The United Nations specified the need for “providing universal access to greenspace for urban residents” in the 11th Sustainable Development Goal. Yet, how far we are from this goal remains unclear. Here, we develop a methodology incorporating fine-resolution population and greenspace mappings and use the results for 2020 to elucidate global differences in human exposure to greenspace. We identify a contrasting difference of greenspace exposure between Global South and North cities. Global South cities experience only one third of the greenspace exposure level of Global North cities. Greenspace exposure inequality (Gini: 0.47) in Global South cities is nearly twice that of Global North cities (Gini: 0.27). We quantify that 22% of the spatial disparity is associated with greenspace provision, and 53% is associated with joint effects of greenspace provision and spatial configuration. These findings highlight the need for prioritizing greening policies to mitigate environmental disparity and achieve sustainable development goals.

Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts.

Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security and environmental sustainability. However, because of the complexity of agricultural landscapes and lack of sufficient training samples, it remains challenging to monitor cropland dynamics at high spatial and temporal resolutions across large geographical extents, especially for regions where agricultural land use is changing dramatically. Here we developed a cost-effective annual cropland mapping framework that integrated time-series Landsat satellite imagery, automated training sample generation, as well as machine learning and change detection techniques. We implemented the proposed scheme to a cloud computing platform of Google Earth Engine and generated a novel dataset of China's annual cropland at a 30 m spatial resolution (namely CACD). Results demonstrated that our approach was capable of tracking dynamic cropland changes in different agricultural zones. The pixel-wise F₁ scores for annual maps and change maps of CACD were 0.79 ± 0.02 and 0.81, respectively. Further cross-product comparisons, including accuracy assessment, correlations with statistics, and spatial details, highlighted the precision and robustness of CACD compared with other datasets. According to our estimation, from 1986 to 2021, China's total cropland area expanded by 30 300 km² (1.79 %), which underwent an increase before 2002 but a general decline between 2002 and 2015, and a slight recovery afterward. Cropland expansion was concentrated in the northwest while the eastern, central, and southern regions experienced substantial cropland loss. In addition, we observed 419 342 km² (17.57 %) of croplands that were abandoned at least once during the study period. The consistent, high-resolution data of CACD can support progress toward sustainable agricultural use and food production in various research applications. The full archive of CACD is freely available at https://doi.org/10.5281/zenodo.7936885 (Tu et al., 2023a).

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

Greenspace exposure metrics can allow for comparisons of green space supply across time, space, and population groups, and for inferring patterns of variation in opportunities for people to enjoy the health and recreational benefits of nearby green environments. A better understanding of greenspace exposure differences across various spatial scales is a critical requirement for lessening environmental health disparities. However, existing studies are typically limited to a single city or across selected cities, which severely limits the use of results in measuring systemic national and regional scale differences that might need policy at above individual city planning level. To close this knowledge gap, our study aims to provide a holistic assessment of multi-scale greenspace exposure across provinces, cities, counties, towns, and land parcels for the whole of China. We mapped the nationwide fractional greenspace coverage at 10 m with Sentinel-2 satellite imagery, and then modeled population-weighted greenspace exposure to examine variation of greenspace exposure across scales. Our results show a prominent scaling effect of greenspace exposure across multi-scale administrative divisions in China, suggesting, as expected, an increase in heterogeneity with finer spatial scales. We also identify an asymmetric pattern of the difference between greenspace exposure and greenspace coverage, across a geo-demographic demarcation boundary (i.e., along the Heihe-Tengchong Line). In general, the greenspace coverage rate will overestimate more realistic human exposure to greenspace in East China while underestimating in West China. We further found that, in China, more recently urbanized areas have much better greenspace exposure than older urban areas. Our study provides a spatially explicit greenspace exposure metric for discovering multi-scale greenspace exposure difference, which will enhance governments' capacity to quantify environmental justice, detect vulnerable greenspace exposure risk hotspots, prioritize greenspace management at the supra-city scale, and monitor the balance between greenspace supply and demand.