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A linear approach for wheat yield prediction by using different spectral vegetation indices

Year 2023, Volume: 8 Issue: 1, 52 - 62, 15.02.2023
https://doi.org/10.26833/ijeg.1035037

Abstract

Yield prediction before harvest is one of the important issues in terms of managing agricultural policies and making the right decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their 21st-century agricultural targets. The aim of this study is to develop a wheat yield model using Landsat-8 and Sentinel-2 satellite data. In this study, the development stages of winter wheat were examined with the help of satellite images obtained between the years 2015-2018 of a selected region in Sanliurfa, Turkey, and it was aimed to predict the yields for other years by establishing a yield estimation model. The yield estimation model was established with the help of Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI) and Modified Soil-adjusted Vegetation Index (MSAVI) obtained from remote sensing satellite images. Linear regression analysis was established between calculated NDVI, SAVI, GNDVI, MSAVI indices, and actual yield values on the pre-flowering, flowering stage, and post-flowering stage. As a result of the study, the highest correlation coefficient was found in the flowering stage between the vegetation indices values and the actual yield values. The values of NDVI, SAVI, GNDVI, and MSAVI and correlation coefficients are obtained in the flowering stage were 0.82, 0.80, 0.86, and 0.87, respectively. With the established model, yield values in 2019 were tried to be estimated for three different fields. The highest correlations were seen in the flowering stage for MSAVI and GNDVI, pre-flowering stage for NDVI and post-flowering stage for SAVI. This clearly shows that the satellite images can be used in yield estimation studies with a remarkable correlation between vegetation indices and actual yield values.

References

  • Wang, Y., Xu, X., Huang, L., Yang, G., Fan, L., Wei, P. & Chen, G. (2019). An improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sensing, 11(9), 1088.
  • Selim, S., & Demir, N. (2019). Detection of ecological networks and connectivity with analyzing their effects on sustainable urban development. International Journal of Engineering and Geosciences, 4(2), 63-70.
  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M. & Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(5967), 812-818.
  • Uyan, M. (2019). Comparison Of Different Interpolation Techniques in Determining of Agricultural Soil Index on Land Consolidation Projects. International Journal of Engineering and Geosciences, 4(1), 28-35.
  • Knox, J. W., Haro-Monteagudo, D., Hess, T., & Morris, J. (2018). Forecasting Changes in Agricultural Irrigation Demand to Support a Regional Integrated Water Resources Management Strategy. Advances in Chemical Pollution, Environmental Management and Protection, 3, 171-213.
  • Bastiaanssen, W. G. M. & Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment, 94(3), 321–340.
  • Apaydin, C., & Abdikan, S. (2021). Fındık bahçelerinin Sentinel-2 verileri kullanılarak piksel tabanlı sınıflandırma yöntemleriyle belirlenmesi. Geomatik, 6(2), 107-114.
  • Li, H., Chen, Z., Liu, G., Jiang, Z. & Huang, C. (2017). Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales. Remote Sensing, 9(3), 190.
  • Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H., Remington, T., Sen, P. T., Sessa, R., Shula, R., Tibu, A. & Torquebiau, E. F. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4(1068-1072).
  • Reynolds, C. A., Yitayew, M., Slack, D. C., Hutchinson, C. F., Huete, A. & Petersen, M. S. (2000). Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data. International Journal of Remote Sensing, 21(18), 3487–3508.
  • Liu, W. T., & Kogan, F. (2002). Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. International Journal of Remote Sensing, 23(6), 1161–1179.
  • Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69(6), 647-664.
  • Fernandez-Ordonez, Y. M., & Soria-Ruiz, J. (2017). Maize crop yield estimation with remote sensing and empirical models. In International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., 3035-3038.
  • Salazar, L., Kogan, F. & Roytman, L. (2007). Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795–3811.
  • Ahmad, I., Saeed, U., Fahad, M., Ullah, A., ur Rahman, M. H., Ahmad, A. & Judge, J. (2018). Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46(10), 1701-1711.
  • Ferguson, M.C. (1982). Evaluation of Trends in Yield Models: Agristars Supporting Research. December, SR J1‐04157, JSC‐17428.
  • Tucker, C. J., Holben, B. N., Elgin, J. H. & McMurtrey, J. E. (1981). Remote sensing of total dry-matter accumulation in winter wheat. Remote Sensing of Environment, 11, 171-189.
  • Craig, M. E. (2001). A resource sharing approach to crop identification and estimation. In ASPRS 2001 Proceedings of the 2001 Annual Conference.
  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., & Schepers, J. S. (2001). In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance. Agronomy Journal, 93(1), 131–138.
  • Ren, J. Q., Chen, Z. X., Zhou, Q. B., & Tang, H. J. (2010). LAI-based regional winter wheat yield estimation by remote sensing. Chinese Journal of Applied Ecology, 21(11), 2883-2888.
  • Şimşek, O., & Çakmak, B. (2012). Agrometshell modeli ile buğdayda geleceğe dönük senaryolar ve risk analizi. Tarım Bilimleri Dergisi, 18(3), 187-196.
  • Narin, O. G., Noyan, O. F., & Abdikan, S. (2021). Monitoring Vegetative Stages of Sunflower and Wheat Crops with Sentinel-2 Images According to BBCH-Scale. Journal of Agricultural Faculty of Gaziosmanpasa University, 38(1), 46-52.
  • Narin, O. G., & Abdikan, S. (2020). Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images. Geocarto International, 1-15.
  • Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6), 1312–1323.
  • Mkhabela, M. S., Bullock, P., Raj, S., Wang, S., & Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3), 385–393.
  • Skakun, S., Vermote, E., Roger, J. C. & Franch, B. (2017). Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale. AIMS Geosciences, 3(2), 163–186.
  • Gontia, N. K., & Tiwari, K. N. (2011). Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS. Journal of the Indian Society of Remote Sensing, 39(1), 27–37.
  • Benek, S. (2006). Şanlıurfa ilinin tarımsal yapısı, sorunları ve çözüm önerileri. Turkish Journal of Geographical Sciences, 4(1), 67-91.
  • Yağmur, N., Tanık, A., Tuzcu, A., Musaoğlu, N., Erten, E., & Bilgilioglu, B. (2020). Oppurtunities provided by remote sensing data for watershed management: Example of Konya closed basin. International Journal of Engineering and Geosciences, 5(3), 120-129.
  • Zabci, C. (2021). Çok bantlı Landsat 8-OLI ve Sentinel-2A MSI uydu görüntülerinin karşılaştırmalı jeoloji uygulaması: Örnek çalışma alanı olarak Doğu Anadolu Fayı boyunca Palu–Hazar Gölü Bölgesi (Elazığ, Türkiye). Geomatik, 6(3), 238-246.
  • Ahady, A. B., & Kaplan, G. (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24-31.
  • Tanaka, D. L. (1989). Spring Wheat Plant Parameters as Affected by Fallow Methods in the Northern Great Plains. Soil Science Society of America Journal, 53(5), 1506–1511.
  • Meier, U. (2001). Growth stages of mono- and dicotyledonous plants. BBCH Monograph.
  • Gündeş, S., & Peştemalci, V. (2008). Türkiye’nin Bitki Örtüsü Değişiminin NOAA Uydu Verileri ile Belirlenmesi. Ç. Ü. Fen Bilimleri Enstitüsü, 17(6).
  • Kundu, A., Dwivedi, S., & Dutta, D. (2016). Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arabian Journal of Geosciences, 9(2), 1–15.
  • Üstüner, M., Şanli, F. B., & Abdikan, S. (2016). Bitki örtüsü indekslerinin tarimsal ürün deseni tespitindeki etkisinin araştırılması. In Proceedings of VI. RS&GIS Symposium (VI. UZAL&CBS Sempozyumu), Adana, Turkey.
  • Aboelghar, M., Ali, A. R., & Arafat, S. (2014). Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index. Arabian Journal of Geosciences, 7(2), 465–474.
  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3–4), 185-200.
  • Konda, V. G. R. K., Chejarla, V. R., Mandla, V. R., Voleti, V., & Chokkavarapu, N. (2018). Vegetation damage assessment due to Hudhud cyclone based on NDVI using Landsat-8 satellite imagery. Arabian Journal of Geosciences, 11(2), 1–11.
  • Peng, W., Wang, J., Zhang, J., & Zhang, Y. (2020). Soil moisture estimation in the transition zone from the Chengdu Plain region to the Longmen Mountains by field measurements and LANDSAT 8 OLI/TIRS-derived indices. Arabian Journal of Geosciences, 13(4), 1–13.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • Ray, T. R. (1994). Vegetation indices in Remote Sensing. A FAQ on Vegetation in Remote Sensing.
  • Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.
  • Rouse, W., Haas, R. H., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS-1 Symposium, 309-329.
  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298.
  • Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126.
Year 2023, Volume: 8 Issue: 1, 52 - 62, 15.02.2023
https://doi.org/10.26833/ijeg.1035037

Abstract

References

  • Wang, Y., Xu, X., Huang, L., Yang, G., Fan, L., Wei, P. & Chen, G. (2019). An improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sensing, 11(9), 1088.
  • Selim, S., & Demir, N. (2019). Detection of ecological networks and connectivity with analyzing their effects on sustainable urban development. International Journal of Engineering and Geosciences, 4(2), 63-70.
  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M. & Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(5967), 812-818.
  • Uyan, M. (2019). Comparison Of Different Interpolation Techniques in Determining of Agricultural Soil Index on Land Consolidation Projects. International Journal of Engineering and Geosciences, 4(1), 28-35.
  • Knox, J. W., Haro-Monteagudo, D., Hess, T., & Morris, J. (2018). Forecasting Changes in Agricultural Irrigation Demand to Support a Regional Integrated Water Resources Management Strategy. Advances in Chemical Pollution, Environmental Management and Protection, 3, 171-213.
  • Bastiaanssen, W. G. M. & Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment, 94(3), 321–340.
  • Apaydin, C., & Abdikan, S. (2021). Fındık bahçelerinin Sentinel-2 verileri kullanılarak piksel tabanlı sınıflandırma yöntemleriyle belirlenmesi. Geomatik, 6(2), 107-114.
  • Li, H., Chen, Z., Liu, G., Jiang, Z. & Huang, C. (2017). Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales. Remote Sensing, 9(3), 190.
  • Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H., Remington, T., Sen, P. T., Sessa, R., Shula, R., Tibu, A. & Torquebiau, E. F. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4(1068-1072).
  • Reynolds, C. A., Yitayew, M., Slack, D. C., Hutchinson, C. F., Huete, A. & Petersen, M. S. (2000). Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data. International Journal of Remote Sensing, 21(18), 3487–3508.
  • Liu, W. T., & Kogan, F. (2002). Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. International Journal of Remote Sensing, 23(6), 1161–1179.
  • Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69(6), 647-664.
  • Fernandez-Ordonez, Y. M., & Soria-Ruiz, J. (2017). Maize crop yield estimation with remote sensing and empirical models. In International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., 3035-3038.
  • Salazar, L., Kogan, F. & Roytman, L. (2007). Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795–3811.
  • Ahmad, I., Saeed, U., Fahad, M., Ullah, A., ur Rahman, M. H., Ahmad, A. & Judge, J. (2018). Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46(10), 1701-1711.
  • Ferguson, M.C. (1982). Evaluation of Trends in Yield Models: Agristars Supporting Research. December, SR J1‐04157, JSC‐17428.
  • Tucker, C. J., Holben, B. N., Elgin, J. H. & McMurtrey, J. E. (1981). Remote sensing of total dry-matter accumulation in winter wheat. Remote Sensing of Environment, 11, 171-189.
  • Craig, M. E. (2001). A resource sharing approach to crop identification and estimation. In ASPRS 2001 Proceedings of the 2001 Annual Conference.
  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., & Schepers, J. S. (2001). In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance. Agronomy Journal, 93(1), 131–138.
  • Ren, J. Q., Chen, Z. X., Zhou, Q. B., & Tang, H. J. (2010). LAI-based regional winter wheat yield estimation by remote sensing. Chinese Journal of Applied Ecology, 21(11), 2883-2888.
  • Şimşek, O., & Çakmak, B. (2012). Agrometshell modeli ile buğdayda geleceğe dönük senaryolar ve risk analizi. Tarım Bilimleri Dergisi, 18(3), 187-196.
  • Narin, O. G., Noyan, O. F., & Abdikan, S. (2021). Monitoring Vegetative Stages of Sunflower and Wheat Crops with Sentinel-2 Images According to BBCH-Scale. Journal of Agricultural Faculty of Gaziosmanpasa University, 38(1), 46-52.
  • Narin, O. G., & Abdikan, S. (2020). Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images. Geocarto International, 1-15.
  • Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6), 1312–1323.
  • Mkhabela, M. S., Bullock, P., Raj, S., Wang, S., & Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3), 385–393.
  • Skakun, S., Vermote, E., Roger, J. C. & Franch, B. (2017). Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale. AIMS Geosciences, 3(2), 163–186.
  • Gontia, N. K., & Tiwari, K. N. (2011). Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS. Journal of the Indian Society of Remote Sensing, 39(1), 27–37.
  • Benek, S. (2006). Şanlıurfa ilinin tarımsal yapısı, sorunları ve çözüm önerileri. Turkish Journal of Geographical Sciences, 4(1), 67-91.
  • Yağmur, N., Tanık, A., Tuzcu, A., Musaoğlu, N., Erten, E., & Bilgilioglu, B. (2020). Oppurtunities provided by remote sensing data for watershed management: Example of Konya closed basin. International Journal of Engineering and Geosciences, 5(3), 120-129.
  • Zabci, C. (2021). Çok bantlı Landsat 8-OLI ve Sentinel-2A MSI uydu görüntülerinin karşılaştırmalı jeoloji uygulaması: Örnek çalışma alanı olarak Doğu Anadolu Fayı boyunca Palu–Hazar Gölü Bölgesi (Elazığ, Türkiye). Geomatik, 6(3), 238-246.
  • Ahady, A. B., & Kaplan, G. (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24-31.
  • Tanaka, D. L. (1989). Spring Wheat Plant Parameters as Affected by Fallow Methods in the Northern Great Plains. Soil Science Society of America Journal, 53(5), 1506–1511.
  • Meier, U. (2001). Growth stages of mono- and dicotyledonous plants. BBCH Monograph.
  • Gündeş, S., & Peştemalci, V. (2008). Türkiye’nin Bitki Örtüsü Değişiminin NOAA Uydu Verileri ile Belirlenmesi. Ç. Ü. Fen Bilimleri Enstitüsü, 17(6).
  • Kundu, A., Dwivedi, S., & Dutta, D. (2016). Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arabian Journal of Geosciences, 9(2), 1–15.
  • Üstüner, M., Şanli, F. B., & Abdikan, S. (2016). Bitki örtüsü indekslerinin tarimsal ürün deseni tespitindeki etkisinin araştırılması. In Proceedings of VI. RS&GIS Symposium (VI. UZAL&CBS Sempozyumu), Adana, Turkey.
  • Aboelghar, M., Ali, A. R., & Arafat, S. (2014). Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index. Arabian Journal of Geosciences, 7(2), 465–474.
  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3–4), 185-200.
  • Konda, V. G. R. K., Chejarla, V. R., Mandla, V. R., Voleti, V., & Chokkavarapu, N. (2018). Vegetation damage assessment due to Hudhud cyclone based on NDVI using Landsat-8 satellite imagery. Arabian Journal of Geosciences, 11(2), 1–11.
  • Peng, W., Wang, J., Zhang, J., & Zhang, Y. (2020). Soil moisture estimation in the transition zone from the Chengdu Plain region to the Longmen Mountains by field measurements and LANDSAT 8 OLI/TIRS-derived indices. Arabian Journal of Geosciences, 13(4), 1–13.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • Ray, T. R. (1994). Vegetation indices in Remote Sensing. A FAQ on Vegetation in Remote Sensing.
  • Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.
  • Rouse, W., Haas, R. H., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS-1 Symposium, 309-329.
  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298.
  • Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126.
There are 47 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Yunus Kaya 0000-0003-2319-4998

Nizar Polat 0000-0002-6061-7796

Publication Date February 15, 2023
Published in Issue Year 2023 Volume: 8 Issue: 1

Cite

APA Kaya, Y., & Polat, N. (2023). A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences, 8(1), 52-62. https://doi.org/10.26833/ijeg.1035037
AMA Kaya Y, Polat N. A linear approach for wheat yield prediction by using different spectral vegetation indices. IJEG. February 2023;8(1):52-62. doi:10.26833/ijeg.1035037
Chicago Kaya, Yunus, and Nizar Polat. “A Linear Approach for Wheat Yield Prediction by Using Different Spectral Vegetation Indices”. International Journal of Engineering and Geosciences 8, no. 1 (February 2023): 52-62. https://doi.org/10.26833/ijeg.1035037.
EndNote Kaya Y, Polat N (February 1, 2023) A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences 8 1 52–62.
IEEE Y. Kaya and N. Polat, “A linear approach for wheat yield prediction by using different spectral vegetation indices”, IJEG, vol. 8, no. 1, pp. 52–62, 2023, doi: 10.26833/ijeg.1035037.
ISNAD Kaya, Yunus - Polat, Nizar. “A Linear Approach for Wheat Yield Prediction by Using Different Spectral Vegetation Indices”. International Journal of Engineering and Geosciences 8/1 (February 2023), 52-62. https://doi.org/10.26833/ijeg.1035037.
JAMA Kaya Y, Polat N. A linear approach for wheat yield prediction by using different spectral vegetation indices. IJEG. 2023;8:52–62.
MLA Kaya, Yunus and Nizar Polat. “A Linear Approach for Wheat Yield Prediction by Using Different Spectral Vegetation Indices”. International Journal of Engineering and Geosciences, vol. 8, no. 1, 2023, pp. 52-62, doi:10.26833/ijeg.1035037.
Vancouver Kaya Y, Polat N. A linear approach for wheat yield prediction by using different spectral vegetation indices. IJEG. 2023;8(1):52-6.