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Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using surface energy balance (SEB) and soil-water balance models. Both modelling approaches use remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS), near-infrared (NIR) and thermal (TIR) observations to solve the surface energy balance equation whereas the soil-water balance model uses only VIS-NIR data to detect the spatial variability of crop parameters. Considering that the study area is characterized by typical spatially sparse Mediterranean vegetation, i.e. olive, citrus and vineyards, alternating bare soil and canopy, we focused the attention on the main conceptual differences between one-source and two-sources energy balance models. Two different models have been tested: the widely used one-source SEBAL model, where soil and vegetation are considered as the sole source (mostly appropriate in the case of uniform vegetation coverage) and the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared vs. the outputs of the agro-hydrological SWAP model, which was applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. Remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. Actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference to be used for energy balance models accuracy assessment. Airborne hyperspectral data acquired during a NERC (Natural Environment Research Council, UK) campaign in 2005 have been used. The results of this investigation seem to prove a slightly better agreement between SWAP and TSEB for some fields of the study area. Further investigations are programmed in order to confirm these indications.

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

For open orchard and vineyard canopies containing significant fractions of exposed soil (>50%), typical of Mediterranean agricultural regions, the energy balance of the vegetation elements is strongly influenced by heat exchange with the bare soil/substrate. For these agricultural systems a "two-source" approach, where radiation and turbulent exchange between the soil and canopy elements are explicitly modelled, appears to be the only suitable methodology for reliably assessing energy fluxes. In strongly clumped canopies, the effective wind speed profile inside and below the canopy layer can strongly influence the partitioning of energy fluxes between the soil and vegetation components. To assess the impact of in-canopy wind profile on model flux estimates, an analysis of three different formulations is presented, including algorithms from Goudriaan (1977), Massman (1987) and Lalic et al. (2003). The in-canopy wind profile formulations are applied to the thermal-based two-source energy balance (TSEB) model developed by Norman et al. (1995) and modified by Kustas and Norman (1999). High resolution airborne remote sensing images, collected over an agricultural area located in the western part of Sicily (Italy) comprised primarily of vineyards, olive and citrus orchards, are used to derive all the input parameters needed to apply the TSEB. The images were acquired from June to October 2008 and include a relatively wide range of meteorological and soil moisture conditions. A preliminary sensitivity analysis of the three wind profile algorithms highlights the dependence of wind speed just above the soil/substrate to leaf area index and canopy height over the typical range of canopy properties encountered in these agricultural areas. It is found that differences among the models in wind just above the soil surface are most significant under sparse and medium fractional cover conditions (15–50%). The TSEB model heat flux estimates are compared with micro-meteorological measurements from a small aperture scintillometer and an eddy covariance tower collected over an olive orchard characterized by moderate fractional vegetation cover (≈35%) and relatively tall crop (≈3.5 m). TSEB fluxes for the 7 image acquisition dates generated using both the Massman and Goudriaan in-canopy wind profile formulations give close agreement with measured fluxes, while the Lalic et al. equations yield poor results. The Massman wind profile scheme slightly outperforms that of Goudriaan, but it requires an additional parameter accounting for the roughness sub-layer of the underlying vegetative surface. The analysis also suggests that within-canopy wind profile model discrepancies become important, in terms of impact on modelled sensible heat flux, only for sparse canopies with moderate vegetation coverage.

Climate change is anticipated to alter the demand and supply of water at the earth's surface. Since many societal impacts from a lack of water happen under drought conditions, it is important to understand how droughts may develop with climate change. This study shows how hydrological droughts will change across Europe with increasing global warming levels (GWLs of 1.5, 2, and 3 K above pre-industrial temperature). We employed a low-flow analysis based on river discharge simulations of the LISFLOOD (De Roo et al., 2000) spatially distributed physically based hydrological and water use model, which was forced with a large ensemble of regional climate model projections under high emissions (RCP8.5) and moderate mitigation (RCP4.5) Representative Concentration Pathways. Different traits of drought, including severity, duration, and frequency, were investigated using the threshold level method. The projected changes in these traits identify four main sub-regions in Europe that are characterized by somehow homogeneous and distinct behaviours with a clear south-west–north-east contrast. The Mediterranean and Boreal sub-regions (defined in Sect. 3.1.1) of Europe show strong but opposite changes at all three GWLs, with the former area mostly characterized by stronger droughts (with larger differences at 3 K), while the latter is expected to experience a reduction in all drought traits. In the Atlantic and Continental sub-regions, the changes are expected to be less marked and characterized by a larger uncertainty, especially at the 1.5 and 2 K GWLs. Combining the projections in drought hazard with population and agricultural information shows that with 3 K global warming an additional 11 million people and 4.5 ×106 ha of agricultural land are projected to be exposed to droughts every year, on average, with the most affected areas located in the Mediterranean and Atlantic regions of Europe.

While climate change will alter the distribution of water in time and space, quantifications of drought risk under global warming remain uncertain. Here, we show that in Europe, drought damages could strongly increase with global warming and cause a regional imbalance in future drought impacts. In the absence of climate action (4 °C in 2100 and no adaptation), annual drought losses in the European Union and United Kingdom combined are projected to rise to more than €65 billion per year compared with €9 billion per year currently, or two times larger when expressed relative to the size of the economy. Drought losses show the strongest rise in southern and western parts of Europe, where drought conditions at 4 °C could reduce regional agriculture economic output by 10%. With high warming, drought impacts will become a fraction of current impacts in northern and northeastern regions. Keeping global warming well below 2 °C would avoid most impacts in affected regions. Climate change impacts precipitation patterns, and thus the risk for drought. Damages from drought in Europe will increase with losses more than €65 billion per year in a scenario without climate mitigation; keeping warming below 2 °C avoids most impacts.

The outcomes of the 2015 Paris Agreement triggered a number of climate impact assessments, such as for floods and droughts, to focus on future time frames corresponding to the years of reaching specific levels of global warming. Yet, the links between the timing of the warming levels and the corresponding greenhouse gas concentration pathways to reach them remain poorly understood. To address this gap, we compared projected changes of annual mean, extreme high, and extreme low river discharges in Europe at 1.5 &deg, C and 2 &deg, C under Representative Concentration Pathways RCP8.5 and RCP4.5 from an ensemble of regional climate model (RCM) simulations. The statistical significance of the difference between the two scenarios for both warming levels was then evaluated. The results show that in the majority of Europe (&gt, 95% of the surface area for the annual mean discharge, &gt, 98% for high and low extremes), the changes projected in the two pathways were statistically indistinguishable. These results suggest that in studies of changes at global warming levels, the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the unified ensemble, the findings show that the projected changes of annual mean, extreme high, and extreme low river discharge were statistically significant in large portions of Europe.

Among the indirect estimation approaches of soil water content in the upper layer of the soil, the &ldquo, triangle method&rdquo, is one of the most common that relies on the simple relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at the surface and within the root zone but also by meteorological forcing including air temperature and humidity, as well as solar radiation. Night- and day-time MODIS composites of land-surface temperature (LST) allowed applying a version of the triangle method that takes into account the temporal admittance of the soil. In this study, it has been applied to a long time-series of pair images to analyze the seasonal influence of the meteorological forcing on a triangle method index (or temperature&ndash, vegetation index, TVX), as well as to discuss extra challenges of the diachronic approach including seasonality effects and the variability of environmental forcing. The Imera Meridionale basin (Sicily, Italy) has been chosen to analyze the method over a time-series of 12 years. The analysis reveals that, under these specific environmental and climatic conditions (strong seasonality and rainfall out of phase with vegetation growth), Normalized Difference Vegetation Index (NDVI) and LST pairs move circularly in time within the optical vs. thermal feature space. Concordantly, the boundaries of the triangle move during the seasons. Results showed a strong correlation between TVX and rainfall normalized amplitudes of the power spectra (r2 ~0.8) over the range of frequencies of the main harmonics.

A remote sensing-based approach to estimate actual evapotranspiration (ET) was tested in an area covered by olive trees and characterized by Mediterranean climate. The methodology is a modified version of the standard FAO-56 dual crop coefficient procedure, in which the crop potential transpiration, T p, is obtained by directly applying the Penman-Monteith (PM) equation with actual canopy characteristics (i.e., leaf area index, albedo and canopy height) derived from optical remote sensing data. Due to the minimum requirement of in-situ ancillary inputs, the methodology is suitable also for applications on large areas where the use of tabled crop coefficient values become problematic, due to the need of corrections for specific crop parameters, i.e., percentage of ground cover, crop height, phenological cycles, etc. The methodology was applied using seven airborne remote sensing images acquired during spring-autumn 2008. The estimates based on PM approach always outperforms the ones obtained using simple crop coefficient constant values. Additionally, the comparison of simulated daily evapotranspiration and transpiration with the values observed by eddy correlation and sap flow techniques, respectively, shows a substantial agreement during both dry and wet days with an accuracy in the order of 0.5 and 0.3 mm d−1, respectively. The obtained results suggest the capability of the proposed approach to correctly partition evaporation and transpiration components during both the irrigation season and rainy period also under conditions of significant reduction of actual ET from the potential one.

Correct estimation of crop actual transpiration plays a key-role in precision irrigation scheduling, since crop growth and yield are associated to the water passing through the crop. Objective of the work was to assess how the combined use of micro-meteorological techniques (eddy covariance, EC) and physiological measurements (sap flow, SF) allows a better comprehension of the processes involving in the Soil–Plant–Atmosphere continuum. To this aim, an experimental dataset of actual evapotranspiration, plant transpiration, and soil water content measurements was collected in an olive orchard during the midseason phenological period of 2009 and 2010. It was demonstrated that the joint use of EC and SF techniques is effective to evaluate the components of actual evapotranspiration in an olive orchard characterized by sparse vegetation and a significant fraction of exposed bare soil. The availability of simultaneous soil water content measurements allowed to estimate the crop coefficients and to assess a simple crop water stress index, depending on actual transpiration that can be evaluated even in the absence of direct measurements of actual transpiration. The crop coefficients experimentally determined resulted very similar to those previously evaluated; in particular, in the absence of water stress, a seasonal average value of about 0.65 was obtained for the “single” crop coefficient, whereas values of a 0.34 and 0.41 were observed under limited water availability in the root zone. The comparison between the values of crop water stress index evaluated during the investigated periods evidenced systematically lower values (less crop water stress) in the first year compared to the second, according to the general trend of soil waters content in the root zone. Further researches are however necessary to extent the experimental dataset to periods characterized by values of soil evaporation higher than those observed, in order to verify the crop coefficients even under different conditions than those investigated.

Reference evapotranspiration (ET 0 ) is widely used for irrigation scheduling, to promote an efficient use of water resources for a sustainable agro-ecosystem productivity, as well as to manage water quality and to face other environmental concerns. As suggested by ASCE-EWRI and FAO, standard Penman–Monteith methods are generally applied for an accurate estimation of ET 0 from hourly to daily scale. In absence of detailed meteorological information several simplified equations, using a limited number of variables, have been proposed as alternative. In this paper, the performance of different reference evapotranspiration methods, at hourly (Penman–Monteith, Pristley–Taylor, Makkink and Turc) and daily scale (Penman–Monteith, Blaney and Criddle, Hargreaves, Pristley–Taylor, Makkink and Turc), was evaluated against scintillometer measurements collected during six month in 2005 in an experimental plot maintained under “reference” conditions (alfalfa crop). The daily values of ET 0 obtained with the examined methodologies were then used as input in the FAO-56 agro-hydrological model, in order to evaluate, for an olive grove in a Mediterranean environment, the impact on simulated actual evapotranspiration. The experiment was carried out in South-West Sicily, in an area where olive groves are the major crop. The comparison between estimated and measured fluxes confirmed that FAO-56 Penman–Monteith (PM) standardized equation is characterized by the lowest mean bias error (−0.15 mm d −1 and 0.06 mm d −1 using daily or hourly data, respectively). Additionally, the analysis also highlighted that the Pristley–Taylor equation can be considered a valid alternative for an accurate estimation of ET 0 (mean bias error of 0.35 mm d −1 and 0.43 mm d −1 using daily or hourly data, respectively). The application of the FAO-56 water balance model on the investigated olive grove evidenced that the best estimations of actual evapotranspiration are obtained when the Pristley–Taylor ET 0 data are used as input, confirming that this approach can be considered a valid alternative to the standard Penman–Monteith.

Summary An operational use of the actual evapotranspiration assessed by remote sensing approaches requires the integration of instantaneous fluxes to daily values. This is commonly achieved under the hypotheses of daytime self-preservation of evaporative fraction and negligible daily ground heat flux. The aim of this study is to evaluate the effect of these assumptions on estimate daily evapotranspiration over a full phenological cycle, including phases characterized by significant changes both in net radiation and vegetation cover. To assess the reliability of these hypotheses, the observations made by a flux tower, installed within a homogeneous field of cereal located in the valley part of the Imera Meridionale basin, were analyzed. Additionally, the widely-known SEBAL (Surface Energy Balance Algorithm for Land) model was applied on the same study area by means of four MODIS (MODerate-resolution Imaging Spectroradiometer) images selected across a three-rainfall events period in March–April 2007 with the aim to analyze the consistency of its estimates in an operational study case under different conditions of water availability. The analysis of in situ data highlights errors on 24-h evapotranspiration characterized by an average value of 20% due to daily soil heat flux neglecting; whereas, the hypothesis of evaporative fraction self-preservation causes an average error equal to −16%. Moreover, the analysis of the observations suggests that a compensation effect of the errors related to each hypothesis occurs in most cases (56%), and this makes suitable the approach for practical daily integration purposes. The application of the SEBAL model at basin scale shows a good capability to detect the increase of the actual 24-h evapotranspiration under the tested hypotheses, also in the case of instantaneous evaporative fraction and daily net radiation not derived form in situ observations.

Summary A coupled energy/hydrologic model was applied to simulate the exchange of energy and water in the soil–plant-atmosphere system (SPA). The model, which uses a “two-source” approach to estimate the energy fluxes, and the “force-restore” approach to represent the water balance, was validated by means of evapotranspiration measurements collected via scintillometry and soil moisture measurements collected via time domain reflectometry (TDR) in a Sicilian olive grove. The comparison between measured and estimated fluxes values at an hourly scale showed good agreement. Additional comparisons on a daily timescale confirmed the model’s applicability for quantifying crop water requirements. Also in terms of daily evapotranspiration and soil water content values, the obtained results confirmed the model’s applicability for those practical applications aiming to quantify the crop water requirement. Moreover, further studies should be conducted to test the feasibility of using this model for long term simulations over a broad range of conditions.

Two regionalized models for the distributed estimation of daily reference evapotranspiration, the temperature-based Hargreaves (HE) and the radiation-based Makkink (MK) equations, are applied in Southern Italy during the years 2007 and 2008. Spatially distributed meteorological inputs, such as air temperature and incoming solar radiation, were derived from geostatistical interpolation of ground data and from the Land Surface Analysis Satellite Application Facility (LSA-SAF) surface radiation product, respectively. Comparison of the latter with 83,394 daily measurements provided by 128 weather stations shows a not negligible seasonal error in daily solar radiation that is corrected by means of a periodic equation. A preliminary local calibration of the MK coefficient highlights its strong dependency on the interactions between moist winds from the Atlantic Ocean and orographic obstructions, leading to two western and eastern subzones. Hence, a regionalization of the MK-adjusted coefficient was performed following a procedure similar to the one already used by the authors in a previous work for the HE coefficient. Application of both regionalized models and their comparison with Penman-Monteith estimates derived from the same stations leads to average whole-year absolute errors of approximately 0.5 mm d-1, further showing the great potential of the MK method in terms of informative content and less dependence on the site density of a ground-based monitoring network. Overall, discussing pros and cons of both regionalized models, it is shown that their integrated use in southern Italy is possible and advisable.

The two-source energy balance (TSEB) model uses remotely sensed maps of land–surface temperature (LST) along with local air temperature estimates at a nominal blending height to model heat and water fluxes across a landscape, partitioned between dual sources of canopy and soil. For operational implementation of TSEB, however, it is often difficult to obtain representative air temperature data that are compatible with the LST retrievals, which may themselves have residual errors due to atmospheric and emissivity corrections. To address this issue, two different strategies in applying the TSEB model without requiring local air temperature data were tested over a typical Mediterranean agricultural area using a set of high-resolution multispectral airborne remote sensing images. Alleviating the need for accurate local air temperature data as input, these two approaches estimate the surface-to-air temperature gradient that drives the sensible heat flux by directly exploiting the information available in the image. The two approaches include: 1) a scene-based internal calibration (TSEB-IC) procedure that estimates air temperature over a well-watered and fully vegetated pixel in the LST image, and 2) a disaggregation scheme (DisALEXI) that uses air temperature estimates from a time-differential coupled TSEB-atmospheric boundary layer model of atmosphere–land exchange (ALEXI). A comparison of the air temperatures modeled by TSEB-IC and DisALEXI with in situ weather station observations shows good agreement, with average differences on the order of 1 K, comparable with the uncertainties in the remotely sensed surface temperature maps. Surface fluxes estimated by each method agree well with micro-meteorological measurements acquired over an olive orchard within the aircraft imaging domain. In comparison with fluxes generated with TSEB using local measurements of air temperature, instantaneous fluxes from these alternative methods show good spatial agreement, with differences of less than 10 W m − 2 across the domain. Finally, a sensitivity analysis of the three models, performed by introducing artificial errors into the model inputs, demonstrates that the DisALEXI and TSEB-IC approaches are relatively insensitive to errors in absolute surface temperature calibration, while turbulent fluxes from TSEB applications using local air temperature measurements show sensitivity of approximately 30 W m − 2 per degree temperature perturbation. This highlights the value of both internal calibration and time-differential estimation of the surface-to-air temperature gradient within a surface energy balance framework.

Remotely sensed observations in the visible to the shortwave infrared (VSWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum can be used synergistically to provide valuable products of land surface properties for reliable assessments of carbon and water fluxes. The high spatial, spectral and temporal resolution VSWIR and TIR observations provided by the proposed Hyperspectral - InfraRed (HyspIRI) mission will enable a new era of global agricultural monitoring, critical for addressing growing issues of food insecurity. To enable predictions at fine spatial resolution (

We are proposing a new method for estimating soil surface water content from thermal inertia distributions retrieved from visible–near infrared (VISNIR)and thermal infrared (TIR) images. A drying experiment was conducted on three fi ne-textured soils while acquiring multispectral VIS-NIR and TIR images. Simultaneous measurements of soil water content and thermal inertia were conducted by the thermogravimetric method and the heat pulse technique, respectively. Direct measurements were used to test the thermal inertia approach proposed by Murray and Verhoef that requires only knowledge of soil porosity and can be easily inverted to derive soil water content from thermal inertia. For the three considered soils, the performance of the Murray and Verhoef model was practically equal to that of the traditional approach based on the direct estimation of thermal conductivity and heat capacity, which requires more detailed information about soil properties. With the aim of simplifying the estimation of thermal inertia from remotely sensed images,a modified Kersten function was proposed in which the normalized thermal inertia is substituted by the normalized apparent thermal inertia. Comparison between the two modifi ed Kersten functions was satisfactory. The proposed approach allowed predictions of the surface soil moisture from apparent thermal inertia distributions with an acceptable level of accuracy for practical purposes (0.028 ≤ RMSE ≤ 0.043 m3 m−3) and therefore it can be considered a simple and effective tool for estimating the spatial and temporal distribution of surface soil moisture from VIS-NIR and TIR remotely sensed data.

In the last years, modeling of surface processes - such as water, energy and carbon budgets, as well as vegetation growth- seems to be focused on integrated approaches that combine aspects of hydrology, biology and meteorology into unified analyses. In this context, remotely sensed data often have a core role due to the cross-cutting impact of this novel source of spatially distributed information on all these research areas. However, several applications - such as drought monitoring, yield forecasting and crop management - require spatially detailed products at sub-field scales, which can be obtained only with support of adequately fine resolution remote sensing data (< 100 m). In particular, observations in the visible to the near infrared (VIS/NIR) spectral region can be used to derive biophysical and biochemical properties of the vegetation (i.e., leaf area index and leaf chlorophyll). Complementarily, the thermal infrared (TIR) signal provides valuable information about land surface temperature, which in turn represents an accurate proxy indicator of the subsurface moisture status by means of surface energy budget analysis. Additionally, the strong link between crop water stress and stomatal closure allows inference of crop carbon assimilation using the same tools. In this work, an integrated approach is proposed to model both carbon and water budgets at field scale by means of a joint use of a thermal-based Two Source Energy Budget (TSEB) model and an analytical, Light-Use-Efficiency (LUE) based model of canopy resistance. This suite of models allows integration of information retrieved by both fine and coarse resolution satellites by means of a data fusion procedure. A set of Landsat and MODIS images are used to investigate the suitability of this approach, and the modeled fluxes are compared with observations made by several flux towers in terms of both water and carbon fluxes.

Agricultural water requirement in arid and semi-arid environments represents an important fraction of the total water consumption, suggesting the need of appropriate water management practices to sparingly use the resource. Furthermore the quality and quantity of some crops products, such as grape, is improved under a controlled amount of water stress. The latter is related, on a side to actual evapotranspiration (ET) through water demand, on the other side to plant water content through leaf water potential. Residual energy balance approaches based on remote sensing allow to estimate the spatial distribution of daily actual ET at plant scale, representing an useful tool to detect its spatial variability across different cultivars and even within each parcel. Moreover, the connection between actual ET and leaf water potential is still not well assessed, especially under water stress conditions, even if farmers use leaf water potential to plan irrigation. However residual energy balance methods are based on the hypothesis that storage terms are negligible, at least during the remote sensor overpass. Indeed, energy balance approaches estimate daily actual ET from the instantaneous value at the overpass time using a daily integration method. The paper first verifies this latter assumption using field data acquired by a flux tower on a whole phenological period. Then, the actual ET values measured by eddy covariance tower were analyzed together with water potential measured using a Scholander chamber; the analysis highlights that, under water stress conditions, daily actual ET is inversely linearly related with water potential. These results suggest the possibility to use remote sensing-based ET as support for irrigation management at plot scale.

Soil humidity plays a key-role in hydrological and agricultural processes. In the rainfall-runoff processes the knowledge of its spatial distribution is fundamental to accurately model these phenomena. Furthermore in agronomy and agricultural sciences, assessing the water content of the root zone is required in order to optimize the plant productivity and to improve the irrigation systems management. Despite the importance of this variable the in situ measurements techniques based on Time Domain Reflectometry (TDR) or on the standard thermo-gravimetric methods, are neither cost-effective nor representative of its spatial and temporal variability. Indirect estimations via Earth Observation (EO) images include the triangle method, which shows that Land Surface Temperature (LST) is prevalently controlled by surface and root zone humidity in bare and vegetated soils respectively. The effects of pre-processing techniques correcting for altimetry and seasonality are analyzed by means of shortwave and longwave airborne images acquired on a vineyard during a whole phenological period. The paper also discusses the advantages induced by replacing the absolute temperatures with relative values, that were obtained subtracting the temperatures measured by micrometeorological station or the surface temperature of high thermal inertia surfaces (as small irrigation reservoir) chosen as reference values. The validation with in situ data also highlights that a higher spatial resolution not necessarily imply a higher accuracy.

Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.

During last two decades the increasing availability of remotely sensed acquisitions in the thermal infrared part of the spectrum has encouraged hydrologist community to develop models and methodologies based on these kind of data. The aim of this paper is to compare three methods developed to assess the actual evapotranspiration spatial distribution by means of remote sensing data. The comparison was focused on the differences between the "single" (SEBAL) and "two" source (TSEB) surface energy balance approaches and the S-SEBI semi-empirical method. The first assumes a semiempirical internal calibration for the sensible heat flux assessment; the second uses a physically based approach in order to assess separately the soil and vegetation fluxes. Finally, the last one is based on the correlation between albedo and surface temperature for evaporative fraction estimations. The models were applied using 7 high resolution images, collected by an airborne platform between June and October 2008, approximately every 3 weeks. The acquired data include multi-spectral images (red, green and near infrared) and thermal infrared images for surface temperature estimation. The study area, located in the south-west cost of Sicily (Italy), is characterised by the presence of typical Mediterranean cultivations: olive, vineyard and citrus. Due to irrigation supplies and rainfall events, the water availability for the crops varies in time and this allowed to perform the comparison in a wide range of the modelled variables. Additionally, the availability of high spatial resolution images allowed the testing of the models performances at field scale despite the high vegetation fragmentation of the study area. The comparison of models performance highlights a good agreements of model estimations, analyzed by means of MAD (Mean Absolute Differences) and MAPD (Mean Absolute Percent Differences) indices, especially in terms of study area averaged fluxes. The analysis in correspondence of various crop fields highlights higher differences for low vegetation coverage and for scarce water availability.

Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using Surface Energy Balance and Agro-Hydrological models. Both modelling approaches require remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS), near-infrared (NIR) and thermal (TIR) observations to solve the surface energy balance equation. To this end two different schemes have been tested: the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately, and the widely used one-source SEBAL model, where soil and vegetation are considered as a sole source. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared with the results of the Agro-Hydrological model SWAP, applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. In this latter model, remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. In the comparison presented here, actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference. Considering that the study area is characterized by typical Mediterranean sparse vegetation, i.e. olive, citrus and vineyards, we focused the attention on the main conceptual differences between SEBAL and TSEB. Airborne hyperspectral data acquired during a NERC campaign in 2005 have been used. The results of the investigation evidenced that the remote sensing two-sources approach used in TSEB model describes turbulent and radiative surface fluxes in a more realistic way than the one-source approach.