Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km². The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination (R²), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al., the values of R², RMSE and bias were obtained as 0.87, 0.57 m²m⁻², and 0.05 m²m⁻² respectively, whereas for WCM model, the values were found as 0.82, 0.67 m²m⁻², and 0.32 m²m⁻² respectively. This investigation showed that the modifications in Oveisgharan et al. model provide marginally better results in the retrieval of LAI using ScatSat-1 data than the WCM model. The models' limitation may be less serious for crop management studies because the majority of crops attains its maturity at LAI values less than 6 m²/m².

European drought events have intensified in recent decades, raising concerns about water resources, agriculture, and ecosystem health. Most existing continental-scale drought assessments provide limited attribution of drought changes to the processes that generate runoff drought, which constrains understanding of how these processes will evolve in a warming climate. In this study, we provide the first continental-scale assessment of future changes in runoff drought generation processes across Europe, focusing on rainfall deficit, rain-to-snow and wet-to-dry season transitions, and snow-related processes. We used a continental-scale hydrological model in combination with observed meteorological data and climate simulations (GCMs) under a middle-of-the-road emission scenario (i.e., RCP4.5, ≈2 °C global warming by 2100). Unlike widely used aggregated drought index-based studies, our mechanism-specific classification approach distinguishes the physical drivers of drought events by classifying runoff droughts into seven event types based on their severity, duration, and frequency and leverages spatial clustering analysis (Getis–Ord G^∗_i method) to assess the climate responses. Our analysis reveals considerable regional differences in drought mechanisms and their responses. Historical observations (1971–2000) highlight that Mediterranean Europe experiences the most severe drought conditions, dominated by rainfall deficit processes and wet-to-dry season transitions, with deficits exceeding (> 4 mm day⁻¹), prolonged durations (> 165 days), and high event frequencies. Future projections (2070–2099) indicate further drought intensification in the Mediterranean driven by increasing rainfall deficit and wet-to-dry transition events, with runoff deficit increases by 2–6 mm day⁻¹ and duration extensions exceeding 200 days, while Northern and Western-Central Europe show predominantly decreasing drought severity due to declining cold-snow season droughts under warming conditions. Importantly, drought related to temperature-driven processes, especially those triggered by rain-to-snow transitions, exhibit the most pronounced projected changes. These findings demonstrate that different drought mechanisms respond distinctly to climate forcing. By attributing projected drought changes to specific generation processes, our results enable region-specific interpretation of drought hazard, which is crucial for effective water-resources planning across Europe.