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[1] The surface energy fluxes and related evapotranspiration processes across the Indus Basin were estimated for the hydrological year 2007 using satellite measurements. The new ETLook remote sensing model (version 1) infers information on actual Evaporation (E) and actual Transpiration (T) from combined optical and passive microwave sensors, which can observe the land-surface even under persistent overcast conditions. A two-layer Penman-Monteith equation was applied for quantifying soil and canopy evaporation. The novelty of the paper is the computation of E and T across a vast area (116.2 million ha) by using public domain microwave data that can be applied under all weather conditions, and for which no advanced input data are required. The average net radiation for the basin was estimated as being 112 Wm À2 . The basin average sensible, latent and soil heat fluxes were estimated to be 80, 32, and 0 Wm ; RE ¼ 6.5% for annual ET). The water balance for all irrigated areas together as one total system in Pakistan and India (26.02 million ha) show a total ET value that is congruent with the ET value from the ETLook surface energy balance computations. An unpublished validation of the same ETLook model for 23 jurisdictional areas covering the entire Australian continent showed satisfactory results given the quality of the watershed data and the diverging physiographic and climatic conditions (R 2 ¼ 0.70; RMSE ¼ 0.31 mmd À1 ; RE ¼ -2.8% for annual ET). Eight day values of latent heat fluxes in Heibei (China) showed a good resemblance (R 2 ¼ 0.92; RMSE ¼ 0.04 mm d À1 ; RE ¼ 9.5% for annual ET). It is concluded that ETLook is a novel model that can be operationalized further-especially after improving the preprocessing of spaceborne soil moisture data. This preprocessing includes (1) downscaling of topsoil moisture from 25 to 1 km pixels, and (2) translation of topsoil moisture into subsoil moisture values.

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Abstract. In order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis.

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

A knowledge of the area‐averaged latent heat flux 〈λE〉 is necessary to validate large‐scale model predictions of heat fluxes over heterogeneous land surfaces. This paper describes different procedures to obtain 〈λE〉 as a weighted average of ground‐based observations. The weighting coefficients are obtained from remote sensing measurements. The remote sensing data used in this study consist of a Landsat thematic mapper image of the European Field Experiment in a Desertification‐Threatened Area (EFEDA) grid box in central Spain, acquired on June 12, 1991. A newly developed remote sensing algorithm, the surface energy balance for land algorithm (SEBAL), solves the energy budget on a pixel‐by‐pixel basis. From the resulting frequency distribution of the latent heat flux, the area‐averaged latent heat flux was calculated as 〈λE〉 = 164 W m−2. This method was validated with field measurements of latent heat flux, sensible heat flux, and soil moisture. In general, the SEBAL‐derived output compared well with field measurements. Two other methods for retrieval of weighting coefficients were tested against SEBAL. The second method combines satellite images of surface temperature, surface albedo, and normalized difference vegetation index (NDVI) into an index on a pixel‐by‐pixel basis. After inclusion of ground‐based measurements of the latent heat flux, a linear relationship between the index and the latent heat flux was established. This relationship was used to map the latent heat flux on a pixel‐by‐pixel basis, resulting in 〈λE〉 = 194 W m−2. The third method makes use of a supervised classification of the thematic mapper image into eight land use classes. An average latent heat flux was assigned to each class by using field measurements of the latent heat flux. According to the percentage of occurrence of each class in the image, 〈λE〉 was calculated as 110 W m−2. A weighting scheme was produced to make an estimation of 〈λE〉 possible from in situ observations. The weighting scheme contained a multiplication factor for each measurement site in order to compensate for the relative contribution of that site to 〈λE〉. It was shown that 〈λE〉 derived as the arithmetic mean of 13 individual in situ observations leads to a difference of 34% (〈λE〉 = 104 W m−2), which emphasizes the need for improved weighting procedures.