Remote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.
The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.