Climate change and water scarcity bring significant challenges to agricultural systems in the Mediterranean region. Novel methods are required to rapidly monitor the water stress of the crop to avoid qualitative losses of agricultural products. This study aimed to predict the stem water potential of cotton (Gossypium hirsutum L., 1763) using Sentinel-2 satellite imagery and machine learning techniques to enhance monitoring and management of cotton’s water status. The research was conducted in Rutigliano, Southern Italy, during the 2023 cotton growing season. Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. The models’ performance was assessed using R2 and root mean square error (RMSE). Feature importance was analyzed using permutation importance and SHAP methods. The random forest model using Sentinel-2 bands’ reflectance as predictors showed the highest performance, with an R2 of 0.75 (±0.07) and an RMSE of 0.11 (±0.02). XGBoost (R2: 0.73 ± 0.09, RMSE: 0.12 ± 0.02) and AdaBoost (R2: 0.67 ± 0.08, RMSE: 0.13 ± 0.02) followed in performance. Visible (blue and red) and red edge bands were identified as the most influential predictors. The trained RF model was used to model the seasonal trend of cotton’s stem water potential, detecting periods of acute and moderate water stress. This approach demonstrates the prospective for high-frequency, non-invasive monitoring of cotton’s water status, which could support smart irrigation strategies and improve water use efficiency in Mediterranean cotton production.

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R² = 0.61), while the support vector machine performed best in predicting electron transport rate (R² = 0.57), Fv/Fm ratio (R² = 0.66) and stomatal conductance (R² = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R² = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change.

Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellites and local meteorological records were used as predictors. Partial least squares, the generalized linear model and three machine learning algorithms (Random Forest, Elastic Net, and Support Vector Machine) were evaluated. Random Forest yielded the highest predictive accuracy with an average R² of 0.74, RMSE of 0.577 mm, and MBE of 0.03 mm. Model interpretability was performed through permutation importance and SHAP, identifying the near-infrared and red spectral bands, average daily temperature, and relative humidity as key predictors. This integrated approach could provide a scalable, precise method for watermelon ETa estimation, supporting data-driven irrigation management and improving water use efficiency in Mediterranean horticultural systems.

The growing demand for renewable energy sources and the need to optimize water use in agriculture, particularly in water-scarce regions, highlights the importance of growing species suitable for semi-arid areas, such as sorghum (Sorghum bicolor L. Moench). Deficit irrigation strategies allow water savings by optimizing water use efficiency. However, the potential of sorghum for bioethanol production with deficit irrigation strategies is still not well studied. This work investigates the impact of three irrigation strategies (full, deficit, and regulated deficit) on the biomass yield, ethanol production, and water use efficiency of sorghum (‘KWS Bulldozer’) in a semi-arid Mediterranean area (the Apulia region, Southeastern Italy) over three growing seasons (2013, 2014, and 2017); irrigation needs were calculated from crop evapotranspiration using standard crop coefficients and soil water content measurements. Harvested biomass was analyzed for cellulose and hemicellulose content, and ethanol production was estimated using conversion models. The full irrigation treatment resulted in the highest biomass and ethanol production in all seasons (22,633 kg × ha⁻¹, 28,367 kg × ha⁻¹, and 23,835 kg × ha⁻¹, in 2013, 2014, and 2017, respectively), highlighting the relationship between a full water supply and yield optimization. However, deficit irrigation showed a higher biomass and ethanol water productivity (10.93 kg × m⁻³ and 3.23 L × m⁻³, respectively) than other treatments, suggesting that moderate irrigation strategies can effectively balance production and sustainable water use. The results suggest the importance of adjusting irrigation practices to specific environmental conditions to improve the efficiency and productivity of sorghum.

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R² and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R² = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R² = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.

The carbon source/sink nature and the water balance of a drip-irrigated and mulched watermelon cultivated under a semi-arid climate were investigated. Biodegradable films, plants and some fruits were left on the soil as green manure. The study spanned from watermelon planting to the subsequent crop (June–November 2023). The eddy covariance technique was employed to monitor water vapor (H₂O) and carbon dioxide (CO₂) fluxes, which were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method.This method utilizesthe Monin–Obukhov similarity theory to separate stomatal (photosynthesis and transpiration) from non-stomatal (respiration and evaporation) processes. The results indicate that mulching films contribute to carbon sequestration in the soil (+19.3 g C m⁻²). However, the mulched watermelon crop presented in this study functions as a net carbon source, with a net biome exchange, representing the net rate of C accumulation in or loss from ecosystems, equal to +230 g C m⁻². This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 g C m⁻²). These findings recommend further research to enhance the sustainability of this crop in terms of both water and carbon balances.