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Information on urban built-up infrastructure is essential to understand the role of cities in shaping environmental, economic, and social outcomes. The lack of data on built-up heights over large areas has limited our ability to characterize urban infrastructure and its spatial variations across the world. Here, we developed a global atlas of urban built-up heights circa 2015 at 500-m resolution from the Sentinel-1 Ground Range Detected satellite data. Results show extreme gaps in per capita urban built-up infrastructure in the Global South compared with the global average, and even larger gaps compared with the average levels in the Global North. Per capita urban built-up infrastructures in some countries in the Global North are more than 30 times higher than those in the Global South. The results also show that the built-up infrastructure in 45 countries in the Global North combined, with ∼16% of the global population, is roughly equivalent to that of 114 countries in the Global South, with ∼74% of the global population. The inequality in urban built-up infrastructure, as measured by an inequality index, is large in most countries, but the largest in the Global South compared with the Global North. Our analysis reveals the scale of infrastructure demand in the Global South that is required in order to meet sustainable development goals. 

Abstract. Near-surface air temperature (Ta) is a key variable in global climatestudies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed inthe scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-basedTa measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-artstatistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates rangedfrom 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affectedprimarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship betweenTa and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporalpatterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and weexpect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data havebeen published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022). 

Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate ResolutionImaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution withglobal coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination,indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporalgap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the datapre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In thespatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in eachpixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for eachday. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values inthe original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by largeareas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation withdifferent missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error(RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime andmid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling,and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhanget al., 2021). 

Abstract Long term, global records of urban extent can help evaluate environmental impacts of anthropogenic activities. Remotely sensed observations can provide insights into historical urban dynamics, but only during the satellite era. Here, we develop a 1 km resolution global dataset of annual urban dynamics between 1870 and 2100 using an urban cellular automata model trained on satellite observations of urban extent between 1992 and 2013. Hindcast (1870–1990) and projected (2020–2100) urban dynamics under the five Shared Socioeconomic Pathways (SSPs) were modeled. We find that global urban growth under SSP5, the fossil-fuelled development scenario, was largest with a greater than 40-fold increase in urban extent since 1870. The high resolution dataset captures grid level urban sprawl over 200 years, which can provide insights into the urbanization life cycle of cities and help assess long-term environmental impacts of urbanization and human–environment interactions at a global scale.