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Soil Moisture Content (SMC) is crucial for sustaining agricultural productivity, ecosystem health, and climate feedback processes. This study investigates the spatiotemporal variation of SMC during two key maize growth stages using a combined physically-based and data-driven approach, which synergizes Water Cloud vegetation correction, Dubois–Dobson dielectric retrieval. The developed SMC inversion method achieving high accuracy with determination coefficients (R 2 ) of 0.75 in the maturity stage and 0.78 in the filling stage, yielding high-resolution SMC product. Machine learning methods, enhanced by Shapley Additive Explanations (SHAP), were employed to analyze the impacts of environmental factors on SMC based on the high-resolution SMC product. Land surface temperature (LST) was identified as the primary driver of spatial variation during maturity, while elevation dominated during the filling stage. The different levels of SMC in two stages were largely dictated by meteorological factors, but the role of maize was deemed inconsequential on SMC’s temporal variation. Furthermore, the relationships between SMC and environmental factors were quantified. SMC exhibits a gradual decrease trend with rising LST, yet this trend escalates sharply when LST surpasses 15 °C. An optimal range of Normalized difference vegetation index (NDVI) value, between 0.2 and 0.5, was discovered to be most effective for preserving SMC. This research offers a comprehensive perspective on the drivers of SMC variation, which is pivotal for informed agricultural practices and the enhancement of climate models.

Radar precipitation nowcasting remains challenging because a model must not only represent the overall motion trends of large-scale precipitation systems, but also capture the fine-grained structural variations of localized strong echo regions while maintaining stable temporal evolution in multi-step forecasting. To address this issue, this paper proposes PEDNet, a predictive encoder–decoder network for radar precipitation nowcasting, and evaluates its performance under a unified 6-to-12 frame forecasting setting. The proposed framework jointly models global contextual perception, local structural refinement, and temporal dependencies within a unified architecture. Specifically, the designed multi-scale global–local spatial modeling strategy is used to simultaneously capture large-scale precipitation organization patterns and local echo details, while the temporal modeling module introduced at the bottleneck stage enhances sequence representation across multiple future lead times. Experimental results on the KNMI radar dataset and the SEVIR VIL benchmark dataset show that PEDNet achieves competitive overall performance across multiple categorical and continuous metrics under the adopted evaluation protocol. Meanwhile, the model maintains a practical computational cost, with 14.84 M parameters, 15.63 G FLOPs, and an inference throughput of 2.12 complete forecast samples per second. These results indicate that PEDNet provides a competitive balance between predictive accuracy and computational efficiency for short-term radar precipitation nowcasting.

The development of 3D anthropomorphic head and neck phantoms is of crucial and timely importance to explore novel imaging techniques, such as radar-based MicroWave Imaging (MWI), which have the potential to accurately diagnose Cervical Lymph Nodes (CLNs) in a neoadjuvant and non-invasive manner. We are motivated by a significant diagnostic blind-spot regarding mass screening of LNs in the case of head and neck cancer. The timely detection and selective removal of metastatic CLNs will prevent tumor cells from entering the lymphatic and blood systems and metastasizing to other body regions. The present paper describes the developed phantom generator which allows the anthropomorphic modelling of the main biological tissues of the cervical region, including CLNs, as well as their dielectric properties, for a frequency range from 1 to 10 GHz, based on Magnetic Resonance images. The resulting phantoms of varying complexity are well-suited to contribute to all stages of the development of a radar-based MWI device capable of detecting CLNs. Simpler models are essential since complexity could hinder the initial development stages of MWI devices. Besides, the diversity of anthropomorphic phantoms resulting from the developed phantom generator can be explored in other scientific contexts and may be useful to other medical imaging modalities.

The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.

Storm events and accompanying heavy rain endanger the silty soils of the fertile and intensively-used agricultural landscape of the Saxon loess province in the European loess belt. In late spring 2016, persistent weather conditions with repeated and numerous storm events triggered flash floods, landslides, and mud flows, and caused severe devastation to infrastructure and settlements throughout Germany. In Saxony, the rail service between Germany and the Czech Republic was disrupted twice because of two mud flows within eight days. This interdisciplinary study aims to reconstruct the two mud flows by means of high-resolution physical erosion modeling, high-resolution, radar-based precipitation data, and Unmanned Aerial Vehicle monitoring. Therefore, high-resolution, radar-based precipitation data products are used to assess the two storm events which triggered the mud flows in this unmonitored area. Subsequently, these data are used as meteorological input for the soil erosion model EROSION 3D to reconstruct and predict mud flows in the form of erosion risk maps. Finally, the model results are qualitatively validated by orthophotos generated from images from Unmanned Aerial Vehicle monitoring and Structure from Motion Photogrammetry. High-resolution, radar-based precipitation data reveal heavy to extreme storm events for both days. Erosion risk maps show erosion und deposition patterns and source areas as in reality, depending on the radar-based precipitation product. Consequently, reconstruction of the mud flows by these interdisciplinary methods is possible. Therefore, the development of an early warning system for soil erosion in agricultural landscapes by means of E 3D and high-resolution, radar-based precipitation forecasting data is certainly conceivable.

A new model is proposed on how to account for the inertia of scatterers in radar-based turbulence intensity retrieval techniques. Rain drop inertial parameters are derived from fundamental physical laws, which are gravity, the buoyancy force, and the drag force. The inertial distance is introduced, which is a typical distance at which a particle obtains the same wind velocity as its surroundings throughout its trajectory. For the measurement of turbulence intensity, either the Doppler spectral width or the variance of Doppler mean velocities is used. The relative scales of the inertial distance and the radar resolution volume determine whether the variance of velocities is increased or decreased for the same turbulence intensity. A decrease can be attributed to the effect that inertial particles are less responsive to the variations of wind velocities. An increase can be attributed to inertial particles that have wind velocities corresponding to an average of wind velocities over their backward trajectories, which extend outside the radar resolution volume. Simulations are done for the calculation of measured radar velocity variance, given a 3-D homogeneous isotropic turbulence field, which provides valuable insight in the correct tuning of parameters for the new model.