Accurate assessment of water use is an important issue in a globally changing climate and environment, where water is
becoming a scarce but essential resource. The concept ‘Water Footprint’ (WF) of a crop is defined as the volume of
water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. This
indicator provides valuable information for a global assessment of how water resources are used. Remote sensing (RS)
provides physically-based, worldwide, and consistent spatial information over space and time, and has been used in
hydrological applications in order to estimate relevant variables at different temporal and spatial scales. The paper
focuses on exploring and exploiting the potential of using RS techniques and data for WF assessment in agriculture.
Based on recent papers initiated in this research topic the investigation focuses on how variables needed in the
calculation of water footprint are obtained (based on non RS and on RS approaches), on identifying the inputs required
for estimating the WF of crops and whether it is feasible to integrate various RS approaches. The results of this study
demonstrate the usefulness of satellite data for water footprint assessment, which were obtained by the Remote Sensing
Working Group in the framework of the ESSEM COST Action ES1106, “Assessment of EUROpean AGRIculture
WATer use and trade under climate change” (EURO-AGRIWAT).
Satellite remote sensing techniques play an important role in crop identification, acreage and production estimation, disease and stress detection, and soil and water resources characterization because they provide spatially explicit information and access to remote locations. The main objective of the study is to highlight the potential of using remote sensing techniques in the research field of water management, especially for “water footprint” assessment. In this paper, several vegetation indices (NDVI, NDWI, etc) and biophysical variables (LAI, fAPAR) are key variables to potentially be estimated by remote sensing and used in water footprint studies. The combination of these input parameters brings several limitations regarding the discrepancies in temporal and spatial resolution and data availability, which are described and discussed in detail. MODIS, Landsat, SPOT Vegetation and Meteosat data were used in order to estimate evapotranspiration and vegetation indices. The results of this study show the usefulness of satellite data for water footprint assessment and were obtained by the Remote Sensing Working Group in the framework of the ESSEM COST Action ES1106, “Assessment of EUROpean AGRIculture WATer use and trade under climate change” (EUROAGRIWAT).
Remote sensing (RS) has long been a useful tool in global and regional applications. The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage. RS provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use. This paper presents an overview of the EU COST Action ES 1106 "Assessment of European agriculture water use and trade under climate change (EURO-AGRIWAT)", regarding the evaluation of the potential of remote sensing to improve the WF and Virtual Water Trade (VWT) assessment. The main objective is the analysis of the role of satellite data in the suitable models and indices concerned with the analysis of WF and VWT. The main tasks include: an inventory of the existing and near future satellite data records for several European regions that could be used for the WF and VWT assessment; the study of satellite data resolution requirements, in time and space; the analysis of the assimilation of satellite data into models for the determination of green and blue water use; conclusions and recommendations concerning the possibility to integrate remote sensing into WF and VWT accounting. The combination of RS data to assess the volume of irrigation applied, and the green and blue WF faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be studied.
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