Abstract. Thermal infrared (TIR) remote sensing of landsurface 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 subsurface 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 Correspondence to: M. C. Anderson (martha.anderson@ars.usda.gov) 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.

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[1] Thermal remote sensing methods for mapping evapotranspiration (ET) exploit the physical interconnection that exists between land-surface temperature (LST) and evaporative cooling, employing principles of surface energy balance (SEB). Unfortunately, while many applications in water resource management require ET information at daily and field spatial scales, current satellite-based thermal sensors are characterized by either low spatial resolution and high repeatability or by moderate/high spatial resolution and low frequency. Here we introduce a novel approach to ET mapping that fuses characteristics of both classes of sensors to provide optimal spatiotemporal coverage. In this approach, coarse resolution daily ET maps generated with a SEB model using geostationary satellite data are spatially disaggregated using daily MODIS (MODerate resolution Imaging Spectroradiometer) 1 km and biweekly Landsat LST imagery sharpened to 30 m. These ET fields are then fused to obtain daily ET maps at 30 m spatial resolution. The accuracy of the fused Landsat-MODIS daily ET maps was evaluated over Iowa using observations collected at eight flux towers sited in corn and soybean fields during the Soil Moisture Experiment of 2002, as well as in comparison with a Landsat-only retrieval. A significant improvement in ET accuracy (reducing errors from 0.75 to 0.58 mm d À1 on average) was obtained by fusing MODIS and Landsat data in comparison with the Landsat-only case, with most notable improvements when a rainfall event occurred between two successive Landsat acquisitions. The improvements are further evident at the seasonal timescale, where a 3% error is obtained using Landsat-MODIS fusion versus a 9% Landsat-only systematic underestimation.

Abstract. Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radiation method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied. In general, the proposed methodology clearly showed the added value of an intercomparison of different upscaling methods under scenarios that account for the uncertainty in eddy covariance flux measurements due to closure errors.

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Abstract:A correct identification of drought events over vegetated lands can be achieved by detecting those soil moisture conditions that are both unusually dry compared with the 'normal' state and causing severe water stress to the vegetation. In this paper, we propose a novel drought index that accounts for the mutual occurrence of these two conditions by means of a multiplicative approach of a water deficit factor and a dryness probability factor. The former quantifies the actual level of plant water stress, whereas the latter verifies that the current water deficit condition is unusual for the specific site and period. The methodology was tested over Europe between 1995 and 2012 using soil moisture maps simulated by Lisflood, a distributed hydrological precipitation-runoff model. The proposed drought severity index (DSI) demonstrates to be able to detect the main drought events observed over Europe in the last two decades, as well as to provide a reasonable estimation of both extension and magnitude of these events. It also displays an improved adaptability to the range of possible conditions encountered in the experiment as compared with currently available indices based on the sole magnitude or frequency. The results show that, for the analyzed period, the most extended drought events observed over Europe were the ones in Central Europe in

a b s t r a c tCorrect 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.