The Tibetan Plateau (TP), known as “The Roof of World”, has expansive alpine grasslands and is a hotspot for climate change studies. However, cropland expansion and increasing anthropogenic activities have been poorly documented, let alone the effects of agricultural activities on food security and environmental change in the TP. The existing cropland mapping products do not depict the spatiotemporal characteristics of the TP due to low accuracies and inconsistent cropland distribution, which is affected by complicated topography and impedes our understanding of cropland expansion and its associated environmental impacts. One of the biggest challenges of cropland mapping in the TP is the diverse crop phenology across a wide range of elevations. To decrease the classification errors due to elevational differences in crop phenology, we developed two pixel- and phenology-based algorithms to map croplands using Landsat imagery and the Google Earth Engine platform along the Brahmaputra River and its two tributaries (BRTT) in the Tibet Autonomous Region, also known as the granary of TP, in 2015–2019. Our first phenology-based cropland mapping algorithm (PCM1) used different thresholds of land surface water index (LSWI) by considering varied crop phenology along different elevations. The second algorithm (PCM2) further offsets the phenological discrepancy along elevational gradients by considering the length and peak of the growing season. We found that PCM2 had a higher accuracy with fewer images compared with PCM1. The number of images for PCM2 was 279 less than PCM1, and the Matthews correlation coefficient for PCM2 was 0.036 higher than PCM1. We also found that the cropland area in BRTT was estimated to be 1979 ± 52 km² in the late 2010s. Croplands were mainly distributed in the BRTT basins with elevations of 3800–4000 m asl. Our phenology-based methods were effective for mapping croplands in mountainous areas. The spatially explicit information on cropland area and distribution in the TP aid future research into the effects of cropland expansion on food security and environmental change in the TP.

Wind‐induced crop lodging can reduce agricultural production and impact food security. However, a systematic evaluation of large‐scale crop lodging and its drivers is lacking, mostly due to the limited observations available. Such knowledge gaps hinder the application of effective management practices to mitigate yield losses. Here, we quantify maize‐lodging induced by three consecutive typhoons in Northeast China in 2020 using direct crop canopy height observations from the Global Ecosystem Dynamics Investigations (GEDI) instrument and optical satellite imagery. We show that the canopy water‐related vegetation index provides a robust wall‐to‐wall mapping of crop lodging, supported by changes in GEDI canopy height. Our spatially contiguous lodging maps show that lodging intensity is mainly due to typhoon intensity (64.34%), with maize plantation fraction (11.94%) and phenology (7.94%) as secondary factors. The shelterbelt forest may partly alleviate the lodging risk, but its effect requires further investigation. Considering the recent expansion of maize cultivation and the poleward shift of landfalling typhoons alongside climate change, the lodging risk may increase for maize and further threaten China's food security.

Avian influenza viruses (AIV) pose a major zoonotic threat with pandemic potential. Waterbirds facilitate AIV spillovers into farm animals and humans through exposure and virus reassortment. Here, we propose waterbird activity entropy (WAE), an indicator of waterbird activity intensity based on monthly distributions of 779 species worldwide. WAE demonstrated high explanative power (AUC = 0.87 ± 0.001) for global avian influenza cases, particularly for H5N1, revealing the potential of WAE for identifying AIV exposure hotspots which cover 14% of global land area. Notably, the AIV exposure hotspots in the USA, EU, China, and India contain 52% of the globally exposed human population, 41% cattle, and 51% poultry. Despite reporting 300 Mha of hotspots area (15% globally), highlighting considerable surveillance gaps. This WAE-based framework enhances AIV risk assessment by incorporating waterbird residency time, offering critical insights for anticipating AIV emergence and improving surveillance.

Climate change, land-use intensification, and biodiversity loss are rapidly reshaping animal distributions and human–animal interfaces, altering the geography and seasonality of zoonotic disease hazards. For avian influenza, migratory wild birds act as long-distance carriers while domesticated hosts amplify transmission and generate major socioeconomic impacts through poultry losses, trade disruption, and livelihood shocks. Yet the global, seasonally varying wildlife–livestock interface that underpins spillover and amplification risk remains poorly quantified. Here we develop a data- and model-based indicator that captures the potential for contact between wild avian hosts and key domesticated host groups across seasons. We combine seasonal distribution estimates for thousands of confirmed or putative avian influenza host species with spatially explicit domestic host layers to derive a gridded, season-resolved "interface intensity" index. We then assess whether spatial and seasonal fluctuations in interface intensity align with reported outbreak occurrence. The indicator reveals pronounced seasonal reorganization of high-interface zones, with peak interface intensity concentrated in low latitudes during boreal winter and expanding toward temperate regions in boreal summer. Persistent high-interface areas emerge in parts of Southeast Asia and several regions in Africa, consistent with long-recognized surveillance priorities. Interface intensity is strongly associated with outbreak reports, particularly for poultry in boreal winter, highlighting its value for anticipating periods and places of elevated transmission pressure. Our approach provides a scalable One Health tool that can be integrated with climate and land-use projections to evaluate future shifts in zoonotic risk and to inform targeted surveillance and preventative interventions.

Avian influenza is a major zoonotic threat with pandemic potential 1. Waterbirds, natural reservoirs for most known subtypes2 of avian influenza viruses (AIVs), facilitate spillover into farm animals and humans through exposure and frequent virus reassortment 3,4. Here we propose waterbird activity entropy (WAE), a novel indicator quantifying waterbird activity intensity based on monthly distributions of 779 waterbird species worldwide. WAE demonstrates high predictive power (AUC = 0.86, s.d. 0.001) for global AIV cases, particularly for highly pathogenic H5N1. It identifies AIV exposure hotspots across 14% of global land area, with key regions—USA, EU, China, and India—housing 52% of exposed humans, 41% of cattle, and 51% of poultry. The USA and India face significant cattle exposure risks, with hotspot areas covering 226 Mha (68% of US hotspots) and 114 Mha (99% of India’s hotspots), respectively. Although sub-Saharan Africa accounts for less than 1% of reported AIV cases, it encompasses over 300 Mha of AIV exposure hotspots, representing approximately 15% of global hotspot areas and considerable surveillance gaps there. Waterfowl and seabirds contribute most to global exposure risk. This WAE-based framework, incorporating waterbird residency time, enhances AIV risk assessment, offering critical insights for anticipating AI emergence and improving surveillance.