Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops
Abstract
:1. Introduction
2. State of the Art
2.1. Water Footprint of Crops
- coarse spatial resolution of the source data, mainly where extracted from statistical databases,
- coarse temporal resolution of the input data, which may imply the use of interpolation techniques,
- assumption of ideal conditions, e.g., optimum soil water conditions,
- outputs are static, i.e., they are given for particular periods of time.
2.2. Irrigation Map**
3. Methodology
3.1. Theory
3.2. Global EO Products
EO product | Source | Spatial coverage | Spatial resolution | Temporal resolution | Main input | Data availability |
---|---|---|---|---|---|---|
P | CMORPH | Global | 8 km at the equator | 30’ monthly | MW IR | 2002–present |
PERSIANN | Global | 0.25° | 6h | IR | 2000–present | |
MPE | Meteosat disk * | Met7:5 km at nadir | Met7: 30’ | MW IR | 2000–present | |
Met8:3 km at nadir | Met8: 15’ | |||||
Met9:3 km at nadir | Met9: 15’ | |||||
ET | MET | Meteosat disk * | 3 km at nadir | 30’ | Radiation fluxes | Pre-operational (available) |
LAI, FVC | ||||||
Climatic data | ||||||
MOD 16 | Global | 1 km | daily | Land cover | Pre-operational (not available) | |
LAI, FAPAR | ||||||
Climatic data | ||||||
S | GRACE | Global | 400 km | monthly | Gravity fields | 2002–present |
Q | GLDAS | Global | 1° | 3h monthly | Land cover | 1979–present |
LAI and soil par. | ||||||
Skin temperature | ||||||
Radiation fluxes | ||||||
Climatic data |
3.3. Estimation of Evapotranspiration from Remote Sensing Data
3.4. Land Use from Remote Sensing Data
3.5. Example
Country | Blue WF (Mm3/year) | Green WF (Mm3/year) |
---|---|---|
India | 81,335 | 44,025 |
China | 47,370 | 83,459 |
Pakistan | 27,733 | 12,083 |
Iran | 10,940 | 26,699 |
Egypt | 5,930 | 1,410 |
United States | 5,503 | 111,926 |
World | 203,744 | 760,301 |
4. Discussion
4.1. Uncertainty
4.2. Limitations
5. Conclusions
Acknowledgement
References
- Hoekstra, A.Y. Virtual water trade. In Proceedings of the International Expert Meeting on Virtual Water Trade, Value of Water Research Report Series No.12. IHE Delft, The Netherlands; 2003. [Google Scholar]
- Hoekstra, A.Y.; Chapagain, A.K. Globalization of Water. Sharing the Planet’s Freshwater Resources; Blackwell Publishing: Oxford, UK, 2008; pp. 1–208. [Google Scholar]
- Allan, J.A. Virtual water: A strategic resource global solutions to regional deficits. Ground Water 1998, 36, 545–546. [Google Scholar] [CrossRef]
- Hoekstra, A.Y.; Hung, P.Q. Globalisation of water resources: international virtual water flows in relation to crop trade. Global Environ. Change 2005, 15, 45–56. [Google Scholar] [CrossRef]
- Chapagain, A.K.; Hoekstra, A.Y. 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. Water Int. 2008, 33, 19–32. [Google Scholar] [CrossRef]
- Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Nino, F.; Weiss, M.; Samain, O.; Roujean, J.L.; Leroy, M. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION—Part 1: Principles of the algorithm. Remote Sens. Environ. 2007, 110, 275–286. [Google Scholar] [CrossRef] [Green Version]
- Wagner, W.; Scipal, K. Large-scale soil moisture map** in western Africa using the ERS scatterometer. IEEE Trans. Geosci. Remote Sens. 2000, 38, 1777–1782. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Romaguera, M. Land surface temperature retrieval from MSG1-SEVIRI data. Remote Sens. Environ. 2004, 92, 247–254. [Google Scholar] [CrossRef]
- Guanter, L.; Gomez-Chova, L.; Moreno, J. Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land. Remote Sens. Environ. 2008, 112, 2898–2913. [Google Scholar] [CrossRef]
- Bartholome, E.; Belward, A.S. GLC2000: A new approach to global land cover map** from Earth observation data. Int. J. Remote Sens. 2005, 26, 1959–1977. [Google Scholar] [CrossRef]
- Joyce, R.J.; Janowiak, J.E.; Arkin, P.A.; ** from MODIS: algorithms and early results. Remote Sens. Environ. 2002, 83, 287–302. [Google Scholar] [CrossRef]
- ESA. GLOBCOVER Products Description Manual v2; European Space Agency: Paris, France, 2008. [Google Scholar]
- Arino, O.; Gross, D.; Ranera, F.; Leroy, M.; Bicheron, P.; Brockman, C.; Defourny, P.; Vancutsem, C.; Achard, F.; Durieux, L.; Bourg, L.; Latham, J.; Di Gregorio, A.; Witt, R.; Herold, M.; Sambale, J.; Plummer, S. GlobCover: ESA service for Global Land Cover from MERIS. In IGARSS 2007; Barcelona, Spain, 2007; pp. 2412–2415. [Google Scholar]
- Zhang, M.W.; Zhou, Q.B.; Chen, Z.X.; Liu, J.; Zhou, Y.; Cai, C.F. Crop discrimination in Northern China with double crop** systems using Fourier analysis of time-series MODIS data. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 476–485. [Google Scholar]
- Blaes, X.; Vanhalle, L.; Defourny, P. Efficiency of crop identification based on optical and SAR image time series. Remote Sens. Environ. 2005, 96, 352–365. [Google Scholar] [CrossRef]
- Rao, N.R. Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery. Int. J. Remote Sens. 2008, 29, 131–144. [Google Scholar] [CrossRef]
- Ebert, E.E.; Janowiak, J.E.; Kidd, C. Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteorol. Soc. 2007, 88, 47–64. [Google Scholar] [CrossRef]
- Tian, Y.; Peters-Lidard, C.D.; Choudhury, B.J.; Garcia, M. Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeorol. 2007, 8, 1165–1183. [Google Scholar] [CrossRef]
- Sapiano, M.R.P.; Arkin, P.A. An intercomparison and validation of high-resolution satellite precipitation estimates with 3-Hourly gauge data. J. Hydrometeorol. 2009, 10, 149–166. [Google Scholar] [CrossRef]
- Kalma, J.D.; McVicar, T.R.; McCabe, M.F. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys. 2008, 29, 421–469. [Google Scholar] [CrossRef]
- Liu, X.; Ditmar, P.; Siemes, C.; Slobbe, D.C.; Revtova, E.A.; Klees, R.; Riva, R.; Zhao, Q. DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation. Geophys. J. Intl. 2010. [Google Scholar] [CrossRef]
- Zaitchik, B.F.; Rodell, M.; Olivera, F. Evaluation of the Global Land Data Assimilation System using global river discharge data and a source to sink routing scheme. Water Resour. Res. 2010. [Google Scholar] [CrossRef]
- Sheffield, J.; Ferguson, C.R.; Troy, T.J.; Wood, E.F.; McCabe, M.F. Closing the terrestrial water budget from satellite remote sensing. Geophys. Res. Lett. 2009, 36, 5. [Google Scholar] [CrossRef]
- Farah, H.O. Estimation of Regional Evaporation under Different Weather Conditions from Satellite and Meteorological Data: A Case Study in the Naivasha basin, Kenya. Ph.D. Thesis, University of Twente, Enschede, The Netherlands, 2001. [Google Scholar]
- Anderson, M.C.; Norman, J.M.; Mecikalski, J.R.; Otkin, J.A.; Kustas, W.P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J. Geophys. Res.-Atmos. 2007, 112, 13. [Google Scholar] [CrossRef]
- Dong, J.; Zhuang, D.F.; Huang, Y.H.; Fu, J.Y. Advances in multi-sensor data fusion: Algorithms and applications. Sensors 2009, 9, 7771–7784. [Google Scholar] [CrossRef] [PubMed]
- ECMWF. ERA-40: ECMWF 45-year reanalysis of the global atmosphere and surface conditions 1957–2002; ECMWF Newsletter 101; ECMWF: Reading, UK, 2004. [Google Scholar]
- ECMWF. Towards a climate data assimilation system: Status update of ERA-Interim; ECMWF Newsletter 115; ECMWF: Reading, UK, 2008. [Google Scholar]
- Barthel, R.; Sonneveld, B.; Gotzinger, J.; Keyzer, M.A.; Pande, S.; Printz, A.; Gaiser, T. Integrated assessment of groundwater resources in the Oueme basin, Benin, West Africa. Phys. Chem. Earth 2009, 34, 236–250. [Google Scholar] [CrossRef]
- Chatterjee, R.; Purohit, R.R. Estimation of replenishable groundwater resources of India and their status of utilization. Curr. Sci. 2009, 96, 1581–1591. [Google Scholar]
- Agbu, P.A.; James, M.E. The NOAA/NASA Pathfinder AVHRR Land Data Set User’s Manual; Goddard Distributed Active Archive Center, NASA, Goddard Space Flight Center: Greenbelt, MD, USA, 1994. [Google Scholar]
© 2010 by the authors; licensee MDPI, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Share and Cite
Romaguera, M.; Hoekstra, A.Y.; Su, Z.; Krol, M.S.; Salama, M.S. Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops. Remote Sens. 2010, 2, 1177-1196. https://doi.org/10.3390/rs2041177
Romaguera M, Hoekstra AY, Su Z, Krol MS, Salama MS. Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops. Remote Sensing. 2010; 2(4):1177-1196. https://doi.org/10.3390/rs2041177
Chicago/Turabian StyleRomaguera, Mireia, Arjen Y. Hoekstra, Zhongbo Su, Maarten S. Krol, and Mhd. Suhyb Salama. 2010. "Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops" Remote Sensing 2, no. 4: 1177-1196. https://doi.org/10.3390/rs2041177