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.

[1]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[2]  P. Q. Hung,et al.  Globalisation of water resources : international virtual water flows in relation to crop trade , 2005 .

[3]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[4]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[5]  Benjamin F. Zaitchik,et al.  Evaluation of the Global Land Data Assimilation System using global river discharge data and a source‐to‐sink routing scheme , 2010 .

[6]  Yong Zhou,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[7]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[8]  Phillip A. Arkin,et al.  An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data , 2009 .

[9]  Thomas Heinemann,et al.  THE EUMETSAT MULTI-SENSOR PRECIPITATION ESTIMATE ( MPE ) , 2004 .

[10]  Christelle Vancutsem,et al.  GlobCover: ESA service for global land cover from MERIS , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Massimo Menenti,et al.  Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements , 2003 .

[12]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[13]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[14]  Matthew F. McCabe,et al.  Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA) , 2005 .

[15]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[16]  Arjen Y. Hoekstra,et al.  The global component of freshwater demand and supply: an assessment of virtual water flows between nations as a result of trade in agricultural and industrial products , 2008 .

[17]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[18]  A. Hoekstra,et al.  A global and high-resolution assessment of the green, blue and grey water footprint of wheat , 2010 .

[19]  Massimo Menenti,et al.  S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance , 2000 .

[20]  A. Hoekstra,et al.  Water footprints of nations: Water use by people as a function of their consumption pattern , 2006 .

[21]  T. Gaiser,et al.  Integrated assessment of groundwater resources in the Oueme Basin, Benin, West Africa , 2009 .

[22]  A. Hoekstra,et al.  Globalisation of water resources: Global virtual water flows in relation to international crop trade , 2005 .

[23]  Xavier Blaes,et al.  Efficiency of crop identification based on optical and SAR image time series , 2005 .

[24]  Arjen Ysbert Hoekstra,et al.  Water Footprint Manual : State of the Art 2009 , 2009 .

[25]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[26]  N. R. Rao,et al.  RETRACTED ARTICLE: Development of a crop‐specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery , 2008 .

[27]  Nicolas Ghilain,et al.  43. Towards a Continuous Monitoring of Evapotranspiration Based on MSG Data , 2007 .

[28]  Yudong Tian,et al.  Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications , 2007 .

[29]  John Wahr,et al.  Monitoring the water balance of Lake Victoria, East Africa, from space. , 2009 .

[30]  N. Batjes ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (ver. 1.2) , 2006 .

[31]  M. Mccabe,et al.  Closing the terrestrial water budget from satellite remote sensing , 2009 .

[32]  Z. Su The Surface Energy Balance System ( SEBS ) for estimation of turbulent heat fluxes , 2002 .

[33]  H. Farah,et al.  Estimation of regional evaporation under different weather conditions from satellite and meteorological data: a case study in the Naivasha Basin, Kenya , 2001 .

[34]  Chen Zhongxin,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008 .

[35]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[36]  A. Hoekstra,et al.  Globalization of Water: Sharing the Planet's Freshwater Resources , 2008 .

[37]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[38]  Kuolin Hsu,et al.  Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation , 1999 .

[39]  J. A. Allan,et al.  Virtual Water: A Strategic Resource Global Solutions to Regional Deficits , 1998 .

[40]  L. S. Pereira,et al.  Revised FAO Procedures for Calculating Evapotranspiration: Irrigation and Drainage Paper No. 56 with Testing in Idaho , 2001 .

[41]  Paul Berrisford,et al.  Towards a climate data assimilation system: status update of ERA-interim , 2008 .

[42]  L. Gómez-Chova,et al.  Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land , 2008 .

[43]  Qile Zhao,et al.  DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation , 2010 .

[44]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[45]  Rana Chatterjee,et al.  Estimation of replenishable groundwater resources of India and their status of utilization , 2009 .

[46]  Hong Yang,et al.  Global consumptive water use for crop production: The importance of green water and virtual water , 2009 .

[47]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[48]  Pamela L. Nagler,et al.  Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration , 2007 .

[49]  Dafang Zhuang,et al.  Advances in Multi-Sensor Data Fusion: Algorithms and Applications , 2009, Sensors.

[50]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .

[51]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[52]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[53]  Albert Olioso,et al.  Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain) , 2009 .

[54]  Hong Yang,et al.  Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China , 2007, Irrigation Science.

[55]  Bart Nijssen,et al.  Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–93 , 2001 .

[56]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[57]  M. Romaguera,et al.  Land surface temperature retrieval from MSG1-SEVIRI data , 2004 .

[58]  Assefa M. Melesse,et al.  Spatially distributed storm runoff depth estimation using Landsat images and GIS , 2002 .

[59]  Klaus Scipal,et al.  Large-scale soil moisture mapping in western Africa using the ERS scatterometer , 2000, IEEE Trans. Geosci. Remote. Sens..

[60]  Albert Olioso,et al.  Application of a simple algorithm to estimate daily evapotranspiration from NOAA–AVHRR images for the Iberian Peninsula , 2007 .

[61]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology , 2007 .

[62]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[63]  Arjen Ysbert Hoekstra,et al.  Globalization of water , 2008 .

[64]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[65]  Petra Döll,et al.  Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation , 2010 .

[66]  Petra Döll,et al.  Development and validation of the global map of irrigation areas , 2005 .