Exploring the actual spatial resolution of 1 km satellite soil moisture products.

High-resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil moisture products both at fine spatial (1 km) and temporal (daily) scales. Some of these products rely on several data sources as input (satellite, in situ, modelling), and therefore an evaluation of their actual spatial and temporal resolution is required.

A spatiotemporal analysis of NO concentrations during the Italian 2020 COVID-19 lockdown.

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space and time-the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data.