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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 highly 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 need 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 highlight the dependence of wind speed just above the soil/substrate to leaf area index and canopy height over the typical canopy properties range of these agricultural area. It is found that differences in wind just above surface among the models is most significant under sparse and medium fractional cover conditions (20–60%). The TSEB model heat flux estimates are compared with micrometeorological 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 height (≈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 describing the roughness of the underlying vegetative surface. This parameter is not directly obtainable using remote sensing, hence this study suggests that the Goudriaan formulation for landscape applications is most suitable when detailed site-specific information regarding canopy architecture is unavailable.

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.

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.

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.

Estimation of evapotranspiration (ET) in Sicilian vineyard is an emerging issue since these agricultural systems are more and more converted from rainfed to irrigated conditions, with significant impacts on the management of the scarce water resources of the region. The choice of the most appropriate methodology for assessing water use in these systems is still an issue of debating, due to the complexity of canopy and root systems and for their high spatial fragmentation. In vineyards, quality and quantity of the final product are dependent on the controlled stress conditions to be set trough irrigation. This paper reports an application of the well-known Penman-Monteith approach, applied in a distributed way, using high resolution remote sensing data to map the potential evapotranspiration (ET p ). In 2008 a series of airborne multispectral images were acquired on "Tenute Rapitala", a wine farm located in the northwest of Sicily. Five airborne remote sensing scenes were collected using a SKY ARROW 351 650 TC/TCNS aircraft, at a height of about 1000 m a.g.l.. The acquisitions were performed encompassing a whole phenological period, period between June and September 2008 (approximately every three weeks). The platform had on board a multi-spectral camera with 3 spectral bands at green (G, 530-570 nm), red (R, 650-690 nm) and near infrared (NIR, 767-832 nm) wavelengths, and a thermal camera with a broad band in the range 7.5-13 µm. The nominal pixel resolution was approximately 0.7 m for VIS/NIR acquisitions, and 1.7 m for the thermal-IR data. Field data were acquired simultaneously to airborne acquisitions. These data include spectral reflectances in VIS-NIR-SWIR (shortwave infrared), leaf area index (LAI), soil moisture at different depths (both in row and below plants). Moreover, meteo variables were measured by a standard weather station whereas fluxes were measured by means of an Eddy correlation tower located within the field. The VIS-NIR bands were atmospherically corrected and calibrated in order to calculate albedo, NDVI and LAI, which represented the distributed inputs of the Penman-Monteith algorithm. Moreover a sensitivity analysis has been carried out on input parameters (such as albedo). A sensitivity analysis was carried out to highlight the variability of outputs (such as ET p ) on the accuracy in the parameters assessment obtainable using high spatial resolution airborne images. Scale effects have been also investigated by means of an artificial degradation of images spatial resolution. Finally the relationship between stress factor evaluated as the ratio between actual and reference ET and pre-dawn leaf water potential has been also investigated.

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 (

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.

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.

Daily high spatial resolution assessment of actual evapotranspiration is essential for water management and crop water requirement estimation under stress conditions. The application of energy balance models usually requires satellite observations of radiometric surface temperat ure with high geometrical and temporal resolutions. By now, however, high spatial resolution (~ 100 m) is available with low time fre quency (approximately every two weeks); at the opposite daily acquisition are characterised by poor spatial resolution. The analysis of vegetation index (VI) and land surface temperature (LST) spatial relationship, shows in substance a scale invariant behaviour [1] ; this consideration allows the application of spatial sharpening algorithms of thermal data, by means of a combination of high spatial resolution data in VIS/NIR range with high temporal acquisition on TIR. In this paper, a sharpening algorithm was applied using the thermal bands of MODIS ( MOderate resolution Imaging Spectroradiometer ) and vegetation indices derived by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer ) sensor; the choice of this sensors is justified by the simultaneous acquisition time. The results of this sharpening process was firstly compared against LST estimation (at the same spatial resolution) by means of the ASTER simultaneous data; then the derived high spatial resolution LST distribution was used in order to investigate the effect of the disaggregation on the outputs of surface energy balance models. The above described application was performed on a Sicilian study area. Keywords: land surface temperature, disaggregation, actual ev apotranspiration, surface energy balance models.