Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The 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. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

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Water requirements and footprint of a super intensive olive grove under Mediterranean climate

Abstract The water footprint of a product can be described as the volume of freshwater used to produce it, associated to a geographic and temporal resolution. For crops, the water footprint relates crop water requirements and yield. The components of water footprint, blue, green and grey water footprints, refer to the volumes of respectively, surface and groundwater, rainfall, and water required to assimilate pollution, used to produce the crop yield. The global standard for crop water footprint assessment relies on evapotranspiration models to estimate green and blue water evapotranspiration. This approach has been used in the present study to estimate the water footprint of a very high density drip irrigated olive grove and further compared with data obtained from evapotranspiration measurements or from its components: the eddy covariance method to quantify latent heat flux, a heat dissipation sap flow technique to determine transpiration and microlysimeters to evaluate soil evaporation. The eddy covariance technique was used for short periods in 2011 and 2012, while sap flow measurements were performed continuously, hence allowing the extension of the data series. Measurements of evapotranspiration with the eddy covariance method provided an average close to 3.4 mm d-1 (2011) and 2.5 mm d-1 (2012). The ratio of evapotranspiration to reference evapotranspiration approached 0.6 and 0.4 for the respective periods. The water footprint of the olive crop under study, calculated with field data, was higher than the water footprint simulated using the global standard assessment and was lower than that reported in literature for olives. Lower values are probably related to differences in cultural practices, e.g., the density of plantation, harvesting techniques and irrigation management. The irrigated high-density olive grove under study had a high yield, which compensates for high water consumption, thus leading to a water footprint lower than the ones of rainfed or less dense groves. Other differences may relate to the procedures used to determine evapotranspiration

Comparing estimates of actual evapotranspiration from satellites, hydrological models, and field data: a case study from Western Turkey

Evapotranspiration / Estimation / Remote sensing / Satellite surveys / Field tests / Measurement / Productivity / Crops / Water requirements / Water balance / Irrigation management / River basins / Hydrology / Models / Turkey / Gediz River

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Quantifying the regional water footprint of biofuel production by incorporating hydrologic modeling

A spatially explicit life cycle water analysis framework is proposed, in which a standardized water footprint methodology is coupled with hydrologic modeling to assess blue water, green water (rainfall), and agricultural grey water discharge in the production of biofuel feedstock at county-level resolution. Grey water is simulated via SWAT, a watershed model. Evapotranspiration (ET) estimates generated with the Penman-Monteith equation and crop parameters were verified by using remote sensing results, a satellite-imagery-derived data set, and other field measurements. Crop irrigation survey data are used to corroborate the estimate of irrigation ET. An application of the concept is presented in a case study for corn-stover-based ethanol grown in Iowa (United States) within the Upper Mississippi River basin. Results show vast spatial variations in the water footprint of stover ethanol from county to county. Producing 1 L of ethanol from corn stover growing in the Iowa counties studied requires from 4.6 to 13.1 L of blue water (with an average of 5.4 L), a majority (86%) of which is consumed in the biorefinery. The county-level green water (rainfall) footprint ranges from 760 to 1000 L L-1. The grey water footprint varies considerably, ranging from 44 to 1579 L, a 35-fold difference, with a county average of 518 L. This framework can be a useful tool for watershed-or county-level biofuel sustainability metric analysis to address the heterogeneity of the water footprint for biofuels

Mapping cropland in smallholder-Dominated Savannas: Integrating Remote Sensing Techniques

Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We present summary results for this methodology applied to a land cover classification of Zambia’s Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%