Advertisement

Evaluation of METRIC-derived ET fluxes over irrigated alfalfa crop in desert conditions using scintillometer measurements

  • K. A. Al-Gaadi
  • V. C. Patil
  • E. Tola
  • R. Madugundu
  • P. H. Gowda
Original Paper
  • 204 Downloads

Abstract

A field study on a 50-ha alfalfa (Medicago sativa L.) irrigated field was conducted to investigate the performance of the remote sensing (RS) based Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model in the estimation of evapotranspiration (ET) under the arid conditions of Saudi Arabia. The METRIC model performance was investigated by comparing the energy fluxes estimated by the model to the output of a surface layer scintillometer (SLS) system installed in the field, given the fact that the SLS is efficient in measuring sensible heat fluxes (H) over vegetative areas. Landsat-8 reflectance data were used as inputs for the METRIC model. Results of the study revealed that the HMETRIC data was strongly correlated with the HSLS data with an R 2 value of 0.74 (P > F = 0.0064) and a mean bias error (MBE) of 6.05 W m−2 (6 %). The METRIC model showed a good performance in estimating the hourly latent heat (LE) fluxes compared with SLS data with an R 2 value of 0.81 (P > F = 0.0023), an MBE of 24.46 W m−2 (8 %) and a Nash–Sutcliffe efficiency (NSE) of 0.91. Furthermore, the hourly ET was estimated with an MBE and an NSE of 0.036 mm h−1 (8 %) and 1.00, respectively. Compared to the SLS data, the METRIC model was found to generally provide an efficient and an accurate means of energy fluxes estimation; therefore, ET estimation over the studied irrigated alfalfa crop.

Keywords

Centre pivot system Evapotranspiration Landsat-8 data Alfalfa field Surface layer scintillometer 

Notes

Acknowledgments

This project was financially supported by King Saud University, Vice Deanship of Research Chairs. The assistance provided by the graduate students M.E. Abass, A.M. Zeyada, and A.G. Kayad in the accomplishment of the field research work was quite valuable. The unstinted cooperation and support extended by Mr. Jack King, Mr. Alan King, and their team in carrying out the research are gratefully acknowledged.

References

  1. Allen GR, Tasumi M, Morse A, Kramber WJ, Bastiaanssen WGM (2005) Computing and mapping of evapotranspiration p 73–90 In: Aswathanarayana U (Eds) Advances in water science methodologies. AA Balkema Publishers Leiden The Netherlands. ISBN0–203–08684-8Google Scholar
  2. Allen RG, Burnett B, Kramber W, Huntington J, Kjaersgaard J, Kilic A, Kelly C, Trezza R (2013) Automated calibration of the METRIC-Landsat evapotranspiration process. J Am Water Resour Assoc 49(3):563–576CrossRefGoogle Scholar
  3. Allen RG, Tasumi M, Morse A, Trezza R, Wright J, Bastiaanssen WGM, Kramber W, Lorite I, Robison C (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—applications. J Irrig Drain Eng 133(4):395–406CrossRefGoogle Scholar
  4. Andreas E (1992) Uncertainty in a path-averaged measurement of the friction velocity u*. J Appl Meteorol Climatol 31:1312–1321CrossRefGoogle Scholar
  5. Andreas E (2012) Two experiments on using a scintillometer to infer the surface fluxes of momentum and sensible heat. J Appl Meteorol Climatol 51:1685–1701 DOI:101175/JAMC-D-11-02481Google Scholar
  6. ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation. In: Allen RG, Walter IA, Elliot RL, Howell TA, Itenfisu D, Jensen ME, Snyder RL (Eds) ASCE standardization of Reference Evapotranspiration Task Committee Final Rep Reston Va: pp 70Google Scholar
  7. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) Remote sensing surface energy balance algorithm for land (SEBAL):1 formulation. J Hydrol 212-213(1–4):198–212CrossRefGoogle Scholar
  8. Beyrich F, Bange J, Hartogensis OK, Raasch S, Braam M, Dinther Dvan, Graf D, Kesteren Bvan, Kroonenberg AC van den, Maronga B, Martin S, Moene AF (2012) Towards a validation of scintillometer measurements:the LITFASS-2009 experiment. Boundary-Layer Meteorol 144:83–112Google Scholar
  9. Brest CL, Goward SN (1987) Driving surface albedo measurements from narrow band satellite data. Int J Remote Sens 8:351–367CrossRefGoogle Scholar
  10. Brohan P, Kennedy JJ, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J Geophys Res 111:D12106. doi:101029/2005JD006548Google Scholar
  11. Brunsell NA, Gillies R (2002) Incorporating surface emissivity into a thermal atmospheric correction. Photogramm Eng Remote Sens 68(12):1263–1269Google Scholar
  12. Brutsaert W (1975) On a derivable formula for long-wave radiation from clear skies. Water Resourc Res 11:742–744CrossRefGoogle Scholar
  13. Carrasco-Benavides M, Ortega-Farías S, Lagos LO, Kleissl J (2013) Assessment of the METRIC model in the estimation of instantaneous values of sensible and latent heat fluxes over a drip-irrigated Merlot vineyard using Landsat images. Anais XVI Simposio Brasileiro de Sensoriamento Remoto - SBSR Foz do Iguaçu PR Brasil 13 a 18 de abril de 2013 INPE (Accessed on August 27 2014 from:http://wwwdsrinpebr/sbsr2013/files/p0656pdf)Google Scholar
  14. Chavez JL, Neale CMU, Hipps LE, Prueger JH, Kustas WP (2005) Comparing aircraft-based remotely sensed energy balance fluxes with eddy covariance tower data using heat flux source area functions. J Hydrometeorol 6:923–940CrossRefGoogle Scholar
  15. Danelichen VHD, Biudes MS, Souza MC, Machado NG, Da-Silva BB, Nogueira JD (2014) Estimation of soil heat flux in a neotropical wetland and region using remote sensing techniques. Revista Brasileira de Meteorologia 29(4):469–482CrossRefGoogle Scholar
  16. DeBruin HAR, Hurk BJJM van den, Kohsiek W (1995) The scintillation method tested over a dry vineyard area. Boundary-Layer Meteorol 76:25–40Google Scholar
  17. Dzikiti S, Jovanovic NZ, Bugan R, Israel S, Maitre DC Le (2014) Measurement and modelling of evapotranspiration in three fynbos vegetation types. Water SA 40(2):189–198Google Scholar
  18. Elhaddad A, Garcia LA (2011) ReSET-raster: surface energy balance model for calculating evapotranspiration using a raster approach. J Irrig Drain Eng 137(4):203–210CrossRefGoogle Scholar
  19. Ezzahar J, Chehbouni A, Er-Raki S, Hanich L (2009) Combining a large aperture scintillometer and estimates of available energy to derive evapotranspiration over several agricultural fields in a semi-arid region. Plant Biosyst 143(1):209–221CrossRefGoogle Scholar
  20. Ezzahar J, Chehbouni A, Hoedjes JCB, Chehbouni A (2007) On the application of scintillometry over heterogeneous grids. J Hydrol 334:493–501CrossRefGoogle Scholar
  21. Gao ZQ, Liu CS, Gao W, Chang NB (2011) A coupled remote sensing and the surface energy balance with topography algorithm (SEBTA) to estimate actual evapotranspiration over heterogeneous terrain. Hydrol Earth Syst Sci 15:119–139CrossRefGoogle Scholar
  22. Gowda PH, Chavez JL, Colaizzi PD, Evett SR, Howell TA, Tolk JA (2007) Remote sensing based energy balance algorithms for mapping ET: current status and future challenges. Trans ASABE 50(5):1639–1644CrossRefGoogle Scholar
  23. Gowda PH, Chavez JL, Colaizzi PD, Evett SR, Howell TA, Tolk JA (2008) ET mapping for agricultural water management: present status and challenges. Irrig Sci 26:223–237CrossRefGoogle Scholar
  24. Gowda PH, Howell TA, Paul G, Colaizzi PD, Marek TH (2011) SEBAL for estimating hourly ET fluxes over irrigated and dryland cotton during BEAREX08. Proc 2011 World Environ Water Resour Congr 2787–2795Google Scholar
  25. Gruber M, Fochesatto GJ (2013) A new sensitivity analysis and solution method for scintillometer measurements of area-averaged turbulent fluxes. Boundary-Layer Meteorol 149:65–83CrossRefGoogle Scholar
  26. Hartogensis O, DeBruin HAR (2005) Monin-Obukhov similarity functions of the structure parameter of temperature and turbulent kinetic energy dissipation rate in the stable boundary layer. Boundary-Layer Meteorol 116(2):253–276CrossRefGoogle Scholar
  27. Hartogensis O, Watts C, Rodriquez JC, DeBruin HAR (2003) Derivation of an effective height for scintillometers: La Poza experiment in Northwest Mexico. J Hydrometeorol 4:915–928CrossRefGoogle Scholar
  28. Hemakumara HM, Chandrapala L, Moene AF (2003) Evapotranspiration fluxes over mixed vegetation areas measured from large aperture scintillometer. Agric Water Manag 58:109–122CrossRefGoogle Scholar
  29. Hipps LE (1989) The infrared emissivities of soil and artemisia tridentate and subsequent temperature corrections in a shrub-steppe ecosystem. Remote Sens Environ 27:337–342CrossRefGoogle Scholar
  30. Hoedjes JCB, Chehbouni A, Ezzah J, Escadafal R, DeBruin HAR (2007) Comparison of large aperture scintillometer and eddy covariance measurements: can thermal infrared data be used to capture footprint-induced differences? J Hydrometeorol 8:144–159CrossRefGoogle Scholar
  31. Kalma JD, McVicar TR, McCabe MF (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469CrossRefGoogle Scholar
  32. Kite G, Droogers P (2000) Comparing evapotranspiration estimates from satellites hydrological models and field data. J Hydrol 229(1–2):3–18CrossRefGoogle Scholar
  33. Krause P, Boyle DP, Base1 F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97Google Scholar
  34. Lagos LO, Lillo-Saavedra M, Fonseca D, Gonzalo C (2013) Evapotranspiration of partially vegetated surfaces from remote sensing. In: Lasaponara R, Masini N, Biscione M (Eds) Proc 33rd EARSeL Sympo p613–624Google Scholar
  35. Lagouarde JP, Jacob F, Gu XF, Olioso A, Bonnefond JM, Kerr YH, McAneney KJ, Irvine M (2002) Spatialization of sensible heat flux over a heterogeneous landscape. Agronomie 22:627–633CrossRefGoogle Scholar
  36. Liu S, Hu G, Lu L, Mao D (2007) Estimation of regional evapotranspiration by TM/ETM data over heterogeneous surfaces. Photogramm Eng Remote Sens 73(10):1169–1178CrossRefGoogle Scholar
  37. Mengistu MG, Savage MJ (2010) Surface renewal method for estimating sensible heat flux. Water SA 36(1):9–18CrossRefGoogle Scholar
  38. Mkhwanazi M, Chavez JL (2012) Using METRIC to estimate surface energy fluxes over an alfalfa field in Eastern Colorado. Hydrology Days, Colorado State University USA (Accessed on August 26th 2014 from: http://hydrologydays.colostate.edu/Papers_2012/Mcebisi_paper.pdf)
  39. Mkhwanazi M, Chavez JL, Rambikur EH (2012) Comparison of large aperture scintillometer and satellite-based energy balance models in sensible heat flux and crop evapotranspiration determination. Int J Remote Sens Appl 2(1):24–30Google Scholar
  40. Odhiambo GO, Savage MJ (2009) Surface layer scintillometry for estimating the sensible heat flux component of the surface energy balance. S Afr J Sci 105:2008–2016Google Scholar
  41. Papadavid G, Hadjimitsis DG, Toulious L, Michaelides S (2013) A modified SEBAL modeling approach for estimating crop evapotranspiration in semi-arid conditions. Water Resour Manag 27:3493–3506CrossRefGoogle Scholar
  42. Pauwels VRN, Timmermans WJ, Loew A (2008) Comparison of the estimated water and energy budgets of a large winter wheat field during AgriSAR 2006 by multiple sensors and models. J Hydrol 349:425–440CrossRefGoogle Scholar
  43. Pocas I, Paco TA, Cunha J, Andrade A, Silvestre J, Sousa A, Santos FL, Pereira LS, Allen RG (2014) Satellite-based evapotranspiration of a super-intensive olive orchard: application of METRIC algorithms. Biosyst Eng 128:69–81CrossRefGoogle Scholar
  44. Rambikur EH, Chavez JL (2012) Scintillometry for evapotranspiration estimation over irrigated alfalfa and dry grassland p 110–118 In: Proc Hydrol Days 2012 March 21–23 2012 North Ball Room-Lory Student Center Colorado State University USAGoogle Scholar
  45. Samain B, Simons GWH, Voogt MP, Defloor W, Bink NJ, Pauwels VRN (2012) Consistency between hydrological model large aperture scintillometer and remote sensing based evapotranspiration estimates for a heterogeneous catchment. Hydrol Earth Sys Sci 16(7):2095–2107CrossRefGoogle Scholar
  46. Savage MJ, Everson CS, Odhiambo GO, Mengistu MG, Jarmain C (2004) Theory and practice of evapotranspiration measurement with special focus on SLS as an operational tool for the estimation of spatially-averaged evaporation. WRC Report No 1335/1/04 Water Res Comm Pretoria S Afr 204 pp (http://wwwreadperiodicalscom/201001/1965015051html#ixzz3Iqa2Ockm)Google Scholar
  47. Savage MJ, Odhiambo GO, Mengistu MG, Everson CS, Jarmain C (2005) Theory and practice of evaporation measurement. The 12th SANCIAHS National Hydrol Symp MidRand SA (September 2005)Google Scholar
  48. Senay GB, Bohms S, Singh RK, Gowda PH, Velpuri NM, Alemu H, Verdin JP (2013) Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. J Am Water Resour Assoc 49(3):577–591CrossRefGoogle Scholar
  49. Singh RK, Irmak A, Irmak S, Martin DL (2008) Application of SEBAL model for mapping evapotranspiration and estimating surface energy fluxes in south-Central Nebraska. J Irrig Drain Eng 134(3):273–285CrossRefGoogle Scholar
  50. Solignac PA, Brut A, Selves JL, Beteille JP, Gastellu-Etchegorry JP, Keravec P, Beziat P, Ceschia E (2009) Uncertainty analysis of computational methods for deriving sensible heat flux values from scintillometer measurements. Atmos Meas Tech 2:741–753CrossRefGoogle Scholar
  51. Thiermann V, Grassl H (1992) The measurement of turbulent surface-layer fluxes by use of bichromatic scintillation. Boundary-Layer Meteorol 58:367–389CrossRefGoogle Scholar
  52. Wright JL, Jensen ME (1978) Development and evaluation of evapotranspiration models for irrigation scheduling. Trans ASAE 21(1):88–96CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  • K. A. Al-Gaadi
    • 1
    • 2
  • V. C. Patil
    • 1
    • 3
  • E. Tola
    • 1
  • R. Madugundu
    • 1
  • P. H. Gowda
    • 4
  1. 1.Precision Agriculture Research ChairKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Agricultural Engineering, College of Food and Agriculture SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Electron Science Research InstituteEdith Cowan UniversityJoondalupAustralia
  4. 4.Forage and Livestock Production Research UnitUSDA-ARS Grazing-lands Research LaboratoryEl RenoUSA

Personalised recommendations