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Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions.

Optical and thermal sensors installed on Unmanned Aircraft Systems (UAS) can be considered a technological innovation for precision farming. The visible and thermal regions of the electromagnetic (EM) spectrum provide useful information to assess the quality of crop growth and monitor plant water status. Accurate measurements of plant water status with high-resolution thermal images associated with high-efficiency irrigation systems can be a suitable solution to improve energy and water saving.The objective of this work was to estimate and compare the Crop Water Stress Index (CWSI) obtained in a citrus orchard irrigated with two different irrigation systems, by using a UAS equipped with a thermal camera.The experiment was carried out in a commercial citrus orchard located in the Northwest of Sicily, Italy, during the irrigation season of 2022. Optical and thermal high-resolution images were acquired at noon on August 23 and 25, and September 2 over two plots, the first of which was irrigated with a subsurface drip irrigation (SDI) and the second with a micro-sprinkler (MSI). Hourly crop reference evapotranspiration, ETo, and Vapour Pressure Deficit (VPD) were calculated by using the weather variables measured by a standard weather station installed in the field, while the plant water status was monitored at an hourly time scale, through three microtensiometers (FloraPulse, Davis, CA) embedded into the woody tissue of trees considered representative of the two irrigation systems. For each thermal image, characterized by a thermal spatial resolution of 15 cm, soil pixels were initially removed; then, the dry and wet reference temperatures, Tdry and Twet, were estimated as the 0.5 and 99.5 percentiles of the canopy temperature. The values of CWSI were finally calculated based on the maximum Tdry and minimum Twet obtained in the two plots during the examined days.Vapor pressure deficit and crop reference evapotranspiration resulted in quite similar values in the three days, with hourly VPD and ETo at noon ranging between 1.49 and 1.65 kPa, and between 0.50 and 0.62 mm, respectively. Irrigation heights provided in the examined period resulted equal to 65 mm in a single application in the MSI plot and 48 mm, equally distributed in eight irrigation events, in the SDI plot. In the latter plot, the values of daily stem water potential ranged between -0.5 and -1.1 MPa during the entire period with values of the corresponding CWSI between 0.22 and 0.28; on the other hand, in the plot irrigated with the MSI system the values tended to decline to a daily range between -1.1 and -1.3 MPa as a consequence of the soil drying between consecutive waterings with values of CWSI ranging between 0.30 and 0.34. The analysis showed that both plots were characterized by low water stress levels. However, despite the lower irrigation volume supplied by the SDI system, the values of CWSI resulted always lower than those obtained under the MSI system, confirming the potential of the SDI system to improve water use efficiency.

Agriculture is the main source of pressure on water resources, so accurate estimates of irrigation demands play a key role in sustainable water management. The INCIPIT (INtegrated Computer modeling and monitoring for Irrigation Planning in Italy) project (funded by Italian Min. Univ. and Research) aims to address the gaps between research and practical application in monitoring irrigation water use in six Italian regions [1]. It is designed to meet the requirements of sustainable water policies, such as the Water Framework Directive and the MIPAAF Ministry Decree, by providing accurate measurements and estimations of irrigated areas and water volumes. The project uses the ESA Sentinel-2 (S2) satellites as a valuable source of information to map irrigated areas and estimate distributed irrigation water requirements. This study presents the results of the IRRISAT methodology, the first Italian satellite-based irrigation advisory service [2], which was applied in the Campania region. The methodology uses a one-step approach, based on the Penman-Monteith equation, and is adjusted with canopy parameters from S2 data, to quantify irrigation water abstraction. Effective irrigated areas were assessed by using pre-existing maps, unsupervised clustering, and supervised machine learning algorithms applied to vegetation index data [3]. The results of the methodology for the irrigation seasons of 2019 and 2020 will be presented for seven Irrigation and Land Reclamation Consortiums, which vary in size, irrigation scheme, farm delivery, irrigation methods, and crop types. [1] INCIPIT Project, www.principit2017.it [2] Vuolo F., D’Urso G., De Michele C., Bianchi B., Cutting M.: Satellite-based irrigation advisory services: a common tool for different experiences from Europe to Australia”. Agricultural Water Management, Elsevier, vol. 147: 82-95; dx.doi.org/10.1016/j.agwat.2014.08.004 (2015). [3] Falanga Bolognesi S., Pasolli E., Belfiore O. R., De Michele C., D’Urso G.: Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy". Remote Sensing, MDPI, vol.12, no. 8: 1275; doi.org/10.3390/rs12081275 (2020).

Introduction Lateral dislocation of the patella is a common injury in active adolescents and young adults. Patients who are ultimately managed surgically have a significantly lower risk of recurrent dislocation. However, determining the optimal surgical treatment remains a challenge, with patients sometimes undergoing multiple surgeries prior to successful stabilization. The aim of this study is to computationally evaluate patients that have undergone multiple surgeries to correct for recurrent lateral patellar dislocation and predict their clinical outcome. Methods Our patient cohort consisted of 16 patients with patella dislocation. Patient-specific imaging were used to create three-dimensional (3D) finite element (FE) models of the knee joint and evaluate patellofemoral (PF) stability at multiple time points pre- and post-surgery for each patient. We applied these models to predict the clinical success or failure of each surgery. Specifically, the FE model simulated a knee extension activity while a tibia external torsion, a recognized cause of patellofemoral pain and instability, was applied to assess PF stability. A healthy control group of 12 participants was also included to assess the ability of the model to identify successful outcomes. In addition, five anatomic factors of risk were measured, and statistical analysis was performed to establish if significant differences exist among pre-surgery, post-surgery and healthy control groups. Lastly, a logistic regression model was implemented, trained with anatomic values, and used to classify subjects into likelihood of dislocation categories in order to differentiate between successful and unsuccessful surgical outcomes. Feature scaling and feature combination (namely, principal component analysis (PCA)) was applied to improve the predictive performance of the regression model. Results Of 12 control participants, 12 pre-surgery subjects (8 patients after an initial unsuccessful MRPLR and 4 without any), and 9 post-surgery subjects (5 after a successful trochleoplasty and 4 patients after MPFLR), the FE model correctly predicted 29 out of 33 surgery outcomes (87.9% accuracy). Post-surgery simulations predicted patellofemoral stability metrics similar to the healthy control group. Particularly, post-trochleoplasty subjects were associated with an increased ability to provide constraint force on the patella lateral facet, and a lower involvement of the medial patellofemoral ligament, particularly close to full extension. A one-way ANOVA showed that four out of five anatomic factors were significantly different between the pre-surgery and the control group, and three of them also between the pre- and post- surgery group, suggesting that the surgery was able to restore a physiological condition. Lastly, logistic regression classification performance demonstrated 72.2% and 78.9% accuracy before and after PCA, respectively. Conclusion The overall aim of this study is to provide surgeons with a useful and validated computational tool that can predict the likelihood of patellar dislocation and differentiate, prior to clinical intervention, between a successful versus unsuccessful surgery, to determine the optimal treatment pathways for individual patients. Preliminary results are promising, but an improvement of the model and a larger clinical dataset are necessary to improve accuracy and comprehensively validate model performance.

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. Among indirect methods to estimate actual evapotranspiration, ETa, the measurements of latent heat fluxes acquired with Eddy Covariance (EC) tower have been largely used. However, the malfunctioning of the commonly installed sensors can cause the loss of data, compromising the temporal continuity of the acquisitions. Machine learning (ML) algorithms can represent a powerful tool to perform gap-filling procedures and provide accurate predictions of missing data.The objective of the research was to assess different ML algorithms to fill daily actual evapotranspiration measurements acquired in a Mediterranean citrus orchard, by using a combination of in-situ or ERA5-Land reanalysis agro-meteorological data and two vegetation indices (VIs) retrieved by the Sentinel 2 platform.The experimental layout consisted of a standard weather station, an EC tower containing an open-patch gas-analyzer, a three-dimension sonic anemometer, a four-component net radiometer. Four “drill and drop” probes (Sentek Pty Ltd, Stepney, Australia) were also installed in the field. Six different algorithms of machine learning were tested, using in input, as climate variables the global solar radiation, mean air temperature and relative air humidity, as well as two VIs (NDVI e NDWI) characterized by a spatial resolution of 10 m and, finally, the average soil water content measured in the root zone (0-50 cm). The number of daily ETa measurements, acquired from March 2019 to September 2021, resulted in about 70% of the total days of the period; the missing data were caused by the malfunctioning of installed instruments, which occurred during the lockdown restrictions caused by the pandemic.Among the different ML algorithms, the best performance was associated with the Gaussian Process Regression (GPR) based on nonparametric kernel probabilistic function (Rasmussen, 2006) when, with the other input variables, the average soil water content was included; in this case, the values of root mean square error (RMSE) and determination coefficient (R2) associated with the cross-validation resulted equal to 0.42 mm/d and 0.85, respectively. However, suitable results were also associated with the GPR model, when assuming, as input variables, on-site meteorological data and VIs (RMSE=0.49 mm/d and R2=0.78), or when considering the ERA5-Land meteorological variables (RMSE=0.56 mm/d and R2=0.73). The joint use of agro-meteorological and remote sensing data, associated with a GPR model, can therefore provide the opportunity to fill the gaps of ETa time-series.

Estimation of evapotranspiration using the crop coefficient method is one of the most common approaches for irrigation water management. The crop coefficient, Kc, can be estimated as the ratio between maximum crop evapotranspiration, ETmax, and reference evapotranspiration, ET0. However, in the last few decades, many correction factors have been proposed to split Kc into separate coefficients to account for water stress conditions, as well as to estimate separately crop transpiration and soil evaporation. Furthermore, the remote sensing data collected from various satellite platforms have shown their full potential in mapping various vegetation indices (VI), which can be directly related to the spatio-temporal variability of Kc values. Despite various VI-Kc relationships have been proposed in the past years, only recently, thanks to the availability of new sensors with higher temporal and spatial resolutions, it is possible to retrieve new relationships able to follow the variability of the crop coefficient during the different crop phenological stages.This study aimed at identifying a VI-Kc relationship suitable to map actual evapotranspiration of a citrus orchard based on an extended time-series of NDVI images retrieved by Sentinel-2 platform and combined with a set of field micro-meteorological measurements.The experiments were carried out during 2019 and 2020 in a commercial citrus orchard (C. reticulata cv. Tardivo di Ciaculli) with tree spacing of 5 x 5 m, located near the city of Palermo, Italy, in which different irrigation systems and management strategies were applied in three different portions of the orchard. In particular, the first portion was irrigated with a traditional micro-sprinkler system (TI) whereas the other two with a subsurface drip system maintained under full irrigation (FI) and deficit irrigation (DI) applied during the phase II of fruit growth (from 1-July to 20-August). The orchard was equipped with a standard weather station (WS) and an Eddy Covariance (EC) tower to acquire, every half-an-hour, precipitation, air temperature and relative humidity, wind speed and direction, global and net solar radiation and, finally, sensible and latent heat fluxes. During the entire period, a weekly dataset of Sentinel-2 images characterized by a spatial resolution of 10 m was acquired and processed in a GIS environment to obtain the spatial and temporal distribution of NDVI. Using the data acquired in 2019, a functional relationship between Kc and NDVI was calibrated accounting only for those periods in which the crop was maintained in the absence of water stress. The values ​​of Kc were determined as the ratio between actual daily ET measured by the EC tower and reference Penman-Monteith ET0 obtained as indicated by the Food and Agriculture Organization of the United Nations. The procedure was then validated with the data recorded in 2020, by comparing estimated crop ET ​and the corresponding measured by the EC tower. The comparative analysis indicated root-mean-square-error and mean-bias-error associated with estimated ET of about 0.5 mm/d and 0.2 mm/d, respectively. Finally, based on the NDVI maps it was possible to derive the spatial variability of Kc and actual ET, under the different irrigation systems and management strategies.