## 1 Introduction
Although Shanghai city entered the stage of microsettlement after 2000 due to the municipal government intervention [41], some local areas are still sinking as a result of large-scale engineering construction. At present, many researchers have studied the time series deformation with leveling and InSAR technology [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The content is roughly divided into the following aspects: Firstly, studying the railway deformation; the subsidence of Subway Line 5, Line 8, and Line 16 is relatively large, and it is positively correlated with groundwater extraction. Secondly, research on individual important areas, such as the central urban areas and the airport, revealed that the annual average settlement of Yangpu District and Hongkou District reached 16 mm/a from 2004 to 2010, Fengxian District and Minhang District reached up to 28 mm/a from 2015 to 2017, and Pudong New Area also had many settlement points. Thus, the uneven settlement in the main urban area was still obvious from 2000 to 2010 due to the large-scale infrastructure; meanwhile, the uneven settlement transited from city to suburb in its spatial distribution during 2010-2020. In addition, land subsidence is the result of a variety of factors. Previous studies have shown that land subsidence in Shanghai is affected by factors such as groundwater extraction and building loads [44]. However, qualitative correlation analyses are not sufficient to accurately quantify the causes of land subsidence and their interrelationships. Therefore, a risk assessment model is needed to accurately predict potential hazards caused by land subsidence [45]. Risks involved are the loss of life and property caused by unavoidable factors such as natural disasters in a certain area over a period, and they also adopted the concept of \"degree of risk (R) = hazard (H) \(\times\) vulnerability (V)\" [46]. The risk assessment literature mainly focuses on sudden geological disasters (such as landslides, debris flows, etc.) and risk assessment, so few studies pay attention to the risk assessment of geological disasters with slow urban deformation [47; 48]. In this study, we will combine InSAR technology and entropy to assess urban risk levels. Therefore, this study evaluated the surface deformation results of Shanghai through SBAS-InSAR technology with 94 scenes from C-band Sentinel-1A images (May 2016 to December 2022), verified the internal accuracy with 9 scenes of X-band descending TerraSAR-X images (2020-2021), and compared this with the results from StaMPS-PS technology. The singularity spectrum analysis (SSA) model was used to analyze the relationship between land subsidence in Shanghai and seasonal rainfall. Finally, according to the actual settlement of Shanghai, ten influencing factors were determined, including land use type, water density, land subsidence rate, and accumulation, as well as population density, GDP per unit area, road density, building density, ground elevation and green space density. The entropy method was used to evaluate the land subsidence risk in Shanghai, which provided a valuable reference for urban management to reduce the potential ground deformation hazard in Shanghai. The specific areas included Huangpu (HP), Changing (CN), Xuhui (XH), Hongkou (HK), Yangpu (YP), Jingan (JA), Putuo (PT), Minhang (MH), Baoshan (BS), Jiading (JD), Pudong (PD), Qingpu (QP), Songjiang (SJ), Jinshan (JS), and Fengxian (FX).