skip to main content
10.1145/2064969.2064970acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

Uncertainty with the scaling-up of remotely sensed evapotranspiration estimation

Published:01 November 2011Publication History

ABSTRACT

In this paper, the uncertainty issues in evapotranspiration estimation using remotely sensed data are discussed. The processes governing the mass, energy, and momentum exchange across the land-atmosphere interface are nonlinear, because of the interdependence of the dominant variables and parameters. Spatial resolution of remote sensing data also has an impact on the spatial pattern of evapotranspiration captured, due to the spatial variation of the surface. In order to study the nonlinear and spatial variation problems, evapotranspiration was first estimated at 30 meter and then aggregated to coarser scales using two approaches. One is to serve as true value of evapotranspiration, while the other one is assumed the linear resulted value. Results show that factors do have a nonlinear impact on evapotranspiration, however, this impact becomes very close to linear at 120m. The impact also varies with scale, as well as on different land cover types.

References

  1. Allen, R., Tasumi, M., and Trezza, R. 2007. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)--Model, Journal of Irrigation and Drainage Engineering. 133(4), 380--394.Google ScholarGoogle ScholarCross RefCross Ref
  2. McCabe, M., and Wood, E. 2006. Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment. 105(4), 271--285.Google ScholarGoogle ScholarCross RefCross Ref
  3. Sellers, P., Randall, D., Collatz, G., Berry, J., Field, C., Dazlich, D., Zhang, C., Collelo, G., and Nounoua, L. 1996. A revised land surface parameterization (SiB2) for atmospheric GCMS, Part 1: Model formulation. Journal of Climate. 9, 676--705.Google ScholarGoogle ScholarCross RefCross Ref
  4. Hong, S., Hendrickx, J., and Borchers, B. 2009. Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale. Journal of Hydrology. 370, 122--138.Google ScholarGoogle ScholarCross RefCross Ref
  5. Tang, Q., and Lettenmaier, D. 2010. Use of satellite snow-cover data for streamflow prediction in the Feather River Basin, California. International Journal of Remote Sensing. 31, 3745--3762. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Makkeasorn, A., Chang, N., Beaman, M., Wyatt, C., and Slater, C. 2006. Soil moisture estimation in a semiarid watershed using RADARSAT-1 satellite imagery and genetic programming. Water Resources Research. 42, 1--15.Google ScholarGoogle ScholarCross RefCross Ref
  7. Verstraeten, W., Veroustraete, F., and Feyen, J. 2008. Assessment of Evapotranspiration and Soil Moisture Content across Different Scales of Observation. Sensors. 8, 70--117.Google ScholarGoogle ScholarCross RefCross Ref
  8. Bastiaanssen, W., 1998. Regionalization of surface flux densities and moisture indicators in composite terrain -- A remote sensing approach under clear skies in Mediterranean climates. Ph.D. thesis, Wageningen Agricultural University, The Netherlands, 273 pp.Google ScholarGoogle Scholar
  9. Mauser, W., and Schadlich, S. 1998. Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data. Journal of Hydrology. 212--213, 250--267.Google ScholarGoogle ScholarCross RefCross Ref
  10. Brunsell, N., Ham, J., and Owensby, C. 2008. Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment. Remote Sensing of Environment. 112, 2977--2987.Google ScholarGoogle ScholarCross RefCross Ref
  11. Brunsell, N., and Gillies, R. 2003. Length Scale Analysis of Surface Energy Fluxes Derived from Remote Sensing. Journal of Hydrometeorology. 4, 1212--1219.Google ScholarGoogle ScholarCross RefCross Ref
  12. Brunsell, N., Gillies, R., Lapenta, B., and Dembeck, S. 2003. Aggregation of remotely sensed vegetation and derived latent heat flux. 83rd AMS Annual Meeting, 9-13 February, Long Beach, CA.Google ScholarGoogle Scholar
  13. Kustas, W., Li, F., Jackson, T., Prueger, J., MacPherson, J., and Wholde, M. 2004. Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment. 92, 525--547.Google ScholarGoogle ScholarCross RefCross Ref
  14. Kustas, W., and Norman, J. 2000. Evaluating the effects of subpixel heterogeneity on pixel average fluxes. Remote Sensing of Environment. 74, 327--342.Google ScholarGoogle ScholarCross RefCross Ref
  15. Moran, S., Humes, K., Pinter, P. 1997. The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes. Journal of Hydrology. 190, 337--362.Google ScholarGoogle ScholarCross RefCross Ref
  16. Su, Z., Pelgrum, H., and Menenti, M. 1999. Aggregation effects of surface heterogeneity in land surface processes. Hydrology and Earth System Sciences. 3(4), 549--563.Google ScholarGoogle ScholarCross RefCross Ref
  17. Wu, J. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology. 19, 125--138.Google ScholarGoogle ScholarCross RefCross Ref
  18. Hong, S., Hendrickx, J., and Borchers, B. 2009. Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale. Journal of Hydrology. 370, 122--138.Google ScholarGoogle ScholarCross RefCross Ref
  19. Hay, G., Niemann, K., and Goodenough, D. 1997. Spatial thresholds, image-objects, and upscaling: a multiscale evaluation. Remote Sensing of Environment. 62, 1--19.Google ScholarGoogle ScholarCross RefCross Ref
  20. Famiglietti, J., and Wood, E. 1991. Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models. Surveys in Geophysics. 12, 179--204.Google ScholarGoogle ScholarCross RefCross Ref
  21. Koster, R., and Milly, P. 1997. The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. Journal of Climate. 10, 1578--1591.Google ScholarGoogle ScholarCross RefCross Ref
  22. Bian, L. 1997. Multiscale nature of spatial data in scaling up environmental models. In Scale in Remote Sensing and GIS, D. A. Quattrochi and M. Goodchild (Eds), pp. 13--26 (Boca Raton, FL: Lewis).Google ScholarGoogle Scholar
  23. Van Rompaey, G. Govers and Baudet, M. 1999. A strategy for controlling error of distributed environmental models by aggregation, International Journal of Geographical Information Science. 13, 577--590.Google ScholarGoogle ScholarCross RefCross Ref
  24. Carmel, Y. 2004. Controlling data uncertainty via aggregation in remotely sensed data, IEEE Transaction on Geoscience and Remote Sensing Letters 1, 39--41.Google ScholarGoogle ScholarCross RefCross Ref
  25. Shuttleworth, W., 1991. The modllion concept. Review of Geophysics. 29, 585--606.Google ScholarGoogle ScholarCross RefCross Ref
  26. Li, B., and Avissar, R. 1994. The impact of spatial variability of land-surface characteristics on land-surface fluxes. Journal of Climate. 7, 527--537.Google ScholarGoogle ScholarCross RefCross Ref
  27. Dixon, R. 2000. Climatology of the Freeman Ranch, Hays County, TEXAS. Freeman Ranch Publication Series No. 3. Southwest Texas Staate University Press, San Marcos, TX, US.Google ScholarGoogle Scholar
  28. Carson, D. 2000. Soil of the Freeman Ranch, Hays County, Texas. Freeman Ranch Publication Series No. 4-2000. San Marcos, TX: Southwest Texas State University Press.Google ScholarGoogle Scholar
  29. Roy, D. P., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M. C., Loveland, T. R., Vermote, E. F., Zhang, C. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ Composited Mosaics of the Conterminous United States, Remote Sensing of Environment. 114: 35--49.Google ScholarGoogle ScholarCross RefCross Ref
  30. Brown, K., and Rosenberg, N. 1973. A resistance model to predict evapotranspiration and its application to a sugar beet field. Agronomy Journal. 65, 341--347.Google ScholarGoogle ScholarCross RefCross Ref
  31. Allen, R. G., Tasumi, M., Morse, A., and Trezza, R. 2005. A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrigation and Drainage Systems. 19, 251--268.Google ScholarGoogle ScholarCross RefCross Ref
  32. Humes, K., Hardy, R., and Kustas, W. 2000 Spatial Patterns in Surface Energy Balance Components Derived from Remotely Sensed Data, The Professional Geographer. 52, 272--288.Google ScholarGoogle ScholarCross RefCross Ref
  33. Lagouarde, J., Jacob, F., Gu, X. F., Olioso, A., Bonnefond, J., Kerr, Y., Mcaneney, K. J., and Irvine, M. 2002. Spazialization of sensible heat flux over a heterogeneous landscape. Agronomie. 22, 627--633.Google ScholarGoogle ScholarCross RefCross Ref
  34. Bastiaanssen, W., Noordman, E., Pelgrum, H., Davids, G., Allen, R. G. 2005. SEBAL for spatially distributed ET under actual management and growing conditions. Journal of Irrigation and Drainage Engineering. 131, 85--93.Google ScholarGoogle ScholarCross RefCross Ref
  35. Tasumi, M., Trezza, R., Allen, R., and Wright, J. 2005. Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid US. Irrigation and Drainage Systems. 19, 355--376.Google ScholarGoogle ScholarCross RefCross Ref
  36. Hendrickx, J., and Hong, S. 2005. Mapping sensible and latent heat fluxes in arid areas using optical imagery. Proceedings of International Society for Optical Engineering, SPIE 5811, 138--146.Google ScholarGoogle Scholar
  37. Namayanga, L. 2002. Estimating terrestrial carbon sequestered in above ground woody biomass from remotely sensed data. International Institute for Geo-information Science and Earth Observation, Netherlands: 10--12.Google ScholarGoogle Scholar
  38. Zwart, S., Bastiaanssen, W. 2007. SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems. Agricultural Water Management. 89, 287--296.Google ScholarGoogle ScholarCross RefCross Ref
  39. Alexandridis, T., Cayrol, P., Moreno, J., Cherif, I., Chemin, Y., de la Vega, C., Escudero, R., Galanis, G., Tsakoumis, G., Silleos, N., Stavrinos, E., Zalidis, G., and Duthil, P. 2009. Agricultural Water Consumption Services for Southern European Basins. Poster presented at the 5th World Water Forum "Bridging Divides for Water", Istanbul, Turkey, March 16--22, 2009.Google ScholarGoogle Scholar
  40. Ferguson, C., Sheffield, J., Wood, E., and Gao, H. 2010. Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA, International Journal of Remote Sensing. 31, 14, 3821--3865. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Uncertainty with the scaling-up of remotely sensed evapotranspiration estimation

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              QUeST '11: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
              November 2011
              42 pages
              ISBN:9781450310376
              DOI:10.1145/2064969

              Copyright © 2011 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 1 November 2011

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article
            • Article Metrics

              • Downloads (Last 12 months)2
              • Downloads (Last 6 weeks)0

              Other Metrics

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader