Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

The challenge of global food security in the face of population growth, conflict, and climate change requires a comprehensive understanding of cropped areas, irrigation practices, and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency-funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and model confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. Validation of the products was done based on best available reference data per product. A global statistical validation for the temporary crop extent product resulted in user's and producer's accuracies of 88.5 % and 92.1 %, respectively. For crop type, a verification was performed against a newly collected street view dataset (overall agreement 82.5 %) and a limited number of publicly available in situ datasets (reaching minimum agreement of 80 %). Finally, global irrigated-area estimates were derived from available maps and statistical datasets, revealing the conservative nature of the WorldCereal irrigation product. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and to inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and to push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023).

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.

Agrometeorological services are a subset of climate services targeted to support farmers' tactical and strategic decisions, with the potential to support farmers' capacity to cope with climate variability and change, as well as strengthen their resilience toward climatic risks. However, the effectiveness of such services is often limited by inadequate and unsuitable means of communication with farmers. Therefore, in recent years, the World Meteorological Organization (WMO) and partners have focussed their efforts on improving communication through these services. At the European Meteorological Society (EMS) Annual Meeting in September 2022, a workshop on effective communication of agrometeorological services was held as a hybrid side event, with the aim of answering the question: “How can we deliver efficient and effective agrometeorological services”? The workshop was a joint endeavour of Met Éireann, the International Society of Biometeorology, the EMS Media and Communication Committee, the Slovenian Environment Agency, the Slovenian Meteorological Society, and the S. W. Tromp Foundation. The aim of this workshop was to advance better communication of services to the agriculture sector as a basis for promoting adaptive strategies for weather and climate change, which would enable sufficient food production at present and in the future. The workshop also provided an opportunity for transdisciplinary discussions between national meteorological and hydrological services, universities, research institutes, private companies, and the WMO. The topics discussed at the workshop included learning about exemplar agrometeorological services at various national hydrometeorological services, strengthening communication of agrometeorological services to end-users, improving data and information sharing, and educating end-users. The workshop resulted in a list of recommendations for the future.