidimensional principal components analysis. The relation between likely climate change and the survival of bees is explored at a regional scale (in this case, the national territory of the Czech Republic) as a case study due to the limitations in bee data availability. Regression analysis was carried out between bee colony winter losses and the slope value of trend lines for both temperature and precipitation, respectively. Although the current study design did not reveal statistically relevant correlations, the methodology applied in the analysis can still provide a new perspective for the research of likely climate change impacts on beekeeping. Moreover, similar patterns of fluctuation were observed from the evolution of bee colony winter loss and temperature anomalies over time, which indicates that high bee colony winter loss might be related to high temperature anomalies. In addition to the analysis mentioned above, a web application "EO4BEE" powered by Google Earth Engine was developed to facilitate the visualization of earth observation variables that are crucial to bee health.This study focuses on evaluating the potential and limitations of earth observation data in the context of likely climate change and beekeeping support. The study area for the first part of the analysis is the European continent, where the pattern of likely climate change is investigated and visualized using multidimensional raster data of temperature and precipitation obtained from Earth observations. Trend analysis was applied to both annual average temperature and yearly total precipitation from 1950 to 2021 in Europe. The spatial pattern of the changing trend of these two variables is visualized, which not only gives an overview of where and how the climate has changed in Europe but also offers bee experts and beekeepers valuable reference information in the decision-making process that can benefit beekeeping and bee conservation. A before-after comparison for the annual average temperature over the 72 years has also been visualized based on the results from the multidimensional principal components analysis. The relation between likely climate change and the survival of bees is explored at a regional scale (in this case, the national territory of the Czech Republic) as a case study due to the limitations in bee data availability. Regression analysis was carried out between bee colony winter losses and the slope value of trend lines for both temperature and precipitation, respectively. Although the current study design did not reveal statistically relevant correlations, the methodology applied in the analysis can still provide a new perspective for the research of likely climate change impacts on beekeeping. Moreover, similar patterns of fluctuation were observed from the evolution of bee colony winter loss and temperature anomalies over time, which indicates that high bee colony winter loss might be related to high temperature anomalies. In addition to the analysis mentioned above, a web application "EO4BEE" powered by Google Earth Engine was developed to facilitate the visualization of earth observation variables that are crucial to bee health.