China has responded to a national land-system sustainability emergency via an integrated portfolio of large-scale programmes. Here we review 16 sustainability programmes, which invested US$378.5 billion (in 2015 US$), covered 623.9 million hectares of land and involved over 500 million people, mostly since 1998. We find overwhelmingly that the interventions improved the sustainability of China's rural land systems, but the impacts are nuanced and adverse outcomes have occurred. We identify some key characteristics of programme success, potential risks to their durability, and future research needs. We suggest directions for China and other nations as they progress towards the Sustainable Development Goals of the United Nations' Agenda 2030.

Maximum light use efficiency (ε max ) is a key parameter for the estimation of net primary productivity (NPP) derived from remote sensing data. There are still many divergences about its value for each vegetation type. The ε max for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The vegetation classification accuracy is introduced to the process. The sensitivity analysis of ε max to vegetation classification accuracy is also conducted.The results show that the simulated values of ε max are greater than the value used in CASA model, and less than the values simulated with BIOME-BGC model. This is consistent with some other studies. The relative error of ε max resulting from classification accuracy is −5.5%-8.0%. This indicates that the simulated values of ε max are reliable and stable.

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China has experienced unprecedented urbanization since the 1980s, resulting in substantial climatic effects from local cities to broad regions. Using the Weather Research and Forecasting model dynamically coupled to an urban canopy model, we quantified the summertime climate effects of urban expansion in China's most rapidly urbanizing regions: Beijing‐Tianjin‐Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). High‐resolution landscape data of each urban agglomeration for 1988, 2000, and 2010 were used for simulations. Our results indicated summertime urban warming of 0.85°C for BTH, 0.78°C for YRD, and 0.57°C for PRD, which was substantially greater than previous estimates. Peak summer warming for BTH, YRD, and PRD was 1.5°C, 1°C, and 0.8°C, respectively. In contrast, the loss of moisture was greatest in PRD, with maximum reduction in 2 m water vapor mixing ratio close to 1 g/kg, followed by YRD and BTH with local peak humidity deficits reaching 0.8 g/kg and 0.6 g/kg, respectively. Our results were in better agreement with observations than prior studies because of the usage of high‐resolution landscape data and the inclusion of key land‐atmospheric interactions. Our study also demonstrated that the warming impacts of polycentric urban forms were less intense but more extensive in space, whereas large concentrated urban aggregations produced much stronger but localized warming effects. These findings provide critical knowledge that improves our understanding of urban‐atmospheric interactions, with important implications for urban landscape management and planning to alleviate the negative impacts of urban heat islands.

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