Research Article
BibTex RIS Cite

Buğdayda Yaprak Alanı İndekslerine (LAI) Dayalı Klorofil Tahmini İçin Çok Bantlı (Hiperspektral) Verilerin Kullanımı

Year 2024, Volume: 6 Issue: 2, 97 - 111
https://doi.org/10.51489/tuzal.1555934

Abstract

Spektral indeksler bitkide yeşil kısmın tahmin edilmesinde sıkça kullanılmaktadır. Bunlar genellikle atmosfer ve toprak gibi harici faktörlerin spektral etkilerini azaltmak için geliştirilmiştir. Bu çalışmanın amacı buğday’da fenolojik gelişim evrelerine göre farklı spektral indekslerin klorofil tahmin edebilme yeteneğini ortaya koymak ve bu indekslerin optimal band kombinasyonlarını hesaplamaktır. Çalışmada öncelikli olarak klorofil tahmininde kullanılan klorofl-pigment ilişkili indeksler ve bunun yanısıra strüktürel ve kırmızı kenar (Red Edge) indeksler kullanılmıştır. Farklı fenolojik dönemler için elde edilen spektral yansıma değerleri SPAD (Minolta-502) değerleri ile korelasyona tabi tutulmuş, öne çıkan hiperspektral indeksleri ve onların optimal band kombinasyonlarını hesaplamak için ise “En Küçük Kısmi Kareler toplamı” (PLS) modeli kullanılmıştır. Bu çalışmada, farklı Spektral İndekslerin klorofil tahmininde fenolojik dönemlerdeki LAI değişimine karşın, tepkileri ve hassasiyetleri incelenmiştir. Sonuçta saturasyon değişiminden en az ve fazla etkilenen indeksler ortaya çıkartılmıştır. Böylece indekslerin kanopinin klorofil kapsamını tahmin etme gücü ortaya konmuştur. Klorofil tahmininde erken dönemde artan LAI değerine bağlı olarak, saturasyon değişiminden en az etkilenen ve yüksek korelasyon gösteren indeks NDVI (705,750) ‘dir (LAI= 2.63, R²= 0.554). Bunu sırasıyla Red Edge (740-720), (LAI= 2.63,1.722), NDVI (550,780), (LAI= 2.63,0.733), SRPI (430,680) (LAI= 2.63,0.661), LCCI (705,750) (LAI= 2.63, 0.554) ve NPCI (430,680) (LAI= 2.63, 0.203) takip etmiştir. Erken dönemde yüksek korelasyon gösteren bu indeksler R2=0.836-0.761 korelasyon aralığında yer almıştır. Haymana 2013-2014 yılları arasında geç dönemde (26 Mayıs,04-12-24 Haziran 2014) LAI değerleri 0.63-3.38 arasında değişmekte korelasyon değerleri ise R2 =0.892-0.862 arasında yer almaktadır. Geç dönemde artan LAI değerine bağlı olarak saturasyon değişiminden en az etkilenen ve yüksek korelasyon gösteren indeks MSR(705,750)‘dir. (LAI=1.904, 0.906). Bunu sırasiyle NDVI670 (LAI=1.904,0.703), NDVI550 (LAI=1.904, 0.651) ve LCCI(705,750) (LAI=1.904, 0.448) takip etmiştir.

Ethical Statement

In the study, the authors declare that there is no violation of research and publication ethics and that the study.

Supporting Institution

REPUBLIC OF TÜRKİYE MINISTRY OF AGRICULTURE AND FORESTRY FIELD CROPS CENTRAL RESEARCH INSTITUTE

Project Number

Project titled “Investigation of the Effects of Different Nitrogen Applications on Yield and Hyperspectral Reflectance Characteristics in Wheat” (TAGEM/TSKAD/14/A13/P08/05).

Thanks

This study was produced from the data of the Project titled “Investigation of the Effects of Different Nitrogen Applications on Yield and Hyperspectral Reflectance Characteristics in Wheat” (TAGEM/TSKAD/14/A13/P08/05).

References

  • Asrar G, Fuchs M, Kanemasu ET & Hatfield JL. (1984). Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76:300–306. https://doi.org/10.2134/agronj1984.00021962007600020029x
  • Başayiğit, L, Dinç, U., (2001). Toprak Etüd ve Haritalama Çalışmalarında Bilgisayar Teknolojilerinin Kullanımı, Tarımda Bilişim Teknolojileri 4. Sempozyumu,Sütçüimam Üniversitesi, Kahramanmaraş, s 283-291” kaynağından özetlenmiştir.
  • Baret, F., Jacquemoud, S., Guyot, G., & Leprieur, C. (1992). Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sensing of Environment, 41, 133– 142. https://doi.org/10.1016/0034-4257(92)90073-S
  • Barraclough, P. B., & Kyle, J. (2001). Effect of water stress on chlorophyll meter readings in winter wheat. In W. J. Horst, et al. (Eds.), Plant nutrition-food security and sustainability of agro-ecosystems (pp. 722–723). Dordrecht, The Netherlands: Kluwer Academic Publishers. https://doi.org/10.1007/0-306-47624-X_350
  • Blackburn, G. A. (1998a). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, 273–285. https://doi.org/10.1016/S0034-4257(98)00059-5
  • Blackburn, G. A. (1998b). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. Interna tional Journal of Remote Sensing, 19, 657–675. https://doi.org/10.1080/014311698215919
  • Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflec tance data. Remote Sensing of Environment, 81, 45–57. https://doi.org/10.1016/S0034-4257(01)00332-7
  • Chaerle, L., & Van Der Straeten, D. (2000). Imaging techniques and the early detection of plant stress. Trends in plant science, 5(11), 495-501.
  • Clevers, J. G., De Jong, S. M., Epema, G. F., Van Der Meer, F., Bakker, W. H., Skidmore, A. K., & Addink, E. A. (2001). MERIS and the red-edge position. International Journal of Applied Earth Observation and Geoinformation, 3(4), 313-320. https://doi.org/10.1016/S0303-2434(01)85038-8
  • Collins, W. (1978). Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing, 26, 43–55.
  • Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sens. Environ. 30:271-278. https://doi.org/10.1016/0034-4257(89)90069-2
  • Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. https://doi.org/10.1080/0143116042000274015
  • Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063
  • Demetriades-Shah, T. H., Steven, M. D., & Clark, J. A. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 55– 64. https://doi.org/10.1016/0034-4257(90)90055-Q
  • Elvidge,C.D., & Z.Chen.(1995). Comparison of broad-band and near-infrared vegetation indices. Remote Sensing of Environment,54, pp. 38-48. https://doi.org/10.1016/0034-4257(95)00132-K
  • Fava,F.,Colombo,R.,Bocchi,S.,Meroni,M.,Sitzia,M.,Fois,N.,et al.(2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11,233-243. https://doi.org/10.1016/j.jag.2009.02.003
  • Feng,W.,Yao,X.,Zhu,Y.,Tian,Y.C., & Cao,W.X. (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Europe Journal of Agronomy, 28,394-404. https://doi.org/10.1016/j.eja.2007.11.005
  • Filella, I., & Pen˜uelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459– 1470.
  • Filella, I., Serrano, L., Serra, J., & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop science, 35(5), 1400-1405. https://doi.org/10.1080/01431169408954177
  • Fischer, R.A., (2001). Selection traits for ımproving yield potential: In Application of physiology in wheat breeding, Eds M.P. Reynolds, J.I. Ortiz-Monasterio, A. McNab., Mexico:CIMMYT p. 148-1159.
  • Gitelson, A. and Merzlyak, M.N. 1994b. Spectral reflectance changes associate with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143, 286-292.
  • Gitelson A.A. & Merzylak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing.J.Plant Physiol. 148:493-500. https://doi.org/10.1016/S0176-1617(96)80284-7
  • Gitelson, A. A., Gritz, Y., % Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of plant physiology, 160(3), 271-282. https://doi.org/10.1078/0176-1617-00887
  • Gitelson, A. A., Keydan, G. P., & Merzlyak, M. N. (2006). Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical research letters, 33(11). https://doi.org/10.1029/2006GL026457
  • Gupta, R.K., Vijayan, D. and Prasad, T.S. 2001. New hyperspectral vegetation characterization parameters, Advances in Space Research, 28(1), 201-206, https://doi.org/10.1016/S0273-1177(01)00346-5
  • Haboudane,D.,j. R.Miller, N.Tremblay, P.J. Zarco-Tejada, & L. Dextraze (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81 (2-3):416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
  • Haboudane, D.Tremblay, N., Miller, J.R. & Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spetral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46, 423-437. https://doi.org/10.1109/TGRS.2007.904836
  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least square regression. https://doi.org/10.1016/S0034-4257(03)00131-7
  • Hatfield J.L., Gitelson,A.A., Scepers,j.s., & Walthall,C.L (2008). Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, 117-131. https://doi.org/10.2134/agronj2006.0370c
  • Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151.
  • Jacobsen, A., K.B. Heidebrecht, A.A. Nielsen (1998): Monitoring Grasslands Using Convex Geometry and Partial Unmixing– a Case Study. Proceedings of 1st EARSel Workshop on Imaging Spectroscopy, Remote Sensing Laboratories, University of Zürich, Switzerland, 6-8 October. Eds. Michael Shaepman, Daniel Schläpfer, Klaus Itten. Pp. 309-316.
  • Jensen A, Lorenzen B, Spelling-Ostergaard H & Kloster-Hvelplund E. (1990). Radiometric estimation of biomass and N content of barley grown at different N levels. Int. J. Remote Sens. 11:1809–1820. https://doi.org/10.1080/01431169008955131
  • Maas, S. J., & Dunlap, J. R. (1989). Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves. Agronomy Journal, 81(1), 105-110. https://doi.org/10.2134/agronj1989.00021962008100010019x
  • Martinez, D.E. & Guiamet, J.J., (2004). Distortion of SPAD 502 chlorophyll meter reading by changes in irradiance and leaf water status, INRA, EDP Sciences, Agronomie 24: 41-46. https://doi.org/ 10.1051/agro:2003060
  • Mauser, W., & Bach, H. (1994). Imaging spectroscopy in hydrology and agriculture—determination of model parameters. In: J. Hill, & J. Megier (Eds.), Imaging spectrometry—a tool for environmental observations ( pp. 261–283). Dordrecht, The Netherlands: Kluwer Academic Publishing. https://doi.org/ 10.1007/978-0-585-33173-7_14
  • Mıao,W. & Gastwırth, J. L (2009). A new two stage adaptive nonparametric test for paired difference. Statistics and Its Interface 2 213–221. MR2516072. https://dx.doi.org/10.4310/SII.2009.v2.n2.a11
  • Minolta. (1989). SPAD-502 owner’s manual. Industrial Meter Div. Minolta Corp., Ramsey, N.J.
  • Myneni, R. B., & Williams, D. L. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49, 200– 211. https://doi.org/10.1016/0034-4257(94)90016-7
  • Penuelas J, Gamon JA, Freeden A, Merino J & Field C (1994). Reflectance indices associated with physiological changes in N and water-limited sunflower leaves. Remote Sens. Environ. 46:100118. https://doi.org/10.1016/0034-4257(94)90136-8
  • Penuelas, J., Filella, I., & Gamon, J. A. (1995). Assessment of photosyn thetic radiation-use efficiency with spectral reflectance. New Phytolo gist, 131, 291–296. https://doi.org/10.1111/j.1469-8137.1995.tb03064.x
  • Peñuelas J., Baret F., Filella I. (1995a). Yaprak spektral yansımasından karotenoid klorofil-a oranını değerlendirmek için yarı deneysel endeksler . Photosynthetica 1995 , 221–230. https://doi.org/10.1007/BF00029464.
  • Peñuelas J., Filella I., Lloret P., Oz FM, Vilajeliu M. (1995b). Elma ağaçlarındaki akar etkilerinin yansıma değerlendirmesi . Int. J. Remote Sens. 16,2727–2733. https://doi.org/10.1080/01431169508954588
  • Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in plant science, 3(4), 151-156. https://doi.org/10.1080/01431169508954588
  • Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51, Issue 3, 4257(94)00114-3. https://doi.org/10.1016/0034-4257(94)00114-3
  • Serrano, L., Filella, I., & Penuelas, J. (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40, 723–731. https://doi.org/10.2135/cropsci2000.403723x
  • Smith RCG , Adams J. , Stephens DJ ve Hick PT , NOAA uydusundan Akdeniz tipi bir ortamda buğday veriminin tahmini, Avustralya Tarım Araştırmaları Dergisi . ( 1995 ) 46 , no. 1, 113 – 125,2-s2.0-0028971783, https://doi.org/10.1071/AR9950113 .
  • Thenkabail, P. S., Smith, R. B., & de Pauw, E. (2001). Hyperspectral vegetation indices and their relationships with agricultural crop charac teristics. Remote Sensing of Environment, 71, 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X.
  • Waldner, F., Fritz, S., Di Gregorio, A., & Defourny, P. (2015). Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps. Remote Sensing, 7(6), 7959-7986. https://doi.org/10.3390/rs70607959.
  • White, J. D., Trotter, C. M., Brown, L. J., & Scott, N. (2000). Nitrogen concentration in New Zealand vegetation foliage derived from labora tory and field spectrometry. International Journal of Remote Sensing, 21, 2525–2531. https://doi.org/10.1080/01431160050030628.
  • Yadava, U.L., (1986). A rapid & nondestructive method to determine chlorophyll in intact leaves, HortScience 21:1449–1450.
  • Yi-Hui, L. (2007). Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Expert Systems with Applications, 33(4), 1090-1096. https://doi.org/10.1016/j.eswa.2006.08.032.
  • Yoder, B. J., & Pettigrew-Crosby, R. E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400 2500 nm) at leaf and canopy scales. Remote Sensing of Environment, 53, 199–211. https://doi.org/10.1016/0034-4257(95)00135-N
  • Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M., De Frutos, A., 2005. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99 (3), 271-287. https://doi.org/10.1016/j.rse.2005.09.002

Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat

Year 2024, Volume: 6 Issue: 2, 97 - 111
https://doi.org/10.51489/tuzal.1555934

Abstract

Spectral Indices are frequently used in the estimation of green parts of plants, usually developed to reduce the spectral effects of external factors such as atmosphere and soil. The aim of this study was to evaluate the ability of different spectral indices to estimate chlorophyll in wheat according to phenological developmental stages and to calculate their optimal band combinations. In this study, chlorophyll-pigment related indices primarily used in chlorophyll estimation, as well as structural and red edge indices were used. Spectral reflectance values obtained for different phenological periods were correlated with SPAD (Minolta-502) values and Partial Least Square (PLS) model was used to calculate the prominent hyperspectral indices and their optimal band combinations. In this study, the responses and sensitivities of different spectral indices for chlorophyll estimation against LAI change in phenological periods were investigated. As a result, the indices that were least and most affected by saturation changes were revealed. Thus, the power of the indices to predict the chlorophyll content of the canopy was demonstrated. In chlorophyll estimation, NDVI (705,750) was the least affected by the saturation change due to the increasing LAI value in the early period and showed a high correlation (LAI= 2.63, R²= 0.554). This was followed by Red Edge (740-720), (LAI= 2.63,1.722), NDVI (550,780), (LAI= 2.63,0.733), SRPI (430,680) (LAI= 2.63,0.661), LCCI (705,750) (LAI= 2.63,0.554), and NPCI (430,680) (LAI= 2.63, 0.203). These indices, which showed high correlation in the early period, were in the range of R2=0.836-0.761. In Haymana in the late period between 2013-2014 (26 May,04-12-24 June 2014) LAI values vary between 0.63-3.38 and correlation values are between R2 =0.892-0.862. MSR (705,750) was the least affected by the saturation change due to the increasing LAI value in the late period and showed high correlation (LAI=1.904, 0.906). This was followed by NDVI670 (LAI=1.904,0.703), NDVI550 (LAI=1.904,0.651) and LCCI (LAI=1.904,0.448).

Ethical Statement

In the study, the authors declare that there is no violation of research and publication ethics and that the study.

Supporting Institution

REPUBLIC OF TÜRKİYE MINISTRY OF AGRICULTURE AND FORESTRY FIELD CROPS CENTRAL RESEARCH INSTITUTE

Project Number

Project titled “Investigation of the Effects of Different Nitrogen Applications on Yield and Hyperspectral Reflectance Characteristics in Wheat” (TAGEM/TSKAD/14/A13/P08/05).

Thanks

This study was produced from the data of the Project titled “Investigation of the Effects of Different Nitrogen Applications on Yield and Hyperspectral Reflectance Characteristics in Wheat” (TAGEM/TSKAD/14/A13/P08/05).

References

  • Asrar G, Fuchs M, Kanemasu ET & Hatfield JL. (1984). Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76:300–306. https://doi.org/10.2134/agronj1984.00021962007600020029x
  • Başayiğit, L, Dinç, U., (2001). Toprak Etüd ve Haritalama Çalışmalarında Bilgisayar Teknolojilerinin Kullanımı, Tarımda Bilişim Teknolojileri 4. Sempozyumu,Sütçüimam Üniversitesi, Kahramanmaraş, s 283-291” kaynağından özetlenmiştir.
  • Baret, F., Jacquemoud, S., Guyot, G., & Leprieur, C. (1992). Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sensing of Environment, 41, 133– 142. https://doi.org/10.1016/0034-4257(92)90073-S
  • Barraclough, P. B., & Kyle, J. (2001). Effect of water stress on chlorophyll meter readings in winter wheat. In W. J. Horst, et al. (Eds.), Plant nutrition-food security and sustainability of agro-ecosystems (pp. 722–723). Dordrecht, The Netherlands: Kluwer Academic Publishers. https://doi.org/10.1007/0-306-47624-X_350
  • Blackburn, G. A. (1998a). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, 273–285. https://doi.org/10.1016/S0034-4257(98)00059-5
  • Blackburn, G. A. (1998b). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. Interna tional Journal of Remote Sensing, 19, 657–675. https://doi.org/10.1080/014311698215919
  • Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflec tance data. Remote Sensing of Environment, 81, 45–57. https://doi.org/10.1016/S0034-4257(01)00332-7
  • Chaerle, L., & Van Der Straeten, D. (2000). Imaging techniques and the early detection of plant stress. Trends in plant science, 5(11), 495-501.
  • Clevers, J. G., De Jong, S. M., Epema, G. F., Van Der Meer, F., Bakker, W. H., Skidmore, A. K., & Addink, E. A. (2001). MERIS and the red-edge position. International Journal of Applied Earth Observation and Geoinformation, 3(4), 313-320. https://doi.org/10.1016/S0303-2434(01)85038-8
  • Collins, W. (1978). Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing, 26, 43–55.
  • Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sens. Environ. 30:271-278. https://doi.org/10.1016/0034-4257(89)90069-2
  • Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. https://doi.org/10.1080/0143116042000274015
  • Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063
  • Demetriades-Shah, T. H., Steven, M. D., & Clark, J. A. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 55– 64. https://doi.org/10.1016/0034-4257(90)90055-Q
  • Elvidge,C.D., & Z.Chen.(1995). Comparison of broad-band and near-infrared vegetation indices. Remote Sensing of Environment,54, pp. 38-48. https://doi.org/10.1016/0034-4257(95)00132-K
  • Fava,F.,Colombo,R.,Bocchi,S.,Meroni,M.,Sitzia,M.,Fois,N.,et al.(2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11,233-243. https://doi.org/10.1016/j.jag.2009.02.003
  • Feng,W.,Yao,X.,Zhu,Y.,Tian,Y.C., & Cao,W.X. (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Europe Journal of Agronomy, 28,394-404. https://doi.org/10.1016/j.eja.2007.11.005
  • Filella, I., & Pen˜uelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459– 1470.
  • Filella, I., Serrano, L., Serra, J., & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop science, 35(5), 1400-1405. https://doi.org/10.1080/01431169408954177
  • Fischer, R.A., (2001). Selection traits for ımproving yield potential: In Application of physiology in wheat breeding, Eds M.P. Reynolds, J.I. Ortiz-Monasterio, A. McNab., Mexico:CIMMYT p. 148-1159.
  • Gitelson, A. and Merzlyak, M.N. 1994b. Spectral reflectance changes associate with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143, 286-292.
  • Gitelson A.A. & Merzylak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing.J.Plant Physiol. 148:493-500. https://doi.org/10.1016/S0176-1617(96)80284-7
  • Gitelson, A. A., Gritz, Y., % Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of plant physiology, 160(3), 271-282. https://doi.org/10.1078/0176-1617-00887
  • Gitelson, A. A., Keydan, G. P., & Merzlyak, M. N. (2006). Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical research letters, 33(11). https://doi.org/10.1029/2006GL026457
  • Gupta, R.K., Vijayan, D. and Prasad, T.S. 2001. New hyperspectral vegetation characterization parameters, Advances in Space Research, 28(1), 201-206, https://doi.org/10.1016/S0273-1177(01)00346-5
  • Haboudane,D.,j. R.Miller, N.Tremblay, P.J. Zarco-Tejada, & L. Dextraze (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81 (2-3):416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
  • Haboudane, D.Tremblay, N., Miller, J.R. & Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spetral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46, 423-437. https://doi.org/10.1109/TGRS.2007.904836
  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least square regression. https://doi.org/10.1016/S0034-4257(03)00131-7
  • Hatfield J.L., Gitelson,A.A., Scepers,j.s., & Walthall,C.L (2008). Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, 117-131. https://doi.org/10.2134/agronj2006.0370c
  • Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151.
  • Jacobsen, A., K.B. Heidebrecht, A.A. Nielsen (1998): Monitoring Grasslands Using Convex Geometry and Partial Unmixing– a Case Study. Proceedings of 1st EARSel Workshop on Imaging Spectroscopy, Remote Sensing Laboratories, University of Zürich, Switzerland, 6-8 October. Eds. Michael Shaepman, Daniel Schläpfer, Klaus Itten. Pp. 309-316.
  • Jensen A, Lorenzen B, Spelling-Ostergaard H & Kloster-Hvelplund E. (1990). Radiometric estimation of biomass and N content of barley grown at different N levels. Int. J. Remote Sens. 11:1809–1820. https://doi.org/10.1080/01431169008955131
  • Maas, S. J., & Dunlap, J. R. (1989). Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves. Agronomy Journal, 81(1), 105-110. https://doi.org/10.2134/agronj1989.00021962008100010019x
  • Martinez, D.E. & Guiamet, J.J., (2004). Distortion of SPAD 502 chlorophyll meter reading by changes in irradiance and leaf water status, INRA, EDP Sciences, Agronomie 24: 41-46. https://doi.org/ 10.1051/agro:2003060
  • Mauser, W., & Bach, H. (1994). Imaging spectroscopy in hydrology and agriculture—determination of model parameters. In: J. Hill, & J. Megier (Eds.), Imaging spectrometry—a tool for environmental observations ( pp. 261–283). Dordrecht, The Netherlands: Kluwer Academic Publishing. https://doi.org/ 10.1007/978-0-585-33173-7_14
  • Mıao,W. & Gastwırth, J. L (2009). A new two stage adaptive nonparametric test for paired difference. Statistics and Its Interface 2 213–221. MR2516072. https://dx.doi.org/10.4310/SII.2009.v2.n2.a11
  • Minolta. (1989). SPAD-502 owner’s manual. Industrial Meter Div. Minolta Corp., Ramsey, N.J.
  • Myneni, R. B., & Williams, D. L. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49, 200– 211. https://doi.org/10.1016/0034-4257(94)90016-7
  • Penuelas J, Gamon JA, Freeden A, Merino J & Field C (1994). Reflectance indices associated with physiological changes in N and water-limited sunflower leaves. Remote Sens. Environ. 46:100118. https://doi.org/10.1016/0034-4257(94)90136-8
  • Penuelas, J., Filella, I., & Gamon, J. A. (1995). Assessment of photosyn thetic radiation-use efficiency with spectral reflectance. New Phytolo gist, 131, 291–296. https://doi.org/10.1111/j.1469-8137.1995.tb03064.x
  • Peñuelas J., Baret F., Filella I. (1995a). Yaprak spektral yansımasından karotenoid klorofil-a oranını değerlendirmek için yarı deneysel endeksler . Photosynthetica 1995 , 221–230. https://doi.org/10.1007/BF00029464.
  • Peñuelas J., Filella I., Lloret P., Oz FM, Vilajeliu M. (1995b). Elma ağaçlarındaki akar etkilerinin yansıma değerlendirmesi . Int. J. Remote Sens. 16,2727–2733. https://doi.org/10.1080/01431169508954588
  • Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in plant science, 3(4), 151-156. https://doi.org/10.1080/01431169508954588
  • Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51, Issue 3, 4257(94)00114-3. https://doi.org/10.1016/0034-4257(94)00114-3
  • Serrano, L., Filella, I., & Penuelas, J. (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40, 723–731. https://doi.org/10.2135/cropsci2000.403723x
  • Smith RCG , Adams J. , Stephens DJ ve Hick PT , NOAA uydusundan Akdeniz tipi bir ortamda buğday veriminin tahmini, Avustralya Tarım Araştırmaları Dergisi . ( 1995 ) 46 , no. 1, 113 – 125,2-s2.0-0028971783, https://doi.org/10.1071/AR9950113 .
  • Thenkabail, P. S., Smith, R. B., & de Pauw, E. (2001). Hyperspectral vegetation indices and their relationships with agricultural crop charac teristics. Remote Sensing of Environment, 71, 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X.
  • Waldner, F., Fritz, S., Di Gregorio, A., & Defourny, P. (2015). Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps. Remote Sensing, 7(6), 7959-7986. https://doi.org/10.3390/rs70607959.
  • White, J. D., Trotter, C. M., Brown, L. J., & Scott, N. (2000). Nitrogen concentration in New Zealand vegetation foliage derived from labora tory and field spectrometry. International Journal of Remote Sensing, 21, 2525–2531. https://doi.org/10.1080/01431160050030628.
  • Yadava, U.L., (1986). A rapid & nondestructive method to determine chlorophyll in intact leaves, HortScience 21:1449–1450.
  • Yi-Hui, L. (2007). Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Expert Systems with Applications, 33(4), 1090-1096. https://doi.org/10.1016/j.eswa.2006.08.032.
  • Yoder, B. J., & Pettigrew-Crosby, R. E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400 2500 nm) at leaf and canopy scales. Remote Sensing of Environment, 53, 199–211. https://doi.org/10.1016/0034-4257(95)00135-N
  • Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M., De Frutos, A., 2005. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99 (3), 271-287. https://doi.org/10.1016/j.rse.2005.09.002
There are 53 citations in total.

Details

Primary Language English
Subjects Remote Sensing
Journal Section Research Articles
Authors

Metin Aydoğdu 0000-0001-6920-1976

Hakan Yıldız 0000-0002-7627-7503

Project Number Project titled “Investigation of the Effects of Different Nitrogen Applications on Yield and Hyperspectral Reflectance Characteristics in Wheat” (TAGEM/TSKAD/14/A13/P08/05).
Early Pub Date December 18, 2024
Publication Date
Submission Date September 26, 2024
Acceptance Date November 4, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Aydoğdu, M., & Yıldız, H. (2024). Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat. Türkiye Uzaktan Algılama Dergisi, 6(2), 97-111. https://doi.org/10.51489/tuzal.1555934
AMA Aydoğdu M, Yıldız H. Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat. TUZAL. December 2024;6(2):97-111. doi:10.51489/tuzal.1555934
Chicago Aydoğdu, Metin, and Hakan Yıldız. “Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat”. Türkiye Uzaktan Algılama Dergisi 6, no. 2 (December 2024): 97-111. https://doi.org/10.51489/tuzal.1555934.
EndNote Aydoğdu M, Yıldız H (December 1, 2024) Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat. Türkiye Uzaktan Algılama Dergisi 6 2 97–111.
IEEE M. Aydoğdu and H. Yıldız, “Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat”, TUZAL, vol. 6, no. 2, pp. 97–111, 2024, doi: 10.51489/tuzal.1555934.
ISNAD Aydoğdu, Metin - Yıldız, Hakan. “Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat”. Türkiye Uzaktan Algılama Dergisi 6/2 (December 2024), 97-111. https://doi.org/10.51489/tuzal.1555934.
JAMA Aydoğdu M, Yıldız H. Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat. TUZAL. 2024;6:97–111.
MLA Aydoğdu, Metin and Hakan Yıldız. “Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat”. Türkiye Uzaktan Algılama Dergisi, vol. 6, no. 2, 2024, pp. 97-111, doi:10.51489/tuzal.1555934.
Vancouver Aydoğdu M, Yıldız H. Use of Hyperspectral Data for Chlorophyll Estimation Based on Leaf Area Index (LAI) in Wheat. TUZAL. 2024;6(2):97-111.

Flag Counter