Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China.

For more efficient development planning, food-energy-water (FEW) nexus indicators should be provided with higher spatial and temporal resolutions. This paper takes Zhangye, a typical oasis city in Northwest China's arid region, as an example, and uses the unweighted, geometric mean method to calculate a standardized, quantitative, and transparent estimation of the FEW nexus for each county. The role of influencing factors is also analyzed. The results showed that (1) the coordination of the FEW nexus in each county gradually increased from 2005 to 2015. Spatially, the distribution of the FEW nexus showed a tendency to be higher in the southwestern region and lower in the northeastern region. (2) Food security and water security were weaker than energy security. Specifically, there were more limitations to food accessibility, water availability, and water accessibility than for other indexes. (3) The FEW indexes are positively associated with per capita GDP (Gross Domestic Product) and negatively correlated with the average evaporation and altitude of each county (district). Decision makers should concentrate on combining industrial advantages, developing water-efficient ecological agriculture, and improving production quality to increase market competitiveness and should actively explore the international market.Since the Bonn conference of 2011 proposed the food-energy-water (FEW) nexus approach to increase efficiency, reduce trade-offs, build synergies, and improve governance across sectors, the FEW nexus have increasingly drawn worldwide attention [7,13,14]. From that point forward, research on FEW nexus has been reported in great detail. In particular, since 2016, an increasing number of papers related to FEW nexus have been published. There are both qualitative and quantitative studies. In terms of qualitative research, some scholars propose that water, energy, and food interact and have intricate interrelationships [15][16][17]. Some researchers indicate that comprehensive thinking should be used to solve the problems and challenges between FEW and to serve the sustainable development of the social economy and resource environment [14,[18][19][20]. Some scientists compare the functions and deficiencies of the existing nexus modeling methods and aim to enable decision makers to determine the tools that best suit their research needs and goals [6,[21][22][23][24][25]. In quantitative studies, in the aspect of research scale, Willis et al. develop a global Pardee RAND (nonprofit corporation in America at https://www.rand.org/) food-energy-water security index (FEW Index) to provide information for development agencies and others studying food, energy, and water resources [9]. Some scientists try to manage the water-energy-food nexus at the regional scale [11,26]. Some researchers quantify the water-energy-food nexus at the watershed scale [7,8,10,[27][28][29]. Li et al., Bai et al., and Wang et al. analyzed China's water-energy-food nexus at the national scale [2,30,31]. Gondhalekar and Ramsauer operationalize...