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RapidEye ve PlanetScope uydu bantları ile pamuk bitkisi yaprak azot içeriğinin belirlenmesi

Yıl 2020, , 169 - 180, 21.08.2020
https://doi.org/10.37908/mkutbd.654258

Öz

Amaç: Bu çalışma, pamuk bitkisinin farklı yaprak azot içeriklerinin uydu görüntülerinde oluşan değişimlerini saptamak, uydu görüntüleri yardımı ile yapraktaki azot içeriğini tahmin etmek, gelecekte uydu görüntülerini kullanarak geniş pamuk üretim alanlarında azot gereksinimlerini saptayarak azotlu gübre uygulama tavsiyelerinde bulunmak amacı ile yapılmıştır.

Yöntem ve Bulgular: Mardin ekolojik koşullarında, tesadüf parselleri deneme deseninde 3 tekerrürlü olarak 6 farklı lokasyonda yürütülmüştür. Çalışmada, PlanetScope uydusunun 4 (Blue, Green, Red ve NIR) bandı ve RapidEye uydusunun 5 (Blue, Green, Red, NIR ve RedEdge) bandı kullanılmıştır. İncelenen yaprak azot içeriği ile uydu görüntüleri arasındaki ikili ilişkilerin saptanması yapılarak, regresyon (yaprak azot içeriği-yansıma) ve ters regresyon (yansıma-yaprak azot içeriği) analizleri yapılmıştır. Yaprak azot içeriği ile RE_Blue (r=-0.58**), RE_Green (r=-0.46**) ve RE_Red (r=-0.67**), PS_Blue (r=-0.54**), PS_Green (r=-0.43**) ve PS_Red (r=-0.42**) yansıma verileri arasında önemli korelasyon saptanmıştır.

Genel Yorum: Yaprak azot içeriğinin tahmin edilmesinde incelenen tüm uydu bantları arasından RE_Blue, RE_Green, RE_Red, PS_Blue, PS_Green, PS_Red uydu bantlarının kullanılması tavsiye edilmektedir.

Çalışmanın Önemi ve Etkisi: Bu çalışma, büyük pamuk üretim alanlarında azotlu gübre kullanım ihtiyacının varlığını tespit etmek yönünden büyük önem taşımaktadır. Özellikle GAP bölgesinde artan pamuk ekim alanları ve pamuk üretiminde azotlu gübreleme dikkate alındığında çalışma yaygın etki ve büyük alanlarda hızlı sonuç elde etme yönünden önemlidir. 

Destekleyen Kurum

Dicle Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (DÜBAP)

Proje Numarası

Ziraat.18.022

Teşekkür

Bu çalışma, Dicle Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından Ziraat.18.022 nolu proje ile mali desteklemiş olup, desteğinden ötürü teşekkür ederiz.

Kaynakça

  • Anonim, (1990). Approved methods of the American association of cereal chemists. 8th ed. St. Paul: AACC.
  • Anonim, (2019). http://www.nik.com.tr/content_sistem_uydu.asp?id=67, http://www.nik.com.tr/content_sistem_uydu_goruntuleri.asp, Erişim tarih: 10.08.2019.
  • Cabrera-Bosquet, L., Molero, G., Stellacci, A.M., Bort, J., Nogués, S. And Araus, J.L. (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 Research Communications, March 2011 DOI: 10.1556/CRC.39.2011.1.15
  • Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Research 154 (2013) 133–144.
  • Carter, G.A., Knapp, A.K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88(4): 677–684. 2001
  • Cassman, K.G., Doberman A. and Walters, D.T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 31, 132-140
  • Chen P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., Li, B. (2010). New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment 114 (2010) 1987–1997.
  • Chen, P., Wang, J., Huang, W., Tremblay, N., Ou, Y. and Zhang, Q. (2013). Critical nitrogen curve and remote detection of nitrogen nutrition index for corn in the North Western Plain of Shandong Province, China. Ieee Journal of Selected Topics In Applied Earth Observatıons And Remote Sensing, Vol. 6, No. 2, Aprıl 2013.
  • Chen, P. (2015). A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Remote Sens. 2015, 7, 4527-4548; doi:10.3390/rs70404527.
  • Clevers, J. G. P.W., and Kooistra, L. (2012). Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. Ieee Journal of Selected Topics In Applied Earth Observations And Remote Sensing, Vol. 5, No. 2, April 2012.
  • Delloyea, C., Weissb, M., Defourny, P. (2018). Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment 216 (2018) 245–261 https://doi.org/10.1016/j.rse.2018.06.037.
  • Eitel, J.U.H., Magney, T.S., Vierling, L.A., Brown, T.T., Huggins, D.R. (2014). LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research Volume 159, 15 March 2014, Pages 21-32 https://doi.org/10.1016/j.fcr.2014.01.008.
  • Erdle ,K., Mistele, B., Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research Volume 124, Issue 1, 9 October 2011, Pages 74-84 https://doi.org/10.1016/j.fcr.2011.06.007.
  • Eroğlu, E., 2008. Bazı tarım ürünlerinin bitki besin element noksanlıkları ile elektromanyetik enerji yansıtma özellikleri arasındaki ilişkiler. Ege Üniversitesi Fen Bilimleri Enstitüsü Toprak ABD., Yüksek Lisans Tezi Bornova/İzmir.
  • Filella, I., Serrano, L., Serra, J. and Peñuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science Vol. 35 No. 5, p. 1400-1405.
  • 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 (2010) 318–324.
  • Fridgen, J.L., Varco, J.J. (2004). Dependency of cotton leaf nitrogen, chlorophyll, and reflectance on nitrogen and potassium availability. Agronomy Journal 96(1)
  • Haliloğlu, H., Oğlakçı, M. (2000). Effects of different nitrogen rates on earliness, yield and yield distribution of cotton. The Interregional Cooperative Research Network on Cotton. A Joint Workshop and Meeting of the All Working Groups 20-24 September, Adana/TURKEY
  • Hibberd, D.E., Ladewig, J.H., Hunter, M.N., Blight, G.W. (1990). Responses in Cotton Yields to Nitrogen and Phosphorus Fertilizers in the Emerald Irrigation Area, Central Queensland. Australian Journal of Experimental Agriculture, 30, 661-667.
  • Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., Yu, W., Gnyp, M. L., Lenz-Wiedemann, V.I.S., Rascher, U. and Bareth, G. (2015). Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sens. 2015, 7, 10646-10667; doi:10.3390/rs70810646.
  • Huang, S., Miao, Y., Yuan, F., Gnyp, M. L., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V. I. S. and Bareth, G. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sens. 2017, 9, 227; doi:10.3390/rs9030227.
  • Kacar, B. (2009). Toprak analizleri, Nobel Akademik Yayıncılık Eğitim Danışmanlık Tic. Ltd. Şti,
  • Kacar, B. ve Katkat, A. (2011). Bitki Besleme, Nobel Akademik Yayıncılık Eğitim Danışmanlık Tic. Ltd. Şti
  • Klema, K., Záhorab, J., Zemeka, F., Trundaa, P., Tůmab, I., Novotnáa, K., Hodaňováa, P., Rapantováa, B., Hanuša, J., Vavříkováb, J., Holub, P. (2018). Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection Agricultural Water Management 210 (2018) 171–184
  • Lee, K., Lee, B. (2013). Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. Europ. J. Agronomy 48 (2013) 57–65.
  • Li, F., Miao, Y., 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. Precision Agriculture August 2010, Volume 11, Issue 4, pp 335–357.
  • Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U. (2013). Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Europ. J. Agronomy 52 (2014) 198–209.
  • Magney, T.S., Eitel, J.U.H., Vierling, L.A. (2016). Mapping wheat nitrogen uptake from RapidEye vegetation indices. Precision Agric (2017) 18:429–451 DOI 10.1007/s11119-016-9463-8.
  • Mullen, R. W., Freeman, K. W., Raun, W. R., Johnson, G.V., Stone, M. L. and Solie, J.B. (2003). Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agronomy Journal 95, 347-351
  • Muñoz-Huerta, R.F., Guevara-Gonzalez, R.G., Contreras-Medina, L.M., Torres-Pacheco, I., Prado-Olivarez, J., and Ocampo-Velazquez, R.V. (2013). A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 2013, 13, 10823-10843; doi:10.3390/s130810823.
  • Mutanga, O., Skidmore, A.K., Prins, H.H.T. (2004). Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89(3):393-408
  • Özer, M.S., Dağdeviren, İ. (1986). Harran Ovası Koşullarında Pamuğun Azotlu Gübre İsteği. Köy Hizmetleri Araştırma Enstitüsü Müdürlüğü Yayınları No:25, Şanlıurfa
  • Patane, P., Vibhute, A. (2014). Chlorophyll and nitrogen estimation techniques: A Review. International Journal of Engineering Research and Reviews Vol. 2, Issue 4, pp: (33-41), Month: October - December 2014.
  • Ramoelo, A., Skidmore, A.K., Cho, M.A., Schlerf, M., Mathieu, R., Heitkönig, I.M.A. (2012). Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19 (2012) 151–162.
  • Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E., Lukina, E.V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94, 815–820.
  • Read, J.J., Tarpleyb, L., McKiniona, J.M. and Reddy, K.R. (2002). Narrow-Waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality Vol.31 No. 5, p. 1442-1452 doi:10.2134/jeq2002.1442.
  • Schönert, M., Zillmann, E., Weichelt, H., Eitel, J.U.H., Magney, T.S., Lilienthalc, H., Siegmannd, B., Jarmer, T. (2015). The tasselled cap transformation for RapidEye data and the estimation of vital and senescent crop parameters. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany.
  • Setatou, H.B., Simonis, A.D. (1994). Response of Cotton to NPK Fertilization the Greek Experience. Proceedings of the World Cotton Research Conf-1, Brisbane Australia, February 14-17, 147-155
  • Spitkó, T., Nagy, Z., Zsubori, Z.T., Szőke, C., Berzy, T., Pintér, J., Marton, C.L. (2016). Connection between normalized difference vegetation index and yield in maize. Plant Soil Environ. Vol. 62, 2016, No. 7: 293–298 DOI: 10.17221/676/2015-PSE
  • Sutton, M.A., C.M. Howard, J.W. Erisman, G. Billen, A. Bleeker, P. Grennfelt, H. van Grinsven, B. Grizzetti., (2011). The European Nitrogen Assessment: Sources, effects and policy perspectives. Cambridge, Cambridge University Press. ISBN: 978110700612.
  • Vigneau, N., Ecarnot, M., Rabatel, G., Roumet, P. (2011). Potential of field hyperspectral imaging as a non-destructive method to assess leaf nitrogen content in Wheat. Field Crops Research 122 (2011) 25–31.
  • Wang, F., Wang, K., Li, S., Gao, S., Xiao, C., Chen, B., Chen, J., Lü Y. And Diao, W. (2011). Estimation of canopy leaf nitrogen status using imaging spectrometer and digital camera in cotton. Acta Agronomica Sinica 2011, 37(6): 1039-1048 ISSN 0496-3490; Coden Tshpa9 DOI: 10.3724/Sp.J.1006.2011.01039
  • Wang, H., Mortensen, A. K., Mao, P., Boelt, B., Gislum, R. (2018). Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera. International Journal of Remote Sensing ISSN: 0143-1161 (Print) 1366-5901 https://doi.org/10.1080/01431161.2019.1569783.
  • Wright, D.L., Rasmussen, V.P., Ramsey, R.D. (2005). Comparing the use of remote sensing with traditional techniques to detect nitrogen stress in wheat. Geocarto International Volume 20, 2005 - Issue 1
  • Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J. and Koti, S. (2005). Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agronomy Journal January 2005 DOI: 10.2134/agronj2005.0089, Source: OAI.

Determination of nitrogen content of cotton plant leaves from RapidEye and PlanetScope satellite data

Yıl 2020, , 169 - 180, 21.08.2020
https://doi.org/10.37908/mkutbd.654258

Öz

Aims: The aim of this study was to determine the changes in nitrogen contents of cotton plant leaves by using satellite images, to estimate nitrogen status in the plant leaves with the help of satellite images, and to determine nitrogen fertilizer requirements in large cotton production areas by using satellite images in the future to make nitrogen application recommendations.

Methods and Results: The experiment was carried out in randomized plot design with three replications in six different locations under Mardin province ecological conditions in Turkey. Data from four bands of PlanetScope and five bands of RapidEye satellites were used in the study. The correlation between leaf nitrogen content and reflectance of satellite bands was determined and regression (leaf nitrogen content-reflectance) and reverse regression (reflectance-leaf nitrogen content) analyses were performed. Significant correlation were found between leaf nitrogen content and RE_Blue (r=-0.58**), RE_Green (r=-0.46**), RE_Red (r=-0.67**), PS_Blue (r=-0.54**), PS_Green (r=-0.43**), PS_Red (r=-0.42**).

Conclusions: It is recommended to use RE_Blue, RE_Green, RE_Red, PS_Blue, PS_Green, PS_Red satellite bands for the estimation of cotton leaf nitrogen content.

Significance and Impact of the Study: This study is of great importance in order to determine the need for nitrogen fertilizer usage in large cotton production areas. The study is important in terms of widespread impact and rapid results in large cotton production areas, especially considering the growing cotton cultivation areas in the GAP (South East Anatolia Project) region for optimum nitrogen fertilization in cotton production.

Proje Numarası

Ziraat.18.022

Kaynakça

  • Anonim, (1990). Approved methods of the American association of cereal chemists. 8th ed. St. Paul: AACC.
  • Anonim, (2019). http://www.nik.com.tr/content_sistem_uydu.asp?id=67, http://www.nik.com.tr/content_sistem_uydu_goruntuleri.asp, Erişim tarih: 10.08.2019.
  • Cabrera-Bosquet, L., Molero, G., Stellacci, A.M., Bort, J., Nogués, S. And Araus, J.L. (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 Research Communications, March 2011 DOI: 10.1556/CRC.39.2011.1.15
  • Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Research 154 (2013) 133–144.
  • Carter, G.A., Knapp, A.K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88(4): 677–684. 2001
  • Cassman, K.G., Doberman A. and Walters, D.T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 31, 132-140
  • Chen P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., Li, B. (2010). New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment 114 (2010) 1987–1997.
  • Chen, P., Wang, J., Huang, W., Tremblay, N., Ou, Y. and Zhang, Q. (2013). Critical nitrogen curve and remote detection of nitrogen nutrition index for corn in the North Western Plain of Shandong Province, China. Ieee Journal of Selected Topics In Applied Earth Observatıons And Remote Sensing, Vol. 6, No. 2, Aprıl 2013.
  • Chen, P. (2015). A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Remote Sens. 2015, 7, 4527-4548; doi:10.3390/rs70404527.
  • Clevers, J. G. P.W., and Kooistra, L. (2012). Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. Ieee Journal of Selected Topics In Applied Earth Observations And Remote Sensing, Vol. 5, No. 2, April 2012.
  • Delloyea, C., Weissb, M., Defourny, P. (2018). Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment 216 (2018) 245–261 https://doi.org/10.1016/j.rse.2018.06.037.
  • Eitel, J.U.H., Magney, T.S., Vierling, L.A., Brown, T.T., Huggins, D.R. (2014). LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research Volume 159, 15 March 2014, Pages 21-32 https://doi.org/10.1016/j.fcr.2014.01.008.
  • Erdle ,K., Mistele, B., Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research Volume 124, Issue 1, 9 October 2011, Pages 74-84 https://doi.org/10.1016/j.fcr.2011.06.007.
  • Eroğlu, E., 2008. Bazı tarım ürünlerinin bitki besin element noksanlıkları ile elektromanyetik enerji yansıtma özellikleri arasındaki ilişkiler. Ege Üniversitesi Fen Bilimleri Enstitüsü Toprak ABD., Yüksek Lisans Tezi Bornova/İzmir.
  • Filella, I., Serrano, L., Serra, J. and Peñuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science Vol. 35 No. 5, p. 1400-1405.
  • 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 (2010) 318–324.
  • Fridgen, J.L., Varco, J.J. (2004). Dependency of cotton leaf nitrogen, chlorophyll, and reflectance on nitrogen and potassium availability. Agronomy Journal 96(1)
  • Haliloğlu, H., Oğlakçı, M. (2000). Effects of different nitrogen rates on earliness, yield and yield distribution of cotton. The Interregional Cooperative Research Network on Cotton. A Joint Workshop and Meeting of the All Working Groups 20-24 September, Adana/TURKEY
  • Hibberd, D.E., Ladewig, J.H., Hunter, M.N., Blight, G.W. (1990). Responses in Cotton Yields to Nitrogen and Phosphorus Fertilizers in the Emerald Irrigation Area, Central Queensland. Australian Journal of Experimental Agriculture, 30, 661-667.
  • Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., Yu, W., Gnyp, M. L., Lenz-Wiedemann, V.I.S., Rascher, U. and Bareth, G. (2015). Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sens. 2015, 7, 10646-10667; doi:10.3390/rs70810646.
  • Huang, S., Miao, Y., Yuan, F., Gnyp, M. L., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V. I. S. and Bareth, G. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sens. 2017, 9, 227; doi:10.3390/rs9030227.
  • Kacar, B. (2009). Toprak analizleri, Nobel Akademik Yayıncılık Eğitim Danışmanlık Tic. Ltd. Şti,
  • Kacar, B. ve Katkat, A. (2011). Bitki Besleme, Nobel Akademik Yayıncılık Eğitim Danışmanlık Tic. Ltd. Şti
  • Klema, K., Záhorab, J., Zemeka, F., Trundaa, P., Tůmab, I., Novotnáa, K., Hodaňováa, P., Rapantováa, B., Hanuša, J., Vavříkováb, J., Holub, P. (2018). Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection Agricultural Water Management 210 (2018) 171–184
  • Lee, K., Lee, B. (2013). Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. Europ. J. Agronomy 48 (2013) 57–65.
  • Li, F., Miao, Y., 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. Precision Agriculture August 2010, Volume 11, Issue 4, pp 335–357.
  • Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U. (2013). Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Europ. J. Agronomy 52 (2014) 198–209.
  • Magney, T.S., Eitel, J.U.H., Vierling, L.A. (2016). Mapping wheat nitrogen uptake from RapidEye vegetation indices. Precision Agric (2017) 18:429–451 DOI 10.1007/s11119-016-9463-8.
  • Mullen, R. W., Freeman, K. W., Raun, W. R., Johnson, G.V., Stone, M. L. and Solie, J.B. (2003). Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agronomy Journal 95, 347-351
  • Muñoz-Huerta, R.F., Guevara-Gonzalez, R.G., Contreras-Medina, L.M., Torres-Pacheco, I., Prado-Olivarez, J., and Ocampo-Velazquez, R.V. (2013). A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 2013, 13, 10823-10843; doi:10.3390/s130810823.
  • Mutanga, O., Skidmore, A.K., Prins, H.H.T. (2004). Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89(3):393-408
  • Özer, M.S., Dağdeviren, İ. (1986). Harran Ovası Koşullarında Pamuğun Azotlu Gübre İsteği. Köy Hizmetleri Araştırma Enstitüsü Müdürlüğü Yayınları No:25, Şanlıurfa
  • Patane, P., Vibhute, A. (2014). Chlorophyll and nitrogen estimation techniques: A Review. International Journal of Engineering Research and Reviews Vol. 2, Issue 4, pp: (33-41), Month: October - December 2014.
  • Ramoelo, A., Skidmore, A.K., Cho, M.A., Schlerf, M., Mathieu, R., Heitkönig, I.M.A. (2012). Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19 (2012) 151–162.
  • Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E., Lukina, E.V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94, 815–820.
  • Read, J.J., Tarpleyb, L., McKiniona, J.M. and Reddy, K.R. (2002). Narrow-Waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality Vol.31 No. 5, p. 1442-1452 doi:10.2134/jeq2002.1442.
  • Schönert, M., Zillmann, E., Weichelt, H., Eitel, J.U.H., Magney, T.S., Lilienthalc, H., Siegmannd, B., Jarmer, T. (2015). The tasselled cap transformation for RapidEye data and the estimation of vital and senescent crop parameters. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany.
  • Setatou, H.B., Simonis, A.D. (1994). Response of Cotton to NPK Fertilization the Greek Experience. Proceedings of the World Cotton Research Conf-1, Brisbane Australia, February 14-17, 147-155
  • Spitkó, T., Nagy, Z., Zsubori, Z.T., Szőke, C., Berzy, T., Pintér, J., Marton, C.L. (2016). Connection between normalized difference vegetation index and yield in maize. Plant Soil Environ. Vol. 62, 2016, No. 7: 293–298 DOI: 10.17221/676/2015-PSE
  • Sutton, M.A., C.M. Howard, J.W. Erisman, G. Billen, A. Bleeker, P. Grennfelt, H. van Grinsven, B. Grizzetti., (2011). The European Nitrogen Assessment: Sources, effects and policy perspectives. Cambridge, Cambridge University Press. ISBN: 978110700612.
  • Vigneau, N., Ecarnot, M., Rabatel, G., Roumet, P. (2011). Potential of field hyperspectral imaging as a non-destructive method to assess leaf nitrogen content in Wheat. Field Crops Research 122 (2011) 25–31.
  • Wang, F., Wang, K., Li, S., Gao, S., Xiao, C., Chen, B., Chen, J., Lü Y. And Diao, W. (2011). Estimation of canopy leaf nitrogen status using imaging spectrometer and digital camera in cotton. Acta Agronomica Sinica 2011, 37(6): 1039-1048 ISSN 0496-3490; Coden Tshpa9 DOI: 10.3724/Sp.J.1006.2011.01039
  • Wang, H., Mortensen, A. K., Mao, P., Boelt, B., Gislum, R. (2018). Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera. International Journal of Remote Sensing ISSN: 0143-1161 (Print) 1366-5901 https://doi.org/10.1080/01431161.2019.1569783.
  • Wright, D.L., Rasmussen, V.P., Ramsey, R.D. (2005). Comparing the use of remote sensing with traditional techniques to detect nitrogen stress in wheat. Geocarto International Volume 20, 2005 - Issue 1
  • Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J. and Koti, S. (2005). Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agronomy Journal January 2005 DOI: 10.2134/agronj2005.0089, Source: OAI.
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ziraat Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Serkan Kılıçaslan 0000-0002-5595-2338

Remzi Ekinci 0000-0003-4165-6631

Sema Başbağ 0000-0002-9324-5175

Proje Numarası Ziraat.18.022
Yayımlanma Tarihi 21 Ağustos 2020
Gönderilme Tarihi 2 Aralık 2019
Kabul Tarihi 30 Nisan 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Kılıçaslan, S., Ekinci, R., & Başbağ, S. (2020). RapidEye ve PlanetScope uydu bantları ile pamuk bitkisi yaprak azot içeriğinin belirlenmesi. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi, 25(2), 169-180. https://doi.org/10.37908/mkutbd.654258

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