Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where ...crop evapotranspiration (ET
) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ET
, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014-2015. ET
measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5
day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.
Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this ...paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.
The occurrence of water shortages ascribed to projected climate change, especially in the Mediterranean region, fosters the interest in remote sensing (RS) applications to optimize water use in ...agriculture. Remote sensing evapotranspiration and water demand estimation over large cultivated areas were used to manage irrigation to minimize losses during the crop growing cycle. The research aimed to explore the potential of the MultiSpectral Instrument (MSI) sensor on board Sentinel-2A to estimate crop parameters, mainly surface albedo (α) and Leaf Area Index (LAI) that influence the dynamics of potential evapotranspiration (ETp) and Irrigation Water Requirements (IWR) of processing tomato crop (Solanum lycopersicum L.). Maximum tomato ETp was calculated according to the FAO Penman-Monteith equation (FAO-56 PM) using appropriate values of canopy parameters derived by processing Sentinel-2A data in combination with daily weather information. For comparison, we used the actual crop evapotranspiration (ETa) derived from the soil water balance (SWB) module in the Environmental Policy Integrated Climate (EPIC) model and calibrated with in-situ Root Zone Soil Moisture (RZSM). The experiment was set up in a privately-owned farm located in the Tarquinia irrigation district (Central Italy) during two growing seasons, within the framework of the EU Project FATIMA (FArming Tools for external nutrient Inputs and water Management). The results showed that canopy growth, maximum evapotranspiration (ETp) and IWR were accurately inferred from satellite observations following seasonal rainfall and air temperature patterns. The net estimated IWR from satellite observations for the two-growing seasons was about 272 and 338 mm in 2016 and 2017, respectively. Such estimated requirement was lower compared with the actual amount supplied by the farmer with sprinkler and drip micro-irrigation system in both growing seasons resulting in 364 (276 mm drip micro-irrigation, and 88 mm sprinkler) and 662 (574 mm drip micro-irrigation, and 88 mm sprinkler) mm, respectively. Our findings indicated the suitability of Sentinel-2A to predict tomato water demand at field level, providing useful information for optimizing the irrigation over extended farmland.
•Remote sensing (RS) for crop water requirements calculation is proposed.•Validations of the canopy variables estimated by RS are reported.•Potential ET by RS was compared with actual ET modelled by EPIC.•Results confirm the usefulness of RS data in supporting irrigation scheduling.
Late spring frost plays a major role in the structure and function of forest ecosystems with potential consequences on species distribution at both local and regional scales. Paradoxically, in a ...warmer world the incidence and impact of frost is increasing because of earlier leaf unfolding and flowering as a response to warmer temperatures. In this regard, European Beech (Fagus sylvatica L.), a native tree species widely distributed in European forests, is considered particularly sensitive to changes in spring temperature regimes associated with climate change and thus especially subject to the risk of frost damage. Although several studies concerning F. sylvatica frost damage have been conducted in northern and central Europe, no extensive studies are available for the southern part of its range, i.e. central and southern Italy as well as Greece.
In this paper the effect of a late spring frost occurring at the end of April 2016 is extensively documented with high spatial detail all along the Apennine Chain through satellite image data. Three different remote-sensing greenness indexes, namely the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the greenness index (GI) derived from Landsat-8 satellite images acquired from May to July in the years 2014, 2015, and 2016 at a spatial resolution of 30 m, were used to gauge the spatial response of common beech forests to this late frost event with relation to latitude, altitude and slope exposure. Frost damage was evaluated as a difference (Δ) of NDVI, GI and EVI between the mean of years 2014 and 2015 (i.e. MRY, mean of reference years), and 2016 (i.e. FEY, frost reference year). The three satellite remote-sensing indexes were efficient at detecting leaf damage with detailed spatial resolution and proved consistent with one another.
The greatest damage occurred in the middle altitudinal range between 1500 and 1700 m a.s.l. with a decreasing trend toward both lower and higher elevations due to warmer temperatures below, and delayed phenology above. Exposure also influenced frost injury, with south-facing slopes of the mountain more damaged than the north. This difference was due to earlier spring leaf phenology of southern beech trees in response to a greater heat sum in the warm weeks preceding. Less damage in the northern Apennines is consistent with the spatial extent of minimum freezing temperatures. To sum up, frost damage is strongly related to site-specific conditions, i.e. on the one hand to minimum temperatures, and on the other to the phenological stage of the trees involving both altitude and exposure. Hence focusing on detailed sub-regional studies can be helpful for predicting future consequences of climate change on forests.
•Spring frost canopy damage of European beech in Apennines is related to site-specific conditions.•Damage severity depends by freezing temperatures coupled with phenological stage.•Frost damage is greater in the southern aspect and at the middle belts rather than at higher ones.•NDVI, EVI and GI are efficient to detect leaf damage with detailed spatial resolution.
The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the ...evapotranspiration and crop coefficients of table grapes vineyards trained on overhead "tendone" systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the "direct" methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm times d-1, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013, and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.
This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal ...observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM.
A research study was conducted in an open field tomato crop in order to: (i) Evaluate the capability of Sentinel-2 imagery to assess tomato canopy growth and its crop water requirements; and (ii) ...explore the possibility to predict crop water requirements by assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model. The pilot area was in Campania, a region in the south west of Italy, characterized by a typical Mediterranean climate, where field campaigns were conducted in seasons 2017 and 2018 on processing tomato. Crop water use and irrigation requirement were estimated by means of three different methods: (i) The AquaCrop model; (ii) an irrigation advisory service based on Sentinel-2 imagery known as IRRISAT and (iii) assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model Sentinel-2 imagery proved to be effective for monitoring canopy growth and for predicting irrigation water requirements during mid-season stage of the crop, when the canopy is fully developed. Conversely, the integration of the Sentinel-2 imagery with a crop growth model can contribute to improve the irrigation water requirement predictions in the early and development stage of the crop, when the soil evaporation is not negligible with respect to the total evapotranspiration.
Soil water balance on a local scale is generally achieved by applying the classical nonlinear Richards equation that requires hydraulic properties, namely, water retention and hydraulic conductivity ...functions, to be known. Its application in agricultural systems on field or larger scales involves three major problems being solved, related to (i) the assessment of spatial variability of soil hydraulic properties, (ii) accounting for this spatial variability in modelling large-scale soil water flow, and (iii) measuring the effects of such variability on real field variables (e.g., soil water storage, biomass, etc.). To deal with the first issue, soil hydraulic characterization is frequently performed by using the so-called pedotransfer functions (PTFs), whose effectiveness in providing the actual information on spatial variability has been questioned. With regard to the second problem, the variability of hydraulic properties at the field scale has often been dealt with using a relatively simple approach of considering soils in the field as an ensemble of parallel and statistically independent tubes, assuming only vertical flow. This approach in dealing with spatial variability has been popular in the framework of a Monte Carlo technique. As for the last issue, remote sensing seems to be the only viable solution to verify the pattern of variability, going by several modelling outputs which have considered the soil spatial variability. Based on these premises, the goals of this work concerning the issues discussed above are the following: (1) analyzing the sensitivity of a Richards-based model to the measured variability of θ(h) and k(θ) parameters; (2) establishing the predictive capability of PTF in terms of a simple comparison with measured data; and (3) establishing the effectiveness of use of PTF by employing as data quality control an independent and spatially distributed estimation of the Above Ground Biomass (AGB). The study area of approximately 2000 hectares mainly devoted to maize forage cultivation is located in the Po plain (Lodi), in northern Italy. Sample sites throughout the study area were identified for hydropedological analysis (texture, bulk density, organic matter content, and other chemical properties on all the samples, and water retention curve and saturated hydraulic conductivity on a sub-set). Several pedotransfer functions were tested; the PTF‒Vereckeen proved to be the best one to derive hydraulic properties of the entire soil database. The Monte Carlo approach was used to analyze model sensitivity to two measured input parameters: the slope of water retention curve (n) and the saturated hydraulic conductivity (k0). The analysis showed sensitivity of the simulated process to the parameter n being significantly higher than to k0, although the former was much less variable. The PTFs showed a smoothing effect of the output variability, even though they were previously validated on a set of measured data. Interesting positive and significant correlations were found between the n parameter, from measured water retention curves, and the NDVI (Normalized Difference Vegetation Index), when using multi-temporal (2004–2018) high resolution remotely sensed data on maize cultivation. No correlation was detected when the n parameter derived from PTF was used. These results from our case study mainly suggest that: (i) despite the good performance of PTFs calculated via error indexes, their use in the simulation of hydrological processes should be carefully evaluated for real field-scale applications; and (ii) the NDVI index may be used successfully as a proxy to evaluate PTF reliability in the field.
A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface ...parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products.
Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have ...emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.
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