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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. Nevertheless, the absence of appropriate ground monitoring networks poses a significant challenge for this assessment. In this study, five high-resolution (1 km) soil moisture products (S1-RT1, S1-COP, SMAP-Planet, SMAP-NSIDC, and ESACCI-Zheng) were analysed and evaluated throughout the Italian territory, together with a coarse resolution (12.5 km) dataset for comparison (ASCAT-HSAF). The main objective is to investigate their actual spatial and temporal resolution, and accuracy. Firstly, a cross-comparison of the products in space and time is carried out, including the use of triple collocation analysis. Secondly, an application-based assessment is implemented, considering irrigation, fire, drought, and precipitation case studies. The results clearly indicate the limitations and the potential of each product. Sentinel-1 based products (S1-COP and S1-RT1) are found able to reproduce high-resolution spatial patterns by detecting localised events for irrigation, fire, and precipitation. Their lower temporal resolution leads to accuracies lower than that of the SMAP-Planet product, and comparable with SMAP-NSIDC and ESACCI-Zheng products. However, SMAP-Planet is found to have an actual spatial resolution coarser than 1 km. The study highlights the need for further research to improve the high-resolution soil moisture products, and particularly to determine accurately the spatial resolution represented in soil moisture products. At the same time, the analysed products are found able to address high-resolution applications for the first time, opening promising activities for their operational use in hydrology and water resources management., Competing Interests: Declaration of competing interest Luca Brocca reports financial support was provided by National Research Council of Italy. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Droughts evolve in space and time without following borders or pre-determined temporal constraints. Here, we present a new database of drought events built with a three-dimensional density-based clustering algorithm. The chosen approach is able to identify and characterize the spatio-temporal evolution of drought events, and it was tuned with a supervised approach against a set of past global droughts characterized independently by multiple drought experts. About 200 events were detected over Europein the period 1981-2020 using SPI-3 (3-month cumulated Standardized Precipitation Index) maps derived from the ECMWF (European Centre for Medium-range Weather Forecasts) 5th generation reanalysis (ERA5) precipitation. The largest European meteorological droughts during this period occurred in 1996, 2003, 2002 and 2018. A general agreement between the major events identified by the algorithm and drought impact records was found, as well as with previous datasets based on pre-defined regions., (© 2023. The Author(s).)