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Chapter 1 Introduction In a push to achieve all 17 SDGs by 2030£¬ the United Nations formally launched in January 2020 the ¡°Decade of Action¡±£¬ calling for accelerating sustainable solutions to the world¡¯s greatest challenges. However£¬ the COVID-19 pandemic has had a serious impact on the global implementation of the 2030 Agenda. It has increased the vulnerability of the global food system£¬ with the number of people facing hunger in 2020 increasing by about 118 million£¬ or 18% higher than that in 2019£¬ and food security emergencies at the highest level in five years. Over the past century£¬ global water use has grown at a speed more than twice the rate of population growth£¬ and the United Nations¡¯ estimates suggest that global freshwater resources will be short of 40% by 2030£¬ making a water crisis highly likely. Prior to the COVID-19 pandemic£¬ some cities had already saw growing numbers of slum-dwellers£¬ more polluted air£¬ minimal public open space and limited public transportation. The COVID-19 pandemic has further exposed and aggravated such vulnerabilities. The concentration of major greenhouse gases in the atmosphere continues to increase£¬ with 2015-2020 being the warmest six years on record. Climate change has made the achievement of many SDGs less likely. The oceans constantly face threats such as pollution£¬ warming and acidification£¬ which are disrupting the marine ecosystem. Deforestation and forest degradation£¬ the continued loss of biodiversity and the degradation of ecosystems are having farreaching impacts on human well-being and survival. The global target of halting biodiversity loss by 2020 was not met (United Nations£¬ 2021a£¬ 2021b). The Sustainable Development Goals Report 2021 of the United Nations points out the need for concerted efforts to support a recovery guided by the 2030 Agenda£¬ and the acquisition and availability of data is one of the key factors in achieving a better recovery. The data that support the monitoring and evaluation of the SDGs has increased significantly over the years£¬ but major gaps remain in terms of geographic coverage and timeliness of data. The SDG Indicator Database of the United Nations reveals that more than 80% of countries have data for only a few SDGs£¬ and for most SDGs£¬ data timeliness is a serious problem (United Nations£¬ 2021c). These data gaps hinder the real-time monitoring of progress towards the goals and the assessment of regional disparities. Data innovation is the key to closing the gaps and accelerating the realization of the SDGs£¬ and an important area of such innovation is the fusion of geospatial information and statistical information. Earth observation data collected by satellites£¬ unmanned aerial vehicles (UAV) and ground sensors can supplement official statistics and survey data£¬ and fuse with traditional data to create high-quality information that is more timely and spatially representative. This type of Earth observation data with spatial attributes£¬ referred to as Big Earth Data£¬ has strong spatiotemporal and physical correlations£¬ and good controllability of data generation methods and sources£¬ in addition to the general properties of big data: massive£¬ multisource£¬ heterogeneous£¬ multi-temporal£¬ multi-scale and nonstationary (Guo£¬ 2017; Guo et al.£¬ 2016). Big Earth Data can help us understand the complex interactions and evolutionary processes between Earth¡¯s natural systems and human social systems£¬ thus contributing to the realization of the SDGs. Big Earth Data science includes the following main technological systems: (1) ubiquitous sensing of Big Earth Data£¬ (2) credible Big Earth Data sharing£¬ (3) multiple Big Earth Data fusion£¬ (4) Big Earth Data digital twin and complex process simulation£¬ and (5) intelligent cognition of Big Earth Data (Figure 1.1). Using Big Earth Data to support the monitoring and evaluation of SDGs has the following unique advantages: First£¬ monitoring results are more transparent and repeatable due to data from diverse sources verifying each other; second£¬ the information on spatial differences and dynamic changes is linked to SDGs indicators£¬ enabling decision-makers to use the former to detect and address the imbalances and weak links in the latter to identify changing trends and policy effects. Figure 1.1 Technological systems of Big Earth Data science CAS uses Big Earth Data to support the SDGs and has established platforms focused on the field. The Big Earth Data Science Engineering Program (CASEarth)£¬ the Big Earth Data Sharing Service Platform£¬ and the Big Earth Data Cloud Service Infrastructure provide data£¬ online calculation and visualization for monitoring and evaluating SDGs indicators (Figure 1.2). As of December 31£¬ 2021£¬ CASEarth has shared a total of about 11 PB of data£¬ and updates 3 PB of data every year. It has more than 410£¬000 unique IP users in 174 countries and regions with 66 billion 4 Big Earth Data in Support of the Sustainable Development Goals (2