The severe impact of the COVID-19 pandemic on socio-economic development has prompted an exploration of its effects on urban activities. Using December 2021 as a case study during the pandemic in the Guanzhong region of China, this paper conducts a multiscale analysis of the area using NPP-VIIRS monthly composite data. The results indicate that during the pandemic, more than 80% of areas in Xi'an, Weinan, and Baoji showed a downward trend in nighttime lights, with Xi'an being the most severely affected but also recovering the fastest, while Tongchuan was the least affected and had the slowest recovery. The spatiotemporal evolution pattern in the Guanzhong region exhibited a northwest-to-southeast directionality, with changes in the standard deviation ellipse area primarily driven by changes in urban lighting in central cities. This study further verified that the Remote sensing of nighttime lights effectively reflects changes in urban lighting and indicated the significance in assessing the impact of policy implementations during major emergencies.
Drought, which frequently occurs in the major soybean producing areas in China, has led to a serious reduction in soybean yields. The objective of the present paper was to study the spectral characteristics of soybean under water stress, and propose the Soybean Water Stress Index (SWSI) to monitor the extent and area of water stress through a field simulated experiment. The experiment was carried out in the Agricultural Experimental Base of Jilin University from May to September in 2020, and canopy spectral data were collected once a week. The result showed that the spectral reflectance of soybean canopy increased in the VIS and SWIR spectral regions and decreased in the NIR with an increase of the water stress. This paper selected NDVI, RDVI, PRI, MCARI, NDWI, WI and SWSI to identify different degrees of water stress of soybean. The result suggested that the RDVI and SWSI were suitable for identifying soybean under water stress. To seek the best identifiable vegetation index, the normalized average distance of vegetation indices under different water stress degrees were calculated. The result indicated that the distance of SWSI is more than that of other indices’ in the whole growth period, illustrated that the identifiable ability of SWSI for different water stress degrees of soybean is better than other indices, then SWSI has the strong sensitivity and stability. Therefore, SWSI can be used to monitor the area and extent of drought and provide information support for disaster relief and decisions.
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