[1] The irrigated Indus Basin in Pakistan has insufficient water resources to supply all its stakeholders. Information on evaporative depletion across the Basin is an important requirement if the water resources are to be managed efficiently. This paper presents the Surface Energy Balance Algorithm for Land (SEBAL) method used to compute actual evapotranspiration for large areas based on public domain National Oceanic and Atmospheric Administration (NOAA) satellite data. Computational procedures for retrieving actual evapotranspiration from satellites have been developed over the last 20 years. The current work is among the first applications used to estimate actual evapotranspiration on an annual scale across a vast river basin system with a minimum of ground data. Only sunshine duration and wind speed are required as input data for the remote sensing flux algorithm. The results were validated in the Indus Basin by comparing results from a field-scale transient moisture flow model, in situ Bowen ratio measurements, and residual water balance analyses for an area of 3 million ha. The accuracy of assessing time-integrated actual annual evapotranspiration varied from 0% to 10% on a field scale to 5% at the regional level. Spatiotemporal information on actual evapotranspiration helps to evaluate water distribution and water use between large irrigation project areas. Wide variations in evaporative depletion between project areas and crop types were found. Satellite-based measurements can provide such information and avoid the need to rely on field databases.

Despite being necessary for effective water management, the assessment of an irrigation system requires a large amount of input data for the estimation of related parameters and indicators, which are seldom measured in a regular and reliable manner. In this work, spatially distributed surface energy balance fluxes and geographical information systems analysis of multiple groundwater parameters were used to estimate water availability, supply, and demand, in order to calculate water-accounting indicators. This methodology was used to evaluate the performance of an irrigation system in the Pinios river basin (Greece) at two selected years of high and low water availability. Time series of archived satellite images and groundwater measurements have been used for past years to support comparative analyses, due to the limited availability of actual water measurements. The resulting maps from the proposed methodology show that the performance of the irrigation system varied across space and time due to differences in its characteristics and changes in its operation, driven by fluctuation of water availability and the response of stakeholders to water depletion. Irrigation districts with unsustainable water management were identified and, together with those with slow and/or limited groundwater recharge, were brought to the attention of water managers. The observed differences in the system operation between the wet and dry years were attributed not only to the hydrological conditions of each year, but also to the changing behaviour of farmers and the improvement actions of the water managers

Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use.

The surface energy balance algorithm for land (SEBAL) has been successfully applied to estimate evapotranspiration (ET) and yield at different spatial scales. However, ET and yield patterns have never been investigated under highly heterogeneous conditions. We applied SEBAL in a salt-affected and water-stressed maize field located at the margin of the Venice Lagoon, Italy, using Landsat images. SEBAL results were compared with estimates of evapotranspiration by the Food and Agriculture Organization (FAO) method (ET c ) and three-dimensional soil-plant simulations. The biomass production routine in SEBAL was then tested using spatially distributed crop yield measurements and the outcomes of a soil-plant numerical model. The results show good agreement between SEBAL evapotranspiration and ET c . Instantaneous ET simulated by SEBAL is also consistent with the soil-plant model results (R 2 = 0.7047 for 2011 and R 2 = 0.6689 for 2012). Conversely, yield predictions (6.4 t/ha in 2011 and 3.47 t/ha in 2012) are in good agreement with observations (8.64 t/ha and 3.86 t/ha, respectively) only in 2012 and the comparison with soil-plant simulations (8.69 t/ha and 5.49 t/ha) is poor. In general, SEBAL underestimates land productivity in contrast to the soil-plant model that overestimates yield in dry years. SEBAL provides accurate predictions under stress conditions due to the fact that it does not require knowledge of the soil/root characteristics.crop-specific relations between ET and yield [6,7], it provides a measure of both water demand and land productivity. Yield is thus the ultimate indicator to describe crop response to water resource management [8] and the quantification of field scale ET is fundamental for managers to maximize land productivity while minimizing water losses [9][10][11]. Crop yield is also a key element for rural development and national food security. For these reasons, forecasting crop yield a few months before harvest can be of paramount importance for timely initiation of the food trade, securing national demand, and organizing food transport within countries [6,7,12].The yield of many agricultural crops is generally predicted from the amount of water used by the crop, i.e., ET [6,7]. Traditionally, ET from fields has been estimated according to the Food and Agriculture Organization (FAO) method [13], i.e., by multiplying a weather-based reference ET 0 by a crop coefficient (K c ) determined according to crop type and growth stage. However, the suitability of the idealized K c coefficient to describe the actual vegetative and growing conditions, especially in water limited areas, was questioned by many authors [14]. In addition, it is difficult to predict the correct growth stage dates for large populations of crops and fields [15].A viable alternative for mapping evaporation at field and regional scales is the use of satellite images that can provide an excellent tool to detect the spatial and temporal structure of ET [16]. Remote sensing (RS) is a reliable and cost-effective method to forecas...

Monitoring of water consumption in irrigation systems has become increasingly important for water managers in the actual trend of integrated water management. Low spatial resolution (LSR) satellite remote sensing has already proven the capacity of monitoring evapotranspiration (ETa) over large areas at high temporal frequencies, by which monitoring for large irrigation systems can be satisfied. However, smaller pixel size is still required for more local management while keeping the return period within a few days. High spatial resolution (HSR) satellite imagery is indeed available for calculation of ETa and has already been used in many studies. However, its practical return period is a major drawback to its implementation for monitoring irrigation systems. This paper is perusing into the use of genetic algorithms to assimilate parameters of an agrohydrological model called soil-water-air-plant for each of the pixels of HSR images contained into one single pixel of an LSR multitemporal image. The methodology developed and experimented here is trying to take advantage of the spatial content of HSR images and the temporal content of LSR images by fusing them by the process of data assimilation.