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Abstract:Cropping intensity is an important indicator of the intensity of cropland use and plays a very important role in food security. In this study, we reconstructed a normalized difference vegetation index (NDVI) time-series from 1982 to 2012 using the Savitzky-Golay (S-G) technique and used it to derive a multiple cropping index (MCI) combined with land use data. Spatial-temporal patterns of variation in the MCI of northern China were as follows: (1) The MCI in northern China increased gradually from north-west to south-east; from 1982 to 2012, the mean cropping index across grid-cells over the study area increased by 4.36% per 10 years (p < 0.001) with fluctuations throughout the study period; (2) The mean MCI across grid-cells over the whole of northern China increased from 107% to 115% with all provinces showing an increasing trend throughout the 1980s and 1990s. Aside from Tianjin, Hebei, Beijing, and Shandong, all provinces also displayed an increasing trend between the 1990s and 2000s. Arable slope played an important role in the variation of the MCI; regions with slope ≤3 • and the regions with slope >3 • were characterized by inverse temporal MCI trends; (3) Drivers of change in the MCI were diverse and varied across different spatial and temporal scales; the MCI was affected by the changing agricultural population, deployment of food policies, and methods introduced for maximizing farmer benefits. For the protection of national food security, measures are needed to improve the MCI. However, more attention should also be given to the negative impacts that these measures may have on agricultural sustainability, such as soil pollution by chemical fertilizers and pesticides.

The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.