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Autor
Tanriverdi Cagatay (Kahramanmaras Sutcu Imam University, Kahramanmaraş,Turkey), Atilgan Atilgan (Süleyman Demirel University in Isparta), Degirmenci Hasan (Kahramanmaras Sutcu Imam University, Kahramanmaraş,Turkey), Akyuz Adil (Kahramanmaras Sutcu Imam University, Kahramanmaraş,Turkey)
Tytuł
Comparasion of Crop Water Stress Index (CWSI) and Water Deficit Index (WDI) by Using Remote Sensing (RS)
Źródło
Infrastruktura i Ekologia Terenów Wiejskich, 2017, nr III/1, s. 879-894, rys., bibliogr. 84 poz.
Infrastructure and Ecology of Rural Areas
Słowa kluczowe
Produkcja rolna, Woda, Zbieranie danych, Dane wrażliwe
Agricultural production, Water, Data collection, Sensitive data
Uwagi
summ.
Abstrakt
Drought, water scarcity and climate changes are very important threats for agriculture on a global basis. Remote sensing (RS) is accepted as a technique to collect data and determine water stress indices. Water Stress Indices (WSI) are useful tools to prevent drought and determine irrigation scheduling. The water stress indices are primarily identified as the Crop Water Stress Index (CWSI) and the Water Deficit Index (WDI). The effect of soil background is major problem to establish CWSI especially during early growth stage measurements of canopy temperature (Ts). Hence, WDI is a better index when it comprised with CWSI because of Ts. CWSI and WDI can be determined by two different techniques. These are determined by using measured by using traditional components to collect data and estimated methods by applying RS components to collect necessary data. Estimated method has many advantages when this method compared with measured method. However, estimated method needs some RS components which are infrared gun (IR), sling psychrometer, Spectro radiometer. With the help of these tools, the necessary data are obtained and WDI is determined. By using Spectro radiometer vegetation indices are defined. Among the many vegetation indices, the Normalized Difference Vegetation Index (NDVI) is mostly used one. By using NDVI determination of vegetation cover is easy and accurate technique to establish WDI. Establishing these both stress indices with less fieldwork and by saving money, time and labor conveys the necessary information for agriculturists using remotely sensed data especially for large agricultural fields.(original abstract)
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  1. Akay, A., Önder, S. (2016). Benefits of rain water usage in urban green areas for water saving. 2nd International Conference on Science Ecology and Technology (ICONSETE, 2016) Barcelona / Spain. Pp:804-812.
  2. Allen, R.G., Pereira, L.S., Raes, D., Smith, M. (1998). Crop Evopotranspiration. FAO Irrigation and Drainage Paper 56, Rome, pp 299.
  3. Al-Solaimani, S.G., Alghabari, F., Ihsan, M.Z., Fahad, S. (2017). Water deficit irrigation and nitrogen response of Sudan grass under arid land drip irrigation conditions. Irrigation and Drainage, Published online in Wiley Online Library DOI: 10.1002/ird.2110.
  4. Azzali A., Menenti M. (2000). Mapping vegetation-soil complexes in southern Africa using temporal Fourier analysis of NOAA AVHRR NDVI data. International Journal of Remote Sensing, 21: 973-996. doi: http://dx.doi.org/10.1080/014311600210380.
  5. Bai J.J., Yu Y., Di L.P. (2017). Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China. Journal of Integrative Agriculture, Vol. 16: 389-397.
  6. Banerjee, A., de Fortier Smit A., Prozzi, J.A. (2012). Modeling the effect of environmental factors on evaporative water loss in asphalt emulsions for chip seal applications. Constr. Build. Mater., 27: 158-164.
  7. Beck, P.S.A., Atzberger, C., Hogda, K.A. (2006). Improved monitoring of vegetation dynamics at very high latitudes, a new method using MODIS NDVI. Remote Sens. Environ. 2006, 100, 321-336.
  8. Belder, P., Bouman, B.A.M., Cabangon, R., Guoan, L., Quilang, E.J.P., Yuanhua, L., Spiertz J.H.J., Tuong, T.P. (2004). Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric. Water Manag., 65: 193-210.
  9. Carlson, T.N. (1986). Regional scale estimation of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sens. Rev. 1:197-247.
  10. Colaizzi, P.D., C.Y. Choi, P.M. Waller, E.M. Barnes, Clarke, T.R. (2000). Determining irrigation management zones in precision agriculture using the water deficit index at high spatial resolutions. 2000 ASAE Annual International Meeting. Pp. 1-19.
  11. Colaizzi, P.D., Barnes, E.M., Clarke, T.R., Choi, C.Y. Waller, P.M. Haberland, J., Kostrzewski, M. (2003). Water stress detection under high frequency sprinkler irrigation with water deficit index. Journal of Irrigation and Drainage Engineering, Vol. 129(1): 36-43.
  12. Cremona, M.V., Stützel, H., Kage, H. (2004). Irrigation scheduling of kohlrabi (Brassica oleracea var. gongylodes) using crop water stress index. Hortic. Sci., 39:276-279.
  13. Colak, Y.B., Yazar, A., Colak, İ., Akça, H., Duraktekin, G. (2014). Evaluation of crop water stress index (CWSI) for eggplant under varying irrigation regimes using surface and subsurface drip systems. Available online at www.sciencedirect.com.
  14. Diker, K. (1998). Use of geographic information management systems (GIMS) for nitrogen management. Ph. D. Thesis, Department of Chemical and Bioresource Engineering, Colorado State University, Spring 1998.
  15. El-Shirbeny, M.A., Ali, A.M., Rashash, A., Badr, M.A. (2015). Wheat yield response to water deficit under central pivot irrigation system using remote sensing techniques. World Journal of Engineering and Technology, Vol. 3: 65-72.
  16. Erdem, Y., Erdem, T., Orta, A.H., Okursoy, H. (2005). Irrigation scheduling for watermelon with crop water stress index (CWSI). Journal Central European Agriculture, 6(4): 449-460.
  17. Erdem, Y., Sehirali, S., Erdem T., Kenar, D. (2006). Determination of crop water stress index for irrigation scheduling of bean (Phaseolus vulgaris L.). Turk. J. Agric. For., 30: 195-202.
  18. Fensholt, R., Sandholt, I., Proud, S.R., Stisen, S., Rasmussen, M.O. (2010). Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data. International Journal of Remote Sensing, 31(23): 6163-6187.
  19. Fensholt, R. Proud, S.R. (2012). Evaluation of earth observation based global long term vegetation trends Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119: 131-147.
  20. Fuchs, M. (1990). Infrared measurement of canopy temperature and detection of plant water-stress. Theoretical Applied Climatology, 42 : 253-261.
  21. Gal, L.Y.P., Rieu, T., Fall, C. (2003). Water pricing and sustainability of Self Governing Irrigation Schemes. Irrigation and Drainage Systems. Vol. 17(3).
  22. Garcia, A., Andre, R.G.B., Ferreira, T. do Paço, (2000). Diurnal and seasonal variations of CWSI and non-water-stressed baseline with nectarine trees. III International Symposium on Irrigation of Horticultural Crops, Vol. 2. DOI: 10.17660/ActaHortic.2000.537.49
  23. Garen, C. D., Moore D.S. (2005). Curve number hydrology in water quality modelling: uses, abuses, and future directions. Journal of the American Water Resources Association. April, 377-388.
  24. Gao, Y., Walker, J.P., Allahmoradi, M., Monerris A., Ryu, D., Jackson, T.J. (2015). Optical Sensing of Vegetation Water Content: A Synthesis Study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 1-9. DOI:10.1109/ JSTARS.2015.2398034.
  25. Giannerini, G. Genovesi, R. (2015). The water saving with irriframe platform for thousands of Italian farms. Journal of Agricultural Informatics. Vol. 6(4):49-55.
  26. Hoffmann, H., Jensen, R., Thomsen, A., Nieto, H., Rasmussen, J. Friborg, T. (2016a). Crop water stress maps for an entire growing season from visible and thermal UAV imagery. Biogeosciences, 13 : 6545-6563.
  27. Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P., Friborg, T. (2016b). Estimating evaporation with thermal UAV data and two-source energy balance models, Hydrol. Earth Syst. Sci., 20, 697-713,
  28. Hou, W., Gao, J., Wu, S., Dai, E. (2015). Interannual variations in growing-season NDVI and its correlation with climate variables in the Southwestern Karst region of China. Remote Sensing, 7: 11105-11124.
  29. Idso, S.B., Jackson, R.D. Reginato, R.J. (1977). Remote sensing of crop yields. Science, 196:19-25.
  30. Idso, S.B., Jackson, R.D, Pinter, P.J., Reginato, R.J, Hatfield, J.L. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24:45-55.
  31. Jackson, R.D., Reginato, R.J., Idso, S.B. (1977). Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resources Research, 13: 651-656.
  32. Jackson, R.D., Idso, S.B., Reginato, R.J. Pinter Jr, P.J.(1981). Canopy temperatures as a crop water stress indicator. Water Resources Research, Vol. 17: 1133-1138.
  33. Jackson, R.D. (1982). Canopy temperatures and crop water stress. In Advances in Irrigation, D.I. Hillel, Editor, Academic Press, 1: 43-85.
  34. Jimenez, C., Prigent, C., Mueller, B., Seneviratne, S.I., McCabe, M.F., Wood, E.F., Rossow W.B., Balsamo, G., Betts, A.K., Dirmeyer, P.A. (2011). Global intercomparison of 12 land surface heat flux estimates. Journal of Geophys. Res. Atmos., 116, DOI:10.1029/2010JD0145.
  35. Kacar, M.M. (2007). Investigation Of Cotton Water Stress Index Variations Under Different Water And Fertilizer Systems. Çukurova Ünv. Fen Bilimleri Enst., Tarımsal Yapılar ve Sulama ABD, Yüksek Lisans Tezi.
  36. Kacira, M., Ling, P.P., Short, T.H., (2002). Establishing crop water stress index (CWSI) threshold values for early, non-contact detection of plant water stress. Transactions of ASAE, Vol. 45(3): 775-780.
  37. Kerr, J.T. Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18 (6): 299-305.
  38. Kustas, W. Anderson, M. (2009). Advances in thermal infrared remote sensing for land surface modeling. Agric. for Meteorol. 149:2071-2081.
  39. Li, B., Wang, T., Sun, J. (2014). Crop water stress index for off-season greenhouse green peppers in Liaoning, China. Int. J. Agric. Biol. Eng., 7: 28-35.
  40. Liu, X., Ferguson, R.B., Zheng, H., Cao, Q., Tian, Y., Cao, W., Zhu, Y. (2017). Using an active-optical sensor to develop an optimal NDVI dynamic model for high-yield rice production. Sensors, 17, 672. DOI:10.3390/s17040672.
  41. Martinez-Fernandez, J., Gonzalez-Zamora, A., Sanchez, N., Gumuzzio, A., HerreroJimenez, C.M. (2016). Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived soil water deficit index. Remote Sensing of Environment, 177: 277-286.
  42. Moran, M.S., Jackson, R.D. (1991). Assessing the spatial distribution of evapotranspiration using remotely sensed inputs. J Environ. Qual. 20:725-737.
  43. Moran, M.S., Clarke, T.R., Inoue, Y., Vidal, A. (1994) Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing Environment, Vol. 32: 125-141.
  44. Mu, S.J., Yang, H.F., Li, J.L., Chen, Y.Z., Gang, C.C., Zhou, W., Ju, W.M. (2013). Spatiotemporal dynamics of vegetation coverage and its relationship with climate factors in Inner Mongolia, China. J Geogr Sci 23(2):231-246.
  45. Mulla. D.J., Schepers. J.S., (1997). Key processes and properties for site-specific soil and crop management. Pp.1-18. In F.J. Pierce and E. J. Sadler (eds.) The State of SiteSpecific Management for Agriculture. American Society of Agronomy, madison, WI.
  46. Nash, M.S., Wickham, J., Christensen J., Wade, T. (2017). Changes in Landscape Greenness and Climatic Factors over 25 Years (1989-2013) in the USA. Remote Sensing, 9,295; DOI:10.3390/rs9030295.
  47. Nielsen, D.C. (1990). Scheduling irrigations for soybeans with crop water stress index (CWSI). Field Crops Res., Vol. 23: 103-116.
  48. O'shaughnessy, S., Evett, S., Colaizzi, P., Howell, T. (2011). Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agric. Water Manag., 98: 1523-1535.
  49. Paltineanu, C., Chitu E., Tanasescu, N. (2009). Correlation between the crop water stress index and soil moisture content for apple in a loamy soil: A case study in southern Romania. VI Int. Symp. Irri. Hort. Crops 889: 257-264.
  50. Park, H.S., Sohn, B. (2010). Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations. J. Geophys. Res., 115.
  51. Payero, J.O., Neale, C.M.U., Wright J.L. (2005). Non-water-stressed baselines for calculating crop water stress index (CWSI) for alfalfa and tall fescue grass. Transactions of ASAE, 48(2): 653-661.
  52. Peng, S., Yang, S., Xu, J., Gao, H. (2011). Field experiments on greenhouse gas emissions and nitrogen and phosphorus losses from rice paddy with efficient irrigation and drainage management. Sci. China Technol. Sci., 54: 1581-1587
  53. Petropoulos, G., Carlson, T.N., Wooster, M.J., Islam, S. (2009). A review of T-s/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr. 33:224-250.
  54. Piao, S.L., Nan, H.J., Huntingford, C., Ciais, P., Friedingstein, P., Sitch, S., Peng, S.S., Ahlstrom, A., Canadell, J.G., Cong, N., Levis, S., Levy, P.E., Liu, L.L., Lomas, M.R., Mao, J.F., Myneni, R.B., Peylin, P., Poulter, B., Shi, X.Y., Yin, G.D., Viovy, N., Wang, T., Wang, X.H., Zaehle, S., Zeng, N., Zeng, Z.Z., Chen, A.P. (2014). Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018.
  55. Ramirez, D.A., Yactayo, W., Rens, L.R., Rolando, J.L., Palacios, S., De Mendiburu, F., Mares, V., et al. (2016). Defining biological thresholds associated to plant water status for monitoring water restriction effects: Stomatal conductance and photosynthesis recovery as key indicators in potato. Agricultural Water Management, 177:369-378.
  56. Rud, R., Cohen, Y., Alchanatis, V., Levi, A., Brikman, R., Shenderey, C., Heuer, B., Markovitch, T., Dar, Z., Rosen, C., Mulla, D., Nigon, T. (2014). Crop water stress index derived from multi yearground and aerial thermal images as an indicator of potato water status. Precision Agric., 15: 273-289.
  57. Romano, G., Zia, S., Spreer, W., Sanchez, C., Cairns, J., Araus, J.L., Müller, J. (2011). Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput. Electron. Agric., 79:67-74.
  58. Rouse, J.W. Haas, R.H., Schell, J.A. and Deering, D.W., 1973. "Monitoring vegetation systems in the Great Plains with ERTS," in Proc. 3rd Earth Resour. Technol. Satell. Symp., Washington, DC, USA, 1973, pp. 309-317.
  59. Sharda, V., Srivastava, P., Kalin, L., Asce, M., Ingram, K., Chelliah, M. (2013). Development of community water deficit index: Drought-forecasting tool for small to midsize communities of the southeastern United States. Journal of hydrologic engineering, Vol. 18: 846-858.
  60. Silber, A., Israeli, Y., Elingold, I., Levi, M., Levkovitch, I., Russo, D., Assouline, S. (2015). Irrigation with desalinated water: A step toward increasing water saving and crop yields. Water Resources Research. 51, 450-464.
  61. Silva, M.D.A., Jifon, J.L., Da Silva J.A., Sharma, V. (2007). Use of physiological parameters as fast tools to screen for drought tolerance in sugarcane. Braz. J. Plant Physiol. 19:193-201.
  62. Tanriverdi, C. (2003). Available water effects on water stress indices for irrigated corn grown in sandy soils. Ph. D. Thesis, Department of Chemical and Bioresource Engineering, Colorado State University, Fall 2003.
  63. Tanriverdi, C. (2005). Using TDR in the agricultural water management. KSU, Journal of Science and Engineering, Vol. 8(2): 108-115.
  64. Turner, D., Lucieer, A., Watson, C. (2011). Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery, in: En Proceedings of 34th International Symposium on Remote Sensing of Environment, 1-4, available at: http://www.isprs.org/ proceedings/2011/ isrse-34/211104015Final00547.pdf (last access: 5 April 2016).
  65. Tucker, C.J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sens. Environ., Vol. 8:127-150.
  66. Tong, X., Brandt, M., Hiernaux, P., Herrmann, S., Tian, F., Prishchepov, A.V., Fensholt, R. (2017). Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: A case study in western Niger. Remote Sensing of Environment, 191: 286-296.
  67. USGS science for a changing world, (2006a). Water science for school: how much water is there on earth? http://ga.water.usgs.gov/edu/earthhowmuch.html Pp. 1-3.
  68. USGS science for a changing world, (2006b). Irrigation water use, from USGS water science. http://ga.water.usgs.gov/edu/wuir.html Pp. 1-4.
  69. Uphoff, N., Kassam, A., Harwood, R. (2010). SRI as a methodology for raising crop and water productivity: productive adaptations in rice agronomy and irrigation water management. Paddy Water Environ., 9: 3-11.
  70. Van Leeuwen, W.J.D, Orr, B.J., Marsh, S.E., Herrmann, S.M. (2006). Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ. 100(1):67-81.
  71. Wang, Q., Takahashi, H. (1999). A land surface water deficit model for an arid and semiarid region: impact of desertification on the water deficit status in the loess plateau, China. Journal of Climate, 12(1):244-257.
  72. Wang, K, Dickinson, R.E. (2012). A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Rev Geophys 50, RG2005:1-54.
  73. Xu, W.X., Gu, S., Zhao, X.Q., Xiao, J.S., Tang, Y.H., Fang, J.Y., Zhang, J., Jiang, S. (2011). High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau. Int. J. Appl. Earth Obs. Geoinf. 13(4):528-535.
  74. Xu, Y., Yang J., Chen Y. (2015). NDVI-based vegetation responses to climate change in an arid area of China. Theor Appl Climatol. 1:213-222.
  75. Xu, J., Lv, Y., Dalson, T., Yang, T., Wu, J. (2015). Diagnosing Crop Water Stress of Rice using Infrared Thermal Imager under Water Deficit Condition. International Journal of Agriculture & Biology, DOI: 10.17957/IJAB/15.0125.
  76. Yang Y., Xu J.H., Hong Y.L., Lv G.H. (2012). The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China. Stoch Environ. Res. Risk. Assess. 26:357-373.
  77. Yang, Z., Wen-bin, W.U., Di, L., Ustundag, B. (2017). Remote sensing for agricultural applications. Journal of Integrative Agriculture, 16(2): 239-241.
  78. Yatapanage, K.G., So, H.B. (2001). The relationship between leaf water potential and stem diameter in sorghum. Agron. J., 93: 1341-1343.
  79. Yi, S.H., Zhou, Z.Y., Ren, S.L., Xu, M., Qin, Y., Chen, S.Y., Ye, B.S. (2011). Effects of permafrost degradation on alpine grassland in a semi-arid basin on the Qinghai- Tibetan Plateau. Environ. Res. Lett. 6 (4), 045103.
  80. Yuan, G., Luo, Y., Sun, X., Tang, D. (2004). Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain. Agric. Water Manag., 64: 29-40.
  81. Zhang, X.X., Ge, Q.S., Zheng, J.Y. (2005). Impacts and lags of global warming on vegetation in Beijing for the last 50 years based on remotely sensed data and phonological information. Chin. J. Ecol., 24:123-130.
  82. Zhang X.Y., Goldberg M., Tarpley D., Friedl M.A., Morisette J., Kogan F., Yu, Y.Y. (2010). Drought-induced vegetation stress in southwestern North America. Environ. Res. Lett. 5:024008. doi:10.1088/1748-9326/5/2/024008.
  83. Zhou, Z.Y., Yi, S.H., Chen, J.J., Ye, B.S., Sheng, Y., Wang, G.X., Ding, Y.J. (2015). Responses of alpine grassland to climate warming and permafrost thawing in two basins with different precipitation regimes on the Qinghai-Tibetan Plateau. Arct. Antarct. Alp. Res. 47(1):125-131.
  84. Zia, S., Du, W., Spreer, W., Spohrer, K., He, X., Müller, J., (2012). Assessing crop water stress of winter wheat by thermography under different irrigation regimes in North China Plain. Int. J. Agric. Biol. Eng., 5: 24-34.
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ISSN
1732-5587
Język
eng
URI / DOI
http://dx.medra.org/10.14597/infraeco.2017.3.1.068
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