Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2010 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2011 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2012 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2013 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2015 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2014 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2016 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2018 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: This dataset contains ceilometer-derived cloud base height detected at the Princess Elisabeth station, East Antarctica (71°57'S - 23°21'E) in 2017 using the polar threshold algorithm. Dataset contains the data over one year, at 15 seconds resolution product: cloud base height (in meters a.g.l.) FOR DETAILS AND DATA AVAILABILITY, SEE : https://ees.kuleuven.be/hydrant/aerocloud/

Description: Crop calendars provide valuable information on the timing of important stages of crop development such as the planting (SOS) and harvesting (EOS) dates. This information is critical for many crop monitoring applications such as crop type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop calendars provides an important asset in this respect, as it allows the algorithms to account for specific local circumstances while also maximizing their robustness and global applicability. Existing global crop calendar products, as produced by the Group on Earth Observations' Global Agricultural Monitoring (GEOGLAM) Crop Monitor, the United States Department of Agriculture Foreign Agricultural Service (USDA-FAS), the Food and Agriculture Organization (FAO), and the European Commission Joint Research Centre's Anomaly hot Spots of Agricultural Production (ASAP), generally provide this information only at national or subnational level. In this work, we present gridded SOS and EOS maps for wheat and maize that represent the crop calendars' spatial variability at 0.5° spatial resolution. These maps are generated in the framework of WorldCereal, which is a European Space Agency (ESA) funded project whose cropland and crop type wheat and maize algorithms at global scale and at 10 m spatial resolution require this information. The proposed maps are built leveraging the above noted global products (Crop Monitor, USDA-FAS, FAO, ASAP) whose datasets are combined into a baseline map and sampled to train a Random Forest algorithm based on climatic and geographic data. Their evaluation against test data from the baseline maps set aside for validation purposes show a good performance with SOS (EOS) R2 of 0.87 (0.92) and a Root Mean Square Error (RMSE) of 27 (26) days for wheat, showing the lowest errors (RMSE 〈 15 days) in North America, Central Europe, South Africa, and Australia, all critical areas for global wheat production and trade. Meanwhile, the largest errors (RMSE between 40 and 60 days) occurred in regions of South America close to the Amazon forest and in Africa close to the Congo Basin. In the case of maize, the SOS (EOS) evaluation shows a R2 of 0.88 (0.79) and a RMSE of 24 (28) days for maize, with the best performing regions (RMSE 〈 15 days) located in the Northern Hemisphere, South Africa, and Australia, important areas for global maize production and trade. Meanwhile, the worst performing regions were in Brazil, Saudi Arabia and India. Additionally, the crop calendars were evaluated using a simple Land Surface Phenology (LSP) model based on Sentinel-2 and Landsat 8 Earth Observation data from Sentinel-2 and Landsat 8 over known wheat and maize fields. The results show a SOS (EOS) R2 of 0.75 (0.88) and a RMSE of 25 (18) days for wheat and SOS (EOS) R2 of 0.80 (0.88) and a RMSE of 35 (24) days for maize. Therefore, the presented calendars present an advancement over the existing crop calendar products in terms of capturing spatial coverage and variability and reporting their accuracy. File description: HTML_Maize_FINAL: Land Surface Phenology model (LSP) for maize HTML_WinterWheat_FINAL: Land Surface Phenology model (LSP) for Winter-wheat M1_EOS_WGS84: Gridded map for end of season of the first season of maize M1_SOS_WGS84: Gridded map for start of season of the first season of maize WW_EOS_WGS84: Gridded map for end of season of the first season of winter wheat WW_SOS_WGS84: Gridded map for start of season of the first season of winter wheat