Multi-polarized Synthetic Aperture Radar (SAR) technology has gained increasing attention in agricultural applications. It offers unique capabilities for monitoring vegetation dynamics thanks to its all-weather, day-and-night operation and high revisit frequency. This study presents, for the first time, a comprehensive analysis combining dual-polarimetric radar vegetation index (DpRVI) with optical indices to characterize vineyard crops. Vineyards exhibit distinct non-isotropic scattering behavior due to their pronounced row orientation, making them particularly challenging and interesting targets for remote sensing. The study further explores the relationship between DpRVI and optical vegetation indices, revealing the complementary nature of their information. The low correlation between DpRVI and optical indices (r ≤ 0.24) suggests they capture distinct vegetation structure and health aspects. Notably, DpRVI exhibits a parabolic trend across the growing season, which may reflect biomass dynamics as inferred from the Winkler Index. Unlike optical indices reflecting vegetation greenness, DpRVI appears more directly related to biomass growth, aligning with specific phenological phases. This research aligns with the objectives of the PNRR-NODES project, which promotes nature-based solutions (NbS) for sustainable vineyard management. The application of DpRVI for monitoring vineyards is part of integrating remote sensing techniques into the broader field of strategies for climate-related change adaptation and risk reduction, emphasizing the role of innovative SAR-based monitoring in sustainable agriculture.

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.

Ground-level air temperature (Tair) is an essential variable for climate monitoring, agricultural management, and hazard prevention. Conventional ground-based measurements often fail to capture the fine-scale spatial variability, especially in regions with complex terrain. Land Surface Temperature (LST) remote sensing offers a complementary solution, providing spatially continuous and temporally frequent observations. This study evaluates the potential of MODIS and Landsat-8 LST products to estimate Tair in a heterogeneous agricultural landscape. We developed spatiotemporal regression models linking satellite-derived LST to ground observations from meteorological stations over the five years 2018–2022. MODIS data provided high temporal coverage through 8- day composites, while Landsat-8 offered higher spatial resolution LST via the Statistical Mono-Window algorithm. The models were validated using Leave-One-Out Cross-Validation, achieving high predictive accuracy for MODIS-based Tair estimation (R² = 0.981, RMSE = 1.1 °C), whereas Landsat-8 captured finer spatial variability (R² = 0.859, RMSE = 3.4 °C). Our results demonstrate that integrating multi-resolution LST products enables accurate, dense mapping of Tair, supporting operational forecasting for precision agriculture. The study also discusses limitations related to land-cover heterogeneity, temporal representativeness, and potential extensions using spatial correlation methods or radar-derived crop-structure information.

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.

This paper investigates the prediction of urban air temperature (Tair) from satellite-derived land surface temperature (LST) in the complex urban and topographic environment of Yerevan, Armenia. Building on previous work that demonstrated the effectiveness of machine learning (ML) approaches for point-based Tair estimation using Partial Least-Squares Regression (PLSR) with multiple environmental variables, this study shifts the focus to the spatial distribution of Tair. Several prediction methods and input variable combinations are evaluated to generate gridded Tair maps, which are assessed for spatial consistency against expected patterns driven by land cover, elevation, local knowledge, and spot observations. In total, five predicting methods were used—one regression approach (PLSR) and four ML methods: random forest (RF), quantile regression forest (QRF), support vector machine (SVM), multilayer perception (MLP). RF and QRF deliver the best overall results, with RF achieving the highest testing R² (0.74) and lowest RMSE (0.56). Spatial patterns are similar for PLSR, RF and QRF, highlighting cooler northern high-altitude areas and warmer southern urban areas. Overall, the results confirm the reliability of the proposed Tair spatial mapping methods in complex urban environments.

In a context of climate change, agricultural systems are increasingly exposed to natural hazards, ranging from prolonged droughts to extreme precipitation events (IPCC, 2023). Monitoring the resilience of high-value crops, such as vineyards, is therefore critical for effective risk management and adaptation strategies. While remote sensing has been widely employed to investigate vineyard phenology (Giovos et al., 2021), current approaches rely predominantly on optical data and UAV platforms, which are limited by weather conditions and often lack the structural sensitivity required for robust biomass estimation (Weiss et al., 2020).This study addresses this gap by exploring the potential of Synthetic Aperture Radar (SAR) technology - specifically X-band data from the COSMO-SkyMed constellation - as a tool for assessing vineyard vulnerability and structural response to environmental stressors. A limited but still significant case study is reported in northern Italy, where the winemaking region of Oltrepò pavese is experiencing a drift in crop suitability; a sample of about 40 vineyards with specific azimuth orientations of rows was defined, to avoid possible anisotropy phenomena, then time series of single-pol (HH) CSK data were identified on each vineyard for year 2024. We posit that precise knowledge of vineyard biomass is not only relevant for carbon sink quantification but is also a key indicator of the crop's capability to withstand increasingly warm and dry conditions.This research analyses the complex interaction between vegetation structure and meteorological hazards, specifically focusing on the influence of accumulated rainfall and dew formation on radar backscatter. Building on previous, multi-sensor SAR observation of vineyards (Bergamaschi et al., 2025) we present an ordinary least squares (OLS) modelling framework to quantify the relationship between hydrometeorological variables and SAR signal variability. Our preliminary results suggest that accumulated recent rainfall acts as a significant predictor of structural changes, with precipitation over the preceding 72 hours explaining over 70% of the local radar signal evolution. This strong correlation underscores the potential of X-band SAR to serve as a reliable proxy for monitoring crop status under hydrological stress. While the proposed OLS model successfully captures the primary drivers of backscatter variability, future developments will aim to enhance risk assessment capabilities through mixed-effects models and the integration of additional biophysical parameters.This work contributes to making Earth-observation data a valuable resource to natural hazard studies, helping to build a pathway toward operational, all-weather monitoring of agricultural risks in a changing climate.ReferencesIPCC. (2023). Summary for Policymakers. In Climate Change 2023: Synthesis Report. Contribution of WGs I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., & Priovolou, A. (2021). Remote Sensing Vegetation Indices in Viticulture: A Critical Review. Agriculture, 11(5), 457. Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402.Bergamaschi, A. Verma, A. Bhattacharya and F. Dell’Acqua (2025). "Joint Analysis of Optical and SAR Vegetation Indices for Vineyard Monitoring: Assessing Biomass Dynamics and Phenological Stages Over Po Valley, Italy", IEEE Access, vol. 13, pp. 153886-153895, 2025.