Search Results (5)

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth cycle by integrating radar remote sensing data with a phenology-informed clustering approach. The research was conducted in three wheat fields in Umbria, Italy, from 30 January to 10 June 2024, using in-field height measurements, phenological observations, and Sentinel-1 acquisitions. Backscatter variables (VH, VV, and CR) were processed using two speckle filters (Lee 7 × 7 and Refined Lee), alongside additional radar-derived parameters (entropy, anisotropy, alpha, and RVI). Fuzzy C-means clustering enabled the classification of observations into two phenological groups, supporting the development of stage-specific linear regression models. Results demonstrated high accuracy during early growth stages (tillering to stem elongation), with R² values of 0.76 (RMSE = 6.88) for Lee 7 × 7 and 0.79 (RMSE = 6.35) for Refined Lee. In later stages (booting to maturity), model performance declined, with Lee 7 × 7 outperforming Refined Lee (R² = 0.51 vs. 0.33). These findings underscore the potential of phenology-based modeling approaches to enhance crop height estimation and improve radar-driven crop monitoring. Full article

Ecosystem services (ES) and urban services (US) can comparably improve human well-being. Models for integrating ES and US with unexpressed and objective needs of defined groups of stakeholders may prove helpful for supporting decisions in landscape planning and management. In fact, they could be applied for highlighting landscape areas with different characteristics in terms of services provided. From this base, a suitability spatial assessment model (SUSAM) was developed and applied in a study area considering different verisimilar scenarios that policy makers could analyse. Each scenario is based on the prioritization of a set of services considering a defined group of stakeholders. Consistent and comparable ES and US indices of spatial benefiting areas (SBA) of services were calculated using GIS spatialization techniques. These indices were aggregated hierarchically with the relevance of services according to a spatial multicriteria decision analysis (S-MCDA). Results include maps for each scenario showing detailed spatial indices of suitability that integrate the local availability of SBA of ES and US, along with their relevance. The results were compared with known landscape classes identified in previous studies, which made it possible to interpret the spatial variation of suitability in the light of known landscape features. A complete sensitivity analysis was performed to test the sensitiveness of the model’s outputs to variations of judgements and their resistance to the indicators’ variation. The application of the model demonstrated its effectiveness in a landscape suitability assessment. At the same time, the sensitivity analysis and helping to understand the model behaviour in the different landscape classes also suggested possible solutions for simplifying the whole methodology. Full article

This study compares simplified and advanced precision nitrogen (N) fertilization approaches for winter wheat relying on Sentinel-2 NDVI, grain yield maps, and protein content. Five N fertilization treatments were compared: (1) a standard rate, calculated by a typical N balance (Flat-N); (2) a variable rate calculated using a simplified linear model, adopting a proportional strategy (NDVI directly related) (Var-N-dir); (3) a variable rate calculated using a simplified linear model, adopting a compensative strategy (NDVI inversely related) (Var-N-inv); (4) a variable rate calculated using the AgroSat model (Var-N-Agrosat); and (5) a variable rate calculated applying the Agricolus model (Var-N-Agricolus). The study was carried out in four fields over two cropping seasons with a randomized blocks design. Results indicate that the weather remains the main factor influencing yield, as it typically happens in a rainfed crop. No substantial differences in crop yield were observed among the N fertilization models within each year and experimental location. However, in the more favorable season, the low-input direct model (Var-N-dir) resulted as the best choice, providing the higher NUE (nitrogen use efficiency) value. In the less favorable season, results showed a better performance of the advanced models (Var-N-Agricolus and Var-N-Agrosat), which limited yield losses and reduced intra-field variability, with relevant importance given to the increasing frequency of abnormal climate phenomena. In general, all these VRT approaches allowed reduction of the excess of fertilizers, preservation of the environment, and saving money. Full article

Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weather conditions, terrain slope, and surface spectral reflectance were considered for this purpose. The accuracy of the temperature data worsened when replacing weather-station measurements with remote-sensing records, though the addition of more complete environmental data resulted in an efficient prediction model of olive-tree phenology. Filtering and embedded feature-selection techniques were employed to analyze the impact of variables on olive-tree phenology prediction, facilitating the inclusion of measurable information in decision support frameworks for the sustainable management of olive-tree systems. Full article

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus’ ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements. Full article