iEarth: an interdisciplinary framework in the era of big data and AI for sustainable development.

The Intelligent Earth (iEarth) framework, composed of four major themes: iEarth data, science, analytics, and decision, is proposed to define and build an interdisciplinary and synergistic framework for research, practice, and education that simultaneously safeguards the sustainable development of our living planet.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios.

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Urban-rural disparities of carbon storage dynamics in China's human settlements driven by population and economic growth.

China has experienced a rapid expansion of human settlement in both urban and rural areas over the past three decades. Regarding the impacts on carbon storage, previous studies that only focus on certain ecosystems cannot reflect urban-rural disparities, resulting in the carbon storage changes in human settlement remaining unknown. In this study, we aimed to explore China's urban-rural disparities in human settlement expansion and direct impacts on carbon storage by using the big Earth data technology. The results showed that from 1990 to 2018, the total amount of China's human settlement expansion reached 175,703.80 km2, and the inner-city, peri-urban, and rural components accounted for 21.00 %, 20.18 %, and 58.82 %, respectively. Along with the general tendency of impervious surface area (ISA) growth, there was more soil organic carbon (SOC) (1254.33 TgC) being sealed beneath ISA (0-100 cm depth), compared to a huge reduction in vegetation biomass carbon (VBC) (91.44 TgC) during the study period. The results further indicated that the change density of either VBC or SOC presented a slightly rising trend along the urban-rural gradient, due to the increasingly common encroachment on vegetation and soil types with higher carbon content. We also found that socioeconomic drivers had a greater influence in urban areas than rural areas, and the related correlation exhibited a descending trajectory in both urban and rural areas. There is thus an urgent need to preserve lands with abundant carbon storage and contain the waste of land resources in rural areas. All stakeholders should pay more attention to concerted and targeted regulation policies for well-planned and eco-friendly human settlement expansion such as enhancing rural land use efficiency and promoting large-scale afforestation and continuous urban greening, which will be critical not only for guiding sustainable urbanization all over China but also for mitigating climate change for the entire world.

Measuring and evaluating SDG indicators with Big Earth Data.

The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals (SDGs) is unfortunately limited in many countries due to lack of data. The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs. Big Earth Data, a special class of big data with spatial attributes, holds tremendous potential to facilitate science, technology, and innovation toward implementing SDGs around the world. Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities, and have successfully carried out case studies to demonstrate their utility in sustainability science. This paper presents implementations of Big Earth Data in evaluating SDG indicators, including the development of new algorithms, indicator expansion (for SDG 11.4.1) and indicator extension (for SDG 11.3.1), introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1, and several new high-quality data products, such as global net ecosystem productivity, high-resolution global mountain green cover index, and endangered species richness. These innovations are used to present a comprehensive analysis of SDGs 2, 6, 11, 13, 14, and 15 from 2010 to 2020 in China utilizing Big Earth Data, concluding that all six SDGs are on schedule to be achieved by 2030.

Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series.

The Tibetan Plateau, a unique cold and dry region recognized as the Earth's third pole, is primarily composed of alpine grasslands (>60 %). While a warming climate in the plateau is being recorded, phenology of alpine grasslands and its climatic dependencies are less investigated. This study tests the feasibility of the frequently observed Moderate Resolution Imaging Spectroradiometer (MODIS) time series (500 m, 8 days) in examining alpine phenology in the plateau. A set of phenological metrics are extracted from the MODIS Normalized Difference Vegetation Index (NDVI) series in each year, 2000-2010. A nonparametric Mann-Kendall trend analysis is performed to find the trends of these phenological metrics, which are then linked to monthly climatic records in the growing season. Opposite trends of phenological change are observed between the east and west of the plateau, with delayed start of season, peak date, and end of season in the west and advanced phenophases in the east. The correlation analysis indicates that precipitation, with a decreasing trend in the west and increasing trend in the east, may serve as the primary driver of the onset and peak dates of greenness. Temperature increases all over the plateau. While the delay of the end of season in the west could be related to higher late-season temperature, its advance in the east needs further investigation in this unique cold region. This study demonstrates that frequent satellite observations are able to extract phenological features of alpine grasslands and to provide spatiotemporally detailed base information for long-term monitoring on the plateau under rapid climate change.