Araştırma Makalesi
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Monitoring of leaf nitrogen content in sugar beet by vegetation index values

Yıl 2020, Cilt: 8 Sayı: 1, 69 - 76, 24.06.2020
https://doi.org/10.33409/tbbbd.757448

Öz

Leaf nitrogen (N) content is one of the most important variables for agricultural applications due to its critical roles in photosynthesis and plant metabolism. In this study, it was aimed to relate the Redge -NDVI values derived from Sentinel 2A satellite images with the leaf N% contents in sugar beet plants for three different vegetation periods on a parcel basis. The study was carried out using leaf samples taken from 26 different farmers' lands selected in Konya-Çumra region between May-June-July 2019 and satellite images provided close to the specified dates. With the research, satisfactory accuracy coefficients (0.74 ≤ r2 ≤ 0.83) were obtained between leaf N contents and Redge - NDVI values during the vegetative and root development phase, but it was determined that the relationship decreased (r2 <0.70) during the sugar beet root growth period. As a result of the study, it was found successful to use Sentinel 2A satellite image and Red Edge spectral band in order to monitoring %N of sugar beet in early – mid vegetation periods rapidly and non-destructively in large areas and short time intervals. In addition, it has been suggested that by conducting similar studies can be provided valuable information to remote sensing applications that may increase the efficiency of nitrogen.

Kaynakça

  • Anonim, 2012. Bitkisel üretim çiftçi rehberi, konyaseker.com.tr/Upload/Files/seker-pancari.pdf.
  • Anonim, 2018. Tarım ve Orman Bakanlığı Meteorolojı Genel Müdürlüğü, https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx (Erişim tarihi 22.06.2019)
  • Anonim, 2020, www.tuik.gow.tr (Erişim tarihi 16.06.2020)
  • Bagheri N, Ahmadi H, Alavipanah S, Omid M, 2012. Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics. 26(2): 103-108.
  • Başayiğit L, Dedeoğlu M, Akgül H, Uçgun K, Altındal M, 2017. Investigation of N deficiency in cherry trees using visible and near-infrared spectra part of the spectrum in field condition. Spectroscopy and Spectral Analysis.37(1): 293-298.
  • Bausch WC, Khosla R, 2010. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture. 11(3): 274-290.
  • Cabrera-Bosquet L, Molero G, Stellacci, A, Bort J, Nogues, S, Araus J, 2011. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Res. Comm. 39(1): 147-159.
  • Clevers JG, Kooistra L, Van den Brande MM, 2017. Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing. 9(5): 405.
  • Clevers JGPW, Gitelson AA, 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int. J. Appl. Earth Obs. Geoinform 23:344–351.
  • Draycott AP, Christenson DR, 2003. Nutrients for sugar beet production: Soil-plant relationships. Cabi.
  • Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P, 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Env. 120: 25-36.
  • Faberio C, Martin de Santa Olalla F, Lopez R, Dominguez A, 2003. Production and quality of the sugar beet cultivated under contrelled deficit irrigation conditions in a semi-arid climate. Agric. Water Manage. 62: 215-227.
  • Fernández-Manso A, Fernández-Manso O, Quintano C, 2016. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International J. App. Earth Obs. Geo. 50: 170-175.
  • Fitzgerald G, Rodriguez D, O’Leary G, 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Research. 116(3): 318-324.
  • Gezgin S, Dursun N, Hamurcu M, Ayaslı Y, 1999. Konya ovasinda şeker pancarı bitkisinde beslenme sorunlarinin toprak ve bitki analizleri ile Belirlenmesi. Konya Pancar Ekicileri Kooperatifi Yayını, 1999, Konya.
  • Gitelson AA, Merzlyak MN, 1997. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18: 2691–2697.
  • Guo BB, Qi SL, Heng YR, Duan JZ, Zhang HY, Wu YP, Feng W, Xie YX, Zhu YJ, 2016. Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. Eur. J. Agron. 82: 113–124.
  • Haboudane D, Tremblay N, Miller JR, Vigneault P, 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans. Geosci. Remote Sens. 46: 423–437.
  • Huang S, Miao Y, Yuan F, Gnyp M, Yao Y, Cao Q, 2017. Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing. 9(3): 227.
  • Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry CS, Perry EM, Akhmedov B, 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. App. Earth Obs. Geo. 21: 103-112.
  • Jackson RD, 1986. Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24: 265–286.
  • Jia L, Yu Z, Li F, Gnyp M, Koppe W, Bareth G, Miao Y, Chen X, Zhang F, 2011. Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
  • Jones JR, Wolf B, Mills HA, 1991. Plant analysis handbook, Micro Macro Publishing Inc.
  • Lambert M, Traoré PCS, Blaes X, Baret P, Defourny P, 2018. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt. Remote Sensing of Environment. 216: 647-657.
  • Li F, Gnyp ML, Jia LL, Miao YX, Yu ZH, Koppe W, Bareth G, Chen XP, Zhang FS, 2008. Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Res. 106: 77–85.
  • Maimaitiyiming M, Ghulam A, Bozzolo A, Wilkins JL, Kwasniewski MT, 2017. Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sensing. 9(7): 745.
  • Mee CY, Siva KB, Ahmad HMH, 2017. Detecting and monitoring plant nutrient stress using remote sensing approaches: A review. Asian J. Plant Sci. 16: 1-8.
  • Mezera J, Lukas V, Elbl J, 2017. Evaluation of crop yield spatial variability in relation to variable rate application of fertilizers. MendelNet. 24(1): 17-37.
  • Min M, Lee WS, 2005. Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASAE. 48(2): 455-461.
  • Pasqualotto N, Delegido J, Van Wittenberghe S, Rinaldi M, Moreno J. 2019. Multi-crop green LAI estimation with a new simple Sentinel-2 LAI index (SeLI). Sensors, 19(4):904.
  • Sharma LK, Bu H, Denton A, Franzen DW, 2015. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA. Sensors. 15(11): 27832-27853.
  • Shou LN, Jia LL, Cui ZL, Chen XP, Zhang FS, 2007. Using high-resolution satellite image to evaluate nitrogen status of winter wheat in the North China Plain. Journal of Plant Nutrition. 30(10): 1669–1680.
  • Verhulst N, Govaerts B, Sayre KD, Deckers J, François IM, Dendooven L, 2009. Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant and Soil. 317(1): 41-59.
  • Wójtowicz M, Wójtowicz A, Piekarczyk J, 2016. Application of remote sensing methods in agriculture. Comm. in Bio. and Crop Sci. 2016(11): 31-50.
  • Wright AF, Bailey JS, 2001. Organic carbon, total carbon, and total nitrogen determinations in soils of variable calcium carbonate contents using a Leco CN-2000 dry combustion analyzer. Comm. in Soil Sci. Plant A. 32(19-20): 3243–3258.
  • Zhao F, Gu X, Verhoef W, Wang Q, Yu T, Liu Q, Zhao H, 2010. A spectral directional reflectance model of row crops. Remote Sensing of Environment. 114(2): 265-285.
  • Zhao H, Song X, Yang G, Li Z, Zhang D, Feng H, 2019. Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing 11(14): 1724.

Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi

Yıl 2020, Cilt: 8 Sayı: 1, 69 - 76, 24.06.2020
https://doi.org/10.33409/tbbbd.757448

Öz

Yaprak azot (N) içeriği fotosentez ve bitki metabolizmasındaki kritik rolleri nedeniyle tarımsal uygulamalar için en önemli değişkenlerdendir. Bu çalışmada Sentinel 2A uydu görüntülerinden türetilen Redge -NDVI değerleri ile üç farklı vejetasyon dönemi için şeker pancarı bitkisinde yaprak %N içeriklerinin parsel bazlı olarak ilişkilendirilmesi amaçlanmıştır. Çalışma Konya-Çumra bölgesinde seçilen 26 farklı çiftçi arazisinden Mayıs-Haziran-Temmuz 2019 tarihlerinde alınan yaprak örnekleri ve belirtilen tarihlere yakın temin edilen uydu görüntüleri kullanılarak yürütülmüştür. Araştırma ile vejetatif gelişim ve kök oluşum evresinde yaprak %N içerikleri ve Redge – NDVI değerleri arasında tatmin edici doğruluk katsayıları (0.74 ≤ r2 ≤ 0.83) elde edilmiş, ancak şeker pancarı kök büyüme dönemi içerisinde ilişkinin azaldığı (r2 <0.70) belirlenmiştir. Çalışma sonucu şeker pancarı bitkisinin erken-orta vejetasyon dönemlerinde %N içeriğinin hızlı ve tahribatsız olarak geniş alanlarda ve kısa zaman aralıklarında izlenmesi için Sentinel 2A uydu görüntüsü ve Red Edge spektral bandının kullanımı başarılı bulunmuştur. Ayrıca benzer araştırmaların yürütülmesi ile azot kullanım etkinliğini artırabilir uzaktan algılama uygulamalarına değerli bilgiler sağlanabileceği önerilmiştir.

Kaynakça

  • Anonim, 2012. Bitkisel üretim çiftçi rehberi, konyaseker.com.tr/Upload/Files/seker-pancari.pdf.
  • Anonim, 2018. Tarım ve Orman Bakanlığı Meteorolojı Genel Müdürlüğü, https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx (Erişim tarihi 22.06.2019)
  • Anonim, 2020, www.tuik.gow.tr (Erişim tarihi 16.06.2020)
  • Bagheri N, Ahmadi H, Alavipanah S, Omid M, 2012. Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics. 26(2): 103-108.
  • Başayiğit L, Dedeoğlu M, Akgül H, Uçgun K, Altındal M, 2017. Investigation of N deficiency in cherry trees using visible and near-infrared spectra part of the spectrum in field condition. Spectroscopy and Spectral Analysis.37(1): 293-298.
  • Bausch WC, Khosla R, 2010. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture. 11(3): 274-290.
  • Cabrera-Bosquet L, Molero G, Stellacci, A, Bort J, Nogues, S, Araus J, 2011. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Res. Comm. 39(1): 147-159.
  • Clevers JG, Kooistra L, Van den Brande MM, 2017. Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing. 9(5): 405.
  • Clevers JGPW, Gitelson AA, 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int. J. Appl. Earth Obs. Geoinform 23:344–351.
  • Draycott AP, Christenson DR, 2003. Nutrients for sugar beet production: Soil-plant relationships. Cabi.
  • Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P, 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Env. 120: 25-36.
  • Faberio C, Martin de Santa Olalla F, Lopez R, Dominguez A, 2003. Production and quality of the sugar beet cultivated under contrelled deficit irrigation conditions in a semi-arid climate. Agric. Water Manage. 62: 215-227.
  • Fernández-Manso A, Fernández-Manso O, Quintano C, 2016. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International J. App. Earth Obs. Geo. 50: 170-175.
  • Fitzgerald G, Rodriguez D, O’Leary G, 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Research. 116(3): 318-324.
  • Gezgin S, Dursun N, Hamurcu M, Ayaslı Y, 1999. Konya ovasinda şeker pancarı bitkisinde beslenme sorunlarinin toprak ve bitki analizleri ile Belirlenmesi. Konya Pancar Ekicileri Kooperatifi Yayını, 1999, Konya.
  • Gitelson AA, Merzlyak MN, 1997. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18: 2691–2697.
  • Guo BB, Qi SL, Heng YR, Duan JZ, Zhang HY, Wu YP, Feng W, Xie YX, Zhu YJ, 2016. Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. Eur. J. Agron. 82: 113–124.
  • Haboudane D, Tremblay N, Miller JR, Vigneault P, 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans. Geosci. Remote Sens. 46: 423–437.
  • Huang S, Miao Y, Yuan F, Gnyp M, Yao Y, Cao Q, 2017. Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing. 9(3): 227.
  • Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry CS, Perry EM, Akhmedov B, 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. App. Earth Obs. Geo. 21: 103-112.
  • Jackson RD, 1986. Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24: 265–286.
  • Jia L, Yu Z, Li F, Gnyp M, Koppe W, Bareth G, Miao Y, Chen X, Zhang F, 2011. Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
  • Jones JR, Wolf B, Mills HA, 1991. Plant analysis handbook, Micro Macro Publishing Inc.
  • Lambert M, Traoré PCS, Blaes X, Baret P, Defourny P, 2018. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt. Remote Sensing of Environment. 216: 647-657.
  • Li F, Gnyp ML, Jia LL, Miao YX, Yu ZH, Koppe W, Bareth G, Chen XP, Zhang FS, 2008. Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Res. 106: 77–85.
  • Maimaitiyiming M, Ghulam A, Bozzolo A, Wilkins JL, Kwasniewski MT, 2017. Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sensing. 9(7): 745.
  • Mee CY, Siva KB, Ahmad HMH, 2017. Detecting and monitoring plant nutrient stress using remote sensing approaches: A review. Asian J. Plant Sci. 16: 1-8.
  • Mezera J, Lukas V, Elbl J, 2017. Evaluation of crop yield spatial variability in relation to variable rate application of fertilizers. MendelNet. 24(1): 17-37.
  • Min M, Lee WS, 2005. Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASAE. 48(2): 455-461.
  • Pasqualotto N, Delegido J, Van Wittenberghe S, Rinaldi M, Moreno J. 2019. Multi-crop green LAI estimation with a new simple Sentinel-2 LAI index (SeLI). Sensors, 19(4):904.
  • Sharma LK, Bu H, Denton A, Franzen DW, 2015. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA. Sensors. 15(11): 27832-27853.
  • Shou LN, Jia LL, Cui ZL, Chen XP, Zhang FS, 2007. Using high-resolution satellite image to evaluate nitrogen status of winter wheat in the North China Plain. Journal of Plant Nutrition. 30(10): 1669–1680.
  • Verhulst N, Govaerts B, Sayre KD, Deckers J, François IM, Dendooven L, 2009. Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant and Soil. 317(1): 41-59.
  • Wójtowicz M, Wójtowicz A, Piekarczyk J, 2016. Application of remote sensing methods in agriculture. Comm. in Bio. and Crop Sci. 2016(11): 31-50.
  • Wright AF, Bailey JS, 2001. Organic carbon, total carbon, and total nitrogen determinations in soils of variable calcium carbonate contents using a Leco CN-2000 dry combustion analyzer. Comm. in Soil Sci. Plant A. 32(19-20): 3243–3258.
  • Zhao F, Gu X, Verhoef W, Wang Q, Yu T, Liu Q, Zhao H, 2010. A spectral directional reflectance model of row crops. Remote Sensing of Environment. 114(2): 265-285.
  • Zhao H, Song X, Yang G, Li Z, Zhang D, Feng H, 2019. Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing 11(14): 1724.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ziraat Mühendisliği
Bölüm Makaleler
Yazarlar

Mert Dedeoğlu Bu kişi benim 0000-0001-8611-3724

Yayımlanma Tarihi 24 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Dedeoğlu, M. (2020). Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi. Toprak Bilimi Ve Bitki Besleme Dergisi, 8(1), 69-76. https://doi.org/10.33409/tbbbd.757448
AMA Dedeoğlu M. Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi. tbbbd. Haziran 2020;8(1):69-76. doi:10.33409/tbbbd.757448
Chicago Dedeoğlu, Mert. “Vejetasyon Indis değerleri Ile şeker Pancarı Yaprak Azot içeriğinin Izlenmesi”. Toprak Bilimi Ve Bitki Besleme Dergisi 8, sy. 1 (Haziran 2020): 69-76. https://doi.org/10.33409/tbbbd.757448.
EndNote Dedeoğlu M (01 Haziran 2020) Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi. Toprak Bilimi ve Bitki Besleme Dergisi 8 1 69–76.
IEEE M. Dedeoğlu, “Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi”, tbbbd, c. 8, sy. 1, ss. 69–76, 2020, doi: 10.33409/tbbbd.757448.
ISNAD Dedeoğlu, Mert. “Vejetasyon Indis değerleri Ile şeker Pancarı Yaprak Azot içeriğinin Izlenmesi”. Toprak Bilimi ve Bitki Besleme Dergisi 8/1 (Haziran 2020), 69-76. https://doi.org/10.33409/tbbbd.757448.
JAMA Dedeoğlu M. Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi. tbbbd. 2020;8:69–76.
MLA Dedeoğlu, Mert. “Vejetasyon Indis değerleri Ile şeker Pancarı Yaprak Azot içeriğinin Izlenmesi”. Toprak Bilimi Ve Bitki Besleme Dergisi, c. 8, sy. 1, 2020, ss. 69-76, doi:10.33409/tbbbd.757448.
Vancouver Dedeoğlu M. Vejetasyon indis değerleri ile şeker pancarı yaprak azot içeriğinin izlenmesi. tbbbd. 2020;8(1):69-76.