SavedAdd your journal

Journals / Soil Studies / 2019 / Özel Sayı

Evaluation of the Relationships Between Wheat Yield and Biomass with Remote Sensing Vegetation Indices on the basis of Agricultural Climate Regions

Buğday Verimi ve Biyokütlesi ile Uzaktan Algılama Vejetasyon İndisleri Arasındaki İlişkilerin Tarımsal İklim Bölgeleri Bazında Değerlendirilmesi

Murat Güven Tuğaç(ANKARA TARLA BİTKİLERİ MERKEZ ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)0000-0001-5941-5487
Harun Torunlar(ANKARA TARLA BİTKİLERİ MERKEZ ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)
Erol Karakurt(ANKARA TARLA BİTKİLERİ MERKEZ ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)
Sinan Aydoğan(ANKARA TARLA BİTKİLERİ MERKEZ ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)
Pages
177–186
Publication Date
2019-12-24
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In ACZ based estimation models, a higher level of relationship was obtained than the model made for the whole area.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Vegetation indices obtained from satellite images are widely used in the monitoring of agricultural crops and the prediction of yields.
📊
Key Findings & Data:The aim of this study is to investigate the relationships between spectral vegetation indices, yield and biomass on the basis of agricultural climatic regions (ACZ) and to determine effective vegetation indices on a temporal basis.
💡
Conclusion & Impact:In ACZ based estimation models, a higher level of relationship was obtained than the model made for the whole area.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Biyokütle · EVI · NDVI

Abstract

Vegetation indices obtained from satellite images are widely used in the monitoring of agricultural crops and the prediction of yields. The changes in meteorological factors and especially the irregular rainfall regime have positive and negative effects on different ecological regions. The aim of this study is to investigate the relationships between spectral vegetation indices, yield and biomass on the basis of agricultural climatic regions (ACZ) and to determine effective vegetation indices on a temporal basis. In this context, 5 different regions were determined by using aridity index and growing degree days (GDD) parameters in establishing ACZ. In the field studies, the plant samples were collected from 108 plots of wheat cultivated in Ankara and Konya provinces and yield and biomass values were obtained. During the crop growing period, Normalized Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) of 15 different periods from MODIS satellite image were used. The relationship between vegetation indices and biomass and yield was examined by Pearson correlation coefficient. The highest correlation between vegetation indices and biomass was observed in May in 12th period (75%) and 13th period (72%) for NDVI, 12th period (71%) and 13th period (72%) for EVI. The highest correlation rate between vegetation indices and yield was determined as NDVI (72%) and EVI (70%) in the 12th period. During the vegetation period, the 12th and 13th periods correspond to the heading / anthesis period. Linear regression models were created according to the significance levels (p <0.05) of the parameters. The R2 values for the vegetation indexes determined for ACZ-based models ranged from 0.37 to 0.87 for biomass-NDVI and 0.56 for the whole area, 0.24 to 0.68 for the biomass-EVI and 0.52 for the whole area. These ratios vary between 0.28 and 0.72 between yield and NDVI, whereas 0.53 in whole area. The yield-EVI ranges from 0.32 to 0.54, while it is 0.48 for the whole area. In ACZ based estimation models, a higher level of relationship was obtained than the model made for the whole area.

Özet

Tarımsal ürünlerin izlenmesi ve verim öngörülerinin yapılmasında uydu görüntülerinden elde edilen vejetasyon indisleri yaygın olarak kullanılmaktadır. Meteorolojik faktörlerdeki değişimler ve özellikle düzensiz yağış rejimi farklı ekolojik bölgelerde olumlu ve olumsuz etkiler göstermektedir. Bu çalışmanın amacı, Tarımsal İklim Bölgeleri (TİB) bazında, spektral vejetasyon indisleri ile verim ve biyokütle arasındaki ilişkilerin araştırılması ve zamansal bazda etkili vejetasyon indislerinin belirlenmesidir. Bu kapsamda, TİB’in oluşturulmasında; Kuraklık indisi ve Etkili Sıcaklık Toplamı (EST) parametreleri kullanılarak 5 farklı bölge belirlenmiştir. Arazi çalışmalarında, Ankara ve Konya illerinde buğday ekili 108 parsellerden bitki örneklemesi yapılarak verim ve biyokütle değerleri elde edilmiştir. Bitki gelişme dönemi boyunca, MODIS uydu görüntüsünden elde edilen 15 farklı döneme ait Normalize Edilmiş Bitki İndeksi (NDVI) ile Zenginleştirilmiş Bitki İndeksi (EVI) kullanılmıştır. Vejetasyon indisleri ile biyokütle ve verim arasındaki ilişki Pearson korelasyon katsayısı ile incelenmiştir. Vejetasyon indisleri ile biyokütle arasında en yüksek ilişki; NDVI için Mayıs ayında, 12. dönem (%75) ve 13. dönem (%72), EVI için 12. dönem (%71) ve 13. dönemde (%72) gerçekleşmiştir. Vejetasyon indisleri ile verim arasında en yüksek korelasyon 12. dönemde, NDVI (%72) ve EVI (% 70) olarak tespit edilmiştir. Bitki gelişim dönemi içinde 12. ve 13. dönemler başaklanma/çiçeklenme dönemine karşılık gelmektedir. Parametrelerin önemlilik düzeylerine (p<0.05) göre doğrusal regresyon modelleri oluşturulmuştur. TİB bazında belirlenen vejetasyon indisleri için oluşturulan modeller de R2 değerleri, biyokütle- NDVI arasında 0.16 ile 0.84 arasında değişirken alanın bütünü için 0.56 oranında ve biyokütle- EVI arasında 0.24 ile 0.68 arasında değişirken alanın bütünü için 0.52 düzeyindedir. Bu oranlar verim ve NDVI arasında 0.28 ile 0.72 arasında değişirken alanın bütününde 0.53 ve verim-EVI arasında 0.32 ile 0.54 arasında değişirken alanın bütünü için 0.48 düzeyindedir. TİB bazında yapılan tahmin modellerinde alanın bütünü için yapılan modele göre daha yüksek düzeyde ilişkiler elde edilmiştir.

🔖

Cite This Article

Tuğaç, M. G., Torunlar, H., Karakurt, E., Aydoğan, S. (2019). Evaluation of the Relationships Between Wheat Yield and Biomass with Remote Sensing Vegetation Indices on the basis of Agricultural Climate Regions. Soil Studies, Özel Sayı, 177–186. https://doi.org/10.21657/topraksu.655617

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (34)

  1. Allen RG, Pereira LS, Raes D, Smith M (1998). Crop evapotranspiration: Guideline for computing crop water requirements.FAO No56. Araya A, Keesstra SD, Stroosnijder L (2010). “A new agroclimatic classification for crop suitability zoning in northern semi-arid Ethiopia”. Agric. Forest Meteorol. 150. 1057–1064.
  2. Balaghi R, Tychon B, Eerens H, Jlibene M (2008). “Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco”, International Journal of Applied Earth Observation and Geoinformation, 10, pp. 438–452.
  3. Bouma E (2005). Development of comparable agroclimatic zones for the international exchange of data on the efficacy and crop safety of plant protection products. OEPP/ EPPO. Bulletin OEPP/EPPO Bulletin. 35. 233– 238 Plant Protection Service. Geertjesweg 15. PO Box 9102. NL-6700 HC Wageningen (Netherlands). Caldiz DO, Haverkort AJ, Struik PC (2002). “Analysis of a complex crop production system in interdependent agroecological zones: a methodological approach for potatoes in Argentina”. Agric. Syst. 73. 297–311.
  4. FAO (1996). Agro-Ecologıcal Zoning Guidelines. Soil Bulletin. vol. 73. FAO. Rome. Fischer G, Shah M, Tubiello FN, Van Velhuizen H (2005). “Socio-economic and climate change impacts on agriculture: an integrated assessment. 1990–2080”. Phil. Trans. R. Soc. Lond. B 360. 2067–2083.
  5. Gallup JL, Sachs JD (2000). “Agriculture. climate. And technology: why are the tropics falling behind?”. Am. J. Agric. Econ. 82. 731–737.
  6. Geerts S, Raes D, Garcia M, Del Castillo C, Buytaert W (2006). “Agro-climatic suitability mapping for crop production in the Bolivian Altiplano: a case study for quinoa”. Agric. Forest Meteorol. 139. 399–412.
  7. Gouri V, Reddy DR, Rao SBSN, Rao AY (2005). Thermal requirement of rabi groundnut in southern Telengana zone of Andhra Pradesh. J. Agrometeorol.7: 90-94.
  8. Hutchinson MF (2000). ANUSPLIN version 4.1. User guide. center for resource and environmental studies, Australian National University, Canberra. Hutchinson MF (1995). Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Info. Systems, 9: 385-403.
  9. Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometricand biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213.
  10. Lu D, Mausel P, Batistella M, Moran E (2005). Landcover binary change detection methods for use in the moist tropical region of the Amazon. International Journal of Remote Sensing. Vol. 26. No. 1. pp. 101–114.
  11. Lusch D P (1999). Introduction to Environmental Remote Sensing: Michigan: Center for Remote Sensing and GIS. Kogan F (1994). NOAA plays leadership role in developing satellite technology for drought watch. Earth Observation Magazine. EMO Inc. Littleton. CO. USA. pp. 18–20.
  12. Kouadio L, Newlands N K, Davidson A, Zhang Y, Chipanshi A (2014). “Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale.” Remote Sensing 6: 10193–10214. doi:10.3390/rs61010193. Mcmaster G S, Wilhelm W (1997). Growing degreedays: one equation. two interpretations. Agric. For. Meteorol. 87(4):291-300. doi:10.1016/S0168-1923(97)00027-0.
  13. Minamiguchi N (2005). The Application of geospatial and disaster information for food insecurity and agricultural drought monitoring and assessment by the FAO GIEWS and Asia FIVIMS // Proceedings of the Workshop on Reducing Food Insecurity Associated with Natural Disasters in Asia and the Pacific. Bangkok. P. 20-21.
  14. Pertziger F, De Pauw E (2002). CLIMAP. An Excel-based software for climate surface mapping. ICARDA, Aleppo, Syria. Rouse J W, Haas R H, Schell J A, Deering DW (1973). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium. NASA SP-351. Washington. DC. NASA. p. 309-317.
  15. Seppelt R (2000). Regionalised optimum control problems for agroecosystem management. Ecol. Model. 131. 121–132.
  16. Tucker C (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote sensing of Environment. 8. 127- 150. Turkes M (1999). Vulnerability of Turkey to desertification with respect to precipitation and aridity conditions, Tr. J. Of Engineering and Environmental Science, 23 (1999), 363- 380. TUBITAK. UN-DESA (2013). World Population Prospects: The 2012 Revision. United Nations Department of Economic and Social Affairs. Population Division. New York. http://esa.un.org/ unpd/wpp/index.htm. UNEP (1993). World atlas of desertification. The United Nations Environment Programme (UNEP), London. Zhang S, Lei Y, Wang L, Li H, Zhao H (2011). Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain. China. Chin. Geogra. Sci. 2011 21(3) 322–333 doi: 10.1007/s11769-011-0472-2.
  17. Williams CL, Liebman M, Edwards JW, James DE, Singer JW, Arritt R, Herzmann D (2008). Patterns of regional yield stability in association with regional environmental characteristics. Crop Sci. 48. 1545-1559.
  18. Allen RG, Pereira LS, Raes D, Smith M (1998). Crop evapotranspiration: Guideline for computing crop water requirements.FAO No56. Araya A, Keesstra SD, Stroosnijder L (2010). “A new agroclimatic classification for crop suitability zoning in northern semi-arid Ethiopia”. Agric. Forest Meteorol. 150. 1057–1064.
  19. Balaghi R, Tychon B, Eerens H, Jlibene M (2008). “Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco”, International Journal of Applied Earth Observation and Geoinformation, 10, pp. 438–452.
  20. Bouma E (2005). Development of comparable agroclimatic zones for the international exchange of data on the efficacy and crop safety of plant protection products. OEPP/ EPPO. Bulletin OEPP/EPPO Bulletin. 35. 233– 238 Plant Protection Service. Geertjesweg 15. PO Box 9102. NL-6700 HC Wageningen (Netherlands). Caldiz DO, Haverkort AJ, Struik PC (2002). “Analysis of a complex crop production system in interdependent agroecological zones: a methodological approach for potatoes in Argentina”. Agric. Syst. 73. 297–311.
  21. FAO (1996). Agro-Ecologıcal Zoning Guidelines. Soil Bulletin. vol. 73. FAO. Rome. Fischer G, Shah M, Tubiello FN, Van Velhuizen H (2005). “Socio-economic and climate change impacts on agriculture: an integrated assessment. 1990–2080”. Phil. Trans. R. Soc. Lond. B 360. 2067–2083.
  22. Gallup JL, Sachs JD (2000). “Agriculture. climate. And technology: why are the tropics falling behind?”. Am. J. Agric. Econ. 82. 731–737.
  23. Geerts S, Raes D, Garcia M, Del Castillo C, Buytaert W (2006). “Agro-climatic suitability mapping for crop production in the Bolivian Altiplano: a case study for quinoa”. Agric. Forest Meteorol. 139. 399–412.
  24. Gouri V, Reddy DR, Rao SBSN, Rao AY (2005). Thermal requirement of rabi groundnut in southern Telengana zone of Andhra Pradesh. J. Agrometeorol.7: 90-94.
  25. Hutchinson MF (2000). ANUSPLIN version 4.1. User guide. center for resource and environmental studies, Australian National University, Canberra. Hutchinson MF (1995). Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Info. Systems, 9: 385-403.
  26. Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometricand biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213.
  27. Lu D, Mausel P, Batistella M, Moran E (2005). Landcover binary change detection methods for use in the moist tropical region of the Amazon. International Journal of Remote Sensing. Vol. 26. No. 1. pp. 101–114.
  28. Lusch D P (1999). Introduction to Environmental Remote Sensing: Michigan: Center for Remote Sensing and GIS. Kogan F (1994). NOAA plays leadership role in developing satellite technology for drought watch. Earth Observation Magazine. EMO Inc. Littleton. CO. USA. pp. 18–20.
  29. Kouadio L, Newlands N K, Davidson A, Zhang Y, Chipanshi A (2014). “Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale.” Remote Sensing 6: 10193–10214. doi:10.3390/rs61010193. Mcmaster G S, Wilhelm W (1997). Growing degreedays: one equation. two interpretations. Agric. For. Meteorol. 87(4):291-300. doi:10.1016/S0168-1923(97)00027-0.
  30. Minamiguchi N (2005). The Application of geospatial and disaster information for food insecurity and agricultural drought monitoring and assessment by the FAO GIEWS and Asia FIVIMS // Proceedings of the Workshop on Reducing Food Insecurity Associated with Natural Disasters in Asia and the Pacific. Bangkok. P. 20-21.
  31. Pertziger F, De Pauw E (2002). CLIMAP. An Excel-based software for climate surface mapping. ICARDA, Aleppo, Syria. Rouse J W, Haas R H, Schell J A, Deering DW (1973). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium. NASA SP-351. Washington. DC. NASA. p. 309-317.
  32. Seppelt R (2000). Regionalised optimum control problems for agroecosystem management. Ecol. Model. 131. 121–132.
  33. Tucker C (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote sensing of Environment. 8. 127- 150. Turkes M (1999). Vulnerability of Turkey to desertification with respect to precipitation and aridity conditions, Tr. J. Of Engineering and Environmental Science, 23 (1999), 363- 380. TUBITAK. UN-DESA (2013). World Population Prospects: The 2012 Revision. United Nations Department of Economic and Social Affairs. Population Division. New York. http://esa.un.org/ unpd/wpp/index.htm. UNEP (1993). World atlas of desertification. The United Nations Environment Programme (UNEP), London. Zhang S, Lei Y, Wang L, Li H, Zhao H (2011). Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain. China. Chin. Geogra. Sci. 2011 21(3) 322–333 doi: 10.1007/s11769-011-0472-2.
  34. Williams CL, Liebman M, Edwards JW, James DE, Singer JW, Arritt R, Herzmann D (2008). Patterns of regional yield stability in association with regional environmental characteristics. Crop Sci. 48. 1545-1559.