Comparison of Different Spectral Vegetation Indices for the Remote Assessment of Winter Wheat Leaf AreaIndex in Mashhad

Document Type : Scientific - Research


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

2 Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

3 Ferdowsi University of Mashhad, Mashhad

4 Ferdowsi University of Mashhad


The role of leaf area index (LAI) in terrestrial ecosystems is undeniable. LAI affects the amount of carbon, water and energy metabolism. Also, many agronomic, environmental and meteorological applications require information on the status of LAI. The time series of the spectral indices obtained from the remote sensing indicates its usefulness in detecting regional-scale LAI changes. So, the desire for the development of models for estimating LAI was increased with using satellite images. Vegetation Indices (VIs), especially the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), are most widely used. According to the different sensitivity of VIs to the value of LAI and vegetation characteristics, in this study, we tried to determine an algorithm with a higher accuracy to estimate the LAI of wheat using more variables (VIs).
Material and Methods
In this study, regarding the wheat growth period in Astan Quds Razavi (AQR) farms, the Landsat 8 satellite images were used from November 22, 2014 to June 20, 2015. LAI was measured simultaneously with passing of Landsat 8 (16-day intervals) from AQR Fields of Mashhad (in five dates from 17 farms) during wheat growing season in 2014-2015.
After pre-processing of satellite images, VIs including the Difference Vegetation Index (DVI), NDVI, RVI, Transformed Difference Vegetation Index (TDVI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), Greenness Index (G1 and G2) and Enhanced Vegetation Index (EVI1 and EVI2) were calculated. To select the best variables and the equation for estimating LAI, simple regression (linear, quadratic and exponential) and multiple linear regression (Backward and Forward) methods were used. Finally, to validate and assess the accuracy of the presented models, the mean square error (RMSE), Mean Absolute Error (MAE), Point accuracy based on a percentage of actual value (E%) and correlation coefficient (r) was used.
Results and Discussion
The results of this study showed that simulation of LAI based on the existing equations in the references using the NDVI, EVI1 and EVI2 indices extracted from Landsat 8 satellite images has low accuracy (RMSE:2.71, 3.65 and 3.65). This confirms the necessity of examining and calibrating equations. The results indicate that the accuracy of the wheat LAI estimation by using the NDVI and SAVI index was increased by exponential functions (RMSE:1.18 and 1, respectively) compared to the linear model (RMSE:1.46 and 1.26, respectively). This increase was due to a more accurate estimation LAI lower than 4 and the fixed value of LAI simulated in a range of actual LAI higher than 6. The accuracy of LAI estimation was increased with combination of two VIs (NDVI and SAVI) compared to the linear model of each index separately. Also, the highest accuracy of LAI estimation from the combination of G2 with SAVI and EVI1 (RMSE: 1.03, 1.03, respectively) was observed due to the higher sensitivity of G2 to medium and high LAI compared to NDVI. In addition, the backward and forward regression model was improved the accuracy of wheat LAI estimation compared to other models, due to the greater sensitivity of this model to LAI higher than 6 (RMSE: 0.87 and 0.95, respectively). Although the accuracy of wheat LAI estimation by the forward regression model was higher than all models, but its calculation requires the use of many parameters.
Since LAI is an important biophysical parameter in ecological modeling. Accurate and fast estimation of this parameter in large scale for ecological models such as yield and evapotranspiration, and carbon exchange is very important. Considering the results of this research and the opinions of other researchers, it can be stated that the accuracy of the exponential functions and multiple linear regression (Forward regression model) for estimating LAI was higher than simple linear regression.


Asrar, G., Fuchs, M., Kanemas, E.T., and Hatfield, J.L. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal 76: 300-306.
Band, L.E., Peterson, D.L., Running, S.W., Dungan, J., Lathrop, R., Coughlan, J., Lammers, L., and Pierce, L.L. 1991. Forest ecosystem processes at the watershed scale: Basis for Distributed Simulation. Ecological Modeling 56: 171–196.
Bannari, A., Asalhi, H., and Teillet, P. 2002. Transformed Difference Vegetation Index (TDVI) for Vegetation Cover Mapping. In Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS 02, IEEE International 5: 3053-3055.
Birth, G.S., and McVey, G.R. 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. Agronomy Journal 60(6): 640–643.
Boegh, E., Soegaard, H., Broge, N., Hasager, C., Jensen, N., Schelde, K., and Thomsen. A. 2002. Airborne Multi-Spectral Data for Quantifying Leaf Area Index, Nitrogen Concentration and Photosynthetic Efficiency in Agriculture. Remote Sensing of Environment 81(2-3): 179-193.
Bondeau, A., Kicklighter, D., and Kaduk, J. 1999. Comparing global models of terrestrial net primary productivity (NPP): Importance of vegetation structure on seasonal NPP estimates. Global Change Biology 5: 35–45.
Bradley, B.A., and Mustard, J.F. 2008. Comparison of phenology trends by land cover class: A case study in the Great Basin, USA. Global Change Biology 14: 334-346.
Broge, N.H., and Leblanc, E. 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76: 156–172.
Cao, X., Zhou, Z., Chen, X., Shao, W., and Wang, Z. 2015. Improving leaf area index simulation of IBIS model and its effect on water carbon and energy-A case study in Changbai Mountain broadleaved forest of China. Ecological Modelling 303: 97–104.
Chen, J.M., and Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment 55: 153–162.
Chen, S., Billings, S.A., and Luo, W. 1989. Orthogonal least squares methods and their application to nonlinear system identification. International Journal of Control 50: 1873-1896.
Cleland, E.E., Chuine, I., Menzel, A., Mooney, H.A., and Schwartz, M.D. 2007. Shifting plant phenology in response to global change. Trends in Ecology and Evolution 22: 357-365.
Crippen, R.E. 1990. Calculating the vegetation index faster. Remote Sensing of Environment 34: 71–73.
Darvishzadeh, R., Skidmore, A., Schlerf, M., and Atzberger, C. 2008. Inversion of a Radiative Transfer Model for Estimating Vegetation LAI and Chlorophyll in a Heterogeneous Grassland. Remote Sensing of Environment 112 (5): 2592–2604.
Daughtry, C.S.T., Gallo, K.P., Goward, S.N., Prince, S.D., and Kustas, W.P. 1992. Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sens. Environ 39: 141–152.
Dijk, V.A., and Bruijnzeel, L.A. 2000. Modeling rainfall interception by vegetation of variable density using an adapted analytical model: Part 1: Model Description. Journal of Hydrology 247: 230–238.
Fan, L., Gao, Y., Brocks, H., and Bernhofer, C. 2009. Investigating the relationship between NDVI and LAI in semiarid grassland in Inner Mongolia using in-situ measurements. Theoretical Applied Climatology 95: 151–156.
Fang, H.L., Liang, S.L., and Kuusk, A. 2003. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment 85: 257–270.
Faridhosseini, A., Astaraei, A.R., Sanaeinejad, S.H., and Mirhoseini Moosavi, P. 2012. Estimation of leaf area index using IRS satellite images. Iranian Journal of Field Crops Research 10 (3): 577-582. (In Persian with English abstract)
Fassnacht, K.S., Gower, S.T., Norman, J.M., and McMurtric, E.R. 1994. A comparison of optical and direct methods for estimating foliage surface area index in forests. Agricultural and Forest Meteorology 71: 183–207.
Gao, F., Anderson, M.C., Kustas, W.P., and Houborg, R. 2014. Retrieving Leaf Area Index from Landsat Using MODIS LAI Products and Field Measurements. IEEE Geosci. Remote Sensing Letters 11: 773–777.
Gitelson, A.A., Peng, Y., Arkebauer, T.J., and Schepers, J. 2014. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environmen, 144: 65–72.
Gitelson, A.A., Vina, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G.P., and Leavitt, B. 2003b. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters 30(5): 1248.
Gitelson, A.A., Wardlow, B.D., Keydan, G.P., and Leavitt, B. 2007. An evaluation of MODIS 250-m data for green LAI estimation in crops. Geophysical Research Letters 34(20): L20403.
Gong, P., Ruiliang, P.U., Biging, S.G., and Larrieu, M.R. 2003. Estimation of forest leaf area index using vegetation indices derived from hyper ion hyper spectral data. I.E.E.E. Transactions on Geosciences and Remote Sensing 41(6): 360-367.
Gray, J., and Song, C. 2012. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sensing of Environment 119: 173–183.
Guindin-Garcia, N., Gitelson, A.A., Arkebauer, T.J., Shanahan, J., and Weiss, A. 2012. An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index. Agricultural and Forest Meteorology 161: 15–25.
Hatfield, J.L., Gitelson, A.A., Schepers, J.S., and Walthall, C.L. 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100: S-117–S-131.
Huete, A. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3): 295–309.
Huete, A.R., Liu, H.Q., Batchily, K., and VanLeeuwen, W. 1997. A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sensing of Environment 59: 440–451.
Jiang, Z., Huete, A.R., Didan, K., and Miura, T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112: 3833–3845.
Karlsen, S.R., Høgda, K.A., Wielgolaski, F.E., Tolvanen, A., Tømmervik, H., Poikolainen, J., and Kubin, E. 2009. Growing-season trends in fennoscandia 1982–2006, determined from satellite and phenology data. Climate Research 39: 275-286.
Khan, J.A., Aelst, S.V., and Zamar, R.H. 2007. Building a robust linear model with forward selection and stepwise procedures. Computational Statistics and Data Analysis 52: 239-248.
Koetz, B., Baret, F., Poilve, H., and Hill, J. 2005. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sensing of Environment 95: 115–124.
Kovacs, J.M., Flores-Verdugo, F., Wang, J., and Aspden, L.P. 2004. Estimating leaf area index of a degraded mangrove forest using high spatial resolution satellite data. Aquatic Botany 80(1): 13-22.
Le Maire, G., Marsden, C., Verhoef, W., Ponzoni, F. J., Lo Seen, D., Begue, A., Stape, Z., and Nouvellon, Y. 2011. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations. Remote Sensing of Environment 115(2): 586–599.
Lu, L., Li, X., Ma, M.G., Che, T., Huang, C.L., Veroustraete, F., Dong, Q.H., Ceulemans, R., and Bogaert, J. 2004. Investigating relationship between Landsat ETMþ data and LAI in a semiarid grassland of Northwest China. Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International 6: 3622–3625.
Masemola, C., Cho., M.A., and Ramoelo, A. 2016. Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa. International Journal of Remote Sensing 37(18): 4401-4419.
Miri, N., Darvishsefat, A.A., Zargham, N., and Shakeri, Z. 2017. Estimation of leaf area index in Zagros forests using Landsat 8 data. Iranian Journal of Forest 9(1): 29-42.
Moulin, S., and Guerif, M. 1999. Impacts of model parameter uncertainties on crop reflectance estimates: A regional case study on wheat. International Journal of Remote Sensing 20: 213–218.
Myneni, R.B., Nemani, R.R., and Running, S.W. 1997. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing 35: 1380–1893.
Nguy-Robertson, A.L., Gitelson, A.A., Peng, Y., Vina, A., Arkebauer, T.J., and Rundquist, D.C. 2012. Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agronomy Journal 104: 1336–1347.
Noori R., Hoshyaripour G.H., Ashrafi, K.H., and Najdar Araabi, B. 2010. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment 44: 476-482.
Persson, S. 2014. Estimating leaf area index from satellite data in deciduous forests of southern Sweden. Student thesis series INES.
Pettorelli, N., Vik. J.O., Mysterud, A., Gaillard, J.M., Tucker. C.J., and Stenseth, N.C. 2005. Using the satellite–derived NDVI to assess ecological responses to environmental change. Trends in ecology and evolution 20(9): 503-510.
Pinter, P.J., J.L., Hatfield, J.S., Schepers, E.M., Barnes, M.S., Moran, C.S.T., Daughtry, and Upchurch, D.R. 2003. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing 69(6): 647–664.
Pinty, B., Lavergne, T., Widlowski, J.L., Gobron, N., and Verstraete, M.M. 2009. On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption. Remote Sensing of Environment 113: 10–23.
Pontailler, J.Y., Hymus, G.J., and Drake, B.G. 2003. Estimation of leaf area index using ground-based remote sensed NDVI measurements: validation and comparison with two indirect techniques. Canadian Journal of Remote Sensing 29: 381–387.
Price, J.C., and Bausch, W.C. 1995. Leaf area index estimation from visible and near-infrared reflectance data. Remote sensing of environment 52: 55–65.
Richardson, A.J., Wiegand, C.L., Wanjura, D.F., Dusek, D., and Steiner, J.L. 1992. Multisite analysis of spectral-biophysical data for sorghum. Remote Sensing of Environment 47: 71–82.
Saito, K., Ogawa, S., Aihara, M., and Otowa, K. 2001. Estimates of LAI for forest management in Okutama. Proc. ACRS 2001 - 22nd Asian Conference on Remote Sensing 5-9 November 2001, Singapore. Vol. 1, pp. 600-605.
Soudani, K., François, C., Le Maire, G., Le Dantec, V., and Dufrêne, E. 2006. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote sensing of environment 102(1): 161-175.
Teillet, P.M., Staenz, K., and Williams, D.J. 1997. Effects of spectral spatial and radiometric characteristics of remote sensing vegetation indices of forested regions. Remote Sensing of Environment 61: 139–149.
Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150.
Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S., and Briggs, J.M. 1999. Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites. Remote Sensing of Environment 70 (1): 52–68.
Van Wijk, M.T., and Williams, M. 2005. Optical instruments for measuring leaf area index in low vegetation: application in arctic ecosystems. Ecological Applications 15(4): 1462–1470.
Vina, A., Gitelson, A.A., Nguy-Robertson, A.L., and Peng, Y. 2011. Comparison of different P0vegetation indi +/85200c14es for the remote assessment of green leaf area index of crops. Remote Sensing of Environment 115: 3468–3478.
Wang, X.X., Chen, S., Lowe, D., and Harris, C.J. 2006. Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing 70: 462-474.
Watson, D.J. 1947. Comparative physiological studies on the growth of field crops: I, Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany 11: 41–76.
White, M.A., Thornton, P.E., and Running, S.W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 11: 217-234.
Zhang, Z., He, G., Wang, X., and Jiang, H. 2011. Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery. International Journal of Remote Sensing 32(19): 5365-5379.