Publicado

2017-05-01

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

Relación entre el estado nutricional de los cultivos, las mediciones espectrales y las imágenes Sentinel 2

DOI:

https://doi.org/10.15446/agron.colomb.v35n2.62875

Palabras clave:

spectral reflectance, spectroradiometry, crop nutrition (en)
reflectancia espectral, espectroradiometría, nutrición de cultivos (es)

Descargas

Autores/as

  • Luis Joel Martínez M. Universidad Nacional de Colombia - Sede Bogotá - Faculty of Agricultural Sciences

In order to monitor the nutritional status of some crops based on plant spectroscopy and Sentinel 2 satellite images in Colombia, spectral reflectance data were taken between 350 and 2,500 nm with a FieldSpec 4 spectrometer in rubber, rice, sugar cane, maize, soybean, cashew, oil palm crops, pastures and natural savanna. Furthermore contents of mineral nutrients in leaves were determined. Several vegetation indexes and red edge positions were calculated using various methods from spectral data and Sentinel 2 satellite images and were correlated with leaf nutrient content. The results showed correlations between spectral indices, mainly those involving a spectral response in the red-edge range with the N, P, K and Cu although the best correlation coefficients were for N. First reflectance derivatives, transformations by the State Normal Variate and second reflectance derivatives showed great potential to monitor N content in crops. The green model index and the red-edge model computed from Sentinel 2 images had the best performance to monitor N content, although in the study area, presence of clouds affected the use of these images. The Sentinel 2 images allowed calculating some vegetation indexes obtained with other images, such as Landsat or SPOT, but additionally other indexes and calculations based on the bands of the red-edge, which is a great contribution to obtain more information of crops on their spatial and temporal variability.

Con el fin de monitorear el estado nutricional de algunos cultivos con base en espectroscopía de plantas e imágenes satelitales 2 en Colombia, se tomaron datos de reflectancia entre 350 y 2.500 nm con un espectrómetro FieldSpec 4 en cultivos de caucho, arroz, caña de azúcar, maíz, soya, marañón y palma de aceite, en pasturas y sabanas naturales y se determinó el contenido de nutrientes minerales en hojas. Se calcularon varios índices de vegetación y posiciones de borde rojo usando varios métodos, a partir de datos espectrales e imágenes de satélite Sentinel 2 y se correlacionaron con el contenido de nutrientes en las hojas. Los resultados mostraron correlaciones entre índices espectrales, principalmente aquellos que involucraron la respuesta espectral en el rango de borde rojo, con N, P, K y Cu aunque los mejores coeficientes de correlación fueron para N. La primera derivada de la reflectancia, su transformación por la state normal variate y la segundas derivadas mostraron un gran potencial para monitorear el contenido de N en los cultivos. El índice del modelo verde y el modelo de borde rojo calculados a partir de imágenes Sentinel 2 tuvieron el mejor desempeño para monitorear el contenido de N, aunque en las condiciones del área de estudio la presencia de nubes afectó el uso de estas imágenes. Las imágenes Sentinel 2 permitieron calcular algunos índices de vegetación que se obtienen con otras imágenes, como Landsat o SPOT, pero adicionalmente otros índices y cálculos basados en las bandas del borde rojo, lo cual es una gran contribución para obtener más información de los cultivos su variabilidad espacial y temporal.

Referencias

Barnes, R., M. Dhanoa, and S. Lister. 1993. Letter: Correction to the description of Standard Normal Variate (SNV) and De-Trend (DT) Transformations in practical spectroscopy with applications in food and beverage analysis. J. Near Infrar. Spectros. 1(1), 185. Doi: 10.1255/jnirs.21

Cao, Q., Y. Miao, G. Feng, X. Gao, F. Li, B. Liu, S. Yue, S. Cheng, S. Lu, and R. Khosla. 2015. Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Comput. Electron Agric. 112, 54-67. Doi: 10.1016/j.compag.2014.08.012

Chen, P., D. Haboudane, N. Tremblay, J. Wang, P. Vigneault, and B. Li. 2010. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ. 114(9), 1987-1997. Doi: 10.1016/j.rse.2010.04.006

Cho, M.A. and A.K. Skidmore. 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens. Environ. 101(2), 181-193. Doi: 10.1016/j.rse.2005.12.011

Cho, M.A. 2007. Hyperspectral remote sensing of biochemical and biophysical parameters: The derivative red-edge “double-peak feature”, a nuisance or an opportunity?, Thesis. Twente University, Enschede, The Netherlands. Congedo, L. 2016. Semi-automatic classification plugin documentation. Doi: 10.13140/RG.2.2.29474.02242/1

Curran, P.J., J. L. Dungan, B. A. Macler, and S. E. Plumer.1991. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sens. Environ. 35(1), 69-76. Doi: 10.1016/0034-4257(91)90066-F

Datt, B. and M. Paterson. 2000. Vegetation-soil spectral mixture analysis. IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No. 00CH37120) 5, 1936-1938. Doi: 10.1109/IGARSS.2000.858186

Delegido, J., J. Verrelst, L. Alonso, and J. Moreno. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11(7), 7063-7081. Doi: 10.3390/s110707063

European Space Agency (ESA). 2016. SENTINEL-2 User Guide. ESA. In: https://earth.esa.int/web/sentinel/user-guides/sentinel- 2-msi; consulted: June, 2016.

Filella, I. and J. Peñuelas. 1994. The red edge position and shape as indicator of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens. 15(7), 1459-1470. Doi: 10.1080/01431169408954177

Frampton, W.J., J. Dash, G. Watmough, and E. J. Milton. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS 82, 83-92. Doi: 10.1016/j.isprsjprs.2013.04.007

Gitelson, A.A., Y.J Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ, 58(3), 289-298. Doi: 10.1016/S0034-4257(96)00072-7

Gitelson, A.A. and M.N Merzlyak. 1996. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. J. Plant Physiol. 148(3-4), 494-500. Doi: 10.1016/S0176-1617(96)80284-7

Gitelson, A.A., M.N. Merzlyak, and H.K Lichtenthaler. 1996. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 148(3-4), 501-508. Doi: 10.1016/S0176-1617(96)80285-9

Gitelson, A.A, A. Viña, V. Ciganda, D. C. Rundquist, and T. J. Arkebauer. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32(8), 1-4. Doi: 10.1029/2005GL022688

Guyot, G. and F. Baret. 1988. Utilisation de la haute résolution spectrale pour suivre l’état des couverts végétaux. pp. 279-286. In: Proc. 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France.

Immitzer, M., F. Vuolo, and C, Atzberger. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 8(3), 2-27. Doi: 10.3390/rs8030166

Jordan, C.F. 1969. Derivation of leaf-area index from quality of light on the forest. Ecol. 50(4), 663-666. Doi: 10.2307/1936256

Kochubey, S.M. and T.A Kazantsev. 2007. Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. J. Plant Physiol. 164(12), 1648-1655. Doi: 10.1016/j.jplph.2006.11.007

Liang, S. 2004. Quantitative remote sensing of land surfaces. Wiley and Sons, Hoboken, NY, USA.

Malthus, T.J. and G. Dekker. 1995. First derivative indices for the remote-sensing of inland water-quality using high-spectralresolution reflectance. Environ. Int. 21(2), 221-232. Doi: 10.1016/0160-4120(95)00012-7

Martinez, L.J. and A. Ramos. 2015. Estimation of chlorophyll concentration in maize using spectral reflectance. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3(May) 65-71. In: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/65/2015; consulted: June, 2016.

Mulla, D.J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. Doi: 10.1016/j.biosystemseng.2012.08.009

Peñuelas, J., M.F. Garbulsky, and I. Filella. 2011. Photochemical reflectance index (PRI) and remote sensing of plant CO2 uptake. New Phytol. 191(3), 596-599. Doi: 10.1111/j.1469-8137.2011.03791.x

Qi, J., Y. Inoue, and N. Wiangwang. 2011. Hyperspectral remote sensing in global change studies. pp. 69–90. In: Hyperspectral remote sensing of vegetation. CRC Press. Doi: 10.1201/b11222-6

Rouse, J.W., Jr., R.H. Haas, J.A. Schell, and D.W. Deering.1974. Monitoring vegetation systems in the Great Plains with ERTS. p. 309. In: Freden, S.C., E.P. Mercanti, and M.A. Becker (eds.).Third Earth Resources Technology Satellite-1 Symposium- Vol. I: Technical Presentations. NASA SP-351. NASA, Washington,D.C.

Ruiz, M. and P. Chen. 1982. Use of the first derivative of spectral reflectance to detect mold on tomatoes. Trans. ASAE 25(3), 759-762.

Savitzky, A. and M.J. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analyt. Chem. 36(8), 1627-1639. Doi: 10.1021/ac60214a047

Schlemmer, M.R., D.D. Francis, J.F. Shanahan, and J.S. Schepers. 2005. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron. J. 97(1), 106-112. Doi: 10.1021/ac60214a047

Thorp, K.R., L. Tian, H. Yao, and L. Tang. 2004. Narrow-band and derivative-based vegetation indices for hyperspectral data. Trans. ASAE 47(1), 291-299. Doi: 10.13031/2013.15854

Tsai, F. and W. Philpot. 1998. Derivative analysis of hyperspectral data. Remote Sens. Environ. 66(1), 41-51. Doi: 10.1016/S0034-4257(98)00032-7

Usha, K. and B. Singh. 2013. Potential applications of remote sensing in horticulture - A review. Sci. Hortic. 153, 71-83. Doi: 10.1016/j.scienta.2013.01.008

Yao, H., Y. Huang, Z. Hruska, S.J. Thomson, and K.N. Reddy. 2012. Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate. Comput. Electron. Agric. 89, 145-157. Doi: 10.1016/j.compag.2012.09.001

Yu, K., V. L. Wiedemann, X. Chen, and G. Bareth. 2014. Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS J. Photogrammetry Remote Sens. 97, 58-77. Doi: 10.1016/j.isprsjprs.2014.08.005

Zhang, C. and J.M. Kovacs. 2012. The application of small unmanned aerial systems for precision agriculture: A review. Precision Agric. 13(6), 693-712. Doi: 10.1007/s11119-012-9274-5

Cómo citar

APA

Martínez M., L. J. (2017). Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agronomía Colombiana, 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62875

ACM

[1]
Martínez M., L.J. 2017. Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agronomía Colombiana. 35, 2 (may 2017), 205–215. DOI:https://doi.org/10.15446/agron.colomb.v35n2.62875.

ACS

(1)
Martínez M., L. J. Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agron. Colomb. 2017, 35, 205-215.

ABNT

MARTÍNEZ M., L. J. Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agronomía Colombiana, [S. l.], v. 35, n. 2, p. 205–215, 2017. DOI: 10.15446/agron.colomb.v35n2.62875. Disponível em: https://revistas.unal.edu.co/index.php/agrocol/article/view/62875. Acesso em: 22 ene. 2025.

Chicago

Martínez M., Luis Joel. 2017. «Relationship between crop nutritional status, spectral measurements and Sentinel 2 images». Agronomía Colombiana 35 (2):205-15. https://doi.org/10.15446/agron.colomb.v35n2.62875.

Harvard

Martínez M., L. J. (2017) «Relationship between crop nutritional status, spectral measurements and Sentinel 2 images», Agronomía Colombiana, 35(2), pp. 205–215. doi: 10.15446/agron.colomb.v35n2.62875.

IEEE

[1]
L. J. Martínez M., «Relationship between crop nutritional status, spectral measurements and Sentinel 2 images», Agron. Colomb., vol. 35, n.º 2, pp. 205–215, may 2017.

MLA

Martínez M., L. J. «Relationship between crop nutritional status, spectral measurements and Sentinel 2 images». Agronomía Colombiana, vol. 35, n.º 2, mayo de 2017, pp. 205-1, doi:10.15446/agron.colomb.v35n2.62875.

Turabian

Martínez M., Luis Joel. «Relationship between crop nutritional status, spectral measurements and Sentinel 2 images». Agronomía Colombiana 35, no. 2 (mayo 1, 2017): 205–215. Accedido enero 22, 2025. https://revistas.unal.edu.co/index.php/agrocol/article/view/62875.

Vancouver

1.
Martínez M. LJ. Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agron. Colomb. [Internet]. 1 de mayo de 2017 [citado 22 de enero de 2025];35(2):205-1. Disponible en: https://revistas.unal.edu.co/index.php/agrocol/article/view/62875

Descargar cita

CrossRef Cited-by

CrossRef citations11

1. Gregoriy Kaplan, Lior Fine, Victor Lukyanov, V. S. Manivasagam, Nitzan Malachy, Josef Tanny, Offer Rozenstein. (2021). Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery. Remote Sensing, 13(6), p.1046. https://doi.org/10.3390/rs13061046.

2. Ansar Ali, Muhammad Imran, Amjad Ali, Muhammad Azam Khan. (2022). Evaluating Sentinel-2 red edge through hyperspectral profiles for monitoring LAI & chlorophyll content of Kinnow Mandarin orchards. Remote Sensing Applications: Society and Environment, 26, p.100719. https://doi.org/10.1016/j.rsase.2022.100719.

3. Abdulhakim A. Aldubai, Abdullah A. Alsadon, Khalid A. Al-Gaadi, ElKamil Tola, Abdullah A. Ibrahim. (2022). Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions. Saudi Journal of Biological Sciences, 29(4), p.2506. https://doi.org/10.1016/j.sjbs.2021.12.030.

4. C. Munyati, H. Balzter, E. Economon. (2020). Correlating Sentinel-2 MSI-derived vegetation indices with in-situ reflectance and tissue macronutrients in savannah grass. International Journal of Remote Sensing, 41(10), p.3820. https://doi.org/10.1080/01431161.2019.1708505.

5. Euseppe Ortiz, Enrique A. Torres. (2018). Assessing water demand with remote sensing for two coriander varieties. Agronomía Colombiana, 36(3), p.266. https://doi.org/10.15446/agron.colomb.v36n3.71809.

6. Zhi Hong Kok, Abdul Rashid Bin Mohamed Shariff, Siti Khairunniza-Bejo, Hyeon-Tae Kim, Tofael Ahamed, See Siang Cheah, Siti Aishah Abd Wahid. (2021). Plot-Based Classification of Macronutrient Levels in Oil Palm Trees with Landsat-8 Images and Machine Learning. Remote Sensing, 13(11), p.2029. https://doi.org/10.3390/rs13112029.

7. Guopeng Jiang, Miles Grafton, Diane Pearson, Mike Bretherton, Allister Holmes. (2019). Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity. Agriculture, 9(11), p.237. https://doi.org/10.3390/agriculture9110237.

8. Daniela Torres Morandi, Luciano Cavalcante de Jesus França, Eduarda Soares Menezes, Evandro Luiz Mendonça Machado, Marcelo Dutra da Silva, Danielle Piuzana Mucida. (2020). Delimitation of ecological corridors between conservation units in the Brazilian Cerrado using a GIS and AHP approach. Ecological Indicators, 115, p.106440. https://doi.org/10.1016/j.ecolind.2020.106440.

9. Marta Estanqueiro, Aleksandar Šalamon, Helen Lewis, Barry Molloy, Dragan Jovanović. (2023). Sentinel-2 imagery analyses for archaeological site detection: an application to Late Bronze Age settlements in Serbian Banat, southern Carpathian Basin. Journal of Archaeological Science: Reports, 51, p.104188. https://doi.org/10.1016/j.jasrep.2023.104188.

10. Carlos Arturo Ramos-García, Luis Joel Martínez-Martínez, Jaime Humberto Bernal-Riobo. (2022). Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis. Revista Colombiana de Ciencias Hortícolas, 16(1) https://doi.org/10.17584/rcch.2022v16i1.13398.

11. Agnieszka Glinko, Cezary Kaźmierowski, Jan Piekarczyk, Sławomir Królewicz. (2022). Assessment of Soil Characteristics Using a Three-Band Agricultural Digital Camera. Quaestiones Geographicae, https://doi.org/10.2478/quageo-2022-0029.

Dimensions

PlumX

Visitas a la página del resumen del artículo

905

Descargas

Los datos de descargas todavía no están disponibles.