Introducing and evaluating a robust statistical model to estimate wheat LAI from satellite data in different climatological locations

Document Type : Original Article

Authors

Department of Agronomy, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad

References

  1. Asadi, S., M. Bannayan, M. Jahan, and A. Faridhosseini, 2018, Comparison of different spectral vegetation indices for the remote assessment of winter wheat leaf area index in Mashhad. Journal of Agroecology. 10(3): 913-934.
  2. Baret, F., and G. Guyot, 1991, Potentials and Limits of Vegetation Indices for LAI and APAR Assessment. REMOTE SENS. ENVIRON. 35:161.
  3. Bréda, N.J.J., 2003, Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany. 54:(392). 2403 −2417.
  4. Chen, J. M., and J. Cihlar, 1996, Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55, 153–162.
  5. Choudhury, B.J., N.U. Ahmed, S.B. Idso, R.J. Reginato, and C.S.T. Daughtry, 1994, Relations between evaporation coefficients and vegetation indexes studied by model simulations. Remote Sensing of Environment, 50(1), 1–17.
  6. Duveiller, G., F. Baret, and P. Defourny, 2011, Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy. Remote Sensing of Environment (115) 2686–2701.
  7. Gallager, L.W., K.M. Soliman, D.W. Rains, C.O. Qualset, and R.C. Huffaker, 1978, Nitrogen assimilation in common wheats differing in potential nitrate reductase activity and tissue nitrate concentrations. Crop Sci. 23: 913–919
  8. Gao, F., M.C. Anderson, W.P. Kustas, and R. Houborg, 2014, Retrieving Leaf Area Index from Landsat Using MODIS LAI Products and Field Measurements. IEEE Geosci. Remote Sensing Letters 11: 773–777.
  9. Gitelson, A.A., Y. Peng, T.J. Arkebauer, and J. Schepers, 2014, Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environment, 144, 65–72.
  10. Gray, J., and C. Song, 2012, Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sensing of Environment 119: 173–183.
  11. Hatfield, J.L., A.A. Gitelson, J.S. Schepers, and C.L. Walthall, 2008, Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100: S-117–S-131.
  12. Ingwersen J., P. Högy, H.D. Wizemann, K. Warrach-Sagi and T. Streck, 2018, Coupling the land surface model Noah-MP with the generic crop growth model Gecros: Model description, calibration and validation. Agricultural and Forest Meteorology Volume 262, 15 November 2018, Pages 322-339.
  13. Janzen, DT., AL. Fredeen, and RD. Wheate, 2006, Radiometric correction techniques and accuracy assessment for Landsat TM data in remote forested regions. Canadian Journal of Remote Sensing, 32(5): 330–340.
  14. Jonckheere, I., S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, 2004, Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology. 121: (1-2), 19 − 35.
  15. Kiniry, J.R., J.A. Landivar, M. Witt, T.J. Gerik, J. Cavero, L.J. Wade, 1998, Radiation use efficiency response to vapor pressure deficit for maize and sorghum. Field Crops Res. 56, 265-270.
  16. Liu, J., E. Pattey, G. Jégo, 2012, Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment 123: 347–358.
  17. McPherson, R.A., 2007, A review of vegetation-atmosphere interactions and their influences on mesoscale phenomena. Prog. Phys. Geog. 31, 261–285.
  18. Myneni, R.B., R.R. Nemani, and S.W. Running, 1997, Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 35, 1380–1393.
  19. Salehnia, N., A. Alizadeh, H. Sanaeinejad, M. Bannayan, A. Zarrin, and G. Hoogenboom, 2017, Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land. 9(6): 797–809.
  20. Samadiyan, F., A. Soleymani, F. Yeganehpoor, V. Beyginiy, 2013, Evaluation of maximum light absorption and light extinction coefficient in different levels of nitrogen and wheat cultivars. International journal of Advanced Biological and Biomedical Research Volume 1, Issue 11, 2013: 1377-1386
  21. Seidel, S.J., T. Palosuo, P. Thorburn, D. Wallach, 2018, Towards improved calibration of crop models – Where are we now and where should we go?. European Journal of Agronomy Volume 94, March 2018, Pages 25-35.
  22. Soleymani, A. 2016. Light extinction of wheat as affected by N fertilization and plant parameters. Crop & Pasture Science, 67, 1075–1086
  23. Thorp, K.R., G. Wang, A.L. West, M.S. Moran, K.F. Bronson, J.W. White, and J. Mon, 2012, Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models. Remote Sensing of Environment (124) 224–233.
  24. Viña A., A. Anatoly, B. Gitelson, L. Anthony, B. Nguy-Robertson, B. Yi Peng, 2011, Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment 115: 3468–3478
  25. Wallach, D., N. Keussayan, F. Brun, B. Lacroix, J.E. Bergez, 2012, Assessing the uncertainty when using a model to compare irrigation strategies. Agron. J. 104, 1274–1283.
  26. Weiss, M., F. Baret, G.J. Smith, I. Jonckheere, and P. Coppin, 2004, Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology. 121: (1-2), 37 − 53.
  27. Xu, D. and X.Guo, 2014, Compare NDVI Extracted from Landsat 8 Imagery with that from Landsat 7 Imagery. American Journal of Remote Sensing. 2(2): 10-14.
  28. Zahed, M., S. Galeshi, N. Latifi, A. Soltani, M. Calate and R. Hosseini, 2013, The Effect of Plant Density on Extinction Coefficient and Radiation Use Efficiency in Modern and Old Wheat (Triticum aestivum L.) Genotypes. Iranian Journal of Field Crops Research, 11(3): 506-514.

 

 

Journal of Climate Research
Volume 1398, Issue 39 - Serial Number 39
Vol. 10- No. 39- Autumn 2019
September 2020
Pages 61-74

Files

Share

How to cite

Statistics