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Food production data — such as crop, livestock, aquaculture and fisheries statistics — are critical to achieving multiple sustainable development goals. However, the lack of reliable, regularly collected, accessible, usable and spatially disaggregated statistics limits an accurate picture of the state of food production in many countries and prevents the implementation of effective food system interventions. In this Review, we take stock of national and international food production data to understand its availability and limitations. Across databases, there is substantial global variation in data timeliness, granularity (both spatially and by food category) and transparency. Data scarcity challenges are most pronounced for livestock and aquatic food production. These challenges are largely concentrated in Central America, the Middle East and Africa owing to a combination of inconsistent census implementation and a global reliance on self-reporting. Because data scarcity is the result of technical, institutional and political obstacles, solutions must include technological and policy innovations. Fusing traditional and emerging data-gathering techniques with coordinated governance and dedicated long-term financing will be key to overcoming current obstacles to sustained, up-to-date and accurate food production data collection, foundational in promoting and monitoring progress towards healthier and more sustainable food systems worldwide. 

Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.