Document Type : علمی - پژوهشی


1 Associate Prof., Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University

2 PhD student, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

3 Assistant Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran


In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important agricultural species, reflectance and best band for selected narrow band indices to discriminating wheat canopies which are under dust stress in different growing stages. Two wheat (Triticum aestivum L.) varieties, Aflak and Pishtaz, were grown in pots under controlled conditions. The treated samples were exposed to simulated dust storm, in the wind tunnel, at two growth stages including Tillering and Heading stages. In each stage the treatments were exposed in 2, 4 and 6 days. Field spectroscopy measurements were carried out at canopy level using a full range spectro-radiometer Fieldspec-3-ASD. New narrow-band vegetation indices from NDVI, RVI, PVI and SAVI2 indices were computed from the all measured canopy spectra, Tillering and Heading stageseparately. To assess the performance of the indices, the RMSE, R2 and cross-validation method were used. For most indices, the selected optimum narrow bands are very close to one another and located in visible and NIR spectral domains. The result showed that the PVI index performed the best for considering the dust effect on wheat crops. The result also show that the selected indices have better performance in the Tillering stage (  0.77; 0.63 0.80)for estimating the dusty days, compared with Heading stage (  0.91; 0.62 0.71). Therefore, determining the dusty days by narrow band indices could be done precisely in the early stage of wheat growing.


  1. شائمی، ا.، حبیبی، م.، 1388، گرمایش جهانی پیامدهای زیستی و اکولوژیکی، انتشارات ترجمان خرد، تهران.
  2. صادقی‌روش، م.ح.، خراسانی، ن.ا.، 1388، بررسی آثار گرد و غبار ناشی از صنایع سیمان بر تنوع و تراکم پوشش گیاهی، مطالعۀ موردی: کارخانۀ سیمان آبیک، فصلنامۀ علوم و تکنولوژی محیط‌زیست، دورۀ دهم، شمارۀ 1، صص. 119-107.
  3. زاهدی‌فر، م.، کریمیان، ن.ع.، رونقی، ع.ح.، یثربی، ج.، امام، ی.، 1390، توزیع فسفر و روی در اندام‌ها و در مراحل مختلف رشد گندم در مزرعه، نشریۀ آب و خاک، جلد 25، شمارۀ 3، صص. 445-436.
  4. عبدل‌زاده، ا.، صفاری، ن.، 1381، بررسی اثرات شوری خاک بر رشد رویشی در یازده رقم ولاین گندم با تکیه بر انباشتگی یون‌ها، مجلۀ علوم کشاورزی و منابع طبیعی، سال نهم، شمارۀ 2، صص. 103-95.
  5. میرزایی، م.، درویش‌زاده، ر.، شکیبا، ع.ر.، متکان، ع.ا.، 1390، انتخاب شاخص‌های فراطیفی (باریک‌باند) بهینه برای تخمین محتوای آب گیاهان با درنظر گرفتن شرایط متفاوت تراکم تاج‌پوشش گیاه و خاک پس‌زمینه، مجلۀ سنجش از دور و GIS ایران، سال سوم، شمارۀ 1، صص. 70-55.
  6. Armbrust, D.V., 1986, Effect of Particulates (Dust) on Cotton Growth, Photosynthesis, and Respiration, Agronomy Journal, 78(6): 1078-1081.
  7. Aziakpono, O.M., Ukpebor, E.E., Ukpebor, J.E., & Nosa, O.G., 2013, Atmospheric Trace Metal Concentrations of Total Suspended Particulate Matter in Isoko land, Southern Nigeria, International Journal of Advanced Research, 1(8): 540-548.
  8. Broge, N.H., & Mortensen, J.V., 2002, Deriving Green Crop Area Index and Canopy Chlorophyll Density of Winter Wheat from Sspectral Reflectance Data, Remote Sensing of Environment, 81(1): 45–57.
  9. Cao, X., Luo, Y., Zhou, Y., Duan, X., & Cheng, D., 2013, Detection of Powdery Mildew in Two Winter Wheat Cultivars Using Canopy Hyperspectral Reflectance, Crop Protection, 45(3): 124-131.
  10. Chavez, R.O., Clevers, J.G.P.W., Herold, M., Ortiz, M., & Acevedo, E., 2013, Modelling the Spectral Response of the Desert Tree Prosopis Tamarugo to Water Stress, International Journal of Applied Earth Observation and Geoinformation, 21: 53–65.
  11. Chen, D., Huang, J., & Jackson, T.J., 2005, Vegetation Water Content Estimation for Corn and Soybeans Uusing Spectral Indices from MODIS near- and Shortwave Infrared Bands, Remote Sensing of Environment, 98(2-3): 225-236.
  12. Darley, E., 1966, Studies on the Effect of Cement-Kiln Dust on Vegetation, Journal of Air Pollution Control Association, 16(3): 145-150.
  13. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsia, F. & Choa, M., 2008, LAI and Chlorophyll Estimation for a Heterogeneous Grassland Using Hyperspectral Measurements, ISPRS Journal of Photogrammetry & Remote Sensing, 63(4): 409–426.
  14. Eller, B.M., 1977, Road Dust Induced Increase of Leaf Temperature, Enviromental Pollution, 13: 99-107.
  15. Eueling, D.W., 1969, Effects of Spraying Plants with Suspensions of Inert Dusts, Annals of Applied Biology, 64: 139- 151.
  16. Farmer, A.M., 1993, The Effects of Dust on Vegetation-Areview, Enviromental Pollution, 79: 63-75.
  17. Greenway, H. & Munns, R., 1980, Mechanisms of Salt Tolerance in Nonhalophytes, Annual Review of Plant Biology, 31: 149-190.
  18. Hamzeh, S., Naseri, A.A., AlaviPanah, S.K., Mojaradi, B., Bartholomeus, H.M., Clevers, J.G.P.W. & Behzad, M., 2013, Estimating Salinity Stress in Sugarcane Fields with Spaceborne Hyperspectral Vegetation Indices, International Journal of Applied Earth Observation and Geoinformation, 21: 282–290.
  19. Hansen, P.M. & Schjoerring, J.K., 2003, Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression, Remote Sensing of Environment, 86(4): 542–553.
  20. Jilili, A., DongWei, L. & GuangYang, W., 2010, Saline Dust Storms and their Ecological Impacts in Arid Regions, Journal of Arid Land, 2(2): 144-150.
  21. Kim, Y., Glenn, D.M., Park, J., Ngugi, H.K. & Lehman, B.L., 2011, Hyperspectral Image Analysis for Water Stress Detection of Apple Trees, Computers and Electronics in Agriculture, 77(2): 155-160.
  22. Lichtenthaler, H.K., Lang, M., Sowinska, M., Heisel, F. & Miehe, J.A., 1996, Detection of Vegetation Stress via a New High Resolution Fluorescence Imaging System, Journal of Plant Physiology, 148(5): 599-612.
  23. Li, F., Hennig, S.D., Gnyp, M.L., Chen, X., Jia, L. & Bareth, G., 2010, Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat at Different Growth Stages, Precise Agriculture, 11(4), 335-357.
  24. Major, D.J., Baret, F. & Guyot, G., 1990, A Ratio Vegetation Index Adjusted for Soil Brightness, International Journal of Remote Sensing, 11 (5): 727-740.
  25. Manning, W.J., I971, Effects of Limestone Dust on Leaf Condition, Foliar Disease Incidence, and Leaf Surface Microflora of Native Plants, Environmental Pollution, 2(1): 69-76.
  26. Nanos, G.D & Ilias, I.F., 2007, Effects of Inert Dust on Olive (Olea europaea L.) Leaf Physiological Parameters, Env Sci Pollut Res, 14 (3): 212-214.
  27. Richardson, A.J. & Wiegand, C.L., 1977, Distinguishing Vegetation from Soil Background Information, Photogrammetric Engineering and Remote Sensing, 43: 1541-1552.
  28. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. & Harlan, J.C., 1974, Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, NASA/GSFC, Type III, final report, Greenbelt, MD.
  29. Savitzky, A. & Golay, M.J.E., 1964, Smoothing and Differentiation of Data by Simplified Least Square Procedure, Analytical Chemistry, 36 (8): 1627-1638.
  30. Schaepman-Strub, G., Limppens, J., Menken, M., Bartholomeus, H.M. & Schaepman, M.E., 2008., Towards Spatial Assessment of Carbon Sequestration in Peatlands: Spectroscopy Based Estimation of Fractional Cover of Three Plant Functional Types, Biogeosciences Discussion, 5(2): 1293-1317.
  31. Shahsavani, A., Naddafi, K., Jaafarzadeh Haghighifard, N.A., Mesdaghinia, A.R., Yunesian, M., Nabizadeh, R., Arhami, M., Yarahmadi, M., Sowlat, M.H., Ghani, M., Jonidi Jafari, A., Alimohamadi, M., Motevalian, S.A. & Soleimani, Z., 2012, Characterization of Ionic Composition of TSP and PM10 during the Middle Eastern Dust (MED) Storms in Ahvaz, Iran, Environmental Monitoring and Assessment, 184(11): 6683-6692.
  32. Stroppiana, D., Boschetti, M., Brivio, P.A. & Bocchi, S., 2009, Plant Nitrogen Concentration in Paddy Rice from Field Canopy Hyperspectral Radiometry, Field Crop Research, 111(1-2): 119-129.
  33. Thenkabail, P., Smith, R. & De Pauw, E., 2000, Hyperspectral Vegetation and their Relationships with Agricultural Crop Characterstics, Remote Sensing of Environment, 71 (2): 158-182.
  34. Tucker, C.J., 1979, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2): 127- 150.
  35. Watanabe, S., Hatanaka, Y. & Inada, K., 1980, Development of a Digital Chlorophyll Meter: I. Structure and Performance, Japanese Journal of Crop Science, 49(1): 89-90.