Improved understanding of the potential growth of urban areas at the national and global levels is needed for sustainable urban development. Current panel data analysis and local scale modeling are limited in projecting global urban area growth with large spatial heterogeneities. In this study, we developed country‐specific urban area growth models using the time series data set of global urban extents (1992–2013) and projected the future growth of urban areas under five Shared Socioeconomic Pathways (SSPs). Our results indicate the global urban area would increase roughly 40–67% under five SSPs until 2050 relative to the base year of 2013, and this trend would continue to a growth ratio of more than 200% by 2100. The growth of urban areas under relatively unsustainable development pathways (e.g., regional rivalry SSP3 and inequality SSP4) is smaller compared to other SSPs. Although developing countries would remain as leading contributors to the increase of global urban areas in the future, they may exhibit different temporal patterns, that is, plateaued or monotonically increasing trends. This variation is primarily attributed to the compounding effect of the growth in population and gross domestic product. Our urban area data set presents a first country‐level urban area projection under the five SSPs, spanning from 2013 to 2100. This data set has a great potential to support various global change studies, for example, urban sprawl simulation, integrated assessment modeling for sustainable development goals, and investigation of the impact of urbanization on atmospheric emissions, air quality, and human health.

We have monitored a newly erupted volcanic island in the Kingdom of Tonga, unofficially known as Hunga Tonga Hunga Ha'apai, by means of relatively frequent high spatial resolution (~50 cm) satellite observations. The new ~1.8 km² island formed as a tuff cone over the course of a month‐long hydromagmatic eruption in early 2015 in the Tonga‐Kermadec volcanic arc. Such ash‐dominated eruptions usually produce fragile subaerial landscapes that wash away rapidly due to marine erosion, as occurred nearby in 2009. Our measured rates of erosion are ~0.00256 km³/year from derived digital topographic models. Preliminary measurements of the topographic expression of the primary tuff cone over ~30 months suggest a lifetime of ~19 years (and potentially up to 42 years). The ability to measure details of a young island's landscape evolution using satellite remote sensing has not previously been possible at these spatial and temporal resolutions.

Aerosol loadings and their spatial distribution are among the most important atmospheric information needed for a range of applications such as air quality monitoring, climate research, and public health. A key measure of aerosol quantity is aerosol optical depth (AOD) and it has been routinely observed from space by Earth observing satellites/instrument, especially the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite its global coverage and daily temporal resolution, the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product is fraught with missing values, severely limiting its use. A gap-filling method which is suitable for large-area application with high efficiency to obtain gapless AOD with reasonable spatial pattern and complete coverage is still lacking. Here, we proposed a novel spatiotemporal fitting algorithm to gap-fill the daily MODIS AOD product. Our algorithm is a multi-stage method aimed to address the non-stationary nature of AOD time series. First, the trend of daily AOD in a year in each pixel was fitted via smoothing splines and the residual was derived based on the original data and the trend. Second, the residual was spatially interpolated, leveraging the spatial correlation between the target pixel and the neighboring pixels. Third, the actual AOD was calculated as the sum of the trend and interpolated residual. We tested the algorithm against ground-based AOD data from 2011 to 2018 in China and further evaluated it via cross-validation at the global scale based on 10 selected MODIS tiles. Compared to the ground-reference AOD, the RMSE of our gapless datasets were 0.24 and 0.27 for Terra and Aqua, respectively; and the cross-validation showed a RMSE ranging from 0.045 to 0.055 (Terra) and 0.047 to 0.057 (Aqua) under different missing ratios. The novel gap-filling method outperforms the Interpolation-based Correlation Weighting (ICW) and Inverse Distance Weighting (IDW) algorithms in accuracy. Meanwhile, the gapless AOD using the novel algorithm shows lower accuracy than original MAIAC AOD, similar accuracy with the AOD from the Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset, higher accuracy than the AOD from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, the accuracy of gapless AOD using this algorithm meets the need of typical applications in relevant studies. The proposed algorithm is transferable to other regions, with the potential to be used even operationally and efficiently for generating accurate gapless global daily AOD datasets with the input of only MODIS MAIAC AOD data.

Livestock grazing occupies ca. 25% of global ice-free land, removing large quantities of carbon (C) from global rangelands (here, including grass- and shrublands). The proportion of total livestock intake that is supplied by grazing (GP) is estimated at >50%, larger than the proportion from crop- and byproduct-derived fodders. Both rangeland productivity and its consumption through grazing are difficult to quantify, as is grazing intensity (GI), the proportion of annual aboveground net primary productivity (ANPP) removed from rangelands by grazing livestock. We develop national or sub-national level estimates of GI and GP for 2000–2010, using remote sensing products, inventory data, and model simulations, and accounting for recent changes in livestock intake, fodder losses and waste, and national cropland use intensities. Over the 11 study years, multi-model average global rangeland ANPP varied between the values of 13.0 Pg C in 2002 and 13.96 Pg C in 2000. The global requirement for grazing intake increased monotonically by 18%, from 1.54 in 2000 to 1.82 Pg C in 2010. Although total global rangeland ANPP is roughly an order of magnitude larger than grazing demand, much of this total ANPP is unavailable for grazing, and national or sub-national deficits between intake requirements and available rangeland ANPP occurred in each year, totaling 36.6 Tg C (2.4% of total grazing intake requirement) in 2000, and an unprecedented 77.8 Tg C (4.3% of global grazing intake requirement) in 2010. After accounting for these deficits, global average GI ranged from 10.7% in 2000 to 12.6% in 2009 and 2010. The annually increasing grazing deficits suggest that rangelands are under significant pressure to accommodate rising grazing demand. Greater focus on observing, understanding, and managing the role of rangelands in feeding livestock, providing ecosystem services, and as part of the global C cycle, is warranted.

Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.